Intelligent and Fuzzy Systems: Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2 (Lecture Notes in Networks and Systems, 505) 3031091752, 9783031091759

Citation preview

Lecture Notes in Networks and Systems 505

Cengiz Kahraman · A. Cagri Tolga · Sezi Cevik Onar · Selcuk Cebi · Basar Oztaysi · Irem Ucal Sari   Editors

Intelligent and Fuzzy Systems Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2

Lecture Notes in Networks and Systems Volume 505

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

Cengiz Kahraman A. Cagri Tolga Sezi Cevik Onar Selcuk Cebi Basar Oztaysi Irem Ucal Sari •

•

•

•

•

Editors

Intelligent and Fuzzy Systems Digital Acceleration and The New Normal Proceedings of the INFUS 2022 Conference, Volume 2

123

Editors Cengiz Kahraman Department of Industrial Engineering Istanbul Technical University Istanbul, Turkey

A. Cagri Tolga Department of Industrial Engineering Galatasaray University Istanbul, Turkey

Sezi Cevik Onar Department of Industrial Engineering Istanbul Technical University Istanbul, Turkey

Selcuk Cebi Department of Industrial Engineering Yildiz Technical University Istanbul, Turkey

Basar Oztaysi Department of Industrial Engineering Istanbul Technical University Istanbul, Turkey

Irem Ucal Sari Department of Industrial Engineering Istanbul Technical University Istanbul, Turkey

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-09175-9 ISBN 978-3-031-09176-6 (eBook) https://doi.org/10.1007/978-3-031-09176-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

INFUS is an acronym for intelligent and fuzzy systems. It is a well-established international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing for applied research in general and for complex engineering and decision support systems. The principal mission of INFUS is to construct a bridge between fuzzy and intelligence systems and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together. INFUS 2019 was an on-site conference organized in Istanbul, Turkey. INFUS 2020 and INFUS 2021 conferences were organized as online conferences because of pandemic conditions. INFUS 2022 conference is organized as both online and on-site conference this year. The theme of INFUS 2022 conference this year is digital transformation and the new normal. Digital transformation plays a vital role in the sustainability of the organization, and it is a long-term investment. As the world continues to fight the devastating impact of the coronavirus pandemic, the need for digital transformation has become more necessary than ever. While companies will undoubtedly face significant challenges in the digital transformation process, it has become a must for managers to accelerate the digital transformation of operations. The new normal is the state to which economies, societies, etc. settle following a crisis that is the coronavirus pandemic in our case. The post-COVID-19 era brought us to a new normal that will accelerate digital transformation in many areas such as digital economy, digital finance, digital government, digital health, and digital education. To prepare for a digital “new normal” and maintain a leadership position among competitors, both manufacturing and service companies need to initiate a robust digital transformation. Digitizing an industrial company is a challenging process, which involves rethinking established structures, processes, and steering mechanisms. INFUS 2022 aims to bring together the latest theoretical and practical intelligent and fuzzy studies on digital transformation, digital acceleration, and the new normal, to present it to the participants and to create a discussion environment.

v

vi

Preface

Researchers from more than 20 countries such as Turkey, Russia, China, Iran, Poland, India, Azerbaijan, Bulgaria, Spain, Ukraine, Pakistan, South Korea, UK, Indonesia, USA, Vietnam, Finland, Romania, France, Uzbekistan, Italy, and Austria contributed to INFUS 2022. Our invited speakers this year are Prof. Krassimir Atanassov, Prof. Vicenc Torra, Prof. Janusz Kacprzyk, Prof. Ahmet Fahri Özok, Prof. Ajith Abraham, Prof. Okyay Kaynak, Prof. Habib Zaidi, and Prof. Vilem Novak. It is an honor to include their speeches in our conference program. We appreciate their voluntary contributions to INFUS 2022, and we hope to see them at INFUS conferences for many years. This year, the number of submitted papers became 364. After the review process, about 46% of these papers have been rejected. More than 50% of the accepted papers are from other countries outside Turkey. We again thank all the representatives of their countries for selecting INFUS 2022 as an international scientific arena. We also thank the anonymous reviewers for their hard works in selecting high-quality papers of INFUS 2022. Each of the organizing committee members provided invaluable contributions to INFUS 2022. INFUS conferences would be impossible without their efforts. We hope meeting all of our participants next year in Turkey at a face-to-face conference. We would like to thank our publisher Springer Publishing Company, Series editor Prof. Janusz Kacprzyk, Interdisciplinary and Applied Sciences and Engineering and Editorial Director Thomas Ditzinger, last but not least, Project Coordinator Viradasarani Natarajan for their supportive, patient, and helpful roles during the preparation of this book. Cengiz Kahraman A. Cagri Tolga Selcuk Cebi Basar Oztaysi Sezi Cevik Onar Irem Ucal Sari

Organization

Program Committee Chairs Kahraman, Cengiz Cevik Onar, Sezi Oztaysi, Basar Tolga, Çağrı Ucal Sari, Irem Çebi, Selçuk

ITU, Industrial Engineering Department, Istanbul, Turkey Istanbul Technical University, Istanbul, Turkey İstanbul Technical University, Istanbul, Turkey Galatasaray University, Department of Industrial Engineering, Istanbul, Turkey Istanbul Technical University, Industrial Engineering, Istanbul, Turkey Yildiz Technical University, Industrial Engineering, İstanbul, Turkey

Program Committee Members Alkan, Nurşah

Aydin, Serhat Boltürk, Eda Dogan, Onur Haktanır Aktaş, Elif Ilbahar, Esra Kahraman, Cengiz

Istanbul Technical University, Department of Industrial Engineering, Maçka, Beşiktaş, Turkey National Defence University, Industrial Engineering Department, Istanbul, Turkey Istanbul Settlement and Custody Bank Inc.-Takasbank, İstanbul, Turkey Izmir Bakircay University, Department of Industrial Engineering, İzmir, Turkey Altinbas University, Industrial Engineering, Istanbul, Turkey Yildiz Technical University, Industrial Engineering Department, İstanbul, Turkey ITU, Industrial Engineering Department, Istanbul, Turkey

vii

viii

Karaşan, Ali Kutlu Gündoğdu, Fatma Otay, Irem Cevik Onar, Sezi Oztaysi, Basar Seker, Sukran Senvar, Ozlem Tolga, Çağrı Ucal Sari, Irem Çebi, Selçuk Çoban, Veysel

Organization

Yildiz Technical University, Istanbul, Turkey National Defence University, Industrial Engineering Department, Istanbul, Turkey Istanbul Bilgi University, Istanbul, Turkey Istanbul Technical University, Istanbul, Turkey İstanbul Technical University, Istanbul, Turkey Yildiz Technical University, Istanbul, Turkey Marmara University, Department of Industrial Engineering, Istanbul, Turkey Galatasaray University, Department of Industrial Engineering, Istanbul, Turkey Istanbul Technical University, Industrial Engineering, Istanbul, Turkey Yildiz Technical University, Industrial Engineering, İstanbul, Turkey Bilecik Seyh Edebali University, Bilecik, Turkey

Contents

Machine Learning An Improved Animal Migration Optimization Approach for Extreme Learning Machine Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miodrag Zivkovic, Ana Vesic, Nebojsa Bacanin, Ivana Strumberger, Milos Antonijevic, Luka Jovanovic, and Marina Marjanovic

3

Store-based Demand Forecasting of a Company via Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmet Tezcan Tekin and Cem Sarı

14

Prediction of the Future Success of Candidates Before Recruitment with Machine Learning: A Case Study in the Banking Sector . . . . . . . . Murat Levent Demircan and Kaan Aksaç

24

Effectiveness of Social Media in Stock Market Price Prediction Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emre Karaşahin, Semih Utku, and Okan Öztürkmenoğlu

36

Deep Learning-Based Cancerous Lung Nodule Detection in Computed Tomography Imageries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sangaraju V. Kumar, Fei Chen, Sumi Kim, and Jaeho Choi

44

Improving Disease Diagnosis with Integrated Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Özge H. Namlı and Seda Yanık

53

Explanation of Machine Learning Classification Models with Fuzzy Measures: An Approach to Individual Classification . . . . . . . . . . . . . . . Daniel Santos, Inmaculada Gutiérrez, Javier Castro, Daniel Gómez, Juan Antonio Guevara, and Rosa Espínola A Machine Learning Based Method for Automatic Identification of Disaster Related Information Using Twitter Data . . . . . . . . . . . . . . . Athina Ntiana Christidou, Maria Drakaki, and Vasileios Linardos

62

70

ix

x

Contents

Human Activity Recognition with Smart Watches Using Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tansel Gönül, Ozlem Durmaz Incel, and Gulfem Isiklar Alptekin

77

Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anil Gürbüz, Ezgi Biçer, and Tolga Kaya

86

Prospects for the Development of Transport Logistics and a Fuzzy Logic Model of the Strategic Goals of the Logistics System of Azerbaijan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahib Imamguluyev and Abil Suleymanov

93

A Multi-channel Deep Learning Architecture for Understanding the Urban Scene Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Tuba Demirtaş and Ismail Burak Parlak Forecasting Greenhouse Gas Emissions Based on Different Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Ilayda Ulku and Eyup Emre Ulku The Computation Trend of Fuzzy Rules for Effective Decision Support Mechanism on Basis of Supervised Learning for Multiple Periods . . . . . 117 Bhupinder Singh and Santosh Kumar Henge Intelligent Systems Utilization in Recommender Systems: A Reinforcement Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Ibrahim Yazici and Emre Ari Equity Portfolio Optimization Using Reinforcement Learning: Emerging Market Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mert Candar and Alp Üstündağ Marketing Campaign Management Using Machine Learning Techniques: An Uplift Modeling Approach . . . . . . . . . . . . . . . . . . . . . . 140 Meltem Sanisoğlu, Tolga Kaya, and Şebnem Burnaz Real Time Big Data Analytics for Tool Wear Protection with Deep Learning in Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 Altan Cakir, Emre Ozkaya, Fatih Akkus, Ezgi Kucukbas, and Okan Yilmaz Instinctive Data Analysis in Machine Learning and Summary Exhibitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 R. Sanjjushri Varshini, T. Madhushree, R. Priyadharshini, K. Yoga Priya, A. S. Akshara, and J. Venkatesh A Supervised Learning Approach to Store Choice Behavior Modeling Using Consumer Panel Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Mozhgan Sobhani and Tolga Kaya

Contents

xi

Categorization of the Models Based on Structural Information Extraction and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Alireza Khalilipour, Fatma Bozyigit, Can Utku, and Moharram Challenger Improving and Assessing the Prediction Capability of Machine Learning Algorithms for Breast Cancer Diagnosis . . . . . . . . . . . . . . . . . 182 Funda Ahmetoğlu Taşdemir A Supervised Learning Algorithms for Consumer Product Returns Case Study for FLO Offline Stores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Derya Yeliz Cosar Sogukkuyu, Ozlem Senvar, Batuhan Aysoysal, Emre Yigit, Volkan Derelioglu, Mehmet Ali Varol, Muhammed Fatih Polat, Salih Sertbas, Gozde Caglar, Burcu Kocas, Kartal Tasoglu, and Huseyin Demirkale Applications Evaluation Model for Supply Chain Agility in a Fuel Oil Supply Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Sukran Seker Efficiency Evaluation of Wastewater Treatment Plants Through Interval Data Envelopment Analysis: A Case Study in Turkey . . . . . . . 208 Selin Aksaç and H. Ziya Ulukan Feature Selection and Feature Extraction-Aided Classification Approaches for Disease Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Minglei Li, Xiang Li, Yuchen Jiang, Shen Yin, and Hao Luo Risk Assessment of WtE Plants by Using a Modified Fuzzy SCEA Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Esra Ilbahar, Selcuk Cebi, and Cengiz Kahraman An Intelligent Retinal Fundus Image Label Sharing Method by Domain Transformation Technique . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Xiang Li, Minglei Li, Yuchen Jiang, Shen Yin, and Hao Luo The Effect of Trust and Price on Satisfaction and Intention to Online Group Buying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 Samira Baratian, Abdul Sattar Safaei, and Ajith Abraham Kidney Transplantation and Allocation Decision Support Analysis Under COVID-19 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Yaser Donyatalab and Fariba Farid A Forecasting Study of Covid-19 Epidemic: Turkey Case . . . . . . . . . . . 263 Omer Faruk Gurcan, Omer Faruk Beyca, Ugur Atici, and Orhan Er

xii

Contents

Social Influence in Fuzzy Group Decision Making with Applications . . . 272 Amirah Nabilah Sedek Abu Bakar Sedek, Nor Hanimah Kamis, Norhidayah A Kadir, Daud Mohamad, and Francisco Chiclana Predicting Firms’ Performances in Customer Complaint Management Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 Serhat Peker Hybrid Models for Vendor Selection Problem in Software Industry: A Pilot Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Servet Soygüder, Babak Daneshvar Rouyendegh, and Aylin Tan Supply Chain Network (SCN) Resilient Pattern Recognition and Intelligent Strategy Recommender Approach for the Post-COVID-19 Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Yaser Donyatalab Remote Access and Management of Plants Experience During Pandemics Time Across the World . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 Nodirdek Yusupbekov, Farukh Adilov, Maksim Astafurov, and Arsen Ivanyan An Autonomous UAV Based Rail Tracking and Sleeper Inspection with Light-Weight Line Segmentation Approach . . . . . . . . . . . . . . . . . . 317 Ilhan Aydın, Erhan Akın, and Emre Güçlü Modeling Urban Human Mobility and Predicting Planning Transportation Facilities Using K-Means Clustering Algorithm . . . . . . . 325 Mwizerwa Maurice and Hanyurwimfura Damien Air Cargo Competition with Modern Layout Methods . . . . . . . . . . . . . 336 Ahmed Oudah Abbood and Gözde Ulutagay Performance of Simultaneous Perturbation Stochastic Approximation for Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 Ramazan Algin, Ali Fuat Alkaya, and Mustafa Agaoglu Intelligent Container Repositioning with Depot Pricing Policies . . . . . . . 355 Ceyhun Güven, Erdinç Öner, and Uğur Eliiyi Improving the Performance of a Network of Signalized Roundabouts via Microscopic Traffic Simulation Tool . . . . . . . . . . . . . . . . . . . . . . . . . 364 Syed Shah Sultan Mohiuddin Qadri, Mahmut Ali Gökçe, and Erdinç Öner Decarbonization of Turkey: Text Mining Based Topic Modeling for the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372 Selin Yilmaz, Ercem Yeşil, and Tolga Kaya The STAMP€ IT Platform: Digitalisation for Modelers . . . . . . . . . . . . . 380 Jérôme Henry and Alexandre Januário

Contents

xiii

Development of Secure Platform for Innovative Processes Implementation in Scientific and Industrial Cluster by VPN Network Segment Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Artur Zaenchkovski, Alexey Lazarev, and Sergey Moiseev Performance Measurement of Healthcare: A Case Study for Pilot Hospital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Babak Daneshvar Rouyendegh Erdebilli and Yavuz Selim Özdemir Store Segmentation in Retail Industry Using Clustering Algorithms . . . 409 Ayşegül Ünal, Merve Önal, Tolga Kaya, and Tuncay Özcan A Comparative Study of Artificial Intelligence Based Methods for Abnormal Pattern Identification in SPC . . . . . . . . . . . . . . . . . . . . . . 417 Umut Avci, Önder Bulut, and Ayhan Özgür Toy A Capacity Allocation Model for Air Cargo Industry: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Dilhan İlgün and S. Emre Alptekin Analysis of Covid-19 News Using Text Mining Techniques . . . . . . . . . . 438 Emine Çağatay, Bahar Y. Sünnetci, Selin Orbay, and Tolga Kaya Development of Digital Twin for Centrifugal Rotating Equipment Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 Nodirbek Yusupbekov, Farukh Adilov, and Arsen Ivanyan Prediction of Stock Price Movements Using Statistical and Hybrid Regression Techniques to Reduce Diversify Risk . . . . . . . . . . . . . . . . . . 456 Bhupinder Singh and Santosh Kumar Henge Lung CT Image Enhancement Using Improved Linear Iterative Clustering for Tumor Detection in the Juxta Vascular Region . . . . . . . . 463 Arun B. Mathews, S. U. Aswathy, and Ajith Abraham A Novel Comprehensive Decision-Making Criteria Weights Proposition Under Fuzzy Preference Judgments for Project Type Selection in the Shipbuilding Industry . . . . . . . . . . . . . . . . . . . . . . . . . . 472 Umut Atalma and Tuncay Gürbüz Assessment of Long - Short Term Relation Between Category Sales and IoT Sensors: A FMCG Retailer Application . . . . . . . . . . . . . . . . . . 480 Defne İdil Eskiocak, Furkan Oral, Buse Mert, Ömer Zeybek, and Burak Kilercik Pilot Location Selection for Cargomatics: Contactless Parcel Pick-Up Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 Tutku Tuncalı Yaman and Serdar Yaylalı

xiv

Contents

Markovian Decision Process Modeling Approach for Intervention Planning of Partially Observable Systems Prone to Failures . . . . . . . . . 497 Oktay Karabağ, Önder Bulut, and Ayhan Özgür Toy Combined Approach to Evaluation of Microcredit Borrowers Solvency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Elchin Aliyev, Elmar Aliev, and Adila Ali Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514 Mohamed Bennawy and Passent el-Kafrawy Determining Annual and Monthly Sales Targets for Stores and Product Categories in FMCG Retail . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 Buse Mert, Defne İdil Eskiocak, İskender Ülgen Oğul, Mustafa Kaan Aslan, and Erem Karalar The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations Guided by Social Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Sevra Çiçekli and Tuncay Gürbüz Evaluation of Cryptocurrencies Dynamically Based on Users’ Preferences Using AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Abdul Razak Zakieh, Semih Utku, and Fady Amroush A GRASP Algorithm for Multi-objective Airport Gate Assignment Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 Mert Paldrak and Mustafa Arslan Örnek Preprocessing Approach Using BADF Filter in MRI Images for Brain Tumor Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 S. U. Aswathy and Ajith Abraham Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 Selen Burçak Akkaya and Mahmut Ali Gökçe Linguistics A MCDM Method for Measuring Digital Capability Maturity Based on Linguistic Variables and Fuzzy Integral . . . . . . . . . . . . . . . . . . . . . . 579 Chen-Tung Chen, Alper Ova, and Wei-Zhan Hung Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588 Yinsheng Zhang

Contents

xv

Social Sentiment Analysis for Prediction of Cryptocurrency Prices Using Neuro-Fuzzy Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606 Şule Öztürk Birim and Filiz Erataş Sönmez Exploring the Critical Factors of Digital Transformation Based on Linguistic Variables and DEMATEL . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 Chien-Wen Chen and Chen-Tung Chen Sentiment Analysis of Elon Musk’s Twitter Data Using LSTM and ANFIS-SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626 Buğra Erkartal and Atınç Yılmaz Surveys Review of Descriptive Analytics Under Fuzziness . . . . . . . . . . . . . . . . . . 639 Elmira Farrokhizadeh and Başar Öztayşi Fuzzy Centrality Measures: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . 646 Fatima-ezzahra Badaoui, Azedine Boulmakoul, Ahmed Lbath, Rachid Oulad Haj Thami, Ghyzlane Cherradi, Lamia Karim, and Adil El Bouziri Review of Fuzzy Multi-criteria Decision Making Methods for Intelligent Supplier Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Dilek Akburak Pythagorean Fuzzy Sets Domination in Pythagorean Neutrosophic Graphs with an Application in Fuzzy Intelligent Decision Making . . . . . . . . . . . 667 D. Ajay, S. John Borg, and P. Chellamani Game Level Design in Mobile Gaming Industry: Fuzzy Pythagorean Similarity Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 Ahmet Tezcan Tekin A Decision Making Approach Using Linear Diophantine Fuzzy Sets with Dombi Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684 J. Aldring, S. Santhoshkumar, and D. Ajay IoT Platform Selection Using Interval Valued Intuitionistic Fuzzy TOPSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693 Sezi Çevik Onar, Cengiz Kahraman, and Başar Öztayşi Interval-Valued Pythagorean Fuzzy AHP&TOPSIS for ERP Software Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702 Tuğba Dalyan, Irem Otay, and Mehmet Gülada

xvi

Contents

Type-2 Fuzzy Sets Stabilization of a Fuzzy Controller Using an Interval Type-2 Fuzzy System Designed with the Bee Colony Optimization Algorithm . . . . . . . 713 Leticia Amador-Angulo and Oscar Castillo Optimal Design and Internet of Things Implementation of a General Type-2 Classifier for Blood Pressure Levels . . . . . . . . . . . . . . . . . . . . . . 722 Oscar Carvajal, Patricia Melin, and Ivette Miramontes Parameter Adaptation in Harmony Search with Shadowed Type-2 Fuzzy Approach for Designing Optimized Interval Type-2 Fuzzy Controllers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730 Cinthia Peraza, Patricia Ochoa, and Oscar Castillo Diagnosis of Diabetes Using Type-2 Fuzzy System . . . . . . . . . . . . . . . . . 739 Hamit Altıparmak, Rahib Abiyev, and Murat Tüzünkan Hesitant Fuzzy Sets Neutrosophic Hesitant Fuzzy Optimization Approach for Multiobjective Programming Problems . . . . . . . . . . . . . . . . . . . . . . 751 Firoz Ahmad and M. Mathirajan How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 763 Bartłomiej Kizielewicz, Andrii Shekhovtsov, and Wojciech Sałabun Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773

Machine Learning

An Improved Animal Migration Optimization Approach for Extreme Learning Machine Tuning Miodrag Zivkovic , Ana Vesic , Nebojsa Bacanin(B) , Ivana Strumberger , Milos Antonijevic , Luka Jovanovic , and Marina Marjanovic Singidunum University, Danijelova 32, 11000 Belgrade, Serbia {mzivkovic,ana.vesic.18,nbacanin,istrumberger, mantonijevic,mmarjanovic}@singidunum.ac.rs, [email protected]

Abstract. Extreme learning machine, a relatively recent learning algorithm that can be applied to single hidden layer feed-forward neural networks, has recently gained much attention from researchers worldwide. By virtue of its characteristics, extreme learning machine can be considered as exceptionally swift learning method with superior generalization capabilities and less required supervising by humans than other techniques. However, there are still open challenges in this domain and one of the biggest open tasks is that the capacity of extreme learning machine at large extent depends on the assigned weights and biases for the hidden layer, which represents NP-hard real-parameter optimization problem. To tackle this issue, in this research a modified variant of the animal migration optimization metaheuristics is applied for optimizing extreme learning machine hidden layer weights and biases. Suggested algorithm was tested on 7 well-recognized classification benchmarking datasets and compared with the basic animal migration optimization metaheuristics and other techniques developed thus far. According to experimental findings, proposed approach obtains improved generalization performance than the other methods. Keywords: Animal optimization algorithm · Optimization · Classification · Swarm intelligence · Extreme learning machine

1

Introduction

For training one hidden layer feed-forward neural network, or SFLNs, a relatively novel algorithm was proposed by [13] and that is Extreme learning machine (ELM) algorithm. The input weight and hidden layer bias values are generated arbitrary, and the output weight values are computed based on the MoorePenrose pseudo inverse [1]. ELM has good generalization performance, though in order for that to be achieved the number of hidden neurons must be significant. ELM also converges fast. The fixed number of input weights and hidden bias c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 3–13, 2022. https://doi.org/10.1007/978-3-031-09176-6_1

4

M. Zivkovic et al.

values that remain during the learning process is the important characteristic of ELM that differs from traditional gradient-descent based algorithms. The improved versions of ELM deal with the problem of the adequate amount of neural cells within the hidden layer. Pruned Extreme learning machine (PELM) has been suggested by [16] to counter the problem of pattern classification. It deals with the relevance of the input neurons which number is high in the beginning, narrowing it down using statistical methods with intention of determining the neurons’ relevance according to the class labels. Those who have low relevance or are completely irrelevant will be eliminated. Evolutionary extreme learning machine (E-ELM) introduced by [20] tunes the input weights and hidden biases using differential evolution (DE), while output weights calculating via Moore-Penrose generalized inverse. Improved version of this model was introduced by [11], self-adaptive evolutionary extreme learning machine algorithm (SaE-ELM). It optimizes the ELM hidden parameters using self-adaptive differential evolution while analytically determining output weights. Two algorithms using swarm intelligence for tuning ELM, namely the ABCELM and the IWO-ELM, were introduced by [1]. ABC-ELM uses artificial bee colony algorithm to, as it is previously mentioned, tune the input parameters while ELM method determines the output weights analytically. IWO-ELM uses the invasive weed optimization algorithm for the input weights and hidden neurons biases optimization while the ELM is utilized for calculating the output weights as in ABC-ELM. This paper introduces an improved animal migration optimization algorithm (AMO) for tuning ELM. The contribution of the research presented here is bifold. First, a modified variant of the AMO has been devised, that uses chaotic initialization to overcome the drawbacks of the original implementation. Second, the new method has been used to ameliorate the ELM capacity further. The structure of the manuscript is as follows. Section 2 introduces important theoretical background associated with ELM and swarm intelligence metaheuristics along with applicable literature sources. Section 3 ref represents Animal migration optimization and diversifications for ELM tuning. Section 4 suggests empirical results, comparative evaluation and brief discussion of the obtained findings. Lastly, Sect. 5 offers the conclusion and indicates future research on this area.

2

Background and Literature Review

This section first introduces the ELM, and later on moves to the swarm intelligence and it’s application in machine learning. 2.1

ELM

Extreme learning machine as a novel algorithm deals with SFLNs. ELM initializes the hidden neurons in a random fashion, and the output weights are

An Improved Animal Migration Optimization Approach for ELM Tuning

5

computed analytically, using Moore-Penrose inverse [20]. Its hidden layer performs input parameters nonlinear transformation, transforming them into the high-dimensional ELM region of features. Input parameters have their probabilities of linear separability the ELM feature space. The probability is often enlarged by this transformation, therefore simplifying the way to the underlying problem solution. Suppose we are starting with a given set of training examples described by: N = (xi , ti )|xi ∈ Rd , ti ∈ Rm , i = 1, ...N

(1)

the output of single-hidden layer feed-forward neural networks with L hidden neurons and activation function denoted as g(x) can be represented by Eq. (2): L 

βi g(wi · xj + bi ) = yj , j = 1, ..., N.

(2)

i=1

where wi = [(wi1 , ..., (wid ]T

(3)

and bi represent the input weight and bi the hidden neuron bias. βi = [βi1 , ..., βim ]T

(4)

makes for the output weight. The inner product of wi and xj is wi · xj . By utilizing the obtained SLFNs, the set of parameters βi , i = 1, ..., L is possible to be estimated in the following way: L 

βi g(wi · xj + bi ) = tj , j = 1, ..., N.

(5)

i=1

The Eq. (5) can be written as Hβ = T where

⎤ g(w1 · x1 + b1 ) . . . g(wL · x1 + bL ) ⎥ ⎢ .. .. H=⎣ ⎦ . ... . g(w1 · xN + b1 ) . . . g(wL · xN + bL ) ⎡ T⎤ β1 ⎢ .. ⎥ β=⎣ . ⎦ ⎡

βLT and

(6)

(7)

(8)

L×m

⎤ tT1 ⎢ ⎥ T = ⎣ ... ⎦ ⎡

tTN

N ×m

(9)

6

M. Zivkovic et al.

H stands for the network’s hidden layer output matrix. The output weight values β are determined systematically via minimum norm least-square solution: β = H ⊕T

(10)

In the previous equation the H ⊕ represents the Moore-Penrose inverse of H. 2.2

Swarm Intelligence and It’s Application in Machine Learning

Swarm intelligence stochastic metaheuristics have been established as excellent optimizers, often used to tackle different NP-hard problems that can not to be solved via the deterministic approach. Metaheuristics have been motivated by natural processes, including the social actions of swarms of ants, moths, bees, bats, fireflies, wolves etc. Swarm intelligence methods are used to solve hard problems with a notable success, such as the global numerical optimization [8], wireless sensors networks challenges [5,21], optimization of various artificial neural network models [2– 4,9,17,24], task scheduling problem of the cloud-based domains [6,23], MRI classifying task used for computer-supported health diagnostic [7,10], and most recently the COVID-19 infection rate forecast [22].

3

Proposed Method: Animal Migration Optimization and Adaptations for ELM Tuning

In this section AMO algorithm that is used in this paper for ELM improvement is described and the solution for the underlying problems is proposed. 3.1

Basic Animal Migration Optimization

Animal Migration Optimization algorithm [15] is a swarm intelligence algorithm that follows three rules: same direction movement of the individual as it’s neighbors, individual’s close proximity to the neighbors and collision avoidance. The algorithm assumes two things considering the swarm: 1. the leader animal that possesses high quality positions shall remain in the swarm in the following generation, 2. The number of animals in the swarm does not change, it always remains the same, whereby instead of the animal that is leaving comes a new animal with a P a probability. In this algorithm there are two processes that are happening and those are migration and population updating. The first process represents certain animals leaving the group and others joining it. While migration process is taking place, each individual obeys three rules: avoids colliding with the neighbors, moves in the same direction as them, and remains close to them. In order for the first rule to be respected each individual should have a different position. Regarding the other two rules, the individual should move to a new position in accordance with the current positions of its neighbors. Local neighborhood is described with a ring topology scheme [15]. A neighbor

An Improved Animal Migration Optimization Approach for ELM Tuning

7

is chosen randomly, while the position of an individual is updated in accordance to the randomly chosen neighbor. This is represented in the equation: Xi,G+1 = Xi,G + δ · (Xneighborhood,G − Xi,G )

(11)

Xneighborhood,G represents neighborhood’s current position, while δ is produced using random number generator controlled by Gaussian distribution, and may be changed in accordance to the real-world problems. The second process simulates the population updating, or rather in what way some units abandon the group and how some are joining it during the migration process. Supposing that there is a global optimization task and the populace consisted of candidate solutions. Each of the individuals is described by a Ddimensional real encoded vector. The population is initiated randomly which utilized NP D-dimension parameter vector bounded by predetermined minimal and maximal boundaries, NP being the population’s size:

 min = x1,min , x2,min , ..., xD,min (12) X

 max = x1,max , x2,max , ..., xD,max X Thus, jth component of the ith vector can be initialized as:   xi,j,0 = xj,min + randi,j 0, 1 · xj,max − xj,min

(13)

(14)

randi , j represents a uniform distribution of random numbers between 0 and 1, i=1, ..., NP and j= 1, ..., D. The basic implementation of AMO is given in Algorithm 1.

Algorithm 1. AMO pseudo-code Initialization of the generation counter G-0; and random generation of the starting populace of NP animals Xi . Evaluation of the fitness value for every individual in P. while finishing criteria is not met do do for i = 1toN P do for j = 1toD do Xi,G+1 = Xi,G + δ · (Xneighborhood,G + Xi,G ) end for end for for i = 1 : N P do Validate the offspring Xi,G+1 if Xi,G+1 isbetterthanXi then Xi = Xi,G+1 end if end for for i = 1toN P do for j = 1toD do choose arbitrary r1 = r2 = i if rand > P a then Xi,G+1 = Xr1,G = rand · (Xb est, G − Xi,G ) + rand · (Xr2,G − Xi,G ) end if end for end for for i = 1toN P do Validate the offspring Xi,G+1 if Xi,G+1 is better than Xi then Xi = Xi,G+1 end if end for Store the best solution obtained until now end while

8

3.2

M. Zivkovic et al.

Improved Animal Migration Optimization

The AMO algorithm is a novel method that has shown excellent optimization capabilities, however, as all other metaheuristics approaches, it has some drawbacks. The simulations with benchmark CEC functions have shown that in some runs of the algorithm, it tends to dwell in the sub-optimal areas of the search region in early iterations. These early best solutions that have missed the ideal portion of the search space will cause other solutions to also converge to suboptimal areas. As it is hard for the basic AMO to escape from the sub-optimal regions in later iterations, this will inevitably lead to worse solutions at the final stages of the run. The proposed improved AMO algorithm is enhanced with chaotic population initialization by incorporating chaotic maps [12]. The stochastic characteristics of basic AMO (and other metaheuristics algorithms) is depending on random number generators. Nevertheless, recent research papers have suggested that the search procedure could be improved if it was contingent on chaotic sequences [14,18]. Many chaotic maps are available nowadays, including the circle, Chebyshev, logistic, sine, tent, and numerous other maps. Following extensive testing with numerous available chaotic maps, the best results have been obtained with the logistic map, and therefore it was selected and implemented in AMO algorithm. The suggested technique puts into the use the chaotic sequence β, that begins with the initial random number β0 , produced by the logistic mapping, as defined with the Eq. (15): βi+1 = μβi × (1 − βi ), i = 1, 2, . . . , N − 1,

(15)

where μ and N represent the chaos control parameter and the number of individuals in the population. The μ is set to 4 [19], to secure the chaotic movement of individual units, while 0 < β0 < 1 and β0 = 0.25, 0.5, 0.75, 1. The solutions are mapped to produced chaotic sequences by using the following equation for every parameter j of entity i: Xic = βi Xi ,

(16)

where Xic denotes the fresh position of entity i in the wake of chaotic disturbances. With this modification, the quality of solutions is enhanced at the start of a run and every search agent can use additional rounds to perform exploitation. The novel approach is called IAMO (improved AMO). 3.3

Proposed ELM Model

The standard ELM model utilizes arbitrary initialization of the input weight and bias values, and therefore is susceptible to certain performance issues. More precisely, it often requires a great amount of neuron cells, that are either unnecessary or sub-optimal. The increased quantity of hidden neural cells can cause slow response of the ELM structure if the new data is fed to the input nodes

An Improved Animal Migration Optimization Approach for ELM Tuning

9

and reduce the applicability of the model for some practical domains. The suggested hybrid metaheuristics-ELM model combines the novel IAMO metaheuristics algorithm and uses it for optimization of the input weight values and hidden layer neurons’ biases, and MP generalized inverse for calculating the output weight values. The novel hybrid model is named IAMO-ELM.

4

Experiments and Discussion

In the first experiment four UCI (University of California, Irvine) benchmark data sets were used, them being Diabetes, Heart disease, Iris and Wine data set. They can be obtained at https://archive.ics.uci.edu/ml/datasets.php. Brief description of each data set follows. Diabetes, The Pima Indians Diabetes data set, can be utilized to determine if a patient is positive or not. The data set has 768 samples belonging to two distinct classes. The next one, Heart disease data set (Disease) is consisted of thirteen properties and contains also two classes. Classes denote if the patient has a heart disease or not. The Fisher Iris data set is made of three flower types measurements namely, viz. Setosa, Verginica and Versicolor. This data set has three classes, each class having 50 patterns. In Wine data set there are 178 patterns that belong to three types of wines. In order to obtain Wine data set chemical analysis is used and applied on wines developed within the same Italian region but produced by three different cultivators. Table 1. Comparative accuracy performances on Diabetes, Disease, Iris and Wine datasets Datasets Methods

Accuracy (Testing) Dev. (Testing) Neurons/SVs

Diabetes SaE-ELM

79.55

2.68

74.73

3.2

20

2.56

35

LM

CFWNN-ELM 78.02

Disease

20

SVM

77.31

2.73

62.28

ABC(1)-ELM

77.13

2.02

10

ABC(2)-ELM

77.19

1.89

13

IWO(1)-ELM

77.03

1.68

9

IWO(2S)-ELM 77.31

1.69

13

AMO-ELM

79.94

2.71

13

IAMO-ELM

81.17

2.58

13

SaE-ELM

82.53

3.86

18

LM

71.75

6.67

20

CFWNN-ELM 76.85

3.43

30

SVM

76.1

3.46

81.6

ABC(1)-ELM

81.35

2.39

7

ABC(2)-ELM

81.49

2.56

8

IWO(1)-ELM

81.61

2.81

11

IWO(2)-ELM

81.92

2.41

15

AMO-ELM

83.17

3.75

15

IAMO-ELM

83.93

3.62

15 (continued)

10

M. Zivkovic et al. Table 1. (continued) Datasets Methods

Accuracy (Testing) Dev. (Testing) Neurons/SVs

Iris

SaE-ELM

97.2

4.12

LM

95.4

3.19

10

3.05

20

CFWNN-ELM 95.92 SVM

Wine

2.76

23.3

ABC(1)-ELM 97.2

2.46

17

ABC(2)-ELM 97.08

2.39

12

IWO(1)-ELM 97.04

2.40

18

IWO(2)-ELM 97.6

2.21

17

AMO-ELM

97.83

2.26

17

IAMO-ELM

98.04

2.21

17

SaE-ELM

97.95

2.54

22

LM

92.97

4.36

20

CFWNN-ELM 95.15

3.13

30

SVM

94.36

18

97.48

1.57

47.3

ABC(1)-ELM 97.79

1.72

19

ABC(2)-ELM 97.97

1.97

20

IWO(1)-ELM 98

2.07

15

IWO(2)-ELM 98.13

1.64

20

AMO-ELM

98.29

1.72

20

IAMO-ELM

98.46

1.62

20

The second experiment utilized three additional UCI datasets, namely Image segmentation, Satellite image and Shuttle. Image segmentation dataset contains 7 outdoor photosm, that have been subjected to segmentation by hand to enable classifying for each of the individual pixels. Satellite image dataset is formed of the multi-spectral pixel values in 3 × 3 neighbourhoods within the satellite images. Shuttle dataset refers to the placement of heaters inside the Space Shuttle, consisting of 58000 patterns. This research and both experiments were inspired by [1]. To enable solid foundations for the comparative study of the suggested model, the identical simulation environment was employed as described in referred research [1]. The experimental results of the proposed IAMO-ELM and basic AMO-ELM on the first group of datasets are presented in the Table 1, merged with the experimental results of competitor algorithms executed on the same datasets, which were obtained from [1]. The outcomes on the second group of datasets have been given in the Table 2. The reported values represent the average results obtained over 50 independent runs. The termination condition for the IAMOELM and AMO-ELM algorithms was limited to 100 iterations, as in [1]. The best accuracy result per each dataset is given in bold text in Tables 1 and 2. The results provided in Tables 1 and 2 point out to superior performances of the presented IAMO-ELM model. IAMO-ELM approach significantly outperformed basic AMO-ELM and other metaheuristics ELM versions, and achieved the best accuracy level on all seven benchmark datasets utilized in this research.

An Improved Animal Migration Optimization Approach for ELM Tuning

11

Table 2. Comparative accuracy performances on Image segmentation, Satellite image and Shuttle datasets Datasets

Methods

Accuracy(Testing)

Dev(Testing) Neurons/SVs

95.26

1.56

ELM

95.07

1.14

200

DE-ELM

88.65

2.05

80

Image segmentation E-ELM

Satellite image

Shuttle

5

70

LM

86.27

1.8

100

GALS

94.27

1.95

80

ABC(1)-ELM 95.36

1.25

170

ABC(2)-ELM 95.33

1.23

170

IWO(1)-ELM 95.64

1.28

150

IWO(2)-ELM 95.25

1.26

170

AMO-ELM

95.69

1.33

170

IAMO-ELM

95.76

1.28

170

E-ELM

88.47

1.36

90

ELM

88.99

1.01

500

DE-ELM

85.31

2.14

80

LM

82.34

1.25

100

GALS

86.5

2.06

80

ABC(1)-ELM 89.01

1.02

410

ABC(2)-ELM 89.17

0.98

400

IWO(1)-ELM 89.10

0.99

400

IWO(2)-ELM 89.02

1.03

410

AMO-ELM

89.68

1.05

400

IAMO-ELM

90.14

0.99

400

E-ELM

99.52

0.11

20

ELM

99.49

0.11

200

DE-ELM

99.31

0.08

20

LM

99.28

0.12

50

GALS

Not enough memory error

ABC(1)-ELM 99.53

0.08

160

ABC(2)-ELM 99.53

0.08

150

IWO(1)-ELM 99.57

0.08

150

IWO(2)-ELM 99.54

0.08

160

AMO-ELM

99.59

0.11

160

IAMO-ELM

99.64

0.08

160

Conclusion

The research described here suggested a metaheuristics-based technique for training feed-forward neural networks that contain single hidden layer. The IAMO metaheuristics was developed to overcome the shortcomings of the basic AMO by incorporating chaotic initialization. It was later used as the selected optimizer for the ELM network, and the model was named IAMO-ELM. The IAMO role is to tune the input weight and bias values, while the ELM determines the output weights. The suggested IAMO-ELM model was rated on seven traditional UCI benchmark datasets, and the findings were evaluated by comparison to other cutting-edge techniques executed on the same datasets. The performed experiments strongly indicate that the suggested approach can obtain better generalization performances, when compared to competitor

12

M. Zivkovic et al.

approaches observed in the analysis. The suggested model achieved the best accuracy values on all seven observed datasets. These promising initial scores indicate bright future for the combined metaheuristics-ELM approach. The future experiments in this domain would include further validation of the introduced technique on other benchmark and real-world datasets. Another possible direction of the future research would include other metaheuristics (in basic implementations or as modified variants) combined with ELM with a goal to achieve even higher accuracy scores.

References 1. Alshamiri, A.K., Singh, A., Surampudi, B.R.: Two swarm intelligence approaches for tuning extreme learning machine. Int. J. Mach. Learn. Cybern. 9(8), 1271–1283 (2017). https://doi.org/10.1007/s13042-017-0642-3 2. Bacanin, N., Alhazmi, K., Zivkovic, M., Venkatachalam, K., Bezdan, T., Nebhen, J.: Training multi-layer perceptron with enhanced brain storm optimization metaheuristics. Comput. Mater. Continua 70(2), 4199–4215 (2022). https://doi.org/10. 32604/cmc.2022.020449, http://www.techscience.com/cmc/v70n2/44706 3. Bacanin, N., et al.: Artificial neural networks hidden unit and weight connection optimization by quasi-refection-based learning artificial bee colony algorithm. IEEE Access 9, 169135–169155 (2021) 4. Bacanin, N., Stoean, R., Zivkovic, M., Petrovic, A., Rashid, T.A., Bezdan, T.: Performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: application for dropout regularization. Mathematics 9(21) (2021). https://doi.org/10.3390/math9212705, https://www.mdpi.com/ 2227-7390/9/21/2705 5. Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., Tuba, M.: Whale optimization algorithm with exploratory move for wireless sensor networks localization. In: Abraham, A., Shandilya, S.K., Garcia-Hernandez, L., Varela, M.L. (eds.) HIS 2019. AISC, vol. 1179, pp. 328–338. Springer, Cham (2021). https://doi.org/10. 1007/978-3-030-49336-3 33 6. Bacanin, N., Zivkovic, M., Bezdan, T., Venkatachalam, K., Abouhawwash, M.: Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Comput. Appl. 34, 1–26 (2022) 7. Basha, J., et al.: Chaotic harris hawks optimization with quasi-reflection-based learning: an application to enhance CNN design. Sensors 21(19), 6654 (2021) 8. Bezdan, T., Petrovic, A., Zivkovic, M., Strumberger, I., Devi, V.K., Bacanin, N.: Current best opposition-based learning salp swarm algorithm for global numerical optimization. In: 2021 Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 5–10. IEEE (2021) 9. Bezdan, T., et al.: Hybrid fruit-fly optimization algorithm with k-means for text document clustering. Mathematics 9(16), 1929 (2021) 10. Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Glioma brain tumor grade classification from MRI using convolutional neural networks designed by modified FA. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 955–963. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2 111 11. Cao, J., Lin, Z., Huang, G.B.: Self-adaptive evolutionary extreme learning machine. Neural Process. Lett. 36(3), 285–305 (2012)

An Improved Animal Migration Optimization Approach for ELM Tuning

13

12. Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. 7(3), 289–304 (2003) 13. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), vol. 2, pp. 985–990. IEEE (2004) 14. Kose, U.: An ant-lion optimizer-trained artificial neural network system for chaotic electroencephalogram (EEG) prediction. Appl. Sci. 8(9), 1613 (2018) 15. Li, X., Zhang, J., Yin, M.: Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput. Appl. (8), 1867–1877 (2013). https://doi.org/10.1007/s00521-013-1433-8 16. Rong, H.J., Ong, Y.S., Tan, A.H., Zhu, Z.: A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(1–3), 359–366 (2008) 17. Strumberger, I., Tuba, E., Bacanin, N., Zivkovic, M., Beko, M., Tuba, M.: Designing convolutional neural network architecture by the firefly algorithm. In: 2019 International Young Engineers Forum (YEF-ECE), pp. 59–65. IEEE (2019) 18. Wang, M., Chen, H.: Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. 88, 105946 (2020) 19. Yu, H., Zhao, N., Wang, P., Chen, H., Li, C.: Chaos-enhanced synchronized bat optimizer. Appl. Math. Model. 77, 1201–1215 (2020) 20. Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recogn. 38(10), 1759–1763 (2005) 21. Zivkovic, M., Bacanin, N., Tuba, E., Strumberger, I., Bezdan, T., Tuba, M.: Wireless sensor networks life time optimization based on the improved firefly algorithm. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 1176–1181. IEEE (2020) 22. Zivkovic, M., et al.: Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc. 66, 102669 (2021) 23. Zivkovic, M., Bezdan, T., Strumberger, I., Bacanin, N., Venkatachalam, K.: Improved harris hawks optimization algorithm for workflow scheduling challenge in cloud–edge environment. In: Pandian, A.P., Fernando, X., Islam, S.M.S. (eds.) Computer Networks, Big Data and IoT. LNDECT, vol. 66, pp. 87–102. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0965-7 9 24. Zivkovic, M., Stoean, C., Chhabra, A., Budimirovic, N., Petrovic, A., Bacanin, N.: Novel improved salp swarm algorithm: an application for feature selection. Sensors 22(5), 1711 (2022)

Store-based Demand Forecasting of a Company via Ensemble Learning Ahmet Tezcan Tekin1(&) and Cem Sarı2 1

2

Soft Towel Games Ltd., Valetta, Malta [email protected] Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey [email protected]

Abstract. Demand forecasting is a topic that is frequently used in the literature and is applied in almost every field. For companies, being able to predict future demand provides a strategic advantage. Especially companies with more than one store have to make demand forecasts for their products on a store basis. The reason for this is that the demand for each product can differ based on the store. For this purpose, although there are many examples of traditional approaches in the literature, machine learning methods have been widely used for demand forecasting in recent years. The use of ensemble learning algorithms along with traditional algorithms in machine learning problems has also positively affected demand forecasting success. In this study; Demand forecasting with store-based historical sales data of a company's products was estimated by machine learning method, and the results of ensemble learning algorithms and traditional machine learning algorithms were compared. To improve the results obtained, hyperparameter optimization was applied to the most successful algorithms and increased prediction success. Keywords: Demand forecasting Feature engineering


Machine learning
Ensemble learning


1 Introduction Demand forecasting is the process of forecasting future demand based on the analysis of past demands. Companies update their strategic positions with short and mediumterm demand forecasts. It is essential for companies that this forecast is correct. Because with these forecasts, companies shape their future plans and strategic decisionmaking processes. Many methods have been proposed in the literature to realize the demand with a high forecasting accuracy. Choosing and applying the most effective method among these suggested methods is very important. Because companies plan many resources such as raw materials, labour, transportation and machinery in line with this forecast, based on this forecast. Although qualitative and quantitative methods are traditionally used in demand forecasting, the use of artificial intelligence-based methods is increasing day by day.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 14–23, 2022. https://doi.org/10.1007/978-3-031-09176-6_2

Store-based Demand Forecasting of a Company via Ensemble Learning

15

In this study, store-based demand forecasting was realized. One of the main contributions is the dataset consists of time series characteristics, but in the feature engineering stage, we transformed this dataset into a regression dataset. Ensemble machine learning algorithms which are the family of bagging and boosting type ensemble learning algorithms and traditional algorithms were applied to the dataset, which is the output of data preprocessing and feature engineering stage. After that, hyperparameter optimization was applied to the three best algorithms to increase the prediction success rate. In the paper, the second section deals with the literature review of machine learing algorithms used in this study and demand forecasting applications. The third section describes the dataset used in this study, data preprocessing and feature engineering steps and comparison of the results. Finally, conclusions of the study are briefly described and future works are presented in the last section.

2 Literature Review It is a challenging task for companies to accurately and clearly forecast the quantity and demand of the product to be produced. Errors in this forecast can profoundly affect the operation and profitability of the company [1]. Predictions made must be shared separately with many departments of the companies, and each must do their internal planning according to these forecasts. Many scientific methods have been proposed to forecast demand, and many traditional demand forecasting methods are used in the private sector. These methods can be evaluated under three main headings [2]. These topics are discussed as qualitative methods, quantitative methods, and artificial intelligence-based methods. Qualitative estimation techniques, also called “subjective” or “criteria-decision-based” techniques, primarily use the human capacity to make predictions and generalizations [3]. For quantitative methods where we can predict that the standard corrected information will continue in the future, it can also be called the estimation of continuity [3]. Artificial intelligence-based methods include understanding how decisions can be made and action plans can be created based on stored information, and how to obtain computer-operable information by learning from sample data or by questioning human experts [4]. Demand forecasting applications have been made in many areas in the literature. It is possible to see different applications, especially in the field of energy. Suganthi and Samuel, Ghalehkhondabi et al., Hsu et al., Singh et al. Applied demand forecasting technics for estimating energy demand [5–8]. In addition to these studies; Fildes and Kumar [9] in the field of telecommunications, Nenni [10] in the area of the fashion industry, Archer [11] in the field of tourism, Bougadis et al. [12] in the field of water consumption carried out different demand forecasting studies. With the widespread use of developing technology in demand forecasting applications, artificial intelligence-based demand forecasting applications have increased, and deep learning and machine learning methods have begun to be used for demand forecasting. At this point, while Abbasimehr et al. [13]., Tan et al. [14], Choi et al. [15], Kong et al. [16] perform demand forecasting applications with the Long Short Term Memory (LSTM) method, while Bennett et al. [17], Al-saba et al. [18], Adamovski and

16

A. T. Tekin and C. Sarı

Karapataki [19], Güven and Şimşir [20] developed demand forecasting applications using artificial neural networks. In addition to deep learning methods, machine learning methods have also been used for demand forecasting applications, and both traditional and ensemble learning algorithms are preferred for these applications. Since the nature of the preferred machine learning algorithms is a regression problem, traditional regression algorithms such as Linear Regression, Ridge Regression, Bayesian Ridge Regression, Huber Regression etc. and algorithms from the ensemble learning algorithm family such as Random Forest, Extreme Gradient Boosting, Catboost, Light Gradient Boosting Machine have been preferred. The working principles and usage areas of these algorithms are given below. • Huber Regressor: Huber Regression is a regression algorithm proposed by Huber [21] in 1964. Instead of using the usual least-squares loss function, the aim is to employ a different loss function. Its working principle is described in Eq. (1). X Minimizeb mi ¼ 1/ðyi  xTibÞ ð1Þ where b 2 Rn for the loss, / is the Huber function with threshold M > 0. This function is the same as the least squares penalty for small residuals, but for high residuals, the penalty is lower and increases linearly rather than quadratically. As a result, it is more tolerant of outliers. • Linear Regression: Linear Regression is the oldest and best-known regression technique. It is generally accepted as the starting point of regression algorithms and its history is quite old. The Linear Regression proposed by Sir Francis Galton in 1886 is shown in Eq. (2). Y ¼ a þ bX

ð2Þ

where X is the explanatory variable and Y is the dependent variable. The line slope is b, and a is the intercept (the value of y when x = 0). • Ridge Regression: Ridge regression fits a new row to the dataset to help refine overfit model. This may increase the model’s training error, but it will help it perform on the test dataset. By doing this, bias is introduced into the model for a better tradeoff. This slight increase in bias will cause a significant decrease in variance in the overfitted model and will help in the long run. Ridge Regression was proposed by Hoerl and Kennard in 1970 [26]. • Random Forest: Random forest is one of the most popular tree-based supervised learning algorithms. It was proposed by Breiman [22] in 1996. It is also the most flexible and easy to use. Individual decision trees tend to overfit the training data, but a random forest can alleviate this problem by averaging the prediction results from different trees. This gives random forests a higher prediction accuracy than a single decision tree. The Random Forest model can be represented as

Store-based Demand Forecasting of a Company via Ensemble Learning

b ð xÞ ¼ m

1X b jðxÞ m j M

17

ð3Þ

b j denotes an individual tree; the prediction is based on the averaging of where m each tree’s prediction. • Light Gradient Boosting Machine: Light Gradient Boosting Machine is a new variant of the GBDT method and it was proposed by Ke et al. in 2017 [23]. It’s used to solve various modelling challenges, including classification and regression. To accommodate many data instances and functions, LightGBM uses two new strategies: gradient-based one-side sampling and exclusive function bundling. LightGBM increases the decision tree vertically, whilst others extend it horizontally when compared to base gradient boosting techniques or Extreme Gradient Boosting. This functionality improves LightGBM's ability to process vast volumes of data. • Catboost: Prokhorenkova introduced Catboost, a new proposed version of the gradient boosting type algorithm, in 2018 [24]. Catboost reliably works with categorical characteristics while minimizing information loss. Other gradient boosting algorithms, such as Extreme Gradient Boosting and LightGBM, is not the same as CatBoost. To solve target leakage, it uses ordered boosting, a useful variation of gradient boosting methods. Catboost model can be represented as X Z ¼ H ð xi Þ ¼ J j ¼ 1cj 1fx 2 Rj g ð4Þ H ðxi Þ is a decision tree function of the explanatory variables xi , and Rj is the disjoint region corresponding to the tree leaves. • Extreme Gradient Boosting: Chen and Guestrin proposed the XGBoost algorithm, a well-known gradient boosting algorithm [25]. XGBoost is an enhanced GBDT algorithm that employs many decision trees and is widely used in classification and regression. To make the tree's classification function more re-producible, XGBoost uses a regularization method to optimize the size of the tree's classification function. Regularization also helps with feature value prediction, essential in huge data situations. It can be represented as Z ¼ F ð xi Þ ¼

X

T t ¼ 1f t ðxi Þ

ð5Þ

where xi denotes the explanatory variables, and f t ðxi Þ is the output function of each tree.

3 Proposed Methodology and Modelling The dataset used in this study is related to a company’s store-based items. This study aims to predict future demand for store-based items using previous sales trends of these items. This prediction is crucial for the companies because they will plan for their short, medium and long term activities according to the demand. Base features that are used

18

A. T. Tekin and C. Sarı

in this study are shown in Table 1. This dataset consists of ten different stores’ fifty different items’ sales information. The base dataset consists of four different columns and 958,000 rows. Initially, data exploration and data preprocessing technics were applied to the dataset. Missing rows and columns were eliminated from the dataset. Also, duplicate rows were located and wiped out from the dataset. In the feature engineering step, the date column was splitted into three groups that show the correspondent date’s year, month, day of the week. After that month, store and item info was transformed into a dummy version and converted to 1 and 0 options. The previous sales information was shifted as columns in the last part of the feature engineering. So, we aimed to transform time series data as a regression approach. This study’s principal contribution is handling the time series problem as a regression problem. For this purpose, feature generation in the machine learning feature engineering step was applied to the dataset deeply. Table 1. Base features used in demand forecasting Feature Date

Store Item Sales

Explanation The date information of the sales Store’s ID Number Item’s SKU Number Number of items sold

Data type Date

Count 958,000

Mean

Categorical

958,000

5.500

Categorical

958,000

25.500

Numerical

958,000

52.250

Std

Min

Max

2872

1

10

14.431

1

50

28.801

0

231

After the data preprocessing and feature engineering step, traditional machine learning algorithms and ensemble learning algorithms were applied to the dataset. The error rates and execution times of each algorithm are shown in Table 2. The results in Table 2 show us that ensemble learning algorithms have the minimum error rates in the prediction and their results are so close to each other. On the other hand, Ridge, Bayesian and Linear Regression have promising results, but they couldn’t reach the ensemble learning algorithms’ success rates in the prediction. After looking at the algorithms’ prediction success results with their default parameters, hyperparameter optimization was applied the most three successful algorithms: Catboost, Light Gradient Boosting Machine and Extreme Gradient Boosting algorithms. In this stage, the grid search approach was applied for finding the most optimum parameters for these algorithms. For this reason, different learning rates, max_depth, number of estimator parameters were combined and the model was fitted separately with these different parameters. At the end of the hyperparameter optimization stage, Light Gradient Boosting Machine has the optimum parameters with a learning rate of 0.2, max_depth 1, number of estimator 120. Extreme Gradient Boosting has the optimum parameters with a learning rate of 0.4, max_depth 5, number

Store-based Demand Forecasting of a Company via Ensemble Learning

19

of estimator 200. Catboost has the optimum parameters with depth 4, learning rate of 0.03. K-Fold Cross-validation is an essential step for testing the model’s generalization and stability. So, 10 fold cross-validation was applied to each algorithm with their best parameter combinations. The results are shown in Tables 3, 4 and 5. Table 2. Algorithms’ results with their default parameters Model CatBoost Regressor Light Gradient Boosting Machine Extreme Gradient Boosting Ridge Regression Bayesian Ridge Linear Regression Least Angle Regression Huber Regressor Random Forest Regressor Passive Aggressive Regressor Extra Trees Regressor Decision Tree Regressor

MAE 8.9815

MSE 143.548

RMSE 11.981

R2 0.828

RMSLE 0.2307

MAPE 0.1985

TT (s) 37.206

9.0803

146.539

12.105

0.825

0.2352

0.2032

0.808

9.1622

148.657

12.193

0.822

0.2395

0.2041

43.106

10.2907

178.689

13.307

0.785

0.3685

0.2524

0.315

10.2807

179.699

13.405

0.785

0.3685

0.2524

1.928

10.2804

179.704

13.408

0.785

0.3685

0.2523

0.791

10.2861

179.765

13.408

0.785

0.3688

0.2526

0.158

10.16

183.171

13.534

0.781

0.3304

0.2394

3.982

10.9633

215.47

14.679

0.742

0.2758

0.2396

90.619

11.2243

215.815

14.683

0.742

0.3646

0.2715

0.772

11.4502

236.329

15.373

0.717

0.289

0.2497

113.763

11.4619

236.79

15.388

0.717

0.2893

0.2499

1.566

20

A. T. Tekin and C. Sarı Table 3. Catboost 10 fold crossvalidation results with its best parameters 0 1 2 3 4 5 6 7 8 9 Mean SD

MAE 8.9112 8.9863 9.0122 8.9317 9.015 8.9711 8.9616 8.9672 8.9747 8.9715 8.9702 0.0302

MSE 141.899 143.679 144.487 142.137 145.151 143.144 142.743 143 143.48 143.157 143.288 0.9364

RMSE 11.9121 11.9866 12.0203 11.9221 12.0478 11.9643 11.9475 11.9583 11.9783 11.9648 11.9702 0.0391

R2 0.8304 0.8284 0.8272 0.8297 0.8273 0.827 0.8289 0.8296 0.8275 0.8305 0.8287 0.0013

RMSLE 0.2302 0.2304 0.2307 0.231 0.23 0.2289 0.2299 0.2301 0.2298 0.2302 0.2301 0.0005

MAPE 0.1988 0.1988 0.1994 0.1997 0.1985 0.1977 0.1984 0.1989 0.1984 0.1988 0.1987 0.0005

Table 4. LightGBM 10 fold crossvalidation results with its best parameters 0 1 2 3 4 5 6 7 8 9 Mean SD

MAE 8.9082 8.9765 9.0079 8.9255 9.0089 8.9686 8.9626 8.9648 8.9694 8.9684 8.9661 0.0295

MSE 141.873 143.457 144.499 141.972 145.03 143.223 142.946 143.219 143.44 143.315 143.297 0.9172

RMSE 11.911 11.9773 12.0208 11.9152 12.0428 11.9676 11.956 11.9674 11.9767 11.9714 11.9706 0.0383

R2 0.8304 0.8287 0.8272 0.8299 0.8275 0.8269 0.8286 0.8293 0.8276 0.8304 0.8286 0.0012

RMSLE 0.2301 0.2303 0.2304 0.231 0.2299 0.2287 0.2298 0.2299 0.2295 0.23 0.23 0.0006

MAPE 0.1985 0.1984 0.199 0.1993 0.1981 0.1973 0.1981 0.1985 0.198 0.1984 0.1983 0.0005

10 fold cross-validation results show that the Light Gradient Boosting Machine has the minimum Mean Absolute Error, but Catboost has the minimum Root Mean Squared Error in the prediction. Also, the results show us the standard deviation of the folds is in the acceptable range for discarding the overfitting issue in the modelling stage. At the end of the evaluation of the results, despite Light Gradient Boosting Machine and Catboost having so similar results in the prediction, Light Gradient Boosting Machine was chosen as the production model. Because, when comparing the runtimes of LightGBM and Catboost, LGBM is approximately 50 times faster than Catboost. This time difference is far from the acceptable tradeoff threshold at the publication of the model.

Store-based Demand Forecasting of a Company via Ensemble Learning

21

Table 5. XGBoost’s 10 fold crossvalidation results with its best parameters 0 1 2 3 4 5 6 7 8 9 Mean SD

MAE 8.9911 9.0641 9.0862 8.9977 9.081 9.0367 9.0339 9.0396 9.0386 9.0515 9.0421 0.0295

MSE 144.037 145.693 146.657 144.04 147.046 144.682 145.157 145.12 145.108 145.411 145.295 0.9333

RMSE 12.0016 12.0703 12.1102 12.0017 12.1262 12.0284 12.0481 12.0466 12.0461 12.0587 12.0538 0.0387

R2 0.8278 0.826 0.8246 0.8274 0.8251 0.8252 0.826 0.827 0.8256 0.8279 0.8263 0.0011

RMSLE 0.2335 0.2335 0.2338 0.2339 0.2332 0.2318 0.2329 0.2332 0.2327 0.2333 0.2332 0.0006

MAPE 0.2004 0.2004 0.201 0.2008 0.1999 0.199 0.1996 0.2002 0.1998 0.2002 0.2001 0.0006

4 Conclusion and Future Work In this study, we intended to predict the product demand, which is store-based for a company. For this purpose, we applied data preprocessing and feature engineering steps to our dataset. Our dataset had the characteristics of time series, but we transformed this dataset into a regression approach dataset. In our approach, we generated new features from the existing dataset, shifted the previous sales data to columns, and expanded the feature space. So, we aimed to increase the learning ability of the regression algorithms. After this process, we applied traditional and new generation machine learning algorithms to the processed dataset with their default parameters. The first results show us ensemble type machine learning algorithms have the most successful results in the prediction stage. Because hyperparameter optimization is one of the essential stages in machine learning problems, we applied hyperparameter optimization to our best three algorithms: Catboost, Light Gradient Boosting Machine and Extreme Gradient Boosting Machine. This process has a positive impact on improving prediction success. The results show that the artificial intelligence-based demand forecasting approach has successful results. Especially, ensemble learning algorithms, Catboost, Light Gradient Boosting Machine and Extreme Gradient Boosting Machine, have robust predictions in the final stage. These algorithms’ results will be compared with traditional demand forecasting methods’ results and time-series approaches Holt-Winter and ARIMA for future work.

References 1. Carbonneau, R., Laframboise, K., Vahidov, R.: Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res. 184(3), 1140–1154 (2008)

22

A. T. Tekin and C. Sarı

2. Ergün, S., Şahin, S.: İşletme Talep Tahmini Üzerine Literatür Araştırması. ulakbilge 5(10), 469–487 (2017) 3. Viglioni, G.M.: Methodology for Railway Demand Forecasting Using Data Mining (2007) 4. Borgelt, C., Kruse, R.: Graphical Models: Methods for Data Analysis and Mining. Wiley, Chichester, UK (2002) 5. Suganthi, L., Samuel, A.A.: Energy models for demand forecasting—a review. Renew. Sustain. Energy Rev. 16(2), 1223–1240 (2012) 6. Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., Young, W.A.: An overview of energy demand forecasting methods published in 2005–2015. Energy Syst. 8(2), 411–447 (2016). https://doi.org/10.1007/s12667-016-0203-y 7. Hsu, C.C., Chen, C.Y.: Applications of improved grey prediction model for power demand forecasting. Energy Convers. Manage. 44(14), 2241–2249 (2003) 8. Singh, A.K., Ibraheem, S.K., Muazzam, M., Chaturvedi, D.K.: An overview of electricity demand forecasting techniques. Netw. Complex Syst. 3(3), 38–48 (2013) 9. Fildes, R., Kumar, V.: Telecommunications demand forecasting—a review. Int. J. Forecast. 18(4), 489–522 (2002) 10. Nenni, M.E., Giustiniano, L., Pirolo, L.: Demand forecasting in the fashion industry: a review. Int. J. Eng. Bus. Manage. 5, 37 (2013) 11. Archer, B.H.: Demand forecasting in tourism (No. Monograph) (1976) 12. Bougadis, J., Adamowski, K., Diduch, R.: Short-term municipal water demand forecasting. Hydrol. Process. 19(1), 137–148 (2005) 13. Abbasimehr, H., Shabani, M., Yousefi, M.: An optimized model using LSTM network for demand forecasting. Comput. Ind. Eng. 143, 106435 (2020) 14. Tan, M., Yuan, S., Li, S., Su, Y., Li, H., He, F.: Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning. IEEE Trans. Power Syst. 35(4), 2937–2948 (2019) 15. Choi, E., Cho, S., Kim, D.K.: Power demand forecasting using long short-term memory (LSTM) deep-learning model for monitoring energy sustainability. Sustainability 12(3), 1109 (2020) 16. Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841– 851 (2017) 17. Bennett, C., Stewart, R.A., Beal, C.D.: ANN-based residential water end-use demand forecasting model. Expert Syst. Appl. 40(4), 1014–1023 (2013) 18. Al-Saba, T., El-Amin, I.: Artificial neural networks as applied to long-term demand forecasting. Artif. Intell. Eng. 13(2), 189–197 (1999) 19. Adamowski, J., Karapataki, C.: Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J. Hydrol. Eng. 15(10), 729–743 (2010) 20. Güven, İ, Şimşir, F.: Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods. Comput. Ind. Eng. 147, 106678 (2020) 21. Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35(1), 73–101 (1964) 22. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996) 23. Ke, G., Meng, Q, Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu T.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17), Curran Associates Inc., Red Hook, NY, USA, pp. 3149–3157 (2017)

Store-based Demand Forecasting of a Company via Ensemble Learning

23

24. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: Catboost: unbiased boosting with categorical features. Adv. Neural Inf. Process. Syst, pp. 6638–6648 (2018) 25. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system, pp. 785–794 (2016) 26. Hoerl, A.E., Kennard, R.W.: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)

Prediction of the Future Success of Candidates Before Recruitment with Machine Learning: A Case Study in the Banking Sector Murat Levent Demircan(&)

and Kaan Aksaç

Galatasaray University, 34349 İstanbul, Turkey [email protected], [email protected]

Abstract. Companies need to employ successful employees to survive in today's competitive and technology-oriented business environment. Most companies started to convert their recruitment processes to online platforms to face digital challenges and reach a massive number of candidates. In addition, the increase in job applications and the number of positions has raised the evaluation process complexity of candidate data. Wrong hiring decisions caused by the difficulty and complexity of evaluation can cause financial and non-financial losses for companies. For this reason, companies need more data-driven and machine learning (ML) based decision support systems to tackle these challenges. This study focuses on predicting candidates’ future performance based on the historical data of successful and unsuccessful employees using ML. We aim to support HR decision-makers to employ the right employee by minimizing the evaluation complexity of big data in the recruitment process. This study covers the data of 597 employees of a private bank serving in Turkey and considers the first two-year performance evaluations of the employees while creating output labels. We have conducted a three-stage methodology: We prepared the data set and organized it as training and testing in the first two stages. Finally, we selected a Logistic Regression model with a 71.19% accuracy by performing five-fold validation for Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Trees (DTs), and Multi-Layer Perceptron (MLP) algorithms. The result is improved by optimizing the parameters to a 73.14% accuracy level. The developed model is tested with another fresh data set and recorded a 71.67% accuracy rate. Keywords: Decision making


Recruitment process
Machine learning

1 Introduction Human resources management (HRM) and studies in this field are developing day by day in number and quality in parallel with the development and trends of technology. While HR professionals still focus on the “human” aspects of running an organization, in just a decade they have become more dependent on technology and data analysis methods that did not exist before. Data analysis and the usage of technology have become an integral part of HR functions because of the need to manage HR functions in a sustainable way, to establish effective and agile decision mechanisms to adapt to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 24–35, 2022. https://doi.org/10.1007/978-3-031-09176-6_3

Prediction of the Future Success of Candidates Before Recruitment

25

changing conditions, and to examine existing and possible scenarios to discover strategic development areas. This situation has led to the development of HR analytics as a research and study area. However, situations, where data-related actions are planned continuously and precisely because of data processing (predictions, classifications, rankings, decisions, etc.), may disrupt the dynamics of HR functions. The functions of HR can be listed as selection and placement, personnel rights management, wage management, performance management, training and development management, career management, as well as various classifications. Recruitment in an organization affects the quality of the employees, so choosing the right person for the right job by analyzing the data is very important in HRM. Using the knowledge obtained, these analyzes can help HR professionals better understand who will be best suited for both a specific role and the company's overall workplace culture. By hiring the “right” person at the right time the first time, HR professionals will focus most of their time on employee retention and spend less time with those they predict will not work in the future. However, it is difficult to predict the job performance and retention of the candidates in the selection process. Traditional selection approaches, including personality tests, job sample tests, job knowledge tests, and interviews, have been widely used for many years. These approaches usually conclude based on the decision maker's subjective judgments. However, personnel selection is a complex process in which many factors must be evaluated simultaneously in the decision-making process. When the competitive positions and strategies of an institution in the sectors in which it operates are evaluated, it is seen that the recruitment and placement function has quite different dynamics. The banking sector is one of the examples where recruitment and placement cannot be managed stably due to its dynamic business structure. As globalization and technology advance, multi-functional roles and cross-functional roles are increasing as new business needs are constantly being created. Recruiting highpotential talent in the banking sector has become more important. In addition to this, demands for the competencies and skills have been diverse and more complex. For this reason, it is considered that traditional recruitment and placement methods, which are valid for stable conditions, will no longer be sufficient and suitable for the banking sector (Lievens et al. 2002). As an innovative approach to assist decision-makers in the recruitment process in their task of identifying the best candidates, ML can provide huge savings in terms of financial resources and time. In this study, a model has been proposed that can predict the future performance of new candidates by learning the historical data of current employees in the recruitment process with ML techniques. This study aims to create a foresight system that can assist decision-makers in recruiting candidates. In the first and second part of the study, information and literature of HR analytics and recruitment processes has been presented historically. In the last section, a three-stage methodology has been followed for the ML approach used in practice. The first stage of the application is the collection and preparation of the data set to be included in ML. In the second stage, five-fold validation has been performed for the prepared data set with LR, SVM, KNNs, DTs, and MLP algorithms, which are widely used in this field. The best model has been proposed according to the calculated evaluation criteria. In the last stage, the parameters of the proposed model have been optimized and the prediction performance in the training data has been improved. Then,

26

M. L. Demircan and K. Aksaç

the performance of the developed model has been tested with the best parameter values and the accuracy score has been calculated.

2 Literature Review Literature studies on the estimation of employee performance in recruitment in HR have been examined in two dimensions as subjects and methods. Early studies of the literature have been seen to focus on performance forecasting with post-employment personnel data. It may be useful to use post-hire personnel data (performance, attendance, etc.) for some processes such as retention, personnel satisfaction, career planning. When the methods are examined for recruitment analytics; traditional statistical approaches such as descriptive statistics, hypothesis testing, analysis of variance, regression, multicriteria decision making, and correlation analysis are frequently used. On the other hand, data mining approaches have been widely used after 2008. Among these approaches, ML-related applications are seen as innovative techniques. Delgado-Gómez et al. (2011) examined the possibility of making better the accuracy of the existing expert system for salesperson selection applying SVMs. Jantan et al. (2011) tried to use this approach in talent management to determine current capabilities by predicting their performance with experience data using classifying algorithms. Thakur et al. (2015) proposed an ideal selection framework for hiring the right candidate and to recenter on the selection criteria by applying the random forest algorithm in the software industry. Li et al. (2016) proposed a model to deal with the employee performance estimation problem which can be a significant part to estimate and enhance the performance of a manufacturing system using a improved KNN algorithm. Kirimi and Moturi (2016) used the data mining classification practice to extract information that is important to estimate employee performance using former assessment records, a public administration development institute in Kenya. Harris (2018) looked at ways in which human feedback could be used to better train ML algorithms, paying special attention to native risks such as data overfitting and bias avoidance. Sarker et al. (2018) showed how data clustering and DTs can be used for predicting the employee’s performance for the following year. Xue et al. (2019) presented a hybrid convolutional recurrent neural network (CRNN) with KNN model, a dataset with 22 attributes, which is used to predict personnel performance in the future and help decision-makers to select the most competent candidates. Mahmoud et al. (2019) asserted a procedure that can help decision-makers and select the best candidate by estimating his\her performance according to produced performance patterns by applying Machine-learning techniques. Santiago and Gara (2018) proposed a model using a naive Bayes classifier to help HR personnel understand the psychological climate and supported their decision to solve turnover by selecting desired applicants who are likely to stay longer in an organization. Lather et al. (2019) predicted the performance of employees in an organization through various factors, including individual, field-specific and socio-economic. They indicated that the predicted performance could be the basis for deciding who to be hired and what type of project to place on. Nasr et al. (2019) created a model with DM techniques (DT, Naive Bayes, and SVM) to estimate employee performance applying a real data set in Egypt. The purpose of this study was to rank all applicants based upon their systems thinking skills and afterward to

Prediction of the Future Success of Candidates Before Recruitment

27

select the candidates most in line with the company’s strategy. Pessach et al. (2020) proposed a hybrid decision support system for HR professionals to improve prerecruitment processing using variable-order Bayesian network (for interpretability) and ML algorithm. Chuang et al. (2020) improved a data-driven Multiple Attribute Decision Making (MADM) method, which integrates MADM techniques and ML, to assist personnel selection more objectively and their competency progress in a Chinese food company. A study by Arora et al. (2020), proposed a predictive study based upon linear and LR. This research has aimed to estimate the educationist’s performance based upon Educationist Evaluation Index and experience in Delhi. Santhosh Kumar et al. (2020) proposed a framework to support HR to observe employee performance by applying the C4.5 classifier, naive Bayes classifier, and generalized fuzzy c-means clustering.

3 Methodology This study aims to present an analytical framework that can be applied to HR recruiters as a decision support tool to recruit new candidates accurately and efficiently by revealing the relationship between the historical data of the employees in the recruitment processes and their job performance after they are hired. Figure 1 shows the framework with the following steps:

Fig. 1. Proposed methodology of this study

Data Collection and Preparation: Analyzing the data sources and types to be used in the study, reaching the right data, and preparing these data for ML form the basis of this section. After these data are obtained, they need to be combined and prepared. However, the collected data can often be dirty, incomplete, and inaccurate. To increase the quality and efficiency of the data and to train algorithms correctly; it is necessary to control the data distribution and outliers, to remove or correctly derive blank or missing data, to perform correlation analysis, to enrich the data, and convert it into suitable formats in which the algorithms can work. Machine Learning Modeling: The data set to be used is divided into training and test sets before the models are run. Five-fold validation has been performed for LR, SVM, KNNs, DTs and MLP algorithms, which are observed to be widely used in the estimation of candidate performance in the literature.

28

M. L. Demircan and K. Aksaç

Evaluation and Improvement: After the models have been trained with the training data, their performance has been measured according to the evaluation criteria. The model with the best training performance has been proposed. Then, the performance of the proposed model has been improved by parameter optimization. Finally, the learning curve of the developed model has been examined and tested with a data set that it had not seen before. The output results have been interpreted by examining the confusion matrix. 3.1

Evaluation Methods

Before moving on to classification analysis evaluation criteria, we need to examine the confusion matrix used in the criteria. The confusion matrix allows the evaluation of the predictions of classification models. Suppose a dataset contains a total of N numbers of data evaluated by the M model. The set of values correctly predicted for the positive class is called true positives, and the set of values correctly predicted for the negative class is called true negatives. The set of values of the positive class that is falsely predicted to be negative is called false negatives, and the set of values of the negative class that is falsely predicted to be positive are called false positives. The numbers of true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) observations (Ruuska et al., 2018) (Table 1). Table 1. Classification of evaluation criteria Accuracy: Accuracy assessment is the most basic and simple assessment criterion. The accuracy value is obtained by dividing the number of correctly predicted values by the number of all predictions. The accuracy model is useful if there is an even distribution among the number of classes Precision: The precision assessment criterion is the ratio of the number of correctly predicted positives to the total number of positive predictions. Precision gives the ratio of the truly positive predictions of the classification model to the total positive predictions. In other words, it shows what percentage of positive predictions are correct Recall: The recall evaluation criterion is the ratio of the number of true positives to the total number of true positives. The total number of true positives is obtained by adding the true positives to the false negatives. It shows the percentage of positive values that were predicted correctly F1-Score: The F1-Score is a classification model evaluation criterion obtained by taking the harmonic mean of the sensitivity and recall criteria. This criterion is widely used in uneven distributions where a class is small

Accuracy%ðM Þ ¼

Precision%ðM Þ ¼

Recall%ðM Þ ¼




TP þ TN  N



TP TP þ FP

TP TP þ FN



F1Score ¼ 2PrecisionRecall Precision þ Recall



Prediction of the Future Success of Candidates Before Recruitment

29

4 Real Life Application In this study, we have created a supervised ML-based classification model to predict the future performance of candidates after they are hired based on a real dataset to support recruiters and HR decision-makers of a private bank serving in the financial system in Turkey. Since there has been a change in the employee performance evaluation methodology of the bank, valid in 2018 and beyond, data for 2017 and before has not been considered. Since the employee performance data for 2021 has not been published at the time of the study, these data have also been excluded from the scope. So only 2018, 2019, and 2020 data has been considered. In addition, while the performances of the employees in the study have been labeled as successful and unsuccessful, they have been determined according to the evaluation forms within the first two years to see the effect of historical data on their future performance. 4.1

Data Collection and Preparation

This section describes the steps taken from collecting data to making it ready for processing for models. These steps are usually the one that takes the longest time in ML processes and needs the most attention in the process. The better the quality and validity of the data, the better the modeling results. In addition, it requires a high amount of expertise. In our study, this process is structured in five steps described below: Step 1: Meetings have been held with business partners to understand the needs in the recruitment process and to provide information about the objectives of the study. In these meetings, the data records of the personnel have also been evaluated. Expert opinions have been received about which data are requested from the candidates during the recruitment process and which of these data may affect the performance of the employee after hiring. Step 2: Input and output attributes have been defined in cooperation with the bank's business partners. All these attributes have been used to predict whether the target class (performance of employees of the bank) will unsuccessful or successful. These attributes, their definitions, expected values, and data types are compiled in detail in Table 2. Step 3: According to the bank's HR historical records, the past candidate forms of the employees hired in 2018, 2019, and 2020 have been examined. The attributes that have been determined in Step 2 have been obtained from the database with the SQL programming language. Relevant records and performance scores have been evaluated and examined. The records that could be thought to be incorrect have been cleared. Attributes containing blank data (such as school importance) have been filled in by looking at the data of employees with similar profiles to prevent data loss. In addition, the performance score of the employees who left without performance evaluation has been produced by considering the way they left the job. The company has three types of layoffs. These are resignation, termination, and others. To increase the significance of the model, resignation and other types of employees have been excluded from the data set. Because employees can be

30

M. L. Demircan and K. Aksaç

successful but quit their jobs without any reason. The performance scores have been considered unsuccessful if the employees are dismissed due to termination. Finally, a data set consisting of 14 attributes and 597 employee data has been used to create a model. Step 4: Non-numeric categorical data columns are digitized so that the data can be recognized by the ML model and mathematical operations can be performed on it. Some algorithms can work directly with categorical data. For example, a DT can be learned directly from categorical data (but the Scikit learn library used does not support this yet) without the need for data transformation. However, many ML algorithms cannot work directly on non-numeric data labels. Step 5: Correlation is a widely applied technique in ML for data analysis. Highly correlated features can harm the learning and generalization of models. The “age” attribute, which was observed to be highly correlated with other variables, has not been included in ML. By applying all these steps, the data set has been made ready for training in models. In addition to this, some data has been added for information purposes and will not be trained in the ML model. These are the “gender” and “marital status” columns in the dataset. The aim is to prevent prejudice, positive or negative distinctions that may occur in recruitment processes, as has been widely observed in the literature recently. Table 2. The attributes used in the model and their possible values Attribute Age

Gender Marital Status Child Status Military Service Status Job Family

Previous Experience

Description It refers to the age of the employee at the time of employment. This data is divided into 4 categories It indicates the gender of the employee It refers to the status of the employee as married or single It tells you whether you have children or not It shows whether the employee has done his military service or not. Women employees are exempted There are business families in the company on which each position depends. It is the job family information of the position from which the employee was hired It refers to the previous work experience of the employee. If it is more than 1 year, it is labeled as “yes”, otherwise “no”

Expected values {< 25 = A, 25– 29 = B, > 30 = C}

Type Input

{Male, Female} {Married, Single}

Input Input

{Yes, No}

Input

{Completed, Exempted, Not Completed} {Branch_Sales, Branch_ Operations, Head Office, IT}

Input

{Yes, No}

Input

Input

(continued)

Prediction of the Future Success of Candidates Before Recruitment

31

Table 2. (continued) Attribute Inter Status

Grade Point Average University Importance Status Foreign Language Certificate Reference Information Loan

Customer Status Performance

4.2

Description It is the information of whether the employee works part-time in the company before starting a full-time job University undergraduate graduation grade point average It is the information that the undergraduate graduation university is scored according to its academic success Whether it has an English or Arabic language certificate Whether the employee has a reference from the company when applying to the company Whether the employee was a customer before applying for a job with the company Whether the employee was a customer before applying for a job with the company Performance score

Expected values {Yes, No}

Type Input

{Low, Medium, High, Excellent} {Low, Medium, High}

Input

{Yes, No}

Input

{Yes, No}

Input

{0, 1}

Input

{Yes, No}

Input

{Unsuccessful, Successful}

Output

Input

Machine Learning Modeling

After the preparation and preprocessing of the data, the necessary python libraries have been loaded first to run the ML models used in the application and to evaluate them. The basic library used for ML in this study is Scikit-learn. After library uploads, the data set has been divided into two as inputs and outputs for ML. Inputs are data that the model will use to make sense of the output (performance) in the decision column. 70% of the data partitioned as input and output have been reserved for training and validation. 30% of the remaining dataset has been used for the final performance evaluation. The performances of the algorithms to be trained in the application have been calculated by performing five-fold validation. The average of the Accuracy, Precision, Recall, and F1-score evaluation criteria have been calculated for each layer. In the analysis of the data, K Neighbors Classifier, Random Forest Classifier, LR, DT Classifier, MLP, Categorical Naive Bayes, and SVM—Linear Kernel models have been used. See results in Table 3.

32

M. L. Demircan and K. Aksaç Table 3. The results and averages for each fold of the executed algorithms Model Logistic Regression

Fold 1 2 3 4 5 Mean SVM 1 2 3 4 5 Mean K Neighbors Classifier 1 2 3 4 5 Mean Decision Tree Classifier 1 2 3 4 5 Mean Multilayer Perceptron 1 2 3 4 5 Mean

4.3

Accuracy 0.7417 0.6667 0.8067 0.6807 0.6639 0.7119 0.7167 0.7167 0.7479 0.6723 0.6891 0.7085 0.6917 0.6917 0.7311 0.5882 0.7059 0.6817 0.7417 0.6667 0.7143 0.6555 0.6471 0.6968 0.7500 0.6667 0.7983 0.6303 0.6639 0.7034

Precision 0.8750 0.6522 0.8784 0.7000 0.5862 0.7384 0.8511 0.6957 0.8889 0.6727 0.6023 0.7421 0.7778 0.6667 0.8507 0.5882 0.6351 0.7037 0.8462 0.6721 0.8358 0.6889 0.5844 0.7312 0.8364 0.6615 0.8873 0.6346 0.5824 0.7303

Recall 0.6269 0.7377 0.8228 0.6034 0.9273 0.7436 0.5970 0.7869 0.7089 0.6379 0.9636 0.7389 0.6269 0.7869 0.7215 0.5172 0.8545 0.7014 0.6567 0.6721 0.7089 0.5345 0.8182 0.6976 0.6866 0.7049 0.7975 0.5690 0.9636 0.7415

F1-score 0.7304 0.6923 0.8497 0.6481 0.7183 0.7278 0.7018 0.7385 0.7887 0.6549 0.7413 0.7250 0.6942 0.7218 0.7808 0.5505 0.7287 0.6952 0.7395 0.6721 0.7671 0.6019 0.6818 0.7060 0.7541 0.6825 0.8400 0.6000 0.7260 0.7237

Improving the Success of the Preferred Model

Hyperparameter tuning severely affects the performance and speed of ML models. In the selected model, the default parameters of the LR algorithm have been used. To improve the performance of the model, the parameter values of the algorithm have been changed and an improvement has been achieved. The default parameters of the selected model are “penalty = l2”, “C = 1”, and “solver = lbfgs”. The suggested parameter set for the LR model is as follows: solvers = {‘newton-cg’, ‘lbfgs’, ‘liblinear’}, penalty = {‘l2’, ‘l1’} and c = {100, 10, 5, 3, 1.0, 0.1, 0.01}. The accuracy values have been calculated by applying five folds to all combinations of the parameter set.

Prediction of the Future Success of Candidates Before Recruitment

33

Incorrect combinations are not considered as some penalties do not work with some solvers. The parameters with the best accuracy are “c = 3”, “penalty = l1” and “solver = liblinear”. By applying five folds in these parameters, an accuracy value of 73.14% has been achieved. With the default parameter values of the LR algorithm, 71.19% accuracy has been obtained. 1.95% improvement has been achieved with the best parameter values of the developed model. Figure 2 shows the learning curve of the LR algorithm on the training data with the relevant parameters to monitor the learning performance of the model. It is seen that the model continues to learn with the training data. It is seen that increasing the number of samples can provide further improvement in the score of the model.

Fig. 2. Learning curve for proposed model

Fig. 3. Confusion matrix for proposed model

Parameter optimization has been performed on the best algorithm selected by training and validation operations on the training data. Finally, the model has been tested with a data set that it has not seen before. So, the test data set that was partitioned before training and validation has been used. The success of the developed model in the test data set has been obtained with good accuracy of 71.67%. Figure 3 shows the confusion matrix for the developed model. This allows us to gain more detailed information about the prediction mechanism of the model to evaluate the behavior and understand the effectiveness of the proposed model. According to the confusion matrix in Fig. 3, 57 employees who failed in the proposed model have been predicted as unsuccessful (TN). 72 employees who succeeded have been predicted to be successful (TP). There have been 29 employees who succeeded but which the model mistakenly predicted (FN) by unsuccessful. There have been 22 employees who succeeded, and the model mistakenly predicted them as successful (FP). The accuracy, precision, recall, and F1-score values of the proposed model are 71.67%, 76.6%, 71.27%, and 73.84%, respectively. In addition, the precision value is especially important when the cost of false-positive estimation is high. It would be a costlier mistake for the model to predict a truly unsuccessful employee for recruitment as successful. In the proposed model, predicting a successful employee to be unsuccessful with a wrong prediction is a preferable error compared to evaluating an unsuccessful employee as successful with a wrong prediction. The high precision value (76.6%) has been a supportive measure for the proposed model.

34

M. L. Demircan and K. Aksaç

5 Conclusion In this study, an analytical framework has been presented that can be used as a decision support tool to predict the post-hire success of the employees by analyzing the historical data in the recruitment processes and recruiting suitable candidates efficiently. Within the scope of the study, the anonymous data of 597 employees of a private bank serving in Turkey have been examined. Past recruitment data of successful and unsuccessful employees have been evaluated together with the company's business partners. Subsequently, possible attributes that can predict the performance of the employee after recruitment have been determined. A supervised ML approach has been applied in this study. Preparation processes have been applied for the data set to be trained in ML. 70% of the data partitioned as input and output have been reserved for training and validation. 30% of the remaining dataset has been used for the final performance evaluation. Firstly, the labeled training data has been trained with supervised ML models such as LR, SVM, K Neighbors Classifier, DT Classifier, and MLP. As a result of five-fold validation, the LR model with the best training performance (71.19% Accuracy) has been selected. Secondly, parameter optimization has been performed to improve the proposed model. With the best parameter values of the developed model, an accuracy rate of 73.14% has been achieved. Finally, the developed model has been tested with the data set that the model had not seen before, which has been separated as the test set. With the proposed model, a successful accuracy score of 71.67% has been achieved on the test data. This study presents the first step in efforts to predict the future performance of pre-employment candidates. The proposed approach is a decision support system, and it aims to support the final decision-makers in reducing the complexity of the recruitment processes and hiring the right employees. In future studies, the accuracy score of the model can be increased by expanding the data set and adding new features (such as personality tests) that can predict performance.

References Lievens, F., Van Dam, K., Anderson, N.: Recent trends and challenges in personnel selection. Personnel review (2002) Jantan, H., Hamdan, A.R., Othman, Z.A.: Towards applying data mining techniques for talent management. In: International Conference on Computer Engineering and Applications, IPCSIT, vol. 2, p. 2011 (2011) Thakur, G.S., Gupta, A., Gupta, S. Data mining for prediction of human performance capability in the software-industry. arXiv preprint arXiv:1504.01934 (2015) Kirimi, J.M., Moturi, C.A.: Application of data mining classification in employee performance prediction. Int. J. Comput. Appl. 146(7), 28–35 (2016) Li, N., Kong, H., Ma, Y., Gong, G., Huai, W.: Human performance modeling for manufacturing based on an improved KNN algorithm. Int. J. Adv. Manuf. Technol. 84(1–4), 473–483 (2016). https://doi.org/10.1007/s00170-016-8418-6 Harris, C.G.: Making better job hiring decisions using “human in the loop” techniques. In: HumL@ ISWC, pp. 16–26 (2018)

Prediction of the Future Success of Candidates Before Recruitment

35

Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., Mononen, J.: Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behav. Proc. 148, 56–62 (2018) Sarker, A., Shamim, S.M., Zama, M.S., Rahman, M.M.: Employee’s performance analysis and prediction using K-means clustering & decision tree algorithm. Global J. Comput. Sci. Technol. (2018) Lather, A.S., Malhotra, R., Saloni, P., Singh, P., Mittal, S.: Prediction of employee performance using machine learning techniques. In: Proceedings of the International Conference on Advanced Information Science and System, pp. 1–6, November 2019 Mahmoud, A.A., Shawabkeh, T.A., Salameh, W.A., Al Amro, I.: Performance predicting in hiring process and performance appraisals using machine learning. In: 2019 10th International Conference on Information and Communication Systems (ICICS), pp. 110–115). IEEE (June 2019) Nasr, M., Shaaban, E., Samir, A.: A proposed model for predicting employees’ performance using data mining techniques: Egyptian case study. Int. J. Comput. Sci. Inf. Secur. 17(1), 31– 40 (2019) Santiago, E.B., Gara, G.P.P. A model based prediction of desirable applicants through employee’s perception of retention and performance. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–6. IEEE Xue, X., Feng, J., Gao, Y., Liu, M., Zhang, W., Sun, X., Zhao, A., Guo, S.: Convolutional recurrent neural networks with a self-attention mechanism for personnel performance prediction. Entropy 21(12), 1227 (2019) Arora, S., Agarwal, M., Kawatra, R.: Prediction of educationist's performance using regression model. In: 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 88–93. IEEE (March 2020) Chuang, Y.C., Hu, S.K., Liou, J.J., Tzeng, G.H.: A data-driven MADM model for personnel selection and improvement. Technol. Econ. Dev. Econ. 26(4), 751–784 (2020) Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H.C., Shmueli, E., Ben-Gal, I.: Employees recruitment: a prescriptive analytics approach via machine learning and mathematical programming. Decis. Support Syst. 134, 113290 (2020) Santhosh Kumar, S., Mohanapriya, G., Shanmugapriya, M.M.: A study on some properties of Qfuzzy normal subgroups. J. Crit. Rev. 7(12), 2818–2821 (2020) Delgado-Gómez, D., Aguado, D., Lopez-Castroman, J., Santacruz, C., Artés-Rodriguez, A.: Expert Syst. Appl. 38, 5129–5132 (2011)

Effectiveness of Social Media in Stock Market Price Prediction Based on Machine Learning Emre Karaşahin1(&) , Semih Utku1 and Okan Öztürkmenoğlu2 1

,

Department of Computer Engineering, The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Izmir 35390, Turkey [email protected] 2 Department of Computer Engineering, Izmir Bakırçay University, Izmir 35665, Turkey

Abstract. Trying to predict the future using social media data and analytics is very popular today. With this motivation, we aimed to make stock market predictions by creating different analysis models for 10 different banks traded in “Borsa Istanbul 100” over 3 different groups that we selected on social media. The groups determined within the scope of the study can be detailed as tweets posted by banks from their accounts, tweets posted with the name of the bank, and tweets with the name of the bank posted from approved accounts. In our analysis, we used various variations, including the tweets’ sentiments, replies, retweet and like counts of the tweets, the effects of daily currency (Dollar, Euro, and Gold) prices, and the changes in stock changes up to 3 days. To apply some pre-processing techniques to the collected data, we defined sentiment classes for sentiment analysis, created 6 different models, and analyzed it using 7 different classification algorithms such as Multi-Layer Perceptron, Random Forest, and deep learning algorithm. After all the models and analysis, we got a total of 1440 different results. According to our results, the accuracy rates vary according to the data groups and models we have chosen. The tweet group in which the name of the banks is mentioned can be shown as the most successful data group and we can easily say that there is a certain relation between social media and stock market prices. Keywords: Stock market Sentiment analysis


Classification
Deep learning
Social media


1 Introduction Social media refers to websites and applications that are designed to allow people to share content quickly, efficiently, and in real-time. It is used by everyone from 7 to 70 and contains very large open-ended data, which can be called big data. Users can share their opinions with other users on social media platforms, even some users lead the market with comments they have made. For example, some users review a product and share their experience with their followers, and they present their positive or negative opinions about this product to their followers. These opinions can affect the sales of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 36–43, 2022. https://doi.org/10.1007/978-3-031-09176-6_4

Effectiveness of Social Media in Stock Market Price Prediction

37

product in a very significant way. In this case, it clearly shows us the effects of the views of people on social media. For this reason, some companies make agreements with some social media accounts which have high followers to advertise their products. Also, users share positive or negative opinions about a company or a product through social media. These shares can greatly affect the brand values of companies. For this reason, most firms have opened accounts on social media to support their customers. In this study, we will try to make estimations about the market value movements of the companies operating in the stock market by using some elements which are the posts shared on social media accounts, the number of likes of these posts, the comments on these posts, the sharing number of these posts. We want to observe whether there exists any relationship between social media usage, the company’s stock price, and currency prices. For this, we will collect daily data from desired social media accounts that are related to the stock market or trading. After that, we will analyze and visualize the collected data and make predictions with it. The rest of this paper is organized as follows. In Sect. 2, we review previous related works. In Sect. 3, we explain how to collect data and prepare a dataset. In Sect. 4, we describe the methodology and used techniques in this study. In Sect. 5, we review solutions and results. In Sect. 6, we review the conclusion of the study.

2 Literature Review In this section, similar studies in the literature are listed. Summary information about the studies can be found in Table 1. Table 1. Similar articles in the literature. Authors 2012 Dara Schniederjans et al. [1]

2015 Halil Akmese et al. [2]

Name of article Enhancing financial performance with social media: An impression management perspective Financial Performance and Social Media: A Research on Tourism Enterprises Quoted in Istanbul Stock Exchange (BIST)

Scope/Aim Analyze how social media impacts the relationship that impression management has on financial performance Analyze and evaluate the relationship between financial performance (market value, net sales, net profits, price/earnings ratio etc.) and efficient use of social media

Methodology SVM-based text mining method, Automated text classification, ordinal logistic regression KolmogorovSmirnov and ShapiroWilk tests, Mann Whitney U test

(continued)

38

E. Karaşahin et al. Table 1. (continued)

Authors 2015 Thien Hai Nguyena et al. [3]

Name of article Sentiment analysis on social media for stock movement prediction

2017 Bhavya Kaushik et al. [4]

Social media usage vs. stock prices: an analysis of Indian firms

2018 Salim Chahine et al. [5]

Impact of social media strategies on stock price: the case of Twitter Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media Big Data Analysis of Volatility Spillovers of Brands across Social Media and Stock Markets

2020 Carosia et al. [6]

2020 Myrthe van Dieijena et al. [7]

2022 Khan et al. [8]

Stock market prediction using machine learning classifiers and social media, news

Scope/Aim Build a model to predict stock price movement using the sentiment from social media. Extracting the mood information by sentiment analysis on social media data Observe whether there exists any relationship between the SM usage and NIFTY 51 company’s stock price Market reaction of a Twitter platform for 312 firms from the Fortune 500 firms Conduct a study of the Brazilian stock market movement through sentiment analysis in Twitter data Investigate whether volatility in usergenerated content spills over to volatility in stock returns and vice versa Discover the impact of this data on stock market prediction accuracy for ten subsequent days

Methodology SVM, Latent Dirichlet Allocation (LDA), JST model, Aspect-based sentiment

Correlation and Regression Analyses, single-factor analysis of variance Event history analysis used

Sentiment Analysis, Naive Bayes, ME, SVM, and MLP

SVM, Natural language processing

Random Forest, Deep Learning, NLP, Sentiment Analysis

3 Data Collection In this section, how the data is obtained will be explained under three subheadings: Twitter data, stock market data, and currency data. 3.1

Twitter Data

To obtain the tweet data, we decided to use an alternative third-party application, Twint [9], due to Twitter API limitations.

Effectiveness of Social Media in Stock Market Price Prediction

39

We collected data for ten different banks traded in BIST100 under three different headings which are called a bank, bank name contained, and bank name contained verified. The time range of the collected data is 01/09/2019 to 25/09/2020. At the end of the tweet gathering, we have thirty different JSON files with a total file size of about 500MB. Collected tweet distributions according to selected data groups for 10 different banks are shown in Fig. 1.

Fig. 1. When the tweet distributions in the table are divided into their groups, the obtained percentiles are shown in the chart.

3.2

Stock Market Data

BIST 100 index, one of the most popular indices of Borsa Istanbul, is an index that is carefully followed by all major investors. Within the scope of this study, we studied the stock market data of 10 popular banks traded in BIST 100. For this reason, we used a web scraping tool to access stock market data for the date range we selected. We collected daily stock prices, high and low prices, volume, and percentage of the daily change for every selected bank and stored them as a JSON file. As a result, we collected 365 different stock price data for every single bank. 3.3

Currency and Gold Price Data

Within the scope of our study, we created our dataset containing Dollar, Euro, and Gold prices in the date range of 10 years from February 2011 to February 2021 using the web scraping tool. We defined a common document format for all price data which contains a date, daily opening, selling, closing, highest, lowest prices, percentage of the daily change and store them as a JSON file. There are approximately 2600 different items in each dataset.

4 Methodology Within the scope of the study, we aim to estimate the stock market prices of the working group that we have selected from social media data and foreign exchange prices.

40

E. Karaşahin et al.

In this section, we will talk about the data set we created with the data we have collected, and how this data set was created. Then, we will examine the analyzes made using this data set under the headings of the working groups we have determined. We aimed to create a meaningful and pre-processed dataset that we can use the data we have collected for analysis. For this reason, we wrote a Python script to perform the related operations. 4.1

Preparation of Dataset for Analysis

In this section, we will talk about the methods we used to create the dataset that we will use as a source in the analysis phase. First, we took the exchange currency, gold prices, and all types of bank tweets that we mentioned in the data collection stage as raw data. We extracted non-trading day tweets from the collected data. A trading day means any day other than any Saturday, and Sunday, any day that is a statutory holiday, or any day that banking institutions are authorized or required to close by law or other government action. The definition of a trading day varies by region. We determined that the JSON format is the best suitable file format for the analysis dataset we created. Data preprocessing: Before the data mining models are established, some corrections are made to the data set, completing the missing data, removing duplicate data, transforming, integrating, cleansing, normalizing, size reduction, etc. We also preprocessed the tweet texts we collected in our study. Preprocessing steps, – – – – – – – –

Lowercased texts. Removing all emojis. Removing all URLs. Removing all user tags with user info like @blabla. Removing all hashtags with hashtag info like #blabla. Removing all special characters %, $, & etc. Removing all integers. Removing all strings whose length is less than 2.

We also passed all the tweets obtained after the preprocessing process through a stemmer for the consistency of our analysis. For the stemming process, we used the Turkish Stemmer [10] project, which is also compatible with the Turkish language. We also performed sentiment analysis on the processed texts we obtained within the scope of the study. We chose the Keyword Processing method for sentiment analysis. We used some example datasets from Kaggle [11] for sentiment lexicons and applied the same preprocessing and stemming techniques to these positive and negative lexicon files. For the sentiment decision, we said that if positive keywords are more common than negative ones then the tweet has positive sentiment, if negative keywords are more common than positive ones then the tweet has negative sentiment and if both are equal then we said that the tweet sentiment is neutral. Within the scope of our study, we also divided the daily changes in currency exchange and gold prices into 3 classes. We named these classes positive, negative, and neutral. By examining the movements in daily changes, we added the class labels that

Effectiveness of Social Media in Stock Market Price Prediction

41

we determined on the data. In our dataset, we also calculated the 3-day historical change values. In this way, we had the opportunity to examine whether past change movements influenced pricing. After all these processes, our datasets to be used for analysis were prepared within the scope of 10 different banks and 3 different data groups. 4.2

Classification and Deep Learning

In classification, a data set is assigned to one of the different and predetermined classes. Classification algorithms learn which data to assign to which class from the given training set. It then tries to assign the test data to the correct classes. Values that specify the classes of data are called labels. We chose 7 different algorithms for classification within the scope of the study. These algorithms are Decision Tree, Multilayer Perceptron, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine, and K Nearest Neighbor. For Deep Learning we defined a separate MLP with TensorFlow. For stock market movements, we labeled the changes under 3 different classes and identified these classes as positive, negative, and neutral. For all algorithms, we select 10-fold cross-validation and stratified sampling method. We created 6 different models within the scope of our study. We determined these models by creating various variations using the values in the dataset we created. The model details created are shown in Table 2. Table 2. Created model details. Model name Model Model Model Model

Model details 1 2 3 4

Model 5 Model 6

Currency Exchange Prices and Gold Prices Model 1 + Stock Change Class 1, 2 and 3 days ago Model 1 + Sentiment Classes Model 3 + Normalized tweet reply counts, Normalized tweet retweet counts, Normalized tweet like counts Model 1 + Model 2 + Model 3 Model 1 + Model 2 + Model 3 + Model 4

5 Results In this section, we will analyze the data in the data set we created according to the models we have determined. For the classification process, we created different models on KNIME and performed our analyzes on these models. KNIME [12] is an opensource and cross-platform (distributed to more than one business system) data analysis, reporting, and integration platform. KNIME’s “nodes” components work with a drag and drop method. In Model 1, we examined the effect of daily currency exchange and daily gold prices on selected 10 banks’ stock market prices. The dataset containing all tweets that

42

E. Karaşahin et al.

contain the keyword “GarantiBBVA” has obtained the highest accuracy with 64%. MLP and Deep Learning were the classification methods that achieved the highest accuracy among the 30 different data sources. The bank with the highest average accuracy %60 in stock market value estimation was determined as GarantiBBVA. In Model 2, we examined the effect of daily currency exchange and daily gold prices and 3 days past stock change classes on selected 10 banks’ stock market prices. The dataset containing all tweets that contain the keyword “Sekerbank” posted by verified accounts has obtained the highest accuracy value with 62%. SVM was the classification method that achieved the highest accuracy. the bank with the highest average accuracy %58 in stock market value estimation was determined as Isbank. In Model 3, we examined the effect of daily currency exchange and daily gold prices and the sentiment of the tweets on selected 10 banks’ stock market prices. The dataset containing all tweets that contain the keyword “GarantiBBVA” and also all tweets that contain the keyword “GarantiBBVA” and posted by verified accounts has obtained the highest accuracy value with 62%. Logistic Regression was the classification method that achieved the highest accuracy. The bank with the highest average accuracy %59 in stock market value estimation was determined as Isbank. In Model 4, we examined the effect of daily currency exchange and daily gold prices sentiment of the tweets, normalized tweet reply counts normalized tweet retweet counts, normalized tweet like counts on selected 10 banks’ stock market prices. The dataset containing all tweets that contain the keyword “GarantiBBVA” has obtained the highest accuracy value with 62%. Logistic Regression was the classification method that achieved the highest accuracy. The bank with the highest average accuracy %58 in stock market value estimation was determined as GarantiBBVA. In Model 5, we examined the effect of daily currency exchange and daily gold prices, 3 days past stock change classes, and sentiment of the tweets on selected 10 banks’ stock market prices. The dataset containing all tweets that contain the keyword “GarantiBBVA” and also all tweets that contain the keyword “Halkbank” has obtained the highest accuracy value with 68%. Deep Learning was the classification method that achieved the highest accuracy. The bank with the highest average accuracy %62 in stock market value estimation was determined as Halkbank. In Model 6, we examined the effect of daily currency exchange and daily gold prices, 3 days past stock change classes, the sentiment of the tweets, normalized tweet reply counts, normalized tweet retweet counts, normalized tweet-like counts on selected 10 banks’ stock market prices. The dataset containing all tweets that contain the keyword “Halkbank” has obtained the highest accuracy value with 69%. Deep Learning was the classification method that achieved the highest accuracy. The bank with the highest average accuracy %61 in stock market value estimation was determined as Halkbank.

6 Conclusion In this study, we tried to predict the stock market with the classification algorithms we have chosen with 6 different methods created by using Twitter data of 10 banks traded in Borsa Istanbul.

Effectiveness of Social Media in Stock Market Price Prediction

43

According to the results obtained, GarantiBBVA is the most likely bank to make stock market predictions with Twitter data, followed by Halkbank. In addition, the accuracy rates of the tweets in which the bank’s name is mentioned on all Twitter are higher than the other selected data groups. As a result of the study, algorithms with high validation rates can be listed as Deep Learning, MLP, Random Forest, and Logistic Regression. According to all the results obtained and all the models analyzed, there is a certain relationship between the currency exchange values of the country and the tweets posted on Twitter, and the stock market values of the selected banks. In the future, the study can be diversified by increasing the selected data groups and creating new model options. We believe that this study will be a good starting point for future studies and models.

References 1. Schniederjans, D., Cao, E.S., Schniederjans, M.: Enhancing financial performance with social media: an impression management perspective. Decis. Support Syst. 55(4), 911–918 (2013) 2. Akmese, H., Aras, S., Akmese, K.: Financial performance and social media: a research on tourism enterprises quoted in Istanbul stock exchange (BIST). Procedia Econ. Finan. 39, 705–710 (2016) 3. Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42(24), 9603–9611 (2015) 4. Kaushik, B., Hemani, H., Ilavarasan, P.V.: Social media usage vs. stock prices: an analysis of Indian firms. Procedia Comput. Sci. 122, 323–330 (2017) 5. Chahine, S., Malhotra, N.K.: Impact of social media strategies on stock price: the case of Twitter. Eur. J. Market. 52, 1526–1549 (2018) 6. Carosia, A.E.O., Coelho, G.P., Silva, A.E.A.: Analyzing the Brazilian financial market through Portuguese sentiment analysis in social media. Appl. Artif. Intell. 34(1), 1–19 (2020) 7. Van Dieijen, M., Borah, A., Tellis, G.J., Franses, P.H.: Big data analysis of volatility spillovers of brands across social media and stock markets. Ind. Mark. Manage. 88, 465–484 (2020) 8. Khan, W., Ghazanfar, M.A., Azam, M.A., Karami, A., Alyoubi, K.H., Alfakeeh, A.S.: Stock market prediction using machine learning classifiers and social media, news. J. Ambient. Intell. Humaniz. Comput. 13, 3433–3456 (2022). https://doi.org/10.1007/s12652-02001839-w 9. Twitter Intelligence Tool Project: https://github.com/otuncelli/turkish-stemmer-python. Accessed 23 Mar 2022 10. Turkish Stemmer Phyton Project: https://github.com/twintproject/twint. Accessed 25 Mar 2022 11. Sentiment Lexicon Dataset: https://www.kaggle.com/rtatman/sentiment-lexicons-for-81languages. Accessed 23 Mar 2022 12. KNIME Analytics Tool: https://www.knime.com. Accessed 25 Mar 2022

Deep Learning-Based Cancerous Lung Nodule Detection in Computed Tomography Imageries Sangaraju V. Kumar1, Fei Chen1, Sumi Kim2, and Jaeho Choi1(&) 1

2

Department of Electronics Engineering, JBNU, CAIIT, Jeonju, Republic of Korea [email protected] Department of Nursing, Seoyoung University, Gwangju, Republic of Korea

Abstract. Computed tomography images have been widely used for lung cancer diagnostics. In the early stages of lung cancers, lung nodules are tiny, and even radiologists struggle to detect and diagnose them. Conventional and manual detection of lung nodules is a sequential and time-consuming process for radiologists. On the other hand, deep-learning-based automatic detection algorithm is an alternative approach that has recently drawn much attention. The deep learning method becomes successful and outperforms physicians in classification, nodule deduction, and false-positive reduction of malignant pulmonary nodules on chest radiograph. Accurate detection of early lung cancer nodules can be critical and hence improve the cure rate of lung cancer. In this paper, a novel deep learning-based lung nodule detection method is presented for automatic detection of malignant tumors in the lungs. The proposed 3-D CNN model classifies the candidates as nodules or non-nodules, while 2-D UNet is used to segment the position of lung nodules. Here, the data augmentation technique is used to generate a large number of training examples, and regularization is also applied to avoid overfitting. The evaluation metrics adopted in this paper are the dice coefficient loss and the area under the receiver operating characteristic curve, which are frequently used in image segmentation tasks. The performance of the proposed method has been verified by using LUNA-16, which is a publicly available medical dataset. The simulation results show that the proposed method can achieve superior detection accuracy, which surpasses the conventional methods. Keywords: Deep learning
Computed tomography
Lung cancer
Nodules
Malignant detection
CNN
U-Net model
LUNA-16

1 Introduction The recent American cancer society statistics show that lung cancer involves one of the highest death rates [1] compared to prostate, breast, colorectal, and brain cancers combined. Global cancer statistics [2] show an estimation of 1.8 million deaths, which is 18% of all deaths caused by cancers; the lung cancer is the leading cause of cancer death globally. Due to the tobacco epidemic, predominantly, lung cancer deaths have increased rapidly in the 20th century. However, early detection of lung cancer, treatment, and stop smoking help to increase the survival rate. Recently, deep learning © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 44–52, 2022. https://doi.org/10.1007/978-3-031-09176-6_5

Deep Learning-Based Cancerous Lung Nodule Detection

45

techniques are making a high impact in the field of medicine. It helps radiologists to increase the diagnosis rate and improves the detection of lung nodules; it can also save time and efforts in comparison to manual detection. In this paper, an automated lung nodule detection system is proposed. Here, in the proposed system, 3-D CNN architecture is used for classification and 2-D U-Net is used to segment lung nodule regions. We group both of the networks to detect whether the patient has been diagnosed with malignant or benign. The computer simulations have been performed by using LUNA-16 grand challenge dataset to evaluate the proposed method. The results are evaluated and the detection accuracy is assessed in terms of sensitivity and receiver operating characteristic (ROC) metrics. This paper is organized as follows. Section 2 provides the related work. The proposed Luna model and U-Net architecture are described in Sect. 3. In Sect. 4, details of the experiment and results of the proposed model are presented. Finally, the conclusions are made in Sect. 5.

2 Related Work The lung nodule detection consists of two stages; one is the false positive reduction and the other is the detection of candidate lung nodules. In recent years, several techniques have been proposed and these methods can be generally divided into two major methods, i.e., machine learning and deep learning methods. In machine learning methods, Akram [3] introduces different thresholding methods to extract the candidate nodules. For the lung nodules classification, it used an artificial neural network and LIDC standard dataset. It achieved 96.6% accuracy and 96.9% sensitivity. Zhang [4] has used a novel 3-D skeletonization feature VRR. For lung segmentation, a global optimal active contour model is used. For lung candidate nodule extractions, thresholding and morphological operations are used. In order to reduce the false-positive rates, a support vector machine is used and it has obtained 93.6% accuracy and 89.3% sensitivity. Jaffar [5] has presented novel ensemble shape gradient features and a random forest classifier to detect the presence of nodules. Their model achieves 98.8% accuracy and 98.4% sensitivity. On the other hand, in deep learning methods, the researchers begin to use the convolutional neural network (CNN) to learn relevant features of the nodules and replace feature extraction methods. Xiao [6] has proposed 3D-Res2Net for image classification and 3D-Res2UNet for candidate nodule detection and segmentation in a CT image [6]. It has shown that the 3-D networks have advantages in detecting lung nodules in comparison to the 2-D networks. The model attained the dice coefficient accuracy of 95.30% and a recall rate of 99.1%. Nasrullah [7] has proposed the 3-D customized mixed link network called CMixNet architecture for lung nodule detection and classification. The model achieves 94% sensitivity and 91% specificity using the LIDC-IDRI dataset. Huang [8] has used a noisy U-Net, which adds particular noise to the hidden layers of the U-Net in training process. This method has achieved a low missed diagnosis rate compared to the conventional U-Net architectures.

46

S. V. Kumar et al.

3 Proposed Luna Model In this section, our automated nodule detection and classification system is presented. The presented method is based on the Luna model for classification and the U-Net architecture for lung nodule segmentation. The Luna model is built on a convolutional neural network (CNN). The convolutional neural network architectures are mostly organized as a head, body, and tail. The tail consists of an input image that processes the input to the system and is followed by a batch normalization layer. Next the backbone of the network, which consists of a series of blocks. Each block performs the same set of layers, but the input size and number of filters change from block to block. Each building block consists of two 3  3 convolutions followed by a rectified linear unit (ReLU) layer, with a maxpooling operation at the end of the block. Finally, a fully connected layer and softmax take the output from the body and change it into the desired output form. In this work, we focus on 3D-CNN architecture, which is typically used for detection in medical images. Figure 1 shows the proposed 3D-CNN Luna model architecture. In this model, the input layer of 32  48  48 is followed by eight convolutional layers with 32, 16, 8, and 4 small 3  3  3 kernels, respectively. Each block consists of a max-pooling layer with overlapping 2  2  2 windows. We use one fully connected layer with a softmax composed of 2 neurons. The ReLUs are used in each block, followed by a convolutional layer. The stochastic gradient descent optimizer is used to update the model parameters. To regularize the model, a learning rate of 0.001 with a momentum of 0.9 is applied. The system involves about 222K parameters. Table 1 depicts the model summary of the proposed system. In addition, the architecture of U-Net [9] is illustrated in Fig. 2. It consists of a down-sampling path (left side) and up-sampling path (right side). The down-sampling path follows the distinctive architecture of a convolutional network. It consists of repeated 3  3 convolutions, each followed by a ReLU and a 2  2 max pooling operation with stride 2 for down-sampling. The network has a total of 23 convolutional layers. In up-sampling, each layer has a feature map followed by a 2  2 convolution

Fig. 1. The architecture of the proposed Luna model.

Deep Learning-Based Cancerous Lung Nodule Detection Table 1. Details of the proposed system model. Type

Output shape

Parameters

BatchNorm3d-1 Conv3d-2 ReLU-3 Conv3d-4 ReLU-5 MaxPool3d_6 Conv3d_7 ReLU-8 Conv3d-9 ReLU-10 MaxPool3d-11 Conv3d-12 ReLU-13 Conv3d-14 ReLU-15 MaxPool3d-16 Conv3d-17 ReLU-18 Conv3d-19 ReLU-20 MaxPool3d-21 Linear-22 Softmax-23

(−1, 1, 32, 48, 48) (−1, 8, 32, 48, 48) (−1, 8, 32, 48, 48) (−1, 8, 32, 48, 48) (−1, 8, 32, 48, 48) (−1, 8, 16, 24, 24) (−1, 16, 16, 24, 24) (−1, 16, 16, 24, 24) (−1, 16, 16, 24, 24) (−1, 16, 16, 24, 24) (−1, 16, 8, 12, 12) (−1, 32, 8, 12, 12) (−1, 32, 8, 12, 12) (−1, 32, 8, 12, 12) (−1, 32, 8, 12, 12) (−1, 32, 4, 6, 6) (−1, 64, 4, 6, 6) (−1, 64, 4, 6, 6) (−1, 64, 4, 6, 6) (−1, 64, 4, 6, 6) (−1, 64, 2, 3, 3) (−1, 2) (−1,2)

2 224 0 1736 0 0 3472 0 6928 0 0 13,856 0 27,680 0 0 55,360 0 110,656 0 0 2306 0

Fig. 2. The architecture of the proposed U-Net.

47

48

S. V. Kumar et al.

that reduces the number of feature channels and two 3  3 convolutions, each followed by a ReLU. Finally, a 1  1 convolution is used to segment each 64-component feature vector to the desired number of classes. In this work, we also have used five data augmentation techniques to prevent overfitting problems: mirroring, shifting, scaling, rotating, and adding noise to the image. When mirroring a sample, we only change the orientation of the image but keep the pixel values the same; shifting the nodule candidate make our model robust to imperfectly centered nodules. Finally, by adding noise to the images, it enables us to examine the robustness and performance of an algorithm.

4 Experiments and Results The proposed method is trained and tested on the LUNA-16 database [10], which has ten subsets and includes 888 sets of CT images with 1186 lung nodules. During the experiment, the data set has been split into two parts, i.e., 70% for training and 30% for validation. Moreover, the nodule diameter larger than 3mm is considered positive samples, and the rest of the samples are referred to as negative samples. The CT data loader consists of two files. The mhd file is a meta image header that allows developers to analyze the images and the raw file converts this into CT data for segmentation. We have used the index, row, column (I, R, C) coordinates to crop a small 3-D slice of a CT image to use as an input to our model. Figure 3 illustrates the accuracy and loss of the proposed model for the LUNA-16 dataset. The epoch history shows that the accuracy gradually increases and achieves 99.5% on training and 98.8% on the validation dataset. Similarly, the loss value on the training set decreases 0.02% and on validation set 0.05%, respectively. One can observe that the model is not overfitting.

Fig. 3. Classification loss and accuracy of Luna model architecture.

Deep Learning-Based Cancerous Lung Nodule Detection

49

Fig. 4. U-Net architecture segmentation results.

Fig. 5. 2-D U-Net network dice coefficient

Figure 4 shows the segmentation results obtained by the U-Net architecture for possible nodule detection. It patches the bounding boxes around the nodule locations; then, it traces outward from the point in all three dimensions until it hits low-density voxels. Afterward, it performs a cropping by taking the intersection between the bounding-box mask and the tissue that is denser than our threshold of −700 HU; it makes the contours of the nodules a bit improved. Instead of the whole CT slices, we have just trained 64  64 crops around positive candidates. These 64  64 patches take randomly from a 96  96 crop centered on the nodule.

50

S. V. Kumar et al.

Fig. 6. Receiver operating characteristic (ROC) for our baseline.

The dice coefficient (DC) [11] is an ordinary loss metric for segmentation tasks and it is defined as follows: DC ¼

2N truepositive 2N truepositive þ N falsepositive þ N falsenegative

ð1Þ

The DC is the ratio between two times intersection and the sum of union and intersection. It is also referred to as F1-score. Figure 5 shows the accuracy of the dice coefficient. The proposed model achieves an accuracy of 76% by using 2D U-Net architecture. On the other hand, Fig. 6 shows the ROC curve for our baseline. The y-axis represents the true positive (TP) rate and the x-axis for the false positive (FP) rate. The sensitivity and specificity are also considered. The sensitivity is defined as follows: Sensitivity ¼

True Positivies True Positivies þ False Negatives

ð2Þ

The sensitivity is the TP rate, which is the ratio between TPs and the sum of TPs and false negatives (FNs). Here, the TPs are the samples that are correctly classified as nodules, and the FNs are actual lung nodules that are incorrectly classified as nonnodules, which is the proportion of actual positive samples predicted as positive by our model. The specificity is related to the FP rate and is defined as follows: FPrate ¼ ð1  SpecificityÞ ¼

False Positivies False Positivies þ True Negatives

ð3Þ

where the FP rate is the ratio between the FPs and the sum of FPs and true negatives (TNs). Here, FPs are the non-nodule samples that are incorrectly classified as lung nodules and the TNs are samples correctly classified as non-nodules, which is the proportion of actual negative samples predicted as negative by our model.

Deep Learning-Based Cancerous Lung Nodule Detection

51

Moreover, Fig. 6 shows the performance of the proposed model in terms of the ROC with respect to AUC = 0.901. The ROC by the way is one of the popular performance measures in detection and it is represented by the ratio between the true positive rate and the false positive rate. From the figure one can see that the curve increases fast up to the FP rate below 0.2 and it maintains TP rate above 0.8 thereafter. Table 2 shows the result of nodule analysis and diagnosis. The proposed model has detected 132 nodules out of 154; it has missed 22 nodules while of this 13 are not considered as candidates by the segmentation. Table 2. Nodule analysis and diagnosis result. Complete miss Filtered out Pred. benign Pred. malignant Non-nodules 164,893 1593 563 Benign 12 3 70 17 Malignant 1 6 9 36

5 Conclusion In this paper, we have presented an automated lung nodule detection system based on deep learning networks. The propose model is built on the 2-D U-Net architecture to segment the position of suspicious lung nodules while the 3-D CNN architecture is used to classify candidates as lung nodules or non-nodules. Then, grouping is performed to combine both models to find the patient, who has been diagnosed with benign or malignant, by fine-tuning to replace the weights of an existing model. Finally, the performance of the proposed model has been evaluated and its detection performance and accuracy are accessed in terms of the sensitivity and specificity. In our future work, we will go further to investigate ways to limit the FP rate and to improve the sensitivity.

References 1. Siegel, R., Miller, K., Jemal, A.: Cancer statistics. CA Cancer J Clin. 70(1), 7–30 (2020) 2. Sung, H., et al.: Global cancer statistics: GLOBOCAN estimates of Incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 71(3), 209–249 (2021) 3. Akram, S., Muhammad, Y.J., Qamar, U., Khanum, A., Hassan, A.: Artificial neural network based classification of lungs nodule using hybrid features from computerized tomographic images. Appl. Math. Inform. Sci. 9(1), 183–195 (2015) 4. Zhang, W., Wang, X., Li, X., Chen, J.: 3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets. Comput Biol. Med. 92, 64–72 (2018) 5. Jaffar, M.A., Zia, M.S., Hussain, M., Siddiqui, A.B., Akram, S., Jamil, U.: An ensemble shape gradient features descriptor based nodule detection paradigm: a novel model to augment complex diagnostic decisions assistance. Multimedia Tools Appl. 79(13–14), 8649–8675 (2018). https://doi.org/10.1007/s11042-018-6092-4

52

S. V. Kumar et al.

6. Xiao, Z., Liu, B., Geng, L., Zhang, F., Liu, Y.: Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry 12(11), 1787 (2020) 7. Nasrullah, N., Jun, S., Alam, M., Mateen, M., Cai, B., Hu, H.: Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors 19(17), 3722 (2019) 8. Huang, W., Hu, L.: Using a noisy U-Net for detecting lung nodule candidates. IEEE Access 7, 67905–67915 (2019) 9. Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10. 1007/978-3-319-24574-4_28 10. Armato, S.G., McLennan, G., et al.: The lung image database consortium and image database resource initiative: a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011) 11. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)

Improving Disease Diagnosis with Integrated Machine Learning Techniques Özge H. Namlı1(&) and Seda Yanık2 1

2

Department of Industrial Engineering, Faculty of Engineering, Turkish-German University, Beykoz, 34820 Istanbul, Turkey [email protected] Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Macka, 34367 Istanbul, Turkey

Abstract. As the digital transformation is constantly affecting every aspect of our lives, it is important to enhance and use machine learning models more effectively also in the healthcare domain. In this study, we focus on the application of machine learning algorithms for disease diagnosis in order to support decision making of physicians. Different classification methods are used to predict the diameter narrowing in the heart using an anonymous dataset. In order to increase the prediction ability of the machine learning algorithms, we employ different feature extraction methods such as Autoencoder, Stacked Autoencoder, Convolutional Neural Network, and Principal Component Analysis methods and integrate each feature extraction method with the classification methods. Then, we compare the prediction performances of individual and feature-extractionintegrated classification methods. It is shown that the prediction performance of the classification methods increase when integrated with feature extraction methods. However, it is concluded that not all feature extraction methods work as well with all classification methods. When a specific classification method is integrated with the appropriate feature extraction method, a better improvement in the prediction performance can be obtained. Keywords: Machine learning diagnosis


Feature extraction
Classification
Disease

1 Introduction Machine learning applications are growing in importance in the healthcare industry as well as in every other industry. Especially thanks to their ability to help diseases diagnosis quickly and accurately. Their use both as stand-alone applications and as advice systems for doctors are among the popular topics. Many classification algorithms have been presented and used in the field of disease diagnosis. However, using only one classification algorithm is not sufficient to accurately detect the disease. The properties of the dataset used and the features considered are also very important. From this viewpoint, in this study, classification applications in the field of disease diagnosis and feature extraction methods that enable us to obtain

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 53–61, 2022. https://doi.org/10.1007/978-3-031-09176-6_6

54

Ö. H. Namlı and S. Yanık

more successful predictions by preventing the overfitting problem of classification algorithms are examined. Studies in the literature that used feature extraction methods in the field of disease diagnosis have been reviewed. Some of the studies are as follows: Li and Zhou [6] develop a model using wavelet packet decomposition and random forest algorithm for ECG classification. Zhang et al. [11] compare the novel landmark-based feature extraction method with regions-of-interest-based and voxel-based methods for Alzheimer's disease diagnosis. Lian et al. [7] develop a deep learning framework using a hierarchical fully convolutional network for joint discriminative atrophy localization and brain disease diagnosis. Chen et al. [1] compare the model, which integrates local fisher discriminant analysis as the feature extractor and supporting vector machine as a classifier, with that of principal component analysis based SVM, fisher discriminant analysis based SVM and standard SVM. Yuvaraj et al. [10] develop a nonlinear approach using higher-order spectra features extracted from electroencephalography signals to enable the automatic diagnosis of Parkinson's disease. In this study, we focus on a disease diagnosis problem. We have the aim of developing integrated classification approaches to predict the angiographic disease status of the patients. We use multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and gradient boosting (GB) algorithms as the classification methods and autoencoder (AE), stacked autoencoder (SAE), convolutional neural network (CNN), and principal component analysis (PCA) methods as the feature extraction methods. Then we compare the prediction results of these classifiers with each of the different feature extraction methods. The original aspect of this study is to try to improve the success of classification algorithms by integrating them with different feature extraction methods for the problem of heart disease diagnosis. The rest of the study is organized as follows: Sect. 1 presents the overview of the topic and the literature review, Sect. 2 explains the machine learning methods used and performance metrics respectively, Sect. 3 describes the application, Sect. 4 summarizes the results and discussion, and finally Sect. 5 demonstrates the conclusion and suggests the future work for research.

2 Methodology 2.1

Feature Extraction

Feature extraction is a data preprocessing method used to clean raw data and make it meaningful. The methods to be used for feature extraction within the scope of this study are presented below. Autoencoder. The AE method is basically an artificial neural network that compresses multidimensional data and then reconstructs the compressed data. This unsupervised learning neural network has 3 layers in its simplest and most basic form. These layers are; the input layer, hidden layer, and output layer. The two main parts of the AE can be summarized as follows [9]:

Improving Disease Diagnosis

55

In the encoding part of the AE method, the hidden representation (hi ) is calculated from the input vector (xi ) as in Eq. (1). hi ¼ f ðW encoder
xi þ bÞ

ð1Þ

Here W encoder shows the encoder weight matrix, b shows the encoder bias vector, and f ðzÞ the sigmoid function. In the decoding part of the method, the new input vector is obtained by reconstructing the hidden representation as in Eq. (2). ~xi ¼ f ðW decoder
hi þ bÞ

ð2Þ

Here W encoder shows the decoder weight matrix and b shows the decoder bias vector. Stacked Autoencoder. SAE is a deep learning neural network, which is widely used for feature extraction. This deep learning neural network arises from the combination of several AE on top of one another. According to the architecture of SAE, AEs are connected to each other by giving the input and output of the previous AE as the input to the next AE. Convolutional Neural Network. CNN, is introduced by LeCun et al. [5], is a specialized neural network designed. This neural network combines three architectural ideas as local receptive fields, weight replication, and subsampling. In the convolutional layer, which is the hub of the CNN structure, the input matrices are filtered and as a result of the filtering, it creates a feature map that enables feature extraction of the input matrix [8]. This mathematical operation is called a feature map is as in Eq. (3). zðm; nÞ ¼ ðx  kÞm;n ¼

1 1 X X

xi;j  kmi;nj

ð3Þ

i¼1 j¼1

Here x shows the input layer and k shows the kernel or feature detector. The second layer after the convolutional layer is the pooling layer. A major aim of this layer is both to prevent overfitting and to reduce the dimensions of the next layer while keeping the properties of the matrix. The convolutional layer and the pooling layer can be used more than once in the model. After these processes, the output value is produced according to the activation function determined. Principal Component Analysis. PCA is a linear technique that reduces the dimensionality of the data set while representing the variation in the data set at the highest level. The aims of this technique are reducing the number of variables, removing the redundant data, and keeping important information. 2.2

Classification

Classification, which is one of the base supervised machine learning techniques, tries to find which class a new observation belongs to by making a connection between the

56

Ö. H. Namlı and S. Yanık

input data and the output data. The classification algorithms used in the study are presented below. Multi-Layer Perceptron. MLP, which is known to be used frequently in the literature, can be defined as a feedforward neural network. This network is majorly composed of three base layers as input layer, hidden layers and output layer. This backpropagation algorithm is based on the error correction learning rule [3]. Support Vector Machine. SVM is a universal feedforward network that is often used for classification and nonlinear regression. The basic principle in SVM is to create a hyperplane that maximizes the separation margin between classes. For this, it works like an approximate application of the structural risk minimization method [3]. Random Forest. RF, one of the ensemble machine learning techniques, is a supervised learning method creating multiple decision trees during the training phase and using majority voting for classification. RF is composed of a collection of the generated trees hð x; hi Þ for i 2 N, here the hi represents independent distributed random vectors and each tree votes a unit vote for the predominant class at input x [4]. Gradient Boosting. GB is an ensemble machine learning technique often used for regression and classification. This powerful machine learning approach is based on the principle of generating strong predictors by iteratively combining weaker models through a greedy procedure corresponding to gradient descent in a function space [2]. 2.3

Performance Measures

Accuracy is a significant performance metric, that shows the success of the prediction made. Recall indicates how many of the values that should have been predicted as Positive were actually predicted as Positive. Precision shows how many of the values predicted as Positive are actually Positive. F-measure is a performance metric calculated with harmonic mean of precision and recall. The measurement of the area under the ROC curve, which takes a value between 0 and 1, shows the ability of the model to distinguish between classes. The formulation of performance metrics considered in this study are as follow: Accuracy ¼

TP þ TN TP þ FP þ FN þ TN

Recall ¼

TP TP þ FN

Precision ¼ F  measure ¼

TP TP þ FP

2Precision  Recall Precision þ Recall

ð4Þ ð5Þ ð6Þ ð7Þ

Improving Disease Diagnosis

57

3 Application To test the performance of integrated machine learning techniques, proposed models are applied on the “heart disease” dataset, which is one of the popular datasets used in the disease diagnosis area. The relevant dataset was accessed through the UCI Machine Learning repository (archive.ics.uci.edu) in October 2021. Summary information on the dataset used is presented in Table 1. Table 1. Summary of the heart disease dataset. No 1 2 3

Feature age sex cp

Feature information Patient’s age Patient’s sex Chest pain type

4

trestbps

5

chol

6

fbs

7

restecg

Patient’s resting blood pressure serum cholesterol level (mg/dl) Fasting blood sugar greater than 120 (mg/dl) Resting ECG results

8

thalach

9 10

exang oldpeak

11

slope

12

ca

13

thal

y

num

Patient’s maximum heart rate exercise-induced angina Exercise-induced ST depression relative to rest The slope of the peak exercise ST segment The number of major vessels colored by fluoroscopy A blood disease called thalassemia Angiographic disease status

Value Numeric Binary [1: man, 0: woman] Categoric [1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic] Numeric Numeric Binary [1: yes, 0: no] Categoric [1: normal, 2: having ST-T wave abnormality, 3: showing probable or definite left ventricular hypertrophy] Numeric Binary [1: true, 0: false] Numeric Categoric [1: upsloping, 2: flat, 3: downsloping] Numeric

Categoric [3: normal, 6: fixed defect, 7: reversable defect] Binary [0 :< 50% diameter narrowing of the heart vessel, 1 :> 50% diameter narrowing of the heart vessel]

The aim of this study is to predict the diameter narrowing in the heart in two classes using these thirteen input variables, which are thought to affect the angiographic disease status of the patients. For this, the categorical variables are restructured with a onehot encoding method. As a result of this restructuring, 20 features are obtained for the application. Afterward, all variables were normalized within themselves between 0 and

58

Ö. H. Namlı and S. Yanık

1. The dataset is divided into 80:20 ratio and 80% of the dataset is used for training the proposed classification models while 20% is used for testing the related models. Each classification algorithm is applied on its own and together with the proposed different feature extraction methods. While applying AE and SAE methods, parameter values are set as shown in Table 2. The SAE approach is formed by the combination of three AE methods. Table 2. Parameter values of autoencoder method.

# of neurons Normalization Activation F

Encoder level 1 Input size + 2 Batch normalization ReLu

Encoder level 2 Input size − 1 Batch normalization ReLu

Code Input size/2 – –

Decoder level 1 Input size − 1 Batch normalization ReLu

Decoder level 2 Input size + 2 Batch normalization ReLu

The parameter values used while applying the CNN structure are also presented in Table 3.

Table 3. Parameter values of convolutional neural network. Convolutional layer 1 Filter 20 Filter size 10 Activation F Sigmoid Normalization Batch normalization

Convolutional layer 2 4 4 Sigmoid Batch normalization

Max pooling layer

Flatten Output layer # of neurons

2

Activation F

Sigmoid

After 20 runs for each of these 3 methods, feature extraction is performed with the weights of the models belonging to the run with the lowest loss value. With the PCA application, the number of factors and total variance explained are examined and the dataset size is reduced in a way that covers 95% of the variance.

Improving Disease Diagnosis

59

4 Results and Discussion This study investigates the effect of using integrated machine learning techniques in the field of disease diagnosis. For this, MLP, SVM, RF and GB algorithms are applied on the heart disease data set alone and together with the proposed feature extractors. The application results obtained from all algorithms and their integrated versions are shown in Table 4.

Table 4. Results of random forest algorithm. Method RF AE + RF SAE + RF PCA + RF CNN + RF GB AE + GB SAE + GB PCA + GB CNN + GB SVM AE + SVM SAE + SVM PCA + SVM CNN + SVM MLP AE + MLP SAE + MLP PCA + MLP CNN + MLP

Accuracy 0.783 0.817 0.85 0.867 0.8 0.833 0.767 0.783 0.85 0.8 0.85 0.783 0.85 0.833 0.883 0.832 0.787 0.848 0.826 0.872

Recall 0.81 0.817 0.853 0.876 0.823 0.849 0.768 0.762 0.862 0.823 0.872 0.772 0.862 0.859 0.879 0.849 0.784 0.859 0.847 0.884

Precision 0.788 0.803 0.837 0.855 0.8 0.826 0.753 0.767 0.84 0.8 0.846 0.767 0.84 0.833 0.873 0.826 0.773 0.838 0.824 0.862

F-measure 0.78 0.808 0.843 0.861 0.796 0.829 0.757 0.764 0.845 0.796 0.847 0.769 0.845 0.83 0.876 0.828 0.777 0.842 0.822 0.867

Average time 0:00:00.257514 0:00:00.231581 0:00:00.245461 0:00:00.257944 0:00:00.394147 0:00:00.050136 0:00:00.042832 0:00:00.039998 0:00:00.030557 0:00:00.026997 0:00:00.021653 0:00:00.015033 0:00:00.014072 0:00:00.013977 0:00:00.007510 0:00:03.577971 0:00:04.359082 0:00:04.710851 0:00:03.968098 0:00:05.309278

ROC area 0.81 0.82 0.85 0.88 0.82 0.85 0.77 0.76 0.86 0.82 0.87 0.77 0.86 0.86 0.88 0.85 0.78 0.86 0.85 0.88

When we examine the application results, we observe that the performance of the RF algorithm is improved with all the proposed feature extraction methods. For the GB algorithm, it is seen that using it only together with PCA provides improving results in terms of performance criteria.

Ö. H. Namlı and S. Yanık

60

ACCURACY

0.832 0.848 0.826 0.872 0.787

0.85 0.85 0.833

0.883

AE+Model PCA+Model

0.783

0.85

0.833

0.767 0.783 0.8

0.783

0.8

0.817

0.85 0.867

Model SAE+Model CNN+Model

ROC Area Model SAE+Model CNN+Model

AE+Model PCA+Model

0.89 0.88 0.87 0.85

0.85

0.86 0.85

0.88 0.87 0.86

0.88 0.86 0.85

0.83 0.81

0.82 0.82 0.81

0.82

0.79 0.77

RF

GB

SVM

MLP

Fig. 1. Accuracy of the models.

0.77 0.76

0.77

GB

SVM

0.78

0.75 RF

MLP

Fig. 2. ROC area of the models.

For SVM and MLP algorithms, it can be said that their applications with SAE and CNN provide improvement in terms of performance metrics. When we examine the accuracy and ROC area graphs from Figs. 1 and 2, we see that the best prediction performance values are obtained with PCA for RF and GB, and with CNN for SVM and MLP.

5 Conclusion The objective of this research is to predict the angiographic disease status of the patients. We use a neural network algorithm, two ensemble decision tree algorithms and SVM for classification application. We implement different feature extraction methods to increase the accuracy and success of our classifiers. Among the classification approaches applied, we achieve the best prediction success with the SVM algorithm with an 85% accuracy rate. When we use classification algorithms together with feature extraction methods, we get the highest prediction success with an 88% accuracy rate from the integrated CNN and SVM application. It is observed that the accuracy rates increased by 4% for MLP, 3% for SVM, 8% for RF, and 2% for GB when integrated with the most appropriate feature extraction methods. Integrated machine learning techniques with feature extraction are found to be superior than individual classification algorithms. More successful prediction results are

Improving Disease Diagnosis

61

obtained in our application when feature extraction is made with PCA for decision tree algorithms, and feature extraction is made with CNN for neural network algorithms. In future research, we will work on a new ensemble structure that combines the different classifiers’ results and increases the model's accuracy. And we will make a comparison whether the prediction success of the new ensemble structure is better than traditional approaches.

References 1. Chen, H.L., Liu, D.Y., Yang, B., Liu, J., Wang, G.: A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Syst. Appl. 38(9), 11796–11803 (2011) 2. Dorogush, A., Ershov,V., Gulin, A.: CatBoost: Gradient boosting with categorical features support. In: Proceedings of the Workshop ML Neural Information Processing Systems (NIPS), pp. 1–7 (2017) 3. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1994) 4. Kulkarni, V.Y., Sinha, P.K.: Pruning of random forest classifiers: a survey and future directions. In: 2012 International Conference on Data Science & Engineering (ICDSE), pp. 64–68. IEEE (2012) 5. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998) 6. Li, T., Zhou, M.: ECG classification using wavelet packet entropy and random forests. Entropy 18(8), 285 (2016) 7. Lian, C., Liu, M., Zhang, J., Shen, D.: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 880–893 (2018) 8. Su, Z.: Design and research on modification method of finite element dynamic model of concrete beam based on convolutional neural network. IOP Conf. Ser. Earth Environ. Sci. 781(2), 022114 (2021) 9. Xia, Y., et al.: An automatic cardiac arrhythmia classification system with wearable electrocardiogram. IEEE Access 6, 16529–16538 (2018) 10. Yuvaraj, R., Rajendra Acharya, U., Hagiwara, Y.: A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural Comput. Appl. 30(4), 1225–1235 (2016). https://doi.org/10.1007/s00521-016-2756-z 11. Zhang, J., Gao, Y., Gao, Y., Munsell, B.C., Shen, D.: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging 35(12), 2524–2533 (2016)

Explanation of Machine Learning Classification Models with Fuzzy Measures: An Approach to Individual Classification Daniel Santos1(B) , Inmaculada Guti´errez1 , Javier Castro1,2 , Daniel G´ omez1,2 , Juan Antonio Guevara1 , and Rosa Esp´ınola1,2 1

2

Facultad de Estudios Estad´ısticos, Universidad Complutense de Madrid, Madrid, Spain {dasant05,inmaguti,juanguev}@ucm.es {jcastroc,dagomez,rosaev}@estad.ucm.es Instituto de Evaluaci´ on Sanitaria, Universidad Complutense de Madrid, Madrid, Spain

Abstract. In the field of Machine Learning, there is a common point in almost all methodologies about measuring the importance of features in a model: estimating the value of a collection of them in several situations where different information sources (features) are available. To establish the value of the response feature, these techniques need to know the predictive ability of some features over others. We can distinguish two ways of performing this allocation. The first does not pay attention to the available information of known characteristics, assigning a random allocation value. The other option is to assume that the feasible values for the unknown features have to be any of the values observed in the sample (in the known part of the database), assuming that the values of the known features are correct. Despite its interest, there is a serious problem of overfitting in this approach, in situations in which there is a continuous feature: the values of a continuous feature are not likely to occur in any other, so there is a large loss of randomization (there will surely be an insignificant number of records for each possible value). In this scenario, it is probably unrealistic to assume a perfect estimation. Then, in this paper we propose a new methodology based on fuzzy measures which allows the analysis and consideration of the available information in known features, avoiding the problem of overfitting in the presence of continuous features. Keywords: Fuzzy measures · Machine learning importance · Explainable artificial intelligence

· Features

Supported by the Government of Spain, Gran Plan Nacional de I+D+i PR108/20-28 and PGC2018096509-B-I00. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 62–69, 2022. https://doi.org/10.1007/978-3-031-09176-6_7

Explanation of Machine Learning Classification Models with Fuzzy Measures

1

63

Introduction

In the field of Game Theory we find a solution concept which stands out from others: the Shapley value [14]. Generally speaking, it allocates among the players a single distribution of the total surplus achieved by the grand coalition with all players. This solution concept has several desirable properties which make it very attractive, so its characterization has been adapted to other fields, as fuzzy measures learning [5,6] and applied in a wide range of areas [15,18]. In particular, the Shapley value has been widely considered in the framework of machine learning [4,8–10,12,17,19]. Due to its flexibility, appealing axiomatization and special notion of ‘fairness’ [11], it has become increasingly popular in assessing the importance of features in a machine learning model, as well as in measuring the predictive ability of some features over others [3,7,13], turning into an essential tool in the field of eXplainable Artificial Intelligence (XAI). At high level, for a given machine learning model, we consider the Shapley value to explain the predictive ability of some features over others, following the philosophy of the SHAP algorithm [9]. Our starting points are the works ˇ of Strumbelj [19–21]. In general, they try to predict the value of an uncharted feature knowing a set of them. To do so, they propose to extreme approaches. In [19] they suggest assigning a random value to the unknown feature, whereas the proposal in [21] is on the consideration of the instances in which the value of the unknown feature exactly matches the fixed instance to be predicted. Our proposal in this paper is an intermediate solution, which mixes random values with consideration of exact values. To do so, we make use of fuzzy measures [16]. That monotonic set functions, which encompasses from capacity and necessity measures to necessity, plausability or possibility functions, are particular useful to represent and analyze vagueness, as well as to make decisions or find good methods and logical operators for connectives and implications [2]. With the use of fuzzy measures, we can represent very accurately the predictive ability of the features of a machine learning model. We agree that, knowing the predictive ability of the features, improves the interpretability of the model [1]. The paper is organized as follows. We introduce preliminaries in Sect. 2. We develop our proposal in Sect. 3, and we conclude in Sect. 4.

2

Preliminary

In this paper we address the problem of measuring the predictive ability of the features of a machine learning model. Although we approach it on the basis of ˇ fuzzy measures, our starting point are the proposals of Strumbelj et al. [19–21]. They worked on the measurement of the importance of the features in a machine learning model using the Shapley value in a cooperative game. Definition 1 (Shapley value in a cooperative game [14]). Given the cooperative game (N, w) with characteristic function w, and being |N | = n the number of players, the Shapley value of the player i ∈ N on the game w is Shi (w) =



S⊆N \{i}

(n − s − 1)!s! (w (S ∪ {i}) − w (S)) , n!

where

s = |S|

(1)

64

D. Santos et al.

Definition 2 (Fuzzy measure [16]). Let N denote a set of individuals, and let the function μ : 2N → [0, 1]. μ is said to be a fuzzy measure if μ(N ) = 1; μ(∅) = 0, and ∀A, B ⊆ N , if A ⊆ B, then μ(A) ≤ μ(B). These set functions encompass many different approaches widely used and very useful, as possibility, capacity, belief and plausibility measures. Then we introduce some notions on machine learning and several measures used to quantify the predictive ability of the features in a model. In this context, on the following we assume that the set of players or individuals previously mentioned, N , actually refers to a set of features in a machine learning model, N = {v1 , . . . , vn }. The cartesian product of the features in N is denoted by A. For the subset S ⊆ N , the corresponding subspace of A is AS = A1 ×A2 ×. . . An , being Ai = Ai , if i ∈ S, and Ai = {} otherwise [20]. Then, given a finite set of labels, C, a mapping f : A → [0, 1]|C| is said to be a classifier. Finally, we present two importance measures found in the literature. Given a database, D, and a specific instance, x, the first proposal [19] denoted by ϕ1 , assigns a random value to the unknown features. On the other hand, when measuring the predictive ability, the proposal in [21], denoted by ϕ2 , only considers the instances in which the value of the unknown features exactly match with x. Definition 3 (Explanation method for an instance ϕ1 [19]). Given a database D with m instances and n features N = {v1 , . . . , vn }, let f describe a classifier and x = (x1 , . . . , xn ) ∈ A represent a specific instance. Being s = |S|, we define the importance of the feature t in the instance x as the Shapley value where w(S) = Δ1 (S). Formally, 

ϕ1t (x) =

S⊆N \{t}

where Δ1 (S) =

 (n − s − 1)!s!  1 Δ (S ∪ {t}) − Δ1 (S) n! 

1 |AN \S |

fc (τ (x, y, S)) −

y∈AN \S

τ (x, y, S) = (z1 , z2 , . . . , zn ),

 being z =

(2)

1  fc (y) |AN | y∈AN

x if  ∈ S /S y if  ∈

Definition 4 (Explanation method for an instance ϕ2 [21]). Given a database D with m instances and n features N = {v1 , . . . , vn }, let f describe a classifier and x = (x1 , . . . , xn ) ∈ A represent a specific instance. Being s = |S|, we define the importance of the feature t in the instance x as the Shapley value where w(S) = Δ2 (S). Formally, ϕ2t (x) =

 S⊆N \{t}

 (n − s − 1)!s!  2 Δ (S ∪ {t}) − Δ2 (S) n!

where Δ2 (S) =

1  1  fc (y) − fc (y) |BS | |AN | y∈BS

being

y∈AN

BS = {y ∈ D : x = y , ∀ ∈ S}

(3)

Explanation of Machine Learning Classification Models with Fuzzy Measures

3

65

Mathematical Model

Our goal is the definition of an importance measure related to the features of a machine learning model. To do so we define m fuzzy measures, one for each feature of the model, related to their predictive ability. Specifically, being N = {v1 , . . . , vn } the set of features of a machine learning model, given the set S ⊆ N , the fuzzy measure μj (S) represents the ability of the features in S to predict the individual feature vj ∈ N , regardless the forecast reached by randomness. For example, μ2 ({v1 , v3 }) = 0.7 can be understood as: knowing the features v1 and v3 , actually we have the 70% of the relevant information of the feature 2. Obviously when j ∈ S, it is trivial that μj (S) = 1. In this scenario, the element of interest, j, belong to set S used to predict. On the other hand, μj (∅) = 0, ∀vj ∈ N . It is trivial because nothing, apart randomness, can be inferred from ∅. Finally, we can assert that μj (N ) = 1, ∀vj ∈ N . Adding a new feature to those used to predict will not worsen the prediction, so, μj (T ) ≤ μj (S), ∀S, T ⊆ N with T ⊆ S. In fact, it happened, the previous predictive model could be used, without considering the new added feature. Definition 5 (Predictive fuzzy measure). Let D denote a database with m instances and N = {v1 , . . . , vn } features. Given the feature vj ∈ N , for every S ⊆ N , we define μj (S) as the predictive ability, regardless randomness, of the features in S over vj in the database D. Formally, μj (S) =

Errorj (∅) − Errorj (S) Errorj (∅)

(4)

where Errorj (∅) and Errorj (S) denote a measure of the error get when predicting vj by randomness or with the features in S, respectively. To simplify the notation, we denote μj ({}) := μj ({v }), and so on. Then we provide an illustrative example of a possible way to calculate μj (S), specifically by considering the absolute error sum. Example 1. Let us consider the database below, with N = {v1 , v2 , v3 } features. Table 1. Database D. v1 v2 v3 1

1

1

0

1

0

0

0

1

0

0

0

It can be seen that for the full knowledge of v1 , v2 and v3 should be known, as every pair of values of v2 and v3 unambiguously sets v1 . Then, μ1 ({2, 3}) = 1.

66

D. Santos et al.

Nevertheless, only knowing v2 , in half of the cases, specifically when v2 = 0, we can exactly know the value of v1 . In the other half of the cases, the estimation is 0, so the absolute error sum is (0.5 + 0.5 + 0 + 0) = 1. The estimation by randomness is 0.25, so the absolute error sum is (0.75 + 1 1.5−1 1 0.25+0.25+0.25) = 1.5, so μ1 ({2}) = 1.5−1 1.5 = 3 . Similarly, μ1 ({3}) = 1.5 = 3 . To quantify the predictive ability of v2 and v3 , we have to calculate: – The prediction by randomness of both features is 0.25, and the absolute error sum is (0.5 + 0.5 + 0.5 + 0.5) = 2. – The estimation for v3 does not change by knowing v2 , neither when v2 = 0 nor when v2 = 0, so μ3 ({2}) = 0. The same situation, but the other way around, happens for μ2 ({3}) = 0. – If we known v1 = 1, then v2 and v3 are exactly known. If we known v1 = 0, then the prediction changes to 0.3 3 in both cases so the absolute error sum is   3 6      (0 + 0.66 + 0.33 + 0.33) = 1.33, so μ2 ({1}) = μ3 ({1}) = 2−1.3 = 0.6 2 2 = 0.33. – If v1 and v2 are known in a half of the cases (v1 = 1, v2 = 1) and (v1 = 0, v2 = 1), then v3 is exactly fitted (v3 = 1 and v3 = 0 respectively); in the other two cases (v1 = 0, v2 = 0) the prediction of v3 is 0.5. Then, the absolute error sum is (0 + 0 + 0.5 + 0.5) = 1, so μ3 ({1, 2}) = 2−1 2 = 0.5. – If v1 and v3 are known, the situation is analogous to the previous one for the pairs (v1 = 1, v3 = 1) and (v1 = 0, v3 = 1), with which v2 is exactly known. In the other two cases, the prediction of v2 is 0.5, so μ2 ({1, 3}) = 2−1 2 = 0.5. The corresponding predictive fuzzy measures for D are μ1 (∅) = 0; μ1 ({2}) = 3; μ1 ({2, 3}) = 1; μ1 ({1}) = μ1 ({1, 2}) = μ1 ({1, 3}) = 0.3 3; μ1 ({3}) = 0.3 3; μ2 ({3}) = 0; μ2 ({1, 3}) = 0.5; μ1 ({1, 2, 3}) = 1; μ2 (∅) = 0; μ2 ({1}) = 0.3 μ2 ({2}) = μ2 ({1, 2}) = μ2 ({2, 3}) = μ2 ({1, 2, 3}) = 1; μ3 (∅) = 0; μ3 ({1}) = 0.3 3; μ3 ({2}) = 0; μ3 ({1, 2}) = 0.5; μ3 ({3}) = μ3 ({1, 3}) = μ3 ({2, 3}) = μ3 ({1, 2, 3}) = 1. Once the values of μj (S) have been calculated, we define the importance measure for each feature, regarding a specific instance. Definition 6 (Explanation method for an instance using fuzzy measures ϕ). Given a database D with m instances and n features N = {v1 , . . . , vn }, let j ∈ N denote one of these features, and let μj (S) be a predictive fuzzy measure of the features in S ⊆ N over j. Let f describe a classifier and x = (x1 , . . . , xn ) ∈ A represent a specific instance. Being s = |S|, we define the importance of the feature t in the instance x as the Shapley value where w(S) = Δ3 (S). Formally, 

ϕt (x) =

S⊆N \{t}

where Δ3 (S, x) =

1 |AN \S |

 (n − s − 1)!s!  3 Δ (S ∪ {t}) − Δ3 (S) n!

 y∈AN \S

fc (τ (x, y  , S)) −

1 |AN \S |

 y∈AN \S

(5)

fc (τ (x, y  , ∅))

Explanation of Machine Learning Classification Models with Fuzzy Measures





τ (x, y , S) = (z1 , . . . , zn ), being z =

67

x if  ∈ S /S y if  ∈

being y an estimation of the  − th feature, when knowing the features in S, with which an explainability percentage of μ (S) is guaranteed. Then we show an example in which the prediction of y knowing μ (S) is done by assigning the real value of μ (S) in some cases and a random value between all the possibilities for 1 − μ (S) cases; i.e.  y , if U (0, 1) ≥ μ (S)  (6) y = x , if U (0, 1) < μ (S) Let us remark that, if the value of ϕ is aggregated for the n instances of the database as in [9], we obtain a general measure of the importance of the features. Example 2. Given the database in Table 1, the predictive fuzzy measures calculated in the Example 1 and ϕC (X), represented by decision tree below, we calculate the importance of v1 , v2 , v3 for the instance x = (0, 0, 1) (Fig. 1).

Fig. 1. Decision tree.

•

27 1  1 (27 ∗ 1 + 37 ∗ 0) = , being 27 and 37 the fc (t (x, y  , ∅)) = |AN | 64 64 y∈AN

number of instances with prediction 1 and 0, respectively. •

1 |AM \{1} |



   fc t x, y  , {1}

=

y∈AM \{1}

1 16





 µ2 ({1})µ3 ({1})fc (v1 , v2 , v3 ) +

y∈AM \{1}

(1 − µ2 ({1})) µ3 ({1})fc (x, y2 , v3 ) + µ2 ({1}) (1 − µ3 ({1})) fc (v1 , v2 , y3 ) + (1 − µ2 ({1})) (1 − µ3 ({1})) fc (v1 , y2 , y3 ) = 13 31 (0 ∗ 1 + 16 ∗ 0) + 23 31 (0 ∗ 1 + 16 ∗ 0) + 13 32 (8 ∗ 1 + 8 ∗ 0) +

2 2 (4 3 3

∗ 1 + 12 ∗ 0) =

1 (0 16

+0+

16 9

+

16 ) 9

=

2 . 9

It is analogous for M \{2} and M \{3}, with results  1 1 • fc (t (x, y  , {1, 2})) = |AM \{1,2} | 4 y∈AM \{1,2}

7 12

and 

1 6

respectively. μ3 ({1, 2})fc (v1 ,

y∈AM \{1,2} 4 ∗ 0) + 12 (2

v2 , v3 ) + (1 − μ3 ({1, 2})) fc (v1 , v2 , v3 ) = 12 (0 ∗ 1 + ∗ 1 + 2 ∗ 0) = 1 1 (0 + 1) = . 4 4 It is analogous for M \{1, 3} and M \{2, 3}, with result 0 for both.

68

•

D. Santos et al.



1 |AM \{1,2,3} |

fc (t (x, y  , {1, 2, 3})) = fc (v1 , v2 , v3 ) = 0.

y∈AM \{1,2,3}

 1

  3   1 3  ϕ1 (x) =  3 Δ3 ({1}) − Δ3 (∅) + 16 Δ3 ({1, Δ ({1, 3})− 2}) − Δ ({2}) + 6  1 3 3 3 Δ ({3}) + 3 Δ ({1, 2, 3}) − Δ ({2,  3})  −0.1433    ϕ2 (x) =  13 Δ3 ({2}) − Δ3 (∅) + 16 Δ3 ({1, 2}) − Δ ({1}) + 16 Δ3 ({2, 3})−  Δ3 ({3}) + 13 Δ3 ({1, 2, 3}) − Δ3 ({1,  3})  0.031 3    ϕ3 (x) =  13 Δ3 ({3}) − Δ3 (∅) + 16 Δ3 ({1, 3}) − Δ ({1}) + 16 Δ3 ({2, 3})−  Δ3 ({2}) + 13 Δ3 ({1, 2, 3}) − Δ3 ({1, 2})  −0.303 See the substantial difference if the values are calculated with ϕ1 and ϕ2 ,  ϕ11 =

−39 192

 ϕ21 =

−5 36

4

 −0.203; ϕ12 =

 −0.138; ϕ22 =

 0.109; ϕ13 =

−63 192

 −0.328

 −0.055; ϕ23 =

−11 36

 −0.305

21 192

−2 36

Conclusions and Further Research

In this paper we deal with the problem of measuring the predictive ability of the features of a machine learning model. To do so, we make use of fuzzy measures, which allow us to represent in a very realistic way the power of the elements of ˇ a machine learning model. In this field, we can highlight the works of Strumbelj et al. [19–21]. Based on cooperative game theory, they propose two solutions to evaluate the predictive ability of a set of features over an unknown one, using the Shapley value of a cooperative game [14]. Due to its particular notion of ‘fairness’, this index is particularly useful to assess the importance of features in a machine learning model and to quantify the predictive ability the features. Following their idea, we suggest an intermediate proposal which combines the ‘good’ parts of [19,21]. Our proposal mixes the consideration of random values with the specification of exact values. The consideration of fuzzy measures [16] allows us to deal with that, so we can represent in a very realistic way the predictive ability of the features of a machine learning model. For the future, we will work on the application of the solution here proposed by considering the linear model. We will work in the specification of the predictive ability of the features in a machine learning model, regardless randomness, by considering the general linear model, specifically by the consideration of the mean squared error as error measurement. Moreover, we will assess some desirable properties about the measures defined. We will also apply the proposal here introduced in some real data sets. Apart from that, we will work in a characterization of μ to be applicated in the consideration of real-life situations.

References 1. Alonso Moral, J., Castiello, C., Magdalena, L., Mencar, C.: Explainable Fuzzy Systems. Studies in Computational Intelligence, vol. 970. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71098-9

Explanation of Machine Learning Classification Models with Fuzzy Measures

69

2. Beliakov, G., G´ omez, D., James, S., Montero, J., Rodr´ıguez, J.: Approaches to learning strictly-stable weights for data with missing values. Fuzzy Sets Syst. 325, 97–113 (2017). https://doi.org/10.1016/j.fss.2017.02.003 3. Chu, C., Chan, D.: Feature selection using approximated high-order interaction components of the Shapley value for boosted tree classifier. IEEE Access 8, 112742– 112750 (2020) 4. Fern´ andez, A., De Jes´ us, M., Cord´ on, O., Marcelloni, F., Herrera, F.: Evolutionary fuzzy systems for explainable artificial intelligence: why, when, what for, and where to? IEEE Comput. Intell. Mag. 14(1), 69–81 (2019) 5. Grabisch, M.: k-order additive discrete fuzzy measures and their representation. Fuzzy Sets Syst. 92(2), 167–189 (1997) 6. Grabisch, M., Nguyen, H., Walker, E.: Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Kluwer Academic, Dordrecht (1995) 7. Ibrahim, L., Mesinovic, M., Yang, K., Eid, M.: Explainable prediction of acute myocardial infarction using machine learning and Shapley values. IEEE ACCESS 8, 210410–210417 (2020) 8. Kumar, I., Venkatasubramanian, S., Scheidegger, C., Friedler, S.: Problems with Shapley-value-based explanations as feature importance measures. In: Proceedings of Machine Learning Research, vol. 119 (2020), International Conference on Machine Learning (ICML), ELECTR NETWORK (2020) 9. Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. ArXiv abs/1705.07874 (2017) 10. Martini, M., et al.: Application of cooperative game theory principles to interpret machine learning models of nonhome discharge following spine surgery. Spine 46(12), 803–812 (2021) 11. Okhrati, R., Lipani, A.: A multilinear sampling algorithm to estimate Shapley values. In: Artificial Intelligence, vol. 298 (2021) 12. Pang, C., Yu, J., Liu, Y.: Correlation analysis of factors affecting wind power based on machine learning and Shapley value. IET Energy Syst. Integr. 3(3), 227–237 (2021) 13. Roder, J., Maguire, L., Georgantas, R., Roder, H.: Explaining multivariate molecular diagnostic tests via Shapley values. BMC Med. Inform. Decis. Mak. 21, 211 (2021) 14. Shapley, L.: A value for n−person games. Ann. Math. Stud. 2, 307–317 (1953) ´ 15. Smith, M., Alvarez, F.: Identifying mortality factors from machine learning using Shapley values? A case of COVID19. Expert Syst. Appl. 176, 114832 (2021) 16. Sugeno, M.: Fuzzy measures and fuzzy integrals: a survey. In: Fuzzy Automata Decision Process, vol. 78, January 1977 17. Sun, X., Liu, Y., Li, J., Zhu, J., Liu, X., Chen, H.: Using cooperative game theory to optimize the feature selection problem. arXiv:2010.12082 (2022) 18. Tan, C., Chen, X.: Intuitionistic fuzzy Choquet integral operator for multi-criteria decision making. Expert Syst. Appl. 37(1), 149–157 (2019) ˇ 19. Strumbelj, E., Kononenko, I.: An efficient explanation of individual classifications using game theory. J. Mach. Learn. Res. 1, 1–18 (2010) ˇ ˇ 20. Strumbelj, E., Kononenko, I., Robnik Sikonja, M.: Explaining instance classifications with interactions of subsets of feature values. Data Knowl. Eng. 68(10), 886–904 (2009) ˇ 21. Strumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41(3), 647–665 (2013). https:// doi.org/10.1007/s10115-013-0679-x

A Machine Learning Based Method for Automatic Identification of Disaster Related Information Using Twitter Data Athina Ntiana Christidou1, Maria Drakaki1(&) and Vasileios Linardos2 1

,

Department of Science and Technology, International Hellenic University of Thessaloniki, 14th km Thessaloniki, Nea Moudania, 570 01 Thessaloniki, Greece [email protected] 2 Archeiothiki S.A., 19400 Athens, Greece

Abstract. The impacts of natural disasters on communities are devastating including loss of human lives and severe damages to properties. Social media data including Twitter and Facebook data can play a critical role in various phases of disaster management, in particular in the phases of disaster response and disaster preparedness. Geospatial data available by Twitter provide valuable real time information for search and rescue operations of emergency response units, damage assessment and disaster monitoring. Online social networks are an integral part of disaster communication and response systems. Machine learning (ML) algorithms can leverage social media data to facilitate disaster management operations. In this paper an ML based method is proposed to swiftly recognize natural disaster events in order to enhance the performance of emergency services to cope with the events. Various machine learning algorithms were used to automatically identify tweets related to natural disasters. Best results were achieved with the Logistic Regression (LR) and Support Vector Machine (SVM) algorithms. The developed method provides promising results to enhance informed decision making during and after disaster events. Keywords: Machine learning
Twitter
Social media
Disaster management
Natural disaster

1 Introduction Disasters can be categorized in two main groups: natural disasters and technological disasters [1]. Adopting the United Nations Office for Disaster Risk Reduction (UNISDR) (2009) terminology, a disaster is a “serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources” [2]. Social media play a critical role in disaster and crisis communication and is increasingly being used in emergency management [3, 4]. The use of social media has shown to reduce operational inefficiencies during emergencies, lead to improved © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 70–76, 2022. https://doi.org/10.1007/978-3-031-09176-6_8

A Machine Learning Based Method for Automatic Identification

71

capabilities of emergency services and reach more people through timely warnings and disaster related messages. Disaster related information posted on Twitter can speed up relief operations, and reduce potential harm to the affected community [3]. Furthermore, social media users during disasters may share disaster related information in order to mobilize emergency services groups in response to their needs and assist effectively in relief operations [5]. Disaster management addresses disasters before and after they strike. Four main disaster phases have been identified: mitigation, preparedness, response and recovery [6]. Mitigation includes the efforts aimed to reduce the hazard risks and the impacts of disasters. Preparedness includes the operations that take place prior to a disaster to enhance readiness. Response includes the operations which take place during a disaster, mainly delivery of humanitarian aid to people in need, damage assessment and search and rescue operations. Recovery includes the long-term actions that aim to bring a state of normalcy to the community. Twitter use for disaster management has various benefits which cannot be attained from other media sources including real-time as well as geotagged information. In the immediate aftermath of a disaster information about location is crucial, especially for search and rescue operations [7]. Furthermore, people not affected by the disaster, may provide help to emergency responders by filtering disaster related information in social media [7]. Moreover, Twitter data has been used for identification of resource needs and availabilities [8], noise reduction of the developed methods [9] and information retrieval [10]. Although Twitter contains a big pool of disaster-related information, it also contains information that is not relevant to the specific disaster. Manually using hashtags and keywords to monitor its content poses volume limitations. The flow of information in popular hashtags cannot be easily monitored since hundreds of tweets per second may be sent due to a popular hashtag. Accordingly, the aim of this paper is to develop a method based on ML to automatically recognize essential information contained in Twitter that is important to emergency responders during a natural disaster and in the aftermath of a natural disaster. The developed technique filters actionable information for the delivery of relief operations and automates the manual procedure of identifying critical information for emergency services. It has been tested on hydrological and climate related disasters. Various ML algorithms were used and the best results were achieved with LR and SVM. In the following, the literature review is presented next. The methodology as well as the implementation and the results using a case study follow. Next, conclusions and future research are presented.

2 Literature Review ML techniques have been widely researched and applied in different phases of disaster management. More specifically, the use of social media datasets for machine learning in disaster management has been researched in recent studies. In this study a short literature review of such studies is presented to provide an informative literature

72

A. N. Christidou et al.

background on the applicability and performance of social media datasets, and Twitter in particular. Layek et al. [11] developed a convolutional neural network (CNN) in order to detect flood images in areas hit by floods using real image data from Twitter. The approach that was used included a classification of the social network images from the CNN and then a color-based filtering to enhance the performance of the overall system. The achieved accuracy was 80.07% while the F1 score of the system was 0.77. Fan et al. [12] proposed a hybrid ML system which was focused on gathering and combining disaster related information from different sources in social media posts. The proposed system aimed to identify the valid information about the situation at hand and distinguish between posts that did not contain the right information therefore negatively impacting the awareness of the situation. The system encased a plethora of methods working together including graph clustering, bert transformer, named entity recognition, and a fusion approach. Similar to the before mentioned study, real Twitter data were used to test the application yielding good results on mapping the disaster related situations. Nguyen et al. [13] proposed a real-time disaster detection and damage assessment system which was based on social media data and CNNs. The system was focused on earthquake data where it managed to perform with an accuracy of 84% and an F1 score of 0.82 on the Nepal earthquake data. A combination of visual and textual data from social media posts for a system that is able to process both to achieve better results was studied by Huang et al. [14]. More specifically, deep learning methods such as embedded CNN and Incpetion-V3 were used to extract features related to disasters from social media posts. Those extracted features were in turn passed on to the classification phase which employed several ML algorithms. When tested on Twitter posts for the 2017 Houston flood, the system showed great results, with the best performing classification algorithm being Logistic Regression, achieving accuracy of 95.2%. Kundu et al. [15] explored Long Short-Term Memory neural networks (LSTM) for the classification of social media posts regarding their relation to different post-disaster actions and activities. This type of recurrent neural network was applied on tweets regarding the Nepal earthquake and achieved a promising accuracy of 92.34% and an F1 score of 0.9159. Alam et al. [10] developed an end-to-end system called Image4Act. This system applied denoising techniques on social media image data and classified the images with neural networks extract which of those images were relevant to an occurred disaster. The authors aimed to overcome the noisy nature of social media posts which can often hinder the extraction of relevant information. The system was evaluated in existing datasets and was proven effective.

3 Methodology, Implementation and Results The system overview includes the following steps: (i) Create python script to collect Tweets via hashtag. (ii) Download prebuilt dataset from Kaggle site.

A Machine Learning Based Method for Automatic Identification

(iii) (iv) (v) (vi)

73

Combine custom and Kaggle dataset with the same format. Preprocessing of combined dataset. Training of machine learning classification algorithms. Results and benchmarking of ML algorithms.

Raw data is collected from the Twitter API using hashtags. Furthermore, a Kaggle dataset, namely the “Natural Language Processing with Disaster Tweets” dataset, was used in combination with the collected raw data to create the dataset used in this study. Additionally, the format of the collected raw data from the Twitter API should follow the format of the Kaggle dataset. Therefore, each tweet contained incremental ID, keyword, location, full text, and target tags. The target tag indicated the state of the post, using a Boolean, in the context of whether the post is disaster relevant or not. A “0” indicated a non-disaster relevant post, and a “1” indicated a disaster relevant post. In the data collection, the keyword search targeted past natural disasters and mainly hydrological and climate related disasters, such as Hurricane Sandy, Kerala flood, and Japan tsunami. The data collection consisted of the following filters: • • • • •

Clear special characters. Clear numbers. Drop phrasal verbs. Remove words that don’t have essential meaning. Remove newline characters and double spaces. Figure 1 shows the system overview and architecture.

Fig. 1. The system overview of the developed method.

74

3.1

A. N. Christidou et al.

Application

The information contained in the tweets of the combined dataset included: the id that is a serial number, the text of the tweet and the Boolean variable target that could have two values, i.e. “1” if the tweet is related to a natural disaster and “0” if it is not related to a natural disaster. In the combined dataset, 43.7% of the tweets were relevant to a natural disaster. A statistical analysis of the dataset was performed next. A new data frame contained information about each tweet including the calculated number of characters, the average word length, the total number of words, the number of hashtags, the number of stop words, the number of uppercase letters and the number of numeric digits. The data frame samples were evaluated in terms of quality. Based on the statistical analysis results, the combined Twitter dataset could be further processed by ML algorithms. Furthermore, the useful special characters were included in a dictionary. Accordingly, stop words, expressions and special characters which were not included in the designed dictionary were removed from the Twitter text. After the data clearing process, the filtered text was transformed into numerical vectors in order to properly train the ML algorithms. Finally, various ML algorithms were used in the training and testing process including Naïve Bayes (NB), LR, gradient boosting (GB), SVM, classification and regression trees (CART), K-Nearest Neighbor (KNN) and Adaboost. Eighty per cent (80%) of the dataset was used for training and twenty per cent (20%) for testing. The PC used to obtain the results had an Intel(R) Core(TM) i7-8700K CPU @ 3.70 GHz and 16.0 GB RAM. An Anaconda virtual environment with Python 3.9 was used for the implementation. The best results were achieved with the LR and SVM algorithms. Table 1 shows the performance results of the ML algorithms based on accuracy, recall, F1 score and average precision. Table 1. Performance metrics of the best performed ML algorithms. ML algorithm Accuracy Recall F1 score Average precision LR 0.813 0.799 0.761 0.857 SVM 0.803 0.788 0.748 0.858

Figure 2 shows a comparison of the accuracy of the ML algorithms.

A Machine Learning Based Method for Automatic Identification

75

Fig. 2. Performance comparison in terms of accuracy of the ML algorithms.

4 Conclusions Natural disasters are on the rise globally. The impacts of disasters on affected communities, the ecosystem and local economies can be devastating. Communication of disaster related information is essential in disaster response in order to deliver relief activities, such as search and rescue operations. Geotagged Twitter data plays an increasingly important role in communication of information for disaster management. In this paper, an automated method of determining informative Twitter data was developed which is useful to enhance the decision making for emergency responders in order to efficiently provide relief operations during and in the aftermath of a natural disaster. Different ML algorithms were used to automatically identify tweets related to natural disasters. The best performance in terms of accuracy was shown by using the LR and SVM algorithms. Future research will explore both ML and DL algorithms using different natural disaster Twitter datasets which contain both image and text data, in order to provide solutions for specific disaster response operations such as damage assessment and postdisaster relief.

References 1. EM-DAT: https://www.emdat.be/guidelines. Accessed 4 March 2022 2. UNISDR: https://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf. Accessed 4 March 2022 3. Platt, A., Hood, C. Citrin, L.: From earthquakes to “#morecowbell”: identifying sub-topics in social network communications. In: Proceedings 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011, pp. 541–544 (2011) 4. Yu, M., Yang, C., Li, Y.: Big data in natural disaster management: a review. Geosciences 8 (5), 165 (2018)

76

A. N. Christidou et al.

5. Briones, R.L., Kuch, B., Liu, B.F., Jin, Y.: Keeping up with the digital age: how the American Red Cross uses social media to build relationships. Public Relations Review 37(1), 37–43 (2011) 6. Alexander, D.E.: Principles of Emergency Planning and Management. Oxford University Press, Oxford (2002) 7. Sit, M.A., Koylu, C., Demir, I.: Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma. Int. J. Digital Earth 12(11), 1205–1229 (2019) 8. Basu, M., Shandilya, A., Khosla, P., Ghosh, K., Ghosh, S.: Extracting resource needs and availabilities from microblogs for aiding post-disaster relief operations. IEEE Trans. Comput. Soc. Syst. 6(3), 604–618 (2019) 9. Oneal, A., Rodgers, B., Segler, J., et al.: Training an emergency-response image classifier on signal data. In: Proceedings 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, pp. 751–756, 17–20 December 2018 10. Alam, F., Imran, M., Ofli, F.: Image4Act: Online social media image processing for disaster response. In: Proceedings 2017 IEEE/ACM International Conference on Advances in Social Networks, Analysis and Mining 2017, Sydney, Australia, 2017 11. Layek, A.K., Poddar, S., Mandal, S.: Detection of flood images posted on online social media for disaster response. In: Proceedings 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Gangtok, India, 2019 12. Fan, C., Wu, F., Mostafavi, A.: A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters. IEEE Access 8, 10478–10490 (2020) 13. Nguyen, D.T., Ofli, F., Imran, M., Mitra, P.: Damage assessment from social media imagery data during disasters. In: Proceedings IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia, 2017 14. Huang, X., Li, Z., Wang, C., Ning, H.: Identifying disaster related social media for rapid response: a visual-textual fused CNN architecture. Int. J. Digital Earth 13(9), 1017–1039 (2019) 15. Kundu, S., Srijith, P.K., Desarkar, M.S.: Classification of short-texts generated during disasters: a deep neural network based approach. In: Proceedings 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018

Human Activity Recognition with Smart Watches Using Federated Learning Tansel G¨ on¨ ul1 , Ozlem Durmaz Incel2 , and Gulfem Isiklar Alptekin1(B) 1

Department of Computer Engineering, Galatasaray University, Istanbul, Turkey [email protected], [email protected] 2 Department of Computer Engineering, Bogazici University, Istanbul, Turkey [email protected]

Abstract. Federated learning (FL) is an emerging technique for training machine learning models in a distributed manner and offers advantages in terms of data privacy and saving communication costs. Wearable devices, such as smart watches, are personal devices and generate lots of personal sensor data. In this respect, FL can offer advantages; hence, their data requires more privacy, and saving communication costs is essential. In this paper, we investigate the performance of FL compared to centralized learning for the domain of human activity recognition (HAR) using wearable devices. We used a dataset from the literature composed of sensor data collected from smart watches and trained three different deep learning algorithms. We compare the performance of the centralized and FL models in terms of model accuracy and observe that FL performs equivalent to the centralized approach.

Keywords: Federated learning Wearable computing

1

· Human activity recognition (HAR) ·

Introduction

After the General Data Protection Regulation [12] was adopted in the EU in 2016, data protection and privacy entered into state protection. As a result of these regulations, traditional machine learning takes a hit. Because conventional machine learning is based on a central server that undertakes training tasks and requires massive training data. Recently Google announced FL [17] for distributed model training that can comply with privacy regulations and save communication costs. FL [9] is a collaborative learning technique where training happens across multiple devices without the need for an exchange or storage of centralized training data. Unlike other cloud-based training methods, FL brings the model training to mobile devices as well. FL operates without sharing data with other entities, leading to more secure and privacy-preserved solutions. This research has been supported by Science Academy’s Young Scientist Awards Program (BAGEP), award holder: Ozlem Durmaz Incel. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 77–85, 2022. https://doi.org/10.1007/978-3-031-09176-6_9

78

T. G¨ on¨ ul et al.

Although FL has promising application potential in different areas, such as sales, financial, and smart healthcare, it is still mainly discussed at the conceptual level [7], and it should be implemented and tested for different application areas. One of the potential areas where FL can bring many advantages is wearable computing, dealing with the data from wearable and mobile devices. These are personal devices, and hence the data generated by them require more privacy. Additionally, they are resource-constrained devices, and saving communication costs is particularly important. The objective of the paper is testing the performance of FL compared to centralized learning for the domain of human activity recognition (HAR) using wearable devices. For this purpose, we utilize a dataset [14] that includes the data logged from the accelerometer, gyroscope, magnetometer, and pressure sensors embedded in smart watches. There are seven activities (mostly related to smoking), and the data is collected from eleven participants with the smart watch placed on their wrists. We allocate each participant’s data to a client in the FL setting. For training, we use three different deep learning architectures: 1-D CNN, 1-layered neural network (NN), and 3-layered NN. As the FL algorithm, we use federated averaging (FedAvg), where all clients participated for the entirety of the training. The experiment results show that FL performs equivalent to the centralized approach. 1-D CNN model achieves the highest accuracy in the centralized and the federated systems. However, the model trained using a FL approach achieves the highest accuracy: 92%. Although there has been recent papers using FL for HAR [4,5,7,16], we use a different and challenging dataset [14] that includes wrist movements and also compare the performance with 3 different deep learning models. The rest of the paper is organized as follows: In Sect. 2, we present the related studies that are particularly focused on HAR and FL. Section 3 summarizes the FL approaches. In Sect. 4 we explain the dataset, the models and the experiments. Section 5 includes the results of the experiments and finally, Sect. 6 draws the conclusions and highlights future work.

2

Related Work

In this section, we briefly summarize the related studies that utilize FL for HAR using motion sensor data. A survey [2] focuses on software and hardware architectures and technologies, protocols, real-life applications, and use-cases of FL. They present several scenarios, especially for the healthcare sector, with their design benefits, challenges, and issues. In recent years, there have been vast amounts of work concentrating on smart healthcare platforms. FedHealth [5] is proposed as a federated transfer learning framework for wearable healthcare. They utilize UCI Smartphone, a public HAR dataset. FL approach is applied to heart activity data collected with smart bands for stress-level monitoring, which can be considered another healthcare platform [4]. This work is one of the first to apply privacy-preserving FL to biomedical informatics data. The authors aim to detect stress levels from the PPG-based heart activity signal. They show

Human Activity Recognition with Smart Watches Using Federated Learning

79

Fig. 1. Federated learning organization [3]

that FL reveals better accuracy values when compared to local models having the lowest performance and traditional systems aggregating all data without privacy concerns. A platform called FL4W is proposed to train a HAR classifier onto a daily and sports activities dataset [8]. They show their model with a client-based architecture and produce high accuracy values with fewer communication rounds. The most similar work is [7]. The authors generate a set of experiments in the field of HAR using a smartphone. They implement three main FL algorithms and reveal that the FedAvg approach leads to a high client accuracy on its own data during high client accuracy on global data. Another work that is based on the Federated Averaging method for sharing model weights is HARFLS [16]. Further, the authors use a perceptive extraction network (PEN) deployed on each mobile device for feature extraction. The experiments are conducted on four different datasets.

3

Federated Learning

With FL, the models are trained on the devices. After training, the model weights are sent to the central server for aggregation. The main server forms a global model with these weights and sends the global model to devices. This cycle continues in this way until the model converges. Nevertheless, with incorporating devices into the training task, there may be disputes in data content and format because of the device diversity. These differences need to be examined and eliminated at the device level. Figure 1 summarizes FL: devices personalize the model locally, based on usage (A). Many updates are aggregated (B) to form a consensus change (C) to the shared model, after which the procedure is repeated. 3.1

Advantages

– Privacy: Because of the regulations like GDPR [12] in different sectors and countries, applying traditional machine learning methods is impractical in most cases. In these situations using FL can alleviate the privacy problems

80

T. G¨ on¨ ul et al.

because training occurs on client devices. Hence users’ sensitive information stays on their devices. Only model parameters are sent to the central server for aggregation. It may be possible to interpret these parameters and find information about users according to [10] but encrypted transmission techniques can be utilized. Even if these parameters are obtained, interpreting the data is mostly impossible. – Training Flexibility: The training process can be carried out when the devices are charging or when WIFI connectivity is available. In this way, the user’s battery and data can be preserved. – Distributed Learning: FL offers distributed training based on references; it provides faster training time and higher accuracy [6]. Also, the current models in the devices can be used even if the devices are offline. Moreover, model results on the new data can be obtained immediately. 3.2

Challenges

– Data Differences: Imbalanced data, missing classes, and missing values can cause problems for FL. Model results can be drastically affected in these situations. – System Differences: There can be many devices with different hardware and network specifications in an FL system. This leads to different computation times, data transfer rates, and storage spaces in different devices. So participating devices can drop out during training because of these differences. – Communication: In FL, devices heavily depend on WiFi to transfer model parameters to the central server, and WiFi bandwidth is lower than central machine learning. Because FL is an iterative process, devices need to be constantly communicating. Hence, FL performance can be impacted due to poor communication. – Client Participation: Because of the lack of communication during some iterations, some devices can drop during the learning process, and their contribution to the central model becomes ineffective. Alternatively, in vanilla FedAvg [11] implementation, if devices cannot catch up with each other or the central server, they turn ineffectually. FedProx [13] tries to overcome this problem by tolerating partial work on devices.

4 4.1

Dataset and Methodology Appliacation and UT Smoking Dataset

We used the UT Smoking Dataset [14] which consists of 45 h of 10 types of smoking and other similar activities collected from 11 participants. Our study used only the 7 shared classes between all 11 persons since some participants did not perform all the activities. These classes are SmokeSD (smoking while standing), DrinkSD (drinking while standing), SmokeST (smoking while sitting), DrinkST (drinking while sitting), Eat, Stand, and Sit. A smart watch (LG Watch

Human Activity Recognition with Smart Watches Using Federated Learning

81

Fig. 2. UT smoking dataset class distributions

R, LG Watch Urbane, and Sony Watch 3 models) placed on the participants’ wrists were used to collect the data. Raw data consist of x, y, and z-axis of accelerometer, linear accelerator, gyroscope, magnetometer, and pressure values collected by a smart watch on the participant’s hand, all gathered at 50 samples per second. Since we use deep learning models, we did not extract high-level features from the data. Further details about the dataset are presented in [14]. For FL experiments, we allocate the data from each participant to a client, and we can see that in Fig. 2, data size is different from client to client, but class sizes inside a client are equal to each other. Different algorithms are proposed for aggregating model parameters, including FedAvg [11], FedProx [13], Federated Matched Averaging [15]. In this work, we chose to experiment with FedAvg. In [7], it was shown that the FedAvg algorithm exhibited better performance against other complex algorithms. FedAvg [11] algorithm starts training on the devices with the command of the central server. This central server contains a global model. The central server picks a subset of clients and transmits the global model. Clients partition their local data into batches and perform selected epochs of Stochastic Gradient Descent (SGD). Then, clients send trained local models to the central server for aggregation. The central server creates a new global model using incoming local models and sends the new global model to a new subset of clients. The algorithm continues to work in this way. One drawback of FedAvg is that it does not tolerate stragglers. If the required amount of epochs is not completed in the required time, the algorithm drops these clients from the selection, resulting in accuracy loss. In future work, we plan to experiment with other more complex algorithms. 4.2

Deep Learning Models

Our data is time-series, so we need to use windowing and decide on a window size to detect smoking activity. In previous work [1], different window sizes were experimented with, and it was reported that longer window sizes, such as 30 s, exhibit better recognition performance. We used 30 s long and 50% overlapping

82

T. G¨ on¨ ul et al. Table 1. Different parameters of the models Architecture Model

Learning Rate

1-NN

Federated

0.005 (Client and Server)

1-NN

Centralized 0.00001

3-NN

Federated

3-NN

Centralized 0.0001

1-D CNN

Federated

1-D CNN

Centralized 0.0005

0.1 (Client and Server) 0.1 (Client and Server)

Table 2. Accuracy results Architecture Model

Accuracy

1-NN

Federated

73%

1-NN

Centralized 71%

3-NN

Federated

86%

3-NN

Centralized 74%

1-D CNN

Federated

1-D CNN

Centralized 87%

92%

window size for our training. The dataset is split as 70% training and 30% validation. For federated training, each client’s data is split as 70% training and 30% validation, respectively. The training was carried out on three different models. Models are 1-layered NN (1-NN), 3-layered NN (3NN), and 1-D Convolutional NN (1-D CNN). Both federated and centralized models are identical. All models have 0.2 Dropout for all layers, all models use SGD optimizer, and all models have a batch size of 32. We initially tried to keep the learning rates the same, but the loss values of the centralized models converged to zero or infinity. Therefore, the learning rate values for all models were fine-tuned to the values in Table 1. We trained these models both in a centralized fashion using TensorFlow and also using FL approach using TensorFlow Federated 1 . TensorFlow Federated is a framework for deep learning computations on decentralized data and offers a simulation runtime for experiments. It provides an API for accessing the contents of the TensorFlow library. One may argue that it may not be possible to run deep learning algorithms on smart watches due to resource constraints. However, recent studies show that [18] if deep learning models can be optimized using quantization, pruning, and other optimization techniques, it is possible to run the algorithms on such devices, even on IoT devices.

1

https://www.tensorflow.org/federated.

Human Activity Recognition with Smart Watches Using Federated Learning

5

83

Performance Evaluation

In this section, we present the results of the experiments and compare the performance of the models with central training with the models trained with FedAvg. Model validation results are presented in Table 2 in terms of accuracy. For all models evaluated, federated counterparts showed equivalent performance compared to centralized learning. In addition to its high performance, the fact that the model weights are not directly shared is also important in terms of privacy. As an example, accuracy and loss graphs for the 1-D CNN model are presented in Figs. 3(a) and 3(b). The proposed 1-D CNN model with a learning rate of 0.0005 and using a centralized approach achieved an accuracy of 87% on the test set. The model converges around 150 epoch (can be seen in Fig. 3(a)). In the federated approach all 11 clients participated for the entirety of the training. The model using a learning rate of 0.1 converges around 400 epochs (communication rounds as can be seen in Fig. 3(b)). The convergence of the federated model took longer than that of the centralized model. We expected this result because the averaging process hurts the convergence of the local models. For all models tested federated approach performed equivalent to the centralized. We believe one of the reasons is that all clients were present during the training, the model generalized well at the end of the averaging process.

Fig. 3. Model losses

6

Conclusion and Future Research

In this paper, we investigated the performance of FL compared to centralized learning on a state-of-the-art HAR dataset composed of sensor data collected from smart watches. For this purpose, we trained three different deep learning algorithms and compared the performance when they were trained using a centralized approach and FL approach using the FedAvg algorithm. We observe that FL performs equivalent to the centralized approach. In future work, we plan to examine the effect of using transfer learning on the personalization of local models. Also, the window size determined as 30 s can be treated as a hyperparameter and changed according to the performance of the algorithms. Having an equal number of samples from the classes indicates an ideal situation in terms of FL.

84

T. G¨ on¨ ul et al.

The effect on performance can be examined by distorting the data distribution manually. The success of FL in cases where not all clients can participate can also be examined.

References 1. Agac, S., Shoaib, M., Durmaz Incel, O.: Smoking recognition with smartwatch sensors in different postures and impact of user’s height. J. Ambient Intell. Smart Environ. 12(3), 239–261 (2020) 2. Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699–140725 (2020). https://doi.org/10.1109/ACCESS.2020.3013541 3. Brendan McMahan, D.R.: Federated learning: collaborative machine learning without centralized training data (2017). https://ai.googleblog.com/2017/04/ federated-learning-collaborative.html 4. Can, Y.S., Ersoy, C.: Privacy-preserving federated deep learning for wearable IoTbased biomedical monitoring. ACM Trans. Internet Technol. 21(1) (2021). https:// doi.org/10.1145/3428152 5. Chen, Y., Qin, X., Wang, J., Yu, C., Gao, W.: FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell. Syst. 35(4), 83–93 (2020). https://doi.org/10.1109/MIS.2020.2988604 6. Dean, J., et al.: Large scale distributed deep networks. In: Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012) 7. Ek, S., Portet, F., Lalanda, P., Vega, G.: Evaluation of federated learning aggregation algorithms: application to human activity recognition. In: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, pp. 638–643 (2020) 8. He, X., Su, X., Chen, Y., Hui, P.: Federated learning on wearable devices: demo abstract. In: Proceedings of the 18th Conference on Embedded Networked Sensor Systems, pp. 613–614. SenSys 2020, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3384419.3430446 9. Liang, P.P., et al.: Think locally, act globally: federated learning with local and global representations (2020) 10. Lim, W.Y.B., et al.: Federated learning in mobile edge networks: a comprehensive survey. CoRR abs/1909.11875 (2019). http://arxiv.org/abs/1909.11875 11. McMahan, H.B., Moore, E., Ramage, D., y Arcas, B.A.: Federated learning of deep networks using model averaging. CoRR abs/1602.05629 (2016). http://arxiv.org/ abs/1602.05629 12. Paul Voigt, A.V.D.B.: The EU General Data Protection Regulation (GDPR), A Practical Guide. Springer, Berlin (2017). https://doi.org/10.1007/978-3-31957959-7 13. Sahu, A.K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., Smith, V.: On the convergence of federated optimization in heterogeneous networks. CoRR abs/1812.06127 (2018). http://arxiv.org/abs/1812.06127 14. Shoaib, M., Scholten, H., Havinga, P.J.M., Incel, O.D.: A hierarchical lazy smoking detection algorithm using smartwatch sensors. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, pp. 1–6 (2016)

Human Activity Recognition with Smart Watches Using Federated Learning

85

15. Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D.S., Khazaeni, Y.: Federated learning with matched averaging. CoRR abs/2002.06440 (2020) 16. Xiao, Z., Xu, X., Xing, H., Song, F., Wang, X., Zhao, B.: A federated learning system with enhanced feature extraction for human activity recognition. Knowl.Based Syst. 229, 107338 (2021) 17. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019) 18. Yao, S., et al.: FastDeepIoT: towards understanding and optimizing neural network execution time on mobile and embedded devices. In: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, pp. 278–291 (2018)

Prediction of Gross Movie Revenue in the Turkish Box Office Using Machine Learning Techniques Anil Gürbüz(&), Ezgi Biçer, and Tolga Kaya Department of Management Engineering, Istanbul Technical University, 34367 Istanbul, Turkey {gurbuza18,bicere17,kayatolga}@itu.edu.tr

Abstract. The gross revenue of a movie in the box office has been a concern of the movie industry. In the last few years, there have been studies on predicting various movie attributes. The field lacks a gross movie revenue prediction model that specifically concerns the gross movie revenues in the Turkish box office. The aim of this study is to build a model to predict the gross movie revenue in the Turkish box office using machine learning techniques. This study is conducted on 150 movies that were in the Turkish box office in 2018. The techniques involved multiple regression analysis including the ridge regression and the lasso, tree-based methods including random forest and boosting, SVM and KNN regression. All models were built using the R programming language. Methods were compared using their MSE values. The lowest MSE was obtained with the Random Forest model. Keywords: Box office
Revenue prediction Supervised learning
Random Forest model


Movies
Machine learning


1 Introduction The participation of the movie industry in people's lives corresponds to the beginning of the 20th century. This industry, which has attracted the attention of cinemagoers over the years, has continued to make progress with the light of technology and production. Although the costs of making movies are high, the gross revenues from the box office satisfy the producer for popular movies. Industries such as Hollywood cinema and Bollywood cinema are among the movie industries with the largest revenue in terms of box office revenue. This major industry has both local and global influences in almost every country. The pandemic crisis, which started with Covid-19 towards the end of 2019, has also deeply affected the movie industry. Particularly the USA and China have been damaged by the effects of the pandemic. Also, the Turkish movie industry has been among those heavily affected by this damage. The aim of this study will be to try to estimate the gross revenue of movies as accurately as possible by using machine learning techniques such as Tree-Based methods Multiple Linear Regression approaches, SVM and KNN regression methods, by using the data of the movies that remained in vision in 2018 in Turkey. To obtain more accurate results in prediction © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 86–92, 2022. https://doi.org/10.1007/978-3-031-09176-6_10

Prediction of Gross Movie Revenue in the Turkish Box Office

87

models, 2018 data was used as the year before the pandemic period. Among the determinants of gross movie revenue in the Turkish box office include week, length of the movie, IMDb, genre, country, production year, and others. Making movies is costly; therefore, we foresee that the estimation of the gross revenue of the film with this study will shed light on those working in this entertainment sector. The remainder of the article is organized as follows: a literature review on various revenue forecasting models in the entertainment industry for blockbuster movies in the second part, a detailed description of the dataset in the third part, the methodology used in the fourth part, the outcome evaluation based on the findings by evaluating the performance of each model in the fifth part, and finally the discussion and suggestions for future work are given in the conclusion part.

2 Literature Review Various revenue prediction studies have been conducted in the entertainment sector for movies in the box office. Dey [1] used one independent method, factor analysis, and one dependent method, multiple linear regression to predict the gross revenue of a movie. During the exploratory factor analysis (EFA), eight out of twenty binary genre variables were selected to be used in regression modelling. Galvao and Henriques [2] constructed a model using data mining, neural network, regression and decision trees to predict the profit of a movie. The dependent variable in the study profit is the box office revenue, and three dependent variable approaches were used. Quader et al. [3] use the support vector machine (SVM) and neural network to propose a decision support system for the movie sector using machine learning techniques. SVM had 83.44% accuracy for prereleased features and 88.87% accuracy for all features, while the neural network had 84.1% accuracy for pre-released features and 89.27% for all features. In a study conducted by Lash et al. [4] to predict the profitability of films in the early stages of film production, various classification algorithms were used, which provided the best results with 10-fold cross-validation, using logistic regression. They noticed some limitations in the model such as collinearity and possible sampling bias. Zhang and Skiena [5] benefited from quantitative news data generated using the large-scale news analysis system Lydia, as opposed to traditional movie gross estimates based on numerical and categorical movie data from The Internet Movie Database. As a result of the analysis using the regression and k-nearest neighbor models, they emphasized that the models using news data only showed similar performance compared to the models using IMDb. The integrated model of IMDb and news data gave the best performance in predictive power. Song and Han [6] used Korean movie data to estimate gross box office revenue for domestic films and used Linear Regression, Random Forest and Gradient Boost regression methods for this estimation. In order to make their analyzes more effective, they applied the clustering method while categorizing the variables. Among the three models, the best performing model was Gradient Boost.

88

A. Gürbüz et al.

3 Data This article, which deals with the data set covering the films that remained in vision during 2018, explores 150 films with a gross box office revenue of over 488,000 TL. The data were collected from the Internet Movie Database (IMDb) [7] and Box Office Turkey [11]. The dependent variable is considered as 2018 gross revenue. Independent variables are week, length of the movie, IMDb, whether the movie is foreign or not, the genre of the movie, and whether it is 3D available. While the type of the gross revenue, week, length of the movie, and IMDb variables is continuous; genres, whether the movie is foreign or not, and the 3D availability of the movie are entered as binary types (Table 1). Table 1. Variables used in the model. No Variable name Variable explanation 1 Gross. The gross revenue the movie has Revenue acquired in the Turkish Box office in 2018 2 Week The number of weeks in theaters 3 Length The runtime of the movie 4 IMDb IMDb rating of the movie 5 Foreign The producer country

Type Value Continuous –

Continuous Continuous Continuous Binary

6

Drama

The genre of the movie

Binary

7

Comedy

The genre of the movie

Binary

8

Adventure

The genre of the movie

Binary

9

Animation

The genre of the movie

Binary

10 Sci.Fi

The genre of the movie

Binary

11 Thriller

The genre of the movie

Binary

12 Action

The genre of the movie

Binary

13 Horror

The genre of the movie

Binary

14 History

The genre of the movie

Binary

15 Love

The genre of the movie

Binary

16 Crime

The genre of the movie

Binary

– – – 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0: 1: 0:

Unit TL

Wk Min – –

Turkey Not Turkey Drama – Not Drama – Comedy Not Comedy Adventure – Not Adventure Animation – Not Animation Sci-Fi – Not Sci-Fi Thriller – Not Thriller – Action Not Action Horror – Not Horror History – Not History Love – Not Love Crime – Not Crime (continued)

Prediction of Gross Movie Revenue in the Turkish Box Office

89

Table 1. (continued) No Variable name Variable explanation 17 Rom.Com The genre of the movie

Type Binary

18 Teen

The genre of the movie

Binary

19 Family

The genre of the movie

Binary

20 3D

The availability of the movie in 3D

Binary

Value 1: Rom-com 0: Not Rom-com 1: Teen 0: Not Teen 1: Family 0: Not Family 1: Yes 0: No

Unit – – – –

4 Methodology The multiple regression model determines the coefficients of p predictors. Y ¼ b0 þ b1 X 1 þ b2 X 2 þ . . . þ bp X p þ


ð1Þ

The coefficients are estimated using the least squares approach. The values that minimize the sum of squared residuals are the coefficient estimates of the multiple regression model [8]. RSS ¼

Xn i¼1

ðyi  ^yi Þ2 ¼

Xn i¼1

^ b ^ xi1 þ b ^ xi2  . . .  b ^ xip Þ2 ðyi  b 0 1 2 p

ð2Þ

The ridge regression improves over the least squares method by shrinking the less important coefficient estimates towards zero [8]. n X

ðyi  b0 

i¼1

p X

2

bj xij Þ þ k

j¼1

p X

b2j ¼ RSS þ k

j¼1

p X

b2j

ð3Þ

j¼1

The lasso and ridge regression are quite similar as they both reduce complexity of the model, but lasso shrinks some coefficients exactly to absolute zero [8]. n X i¼1

ðyi  b0 

p X

2

bj xij Þ þ k

p   p   X X bj  ¼ RSS þ k bj 

j¼1

j¼1

ð4Þ

j¼1

Both regression and classification problems can benefit from the use of decision trees. Decision tree-based models that want to keep the RSS value to a minimum are based on the following formula [8]. RSS ¼

J X n X ðyi  ^yRj Þ2 j¼1 i2Rj

ð5Þ

90

A. Gürbüz et al.

In the Random Forest model, an essential part of building each tree is adding another layer on top of the bagging. While randomization is included in the tree structure two separate proposals are considered; random split selection, in which each node is split by randomly choosing one of the best splits in that node, and random input selection, in which the split at each node is decided by random selection of a subset of the R input properties [9]. As the advantage of this method, the variance of the estimator can be reduced, thereby contributing to performance improvement. Another model is the Boosting, which again can be applied to both regression and classification. In this method, each tree is fitted to a modified version of the original dataset, without bootstrap sampling [8]. Support Network Machines also known as Support Vector Networks, are supervised learning models that help find a decision boundary between the two classes that are furthest from any point in the training data and are used in both classification and regression analysis. The SV algorithm is generalized to the regression case by constructing a soft margin analog in the space of the target values y [10]. KNN regression method identifies K nearest training observations that are closest to a given point x0 and estimates f(x0) by taking an average of those K training responses [8].

5 Findings A box plot graph based on the genre of the movies was created to see differences in movie revenue based on the genre of the movie (Fig. 1). The largest market genres are comedy, adventure, drama and animation.

Fig. 1. Box plot graph based on genre.

Multiple linear regression has been performed with all variables. According to multiple linear regression results, the variables IMDb, Length, Foreign were found to be significant. Since the response variables Gross.Revenue of observations show a large range, ln(Gross.Revenue) of the response variables were utilized for simplicity reasons. Later, ridge and lasso regressions were performed. Both models were compared

Prediction of Gross Movie Revenue in the Turkish Box Office

91

according to their test MSE values using cross validation. The lasso regression gave the best model out of multiple regression methods with the lowest MSE value (Table 2). Table 2. MSE comparison. Method Ridge regression Lasso regression Random Forest Boosting SVM KNN

MSE 1.243 1.184 1.182 1.818 1.819 1.258

Following the multiple regression methods, tree methods were utilized. The treebased methods used to build models include random forest and boosting. First, a random forest method was performed which gave an MSE of 1.181661. For the best prediction, the number of predictors sampled for splitting at each node mtry was selected as 3 and random forest regression was applied with a better performance in this context. The boosting model was also utilized as another tree-based method. The MSE value obtained with boosting was calculated as 1.81794. It is observed that the lowest MSE value among the tree-based methods belongs to the random forest. Moreover, the SVM regression method, which is thought to be a powerful method, has also been applied to the dataset. MSE value has been calculated again, and this value is 1.818682. At this juncture, it has emerged that tree-based methods are more successful in predicting than SVM. Lastly, KNN regression method was utilized. Various models with different K values were fit and the KNN model with the lowest MSE was found to be the one with K = 40. The KNN regression method was calculated to be 1.258291. Table 3. Several movies from the Turkish Box office in 2018. Movie name A Quiet Place Alpha The Equalizer 2 Red Sparrow Sherlock Gnomes Cumali Ceber 2

Gross revenue 2,728,379 2,869,683 2,823,633 2,876,240 2,303,950 2,133,742

Predicted gross revenue 3,300,770 2,944,207 4,942,371 5,929,238 2,633,517 3,923,392

The model with the lowest MSE, Random Forest method, was utilized to predict some movies and see the predicted results of the model (Table 3).

92

A. Gürbüz et al.

6 Conclusion In this study of gross revenue movie prediction in the Turkish Box Office, six different regressions methods were utilized. These are ridge regression and lasso. The used tree methods are random forest and boosting. Lastly, SVM and KNN regression methods were utilized. Out of the six, the random forest method has yielded the best results with the lowest MSE. In future research, more variables can be considered in model development. For example, a more complex model that includes variables such as the producer, the distributor, or the actors in the film can yield near-perfect results in the estimation process. In addition, a study covering more years can be carried out for the prediction of gross movie revenue in the Turkish Box Office with the same regression methods.

References 1. Dey, S.: Predicting Gross Movie Revenue (2018). https://arxiv.org/pdf/1804.03565.pdf 2. Galvão, M., Henriques, R.: Forecasting movie box office profitability. J. Inf. Syst. Eng. Manage., 3(3): 22 (2018)https://doi.org/10.20897/jisem/2658 3. Quader, N., Gani, M.O., Chaki, D., Ali, M.H.: A machine learning approach to predict movie box-office success. In: 20th International Conference of Computer and Information Technology, ICCIT 2017, 2018 January, pp. 1–7 (2017). https://0-doi-org.divit.library.itu. edu.tr/https://doi.org/10.1109/ICCITECHN.2017.8281839 4. Lash, M., Fu, S., Wang, S., Zhao, K.: Early prediction of movie success — what, who, and when. In: Agarwal, N., Xu, K., Osgood, N. (eds.) SBP 2015. LNCS, vol. 9021, pp. 345–349. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16268-3_41 5. Zhang, W., Skiena, S.: Improving movie gross prediction through news analysis. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology vol. 1, pp. 301–304. doi:https://doi.org/10.1109/wi-iat.2009.53 (2009) 6. Song, J., Han, S.: Predicting gross box office revenue for domestic films. Commun. Stat. Appl. Methods 20(4), 301–309 (2013). https://doi.org/10.5351/CSAM.2013.20.4.301 7. IMDb: Ratings, Reviews, and Where to Watch the Best Movies, https://www.imdb.com/. Accessed 5 January 2022 8. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R, 1st edn. Springer Science + Business Media, New York (2013) 9. Izenman, A. J.: Modern Multivariate Statistical Techniques. Springer Texts in Statistics, New York, NY (2008). doi:https://doi.org/10.1007/978-0-387-78189-1 10. Schölkopf, B., Smola, A.J., Bach, F.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA (2002) 11. Box office Türkiye, https://boxofficeturkiye.com/. Accessed 5 January 2022

Prospects for the Development of Transport Logistics and a Fuzzy Logic Model of the Strategic Goals of the Logistics System of Azerbaijan Rahib Imamguluyev1(&) 1

and Abil Suleymanov2

Odlar Yurdu University, Baku AZ1072, Azerbaijan [email protected] 2 Odlar Yurdu University, Baku, Azerbaijan [email protected]

Abstract. The volume of trade in Azerbaijan is constantly growing. The sustainable development of the country’s LC helps to increase the republic’s role in zoning and international trade. Especially due to the good geographical position of Azerbaijan, where transport corridors are connected. The east-west and northsouth regions of the republic have the opportunity to earn income from transit trade and export-import operations. Currently, the implementation of certain infrastructure projects related to the construction of the seaport and railway. As a result, it is planned to implement a number of initiatives to increase trade turnover by creating intermodal and multimodal logistics infrastructure in the country, to achieve higher revenues from transit trade and increase participation in logistics projects. At present, international transportation of goods in the country is carried out mainly by rail. They have a relatively high volume of trade and economic transactions, and a relatively low volume of transit cargo. The share of transit cargo in the total volume of cargo transported by ZhT in our country was 25%. A fuzzy logic model has been developed to select the most suitable one for cargo transportation. Keywords: Logistic


Transport
Development fuzzy logic
Fuzzy set

1 Introduction Currently, there are three main trends in international trade that can affect world destinations. I. Macroeconomic factors have a positive effect on the growth of trade between individual countries. For the period from 2000 to 2020. this development approach based on market relations has become the main model of economic growth. International economic relations, simplifying trade agreements in the WTO system, gave a strong impetus to the acceleration of these areas [1]. II. At the macroeconomic level, i.e. enterprises and firms, the system of distribution of commercial segments is being improved. In particular, businesses, in order to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 93–100, 2022. https://doi.org/10.1007/978-3-031-09176-6_11

94

R. Imamguluyev and A. Suleymanov

expand into new markets, are shaping businesses in new regional markets, while simultaneously expanding business to leverage their cost-cutting opportunities [2]. III. The introduction of effective development tools is adjusted to factors at the macro and microeconomic levels. In this direction, ICT is seen as one of the main factors. Development in the field of investment trade and the creation of new investment mechanisms also contribute to the growth of trade turnover. At the same time, at the present stage, developed countries are striving to increase added value by creating logistics centers (LCs) created on all vehicles. The success of logistics centers depends on the following factors. I. The systematic development of the THB infrastructure should be ensured, taking into account efficiency in supply chains. II. Development of a strategy in the design system for capital investments in the LC. The creation of a LC requires large investments, which necessitates preliminary assessments. III. The private sector does not take an active part in the process of LC formation, although it can contribute to the optimization of the supply chain. So, in general, there is a lack of effective LCs acting as a “third judge” and knowing about the effective optimization of drugs and drug trafficking in the field of organizing supply chains. As for Azerbaijan, our republic is constantly increasing the volume of trade. The sustainable development of the country’s LC contributes to the growth of the republic’s role in zonal and international trade. In particular, due to the good geographical location of Azerbaijan, where transport corridors connect. The east–west and north– south of the republic has the opportunity to receive income from transit trade and export–import operations. Currently, the implementation of certain infrastructure projects related to the construction of a seaport and railway. As a result, it is planned to implement a number of initiatives to increase trade turnover, obtain higher income from transit trade and increase the level of participation in logistics projects by creating an intermodal and multimodal logistics infrastructure in the republic [3]. At present, international transportation of goods in the republic is carried out mainly by railway. The volumes of trade and economic transactions in them are relatively high, and the volume of transit cargo is relatively low. The share of transit cargo in the total volume of cargo transported in our republic by ZhT was 25%. At the same time, a large share of transit freight flows through seaports. The positives in TL make it possible, after 2025, to turn the country into a leading logistics and trade hub of zonal significance through efficiently functioning LCs with the simultaneous presence of stable relations with other countries. To accomplish this task, LCs with stable connections with world markets will be created in the republic. To ensure the efficiency of the operations carried out in the LC, modern ICT will be introduced [4]. Currently, the positive features of THB in the republic can be considered the introduction of significant infrastructure projects in the country, such as the creation of a new port complex and the establishment of logistics with foreign countries; legal strengthening of preferential instruments.

Prospects for the Development of Transport Logistics

95

The negative aspects include the following: poor provision of drugs, qualified personnel; weak level of labor efficiency; underfunding; low degree of marketing services; flaw in the modern business model [5]. At the same time, the republic has all the necessary conditions for the formation of an effective TL: the formation of added value for transit cargo through the creation of free economic zones in the regions of the Baku International Sea Trade Port and the LC at the International Airport; state support for the development of drugs and trafficking in the country; attracting foreign investors to the implementation of these projects; good territorial position of the country and the potential to transform it into drugs and TL of zonal significance; growth in trade volumes and increased competition on an international scale. In the long term, the republic until 2025 is aimed at significantly improving drugs and drug trafficking, as well as ensuring the country’s attractiveness in this sector and turning it into a significant LC. In order to turn the country into a LC of zonal significance until 2025, work will be done to create firms in the republic that exist at the zonal level, as well as ensure that the business environment is consistent to stimulate investment. By ensuring targeted development in the area of drugs and drug trafficking, the country will become a LC for regional firms [6]. In order to integrate the Baku International Sea Trade Port into the LAN of the country’s International Airport, AT and ZhT communication will be strengthened. The study of target indicators for drugs and THB is very interesting. So, for example, at present, transit traffic along the E–W corridor, although it amounted to about 200 million tons, the specific in the republic is very low. The volume between Central Asia and Europe is about 50 million tons, and by 2020 this indicator is projected to grow over 60 million tons. Only 10 million tons of cargo was transported along this corridor through the territory of the republic. At the same time, the trade turnover between Russia and Iran along the north–south corridor amounted to 4 million tons, and the trade turnover between Iran and the BSEC countries was about 3.0 million tons. It is planned to increase the volume of transit cargo attracted in the republic until 2020 [7]. According to the measures taken in the republic in the field of transport corridors, as well as the formation of infrastructure and other works in drugs and TL, the following will be achieved: • it is expected to increase by 2020 the GDP of Azerbaijan by 605 manats; • a total of 18,900 new jobs will be created, of which 10,900 jobs in this sector; • LCs will reach the indicator 5–6. The strategic goals of drugs and drug trafficking in the republic include: • Medicines is an important area of development of the non-oil sector, acts as the main factor in the integration of other sectors of the economy; • involvement of transit cargoes to transport corridors across the territory of the republic; formation in LC zones, growth of attractiveness by conceptual assessment of the potential of drugs and TL elements, including the conditions of business processes and the ability to receive the added value for the purpose of turning the republic into a regional LC;

96

R. Imamguluyev and A. Suleymanov

• in order to gain competitiveness in the republic on the E–W and N–S transport corridors, it is necessary to optimize the time and costs for export-import operations and the transportation of transit goods [8]. The transport activity of the Republic of Azerbaijan in Europe-Caucasus-Asia is presented in Table 1. Table 1. Transport activity in the Europe-Caucasus-Asia transport corridor (In the Azerbaijani part). Cargo was transported, thousand tons Railway The sea Car Transit cargo was transported, thousand tons Railway The sea Car Freight turnover, million ton-km Railway The sea Car Turnover of transit cargo, million ton-km Railway The sea Car

2000 29,091 15,200 5779 8112 8572 3538 5034 … 8405 5240 2098 1067 3626 1757 1869 …

2005 46,741 24,685 8488 13,568 15,051 7328 7723 … 13,446 8534 3162 1750 6680 3767 2913 …

2010 51,688 20,578 9370 21,740 15,971 8253 7718 … 13,222 6874 3672 2676 7120 4040 3080 …

2015 52,240 15,521 6067 30,652 9494 3927 5567 … 10,956 4547 2354 4045 4360 2190 2170 …

2020 52,773 13,074 7423 32,236 10,186 3346 6840 … 11,427 4158 3020 4249 4566 1758 2808 …

• 2000–2020 in this sector, freight traffic increased from 29.1 million tons to 52.8 million tons, including railways – from 15.2 to 13.1; sea – from 5.8 to 7.4; automobiles – from 8.1 to 32.2. • Accordingly, the transit cargo turnover increased from 8.5 million tons to 10.2 million tons, including railway – from 3.5 to 3.3; marine – 5.0 to 6.8. • The cargo turnover in this sector also increased from 8.4 million ton-km to 11.4 million ton-km, including railways – from 5.2 to 4.1; sea – from 2.1 to 3.0; automobiles – from 1.1 to 4.2. • As for the transit cargo turnover, it increased from 3.6 million ton-km to 4.6 million ton-km. As you can see, the transportation of goods by the transport sector of the republic occupies the fifth position among the CIS countries. In terms of cargo turnover, the republic also ranks fifth. Next, we will consider a number of problems associated with international transport logistics (MTL). It should be noted that MTL systematically solves a large number of tasks, including route and warehouse operations.

Prospects for the Development of Transport Logistics

97

According to SI Lyashko, these features include [5]: • development of the optimal route; • selection of feasibility studies; • measures for the storage of cargo are created. This list does not reflect all the functions of drugs, but its main components are TL, which carry out transportation. But MTL is not limited to transportation and storage. At present, it acts as a concept for managing CHM between countries. The composition of the MTL also includes the World Wide Web, since the information support of this network increases the efficiency of the transportation of goods. The current MTL trend is online transactions. This is elogistics, which is an integral part of e-commerce. Medicines and international distribution operations include: transportation; warehousing and storage; loading and unloading works; packaging and labeling; production inventory management [2–4]. The main task of MTL is to deliver the goods in compliance with the essential requirements, within the appropriate time frame. Medicines cover the entire MP from the purchase of raw materials to the delivery of final products and related IP. In the current conditions, the concept of “intermo-range” is mainly used, i.e. efficiency of international trade, stable development of export-import relations. “Intermodality” is the development and use of different types of transport in a single vehicle; are expressed as the integration of various types of transport into a single system for the supply of commercial services. They act as a system of international transportation of goods using international transport corridors according to a standard shipping document. It allows customs sealing based on international requirements with the exclusion of access to the cargo without breaking the seal. The basis of intermodal transportation is containers of the international standard ISO (International Organization of Standardization). Currently, there are various interpretations of intermodal transportation: “Intermodal transportation is the transportation of goods by different modes of transport, in which one of the carriers organizes the entire delivery, and depending on the division of responsibility for transportation, various types of transport documents are issued”; “Intermodal transportation is carried out by one type of transport, but with reloading of a sealed container along the way.” Fuzzy logic is the most optimal method for solving this type of problem.

2 Application of Fuzzy Logic The concept of Fuzzy Logic, or fuzzy logic, is a theory first introduced by Lotfi Zadeh. Zadeh laid the foundations of the idea of creating a rule-working and transferring it to the machine by making use of human life experiences and all kinds of knowledge [9, 10, 12, 15]. Fuzzy logic can be defined as a decision mechanism design in its simplest and simplest form [11, 12, 16]. When the working principles of classical logic are expressed mathematically, we see a table consisting of “1” and “0” values. Here an entity can belong to a particular

98

R. Imamguluyev and A. Suleymanov

cluster. Otherwise, it may not belong to this cluster [13–15]. “True” is expressed as “1”, while “false” is evaluated as “0”. On the other hand, it seems impossible that the natural, verbal and human things we mentioned at the beginning always fit into these categories. This is why the approaches of classical sets, in other words, exact sets, fall short. At this point, the alternative, fuzzy logic, comes into play. Between the two main values, “1” and “0”, other possibilities are also allowed. For fuzzy logic, 1 and 0 are seen as border regions, not absolute values [9–17]. A fuzzy logic model has been developed for the analysis of transport. So that, 56 production rules with 3 inputs and 1 output were developed in the MATLAB program for the implementation of the task solution (Figs. 1 and 2). Input parameter Very bad, Bad, Normal, Good, Very good Output The parameter is unique: Value.

Fig. 1. Belonging functions of the transport linguistic variable.

Fig. 2. Description of the rules of logical derivation.

Prospects for the Development of Transport Logistics

99

Thus, based on the fuzzy logic model, we can draw the following conclusions: 1. To increase the efficiency of the zone TPP through the application of terminal technologies, reduction of warehouse stocks of enterprises in the economy, management of transport and logistics operations; 2. Establishment of regional transport centers, freight terminals, which are guaranteed to provide a comprehensive feasibility study and warehousing services to customers, which will reduce the storage area and reduce the transport costs of production on the basis of the TL transportation process. 3. The structure of the rolling stock should be improved and the cost of fuel and energy sources should be significantly reduced. 4. Restrict access to urban areas for large non-residential vehicles. 5. Improving the environmental situation in the city by reducing the total number of transport units and March. 6. Improving the quality of transport services, the efficiency of the LC by ensuring the coordination and effective interaction of the main modes of transport and modes of transport in the TL system. 7. With the application of terminal technologies widely used in the world practice, it is necessary to increase the efficiency of transportation of goods in international traffic, as well as to ensure the country’s entry into the international drug trade as a whole. The need to pay more attention to the development of railway infrastructure is based on the economic effect expected after the modernization of the railway industry. On the “N–Yu” and “V–Z” lines: • transportation costs will be reduced in the final price of goods; • Improving the quality of roads, the creation of convenient road networks will lead to the redistribution of traffic between modes of transport, the creation of new routes; • The development of transport infrastructure leads to the optimal distribution of productive forces, production areas.

References 1. 2. 3. 4. 5. 6. 7.

Azərbaycanda nəqliyyat. ARDSK. Bakı.: 2018, -144 s Azərbaycanda ticarət. AR DSK. Bakı: 2019, - 184 s Azərbaycanın xarici ticarəti. ARDSK. Bakı.: 2019, -216 s Bülleten: Nəqliyyat, informasiya və rabitə üzrə əsas göstəricilər. AR DSK. 2018, - 23 s Şabanlı, N: Nəqliyyat logistikası. Bakı: Azərkitab (2019) Aлeкcaндpoв O.A. Лoгиcтикa. M.: Инфpa-M. 2019, -216 c Зaк Ю.A. Пpиклaдныe зaдaчи pacпиcaний и мapшpyтизaции пepeвoзoк. M.: URSS. 2018, - 394 c 8. Caттapoв P., Лeвкин Г. Лoгиcтикa в тpaнcпopтныx cиcтeмax. M.: Пpocпeкт. 2019, -160 c 9. Imamguluyev, R.: Determination of correct lighting based on fuzzy logic model to reduce electricity in the workplace. In: International Conference on Eurasian Economies, Baku, Azerbaijan, September 2020. DOI: https://doi.org/10.36880/C12.02456

100

R. Imamguluyev and A. Suleymanov

10. Imamguluyev, R.: Optimal solution of indoor lighting system based on fuzzy logic. In: 2nd international conference Baku conference on scientific research, Baku, Azerbaijan, May 2021 11. Valiyev, A., Imamguluyev, R., Ilkin, G.: Application of fuzzy logic model for daylight evaluation in computer aided interior design areas. In: 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing – ICAFS-2020, January 2021. DOI: https://doi.org/10.1007/978-3-030-64058-3_89 12. Aliev, R., Tserkovny, A.: Fuzzy logic for incidence geometry. In: Kosheleva, O., Shary, S. P., Xiang, G., Zapatrin, R. (eds.) Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and their Applications. SCI, vol. 835, pp. 49–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31041-7_4 13. Imamguluyev, R., Suleymanli, T., Umarova, N.: Evaluation of the effectiveness of integration processes in production enterprises based on the fuzzy logic model. In: Aliev, R. A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds.) ICAFS 2020. AISC, vol. 1306, pp. 133–139. Springer, Cham (2021). https://doi.org/10.1007/978-3030-64058-3_17 14. Imamguluyev, R.: Application of fuzzy logic model for correct lighting in computer aided interior design areas. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 1644–1651. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_192 15. Zadeh, L.A., Aliev, R.: Fuzzy Logic Theory and Applications: Part I and Part II, p. 61 (2018). https://doi.org/10.1142/10936 16. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997) 17. Valiyev, A., Imamguluyev, R., Gahramanov, I.: Staff selection with a fuzzy analytical hierarchy process in the tourism sector. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds.) ICSCCW 2021. LNNS, vol. 362, pp. 437–444. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92127-9_59

A Multi-channel Deep Learning Architecture for Understanding the Urban Scene Semantics Tuba Demirta¸s1

and Ismail Burak Parlak2(B)

1

2

Department of Intelligent Systems Engineering, Galatasaray University, Ciragan Cad. No:36, 34349 Ortakoy, Istanbul, Turkey [email protected] Department of Computer Engineering, Galatasaray University, Ciragan Cad. No:36, 34349 Ortakoy, Istanbul, Turkey [email protected]

Abstract. Smart city analysis becomes an emerging field in autonomous urban problems. Image semantics is a complex problem where the image classification, the semantic segmentation and the object detection subroutines are staged in a cascade framework through spatio-temporal datasets. Urban scene analysis has been coupled in several applications such as security, autonomous vehicles, and mass transport. The initial problem of an urban scene is characterized as the pursuit of discrete 2D-3D movements on the street. Therefore, an accurate segmentation of the scene is required to minimize the spatial gap in the scene. The complete framework provides critical decision-making tools to protect the human and the moving objects. New generation autonomous systems turn on their real time sensors to monitor all possible movements in a street. However, the sensing stage must be interpreted within semantics to generate the city insights. The task of semantic segmentation is to label every pixel including the background into a semantic class. The object detection locates the presence of objects and types or classes of the located objects in an image. The instance segmentation contains these two tasks. This paper is composed of the instance segmentation of base objects through Cityscapes dataset. YOLACT deep learning architecture has been applied on high resolution images. The method has been found fast as it requires one stage segmentation. We conclude that YOLACT architecture generates feasible labels in an accurate dataset where spatial gaps are lower. The smart city analysis would be processed better with new hierarchical labels. Keywords: Smart city · Urban scene learning · Artificial neural networks

1

· Image semantics · Machine

Introduction

Over the last decade, digitized maps became more efficient to identify the localization problem in a city and to characterize the sociocultural effects in the c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 101–108, 2022. https://doi.org/10.1007/978-3-031-09176-6_12

102

T. Demirta¸s and I. B. Parlak

digital urban environments with the user interaction. On the other hand, three dimensional panoramic view is still considered a challenge in smart city applications due to their semantic bottlenecks related to the image understanding. As the use of smart devices requires more interaction with online urban services, the dynamic route generation becomes critical to provide meaningful feedback and increase the service quality in recreational, residential and commercial requests. In order to understand the city semantics, the detection of visual patterns is considered as the primary task from coarse to fine levels in street views. The detected objects are located in a map in order to generate the smart labels through the semantic segmentation techniques. The semantic segmentation encapsulates the smart labels either in the bounding boxes or the centroids. Furthermore, the instance segmentation considers different labels in the same class in order to generate a broad spectrum of instances of similar objects. This paper aims to address the instance segmentation problem by using the detection of base objects through Cityscapes dataset [1]. The deep learning architecture has been created on YOLACT architecture [2] with high resolution images. The global architecture includes all required classes in an urban scene such as human, bicycle, car, traffic light, and stop sign. The originality of our study is the analysis of instance segmentation scores for Cityscapes dataset through YOLACT architecture. The method has been found fast as it requires one stage segmentation. The quality of the masks was better for the large scale objects. We conclude that YOLACT architecture generates feasible image labels in an accurate dataset where spatial gaps are lower. The smart city analysis would be processed better with new hierarchical labels. The study is organized as it follows. Section 2 addresses recent studies in the semantic and instance segmentation problem through deep learning architectures. Section 3 encompasses the methodology and the main steps of YOLACT architecture, the dataset and the evaluation process. Moreover, the classification results have been reviewed in Sect. 4 where the results have been presented through the accuracy scores in validation datasets. Consequently, we have concluded our study by highlighting the key points and our future steps in Sect. 5.

2

Related Works

In the last decade, image semantic studies have separated into semantic segmentation, instance segmentation and panoptic segmentation. Various convolutional neural networks using the same backbone but with different speed and accuracy have been published for three topics. In addition, studies differentiate into based on the image type that is the subject of the study, and aerial images, panoramic images, night images [3], street images [4–6] that change depending on weather conditions such as rain and snow are analyzed. The networks focused on street view are prioritized in this paper. Mask R-CNN [7], ResNet (with different number of layers) [8], FCN [9], Faster R-CNN [10] and YOLACT [2,11] are prominent networks for the instance segmentation. These networks are also used in realtime video processing studies. As real-time video processing is more important in

Multi-channel Deep Learning Architecture for Scene Semantics

103

street scene semantics and semantic segmentation, YOLACT [2], which is based on ResNet-101 [12], shines as a robust and fast network. Although the focused objects or images type differs according to the subject of the study, MS Coco, Cityscapes, Pascal Voc 2012 free datasets are generally preferred as datasets. To measure success, ground truth is also needed in labeled form with the dataset. Many datasets used in the studies also contain labeled versions. Almost all the networks used for the urban scene and smart city focus on certain labels such as person, rider, car, bus, and building etc. Instance segmentation has two stages: object detection and semantic segmentation. Object detection provides the classes and the location of the image objects. Semantic segmentation gives sensitive predicting labels for every pixel according to the object class which it is included in. On the other hand, instance segmentation ensures that the objects of the same class detected are separated. The idea is to differentiate the pixels that form one car, one instance of a car and differentiate them from the pixels that form another instance of another car. It differs from semantic segmentation because different instances of the same object are segmented with different color maps. The techniques used for instance segmentation could be listed as classification of mask proposals (e.g., RCNN, Fast RCNN [14]), ‘detection followed by segmentation’ (e.g., PANet, Mask RCNN, YOLACT), ‘labeling pixels followed by clustering’ (e.g., Deep Watershed Transform, Instance Cut), and ‘dense sliding window methods’ (e.g., Deep Mask, Tensor Mask). Although there are frameworks that have been developed for each technique and generate successful results depending on the evaluation metric, many of them cannot be used for autonomous vehicles, urban scene analysis and real-time due to their complex architecture [13]. In addition, their computation could need relatively expensive hardware, long time and continually maintenance. Due to the need of instant urban scene analysis for autonomous vehicles, the robustness, speed, and accuracy of these networks in the real-time stand out as the reason for preference. YOLACT ensures speed and high-quality because of its parallel structure, extremely lightweight assembly process, and without any loss of quality from repooling. In addition, YOLACT provided a new idea of generating the prototypes and mask coefficients could be added to any modern detector.

3

Methodology

Semantic image segmentation consists of the classification of an input image at the pixel level where the pixel membership prediction is being achieved through the object class. The prediction procedure computes the membership score for each pixel to infer the detailed semantic information about the input image. As a matter of fact, urban scenes might be analyzed and every pixel in the street view could be labeled with the target classifiers such as background, car, person and so on. YOLACT is an optimized framework for instance segmentation with its speed and accuracy, especially in real-time. YOLACT reduces the time and requirements of computational functionalities by using ResNet - 101 with FPN

104

T. Demirta¸s and I. B. Parlak

(Feature Pyramid Networks) that helps in creating pyramids of feature maps of high-resolution images instead of the conventional Pyramid of images approach. The backbone ResNet-101 and the model features are computed at different skills. The feature pyramid which is present in all object detectors and segmentation is set as the instance segmentation methods. Protonet is responsible for generating K prototype masks and this K has no relationship with the number of semantic classes, but it is rather a hyper parameter. Therefore, a fixed number of prototype masks which can somehow be related to the anchors that were in the bounding box can be generated. The architecture of Protonet is a Fully Convolutional Network which consists in a series of 3 × 3 convolutions and then a final 1 × 1 convolution that simply converts the number of channels into these k predictions. There are k possible mask prototypes at the end on this feature map of 138 × 130 × k. This is similar to the mask branch in Mask-RCNN but there is no loss function applied at this stage, and this makes a crucial difference. Once this Protonet has generated the mask prototypes, there is another network that predicts a coefficient for every predicted mask and just judges how reliable the mask is. The mask coefficient is essentially nested also with the box predictions. Therefore, there is a series of anchor boxes where one can predict the class of the anchor box and the regression to fit this anchor box tightly to the object. Then, this scale coefficients one per prototype mask and per anchor. There is W xH is multiplied by k which is the number of prototype masks and multiplied by a which is the number of anchor boxes. These k coefficients per anchor are the ones that define the mask. The mask predicts a linear combination and then passes through a non-linearity. The bounding box for YOLACT has a simple cross entropy between the assemble mask and the ground truth, also to the standard losses which are the regression for the bounding box, and the classification for the actual semantic class of the object/mask. The Cityscapes Dataset is a preferred dataset for image segmentation and focuses on semantic understanding of urban street scenes. This dataset comprises about 5000 images with fine annotation and 20000 with coarse annotation. The dataset has large diversities in terms of large area (50 cities images), different weather conditions, daytime, etc. Furthermore, the dataset ensures pixel-base class annotations for semantic and instance segmentation on a vehicle’s perspective. The annotations in this dataset define each object by the segmentation mask and a unique instance identification. This information provides the 2D bounding box, and specifies the area is contained by the instance (which is called the mask). And finally, the labels are acquired for segmentation. The Cityscapes dataset involves 30 different classes grouped by 8 categories related to their urban scenes like flat surfaces, human, construction, etc. All of them are not relevant for this paper, therefore only nine classes are included in the scope. These are ‘background’, ‘person’, ‘rider’, ‘car’, ‘bicycle’, ‘motorcycle’, ‘bus’, ‘truck’, and ‘train’. The detection is sometimes not meaningful because some segmentation annotations are quite small, and no object could be created. For this reason, this type of objects have been skipped.

Multi-channel Deep Learning Architecture for Scene Semantics

105

YOLACT is a kind of method with the detection followed by segmentation. Therefore, YOLACT and other similar networks are relatively easy to train, better generalization, and relatively faster. In addition, these networks provide good segmentation accuracy, however, they depend on a complicated training pipeline which is difficult to train, and to optimize. Because of this difficulty, training the Cityscapes Dataset (gtFine train set) with YOLACT has taken over two days with 4-GPU hardware. The Resnet backbone with 50 layers (ResNet-50) has been used for training. Tanh, sigmoid, softmax, relu are used as activation functions. Relu is for only regular layers and prototype activation, not downsampled layers. Sigmoid is for mask activation. Tanh is for coefficient activation. Maximum iteration number is 220000, and maximum number of detections for evaluation is 100. The number of features in each FPN layer is 256. The maximum image size is 550, over this size has not been able to train on 4 GPU. The training with ResNet-50 is slower than ResNet-101, but mAP results are better. In the validation step, as the network continues to learn, how the bounding boxes and the masks rapidly converge without the need for feature localization is observed. Furthermore, the prototype masks are used by YOLACT, and then the network learns how to localize these masks on its own by setting validation images. The weights which are named by the used backbone, the number of epochs and the number of iterations, have been obtained after the training.

4

Results

Instance segmentation results on Cityscapes are reported using standard metrics. The training was carried out with gtFine train dataset, and then evaluated validation and test dataset, for Cityscapes. All models are trained with batch size 2 on one GPU using ImageNet pretrained weights. The batch size could not be increased due to the memory capacity of the computer. The training with ResNet50-FPN is achieved for 220 k iterations and divided by 10 at iterations 10 k, 20, 30 k, 50 k, 80 k, 100 k and 120 k, (divided the learning rate at 60 k and 100 k) with SGD. Training takes till 2 days (depending on config) on one NVIDIA GeForce GTX 1650 for Cityscapes. For 800k iterations like in the original paper, it needs at least 4 days. Since YOLACT-550 (550 represents image size) is the fastest instance segmentation method on COCO in the original paper, the mAP results were compared 500 and 550 image size through Resnet-50 and Resnet-101 backbones. Downsizing the image causes a large reduction in performance, this shows that instance segmentation needs larger images. Increasing the image size reduces the speed notably, but simultaneously increases performance. The masks obtained from YOLACT are quite qualified because of no loss from the repooling. Table 1 shows mean Average Precision (mAP) for both box and mask on Cityscapes Dataset. When the results are compared with YOLACT performance on COCO, the mAP results are quite lower on Cityscapes than COCO due to the number of training iterations. The method is plenty fast because it is a one stage segmentation. For the large objects, the quality of the masks is even better. But the quality is a little bit reduced for small objects. The vehicles such as

106

T. Demirta¸s and I. B. Parlak

Fig. 1. The class-based detection after the annotation of the Cityscapes dataset.

Fig. 2. The semantic labels on the validation dataset.

cars, bicycles etc. are detected very strongly and instantaneously. But the persons, especially with little size in the image, are not detected successfully. Some results for Cityscapes are shown in Fig. 1. YOLACT clearly distinguishes instances from each other. It can successfully decompose a large proportion of objects, even in overlapping boxes. The visual results show that YOLACT trained with Cityscapes train dataset is able to find all 9 classes. However, the accuracy of class detection remained below 80% in some classes, especially in person class. Comparing YOLACT trained with COCO train dataset (for the original paper) and YOLACT trained with Cityscapes train dataset (for this paper), almost similar instance segmentation results were obtained. The prominent problem is that YOLACT is seriously inadequate in detecting person and rider classes in both weights as shown in Fig. 2.

Multi-channel Deep Learning Architecture for Scene Semantics

107

Table 1. Evaluation metrics on Cityscapes dataset using mean average precision (mAP).

5

Image size Backbone

mAP (box) mAP (mask)

500

ResNet50-FPN

13.54

12.47

550

ResNet50-FPN

17.82

14.35

750

ResNet50-FPN

17.61

16.37

1000

ResNet50-FPN

18.63

16.44

550

ResNet50-FPN (Original Paper) 29.79

27.97

Discussion and Conclusion

Instance segmentation has two stages: object detection and semantic segmentation. Object detection provides the classes and the location of the image objects. Semantic segmentation gives sensitive predicting labels for every pixel according to the object class which it is included in. The Cityscapes dataset involves 30 different classes grouped by 8 categories related to their urban scenes YOLACT is an optimized framework for instance segmentation with its speed and accuracy, especially in real-time. YOLACT reduces the time and requirements of computational functionalities by using ResNet - 101 with FPN. However, there are also some scenes where YOLACT trained with Cityscapes is more successful especially for person class than YOLACT trained with COCO YOLACT has obvious detection errors such as misclassification, positioning failure of the box, etc. Although two errors, localization failure and leakage, were highlighted in the original article, only localization failure has encountered frequently in the Cityscapes evaluation. If there are too many large objects in the image, YOLACT cannot fully cover smaller objects regardless of the distance from each other and cannot accurately detect the location of and corners of the object. According to the angle and position of the camera, the object can be classified with more than one class, even if the object is in spot of image. Instance segmentation for urban scene analysis is still a complex task. The expense of time and hardware requirements for the real time image processing and interpretending results makes this task harder, especially for high resolution images. In other studies on instance segmentation, even with a robust hardware, the success of results can not be increased because of the complexity of algorithm/network. Therefore, the future works will require to balance the hardware requirement with the network complexity. Another issue is the accurate detection rate which decreases as the objects get smaller, depending on the scene angle. The successful detection rate of small objects depends on the camera angle. In addition, human and driver cannot be separated successfully and as expected. For a safe urban scene analysis, the solution of these two problems would become a prospective challenge.

108

T. Demirta¸s and I. B. Parlak

References 1. Cordts, M., et al.: The Cityscapes dataset for semantic urban scene understanding. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223 (2016) 2. Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. In: IEEE International Conference on Computer Vision–ICCV (2019) 3. Tan, X., Zhang, Y., Cao, Y., Ma, L., Lau, R.W.: Night-time semantic segmentation with a large real dataset. arXiv e-prints, arXiv-2003 (2020) 4. Lin, C.Y., Chiu, Y.C., Ng, H.F., Shih, T.K., Lin, K.H.: Global-and-local context network for semantic segmentation of street view images. Sensors 20(10), 2907 (2020) 5. Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684–3692 (2018) 6. Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4151–4160 (2017) 7. He, K., Gkioxari, G., Doll´ ar, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) 8. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 7(3), 171–180 (2015). Arxiv.Org 9. Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2359–2367 (2017) 10. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NeurIPS (2015) 11. Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT++: better real-time instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 1108–1121 (2020) 12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2016) 13. Liu, S., Jia, J., Fidler, S., Urtasun, R.: SGN: sequential grouping networks for instance segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3496–3504 (2017) 14. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

Forecasting Greenhouse Gas Emissions Based on Different Machine Learning Algorithms Ilayda Ulku1(&) 1

2

and Eyup Emre Ulku2

Faculty of Engineering, Industrial Engineering Department, Istanbul Kultur University, 34156 Istanbul, Turkey [email protected] Faculty of Technology, Computer Engineering Department, Marmara University, 34722 Istanbul, Turkey

Abstract. With the increase in greenhouse gas emissions, climate change is occurring in the atmosphere. Although the energy production for Turkey is increased at a high rate, the greenhouse gas emissions are still high currently. Problems that seem to be very complex can be predicted with different algorithms without difficulty. Due to fact that artificial intelligence is often included in the studies to evaluate the solution performance and make comparisons with the obtained solutions. In this study, machine learning algorithms are used to compare and predict greenhouse gas emissions. Carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and fluorinated gases (F-gases) are considered direct greenhouse gases originating from the agriculture and waste sectors, energy, industrial processes, and product use, within the scope of greenhouse gas emission statistics. Compared to different machine learning methods, support vector machines can be considered an advantageous estimation method since they can generalize more details. On the other hand, the artificial neural network algorithm is one of the most commonly used machine learning algorithms in terms of classification, optimization, estimation, regression, and pattern tracking. From this point of view, this study aims to predict greenhouse gas emissions using artificial neural network algorithms and support vector machines by estimating CO2, CH4, N2O, and F-gases from greenhouse gases. The data set was obtained from the Turkish Statistical Institute and the years are included between 1990 and 2019. All analyzes were performed using MATLAB version 2019b software. Keywords: Machine learning algorithm


Greenhouse gases
Forecasting

1 Introduction Energy consumption and carbon footprint topics have been important issues in today's world in comparison with the past decade. With the increase in energy consumption, electricity generation including wind power, hydropower, and also solar power is the preferred option for electricity generation from renewables. However, renewable energy can meet only around half of the forecast of current trends of global electricity demand according to the IEA report [8]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 109–116, 2022. https://doi.org/10.1007/978-3-031-09176-6_13

110

I. Ulku and E. E. Ulku

150.00 100.00

Natural Gas Renewable Energy And Wastes Hydroelectric Energy Coal And Coal Derivatives Liquid Fuel

50.00 0.00

Year

19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 20 16 20 18 20 20

Electricity Generation (Twh)

According to economic trends about the electricity generation with the help of renewables grow significantly around all over the world by 8% in 2021 and also by more than 6% in 2022 where renewables include wind and solar PV and hydropower. Although, this significant increase in the electricity generation from the renewables can meet only half of the global electricity demand for the reported years 2021 and 2022, according to the new IEA report. The electricity production information is presented by Turkey Electricity Distribution Inc. (TEDAŞ) on the basis of measurement of natural gas, renewable energy and wastes, hydroelectric energy, coal and coal derivatives, and liquid fuel data. The dataset covers the years between 1990 and 2020 and is obtained from TEDAŞ data [1]. According to the dataset represented in Fig. 1, the electricity production from coal decreases in Turkey, but it is not reflected in greenhouse gas emissions.

Fig. 1. Electricity generation in Turkey between 1990 and 2020.

On the contrary, there is a significant increase in the environmental benefits of renewable energy and waste since 2017. According to the dataset, electricity generation increased by 15.7% in 2020 compared to the previous year. Also, this electricity generation in renewable energy increased its share to 16.8% of total electricity generation with 51.54 TWh. Accelerating renewable energy investments will reduce the greenhouse gas emissions from coal power plants. Thus, renewable energy investments provide of great importance in preventing climate change in the atmosphere. It is obvious that there are several studies in the literature for forecasting CO2 emissions [5, 6, 9]. In this study, the energy production, number of road vehicles, and the amount of municipal waste collected in tonnes per year are considered. There is a strong correlation between each input pair in terms of CO2 as an output parameter. In order to analyze the efficiency of machine learning algorithms, the data set obtained from the estimation of the CO2 emissions was first used. To train the algorithms, the data between 1990 and 2009 is used. Then, the last 5 years covering the years 2015 and 2019 were estimated with 2 algorithms. Among several statistical metrics the coefficient of determination, R2 (R-Squared) is used to determine how well the data fit the developed model to address the acceptability of the algorithms. Artificial neural network and support vector machine are used to predict greenhouse gas

Forecasting Greenhouse Gas Emissions

111

emissions by using year, electricity generation (MWh), the number of road motor vehicles, Turkey's population, and the amount of municipal waste collected tonnes per year as input parameters. The remainder of this study is organized as follows: In Sect. 2 the descriptive statistics of the data set are represented. Also, mathematical models and machine learning algorithms are discussed with the relationship between input and output variables. Statistical comparisons, formulas, explanations, and success criteria are also presented under Sect. 2. The results of the research are given under Sect. 3. Finally, the paper concludes with the conclusion and recommendations in Sect. 4.

2 Methodology In this study, the year, the population in million, the energy production, number of road vehicles, and the amount of municipal waste collected in tones per year are used as the input dataset. The CO2 emissions are used as the output of the data. The main greenhouse gas that is responsible for the environmental pollution effect on the heat balance is CO2. The Climate Convention reported that carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and fluorinated gases (F-gases) are values that imply global warming [2]. The sum of CH4, CO2, N2O, and F-gases between the 1990 and 2019 periods are used as CO2 equivalent greenhouse gas emissions for this study. There is a 72% of total greenhouse gas emissions in 2019 which had the largest share with energy-related CO2 equivalent as represented in Fig. 2. On the other hand, there is a significant increase which is recorded as 161% in 2019 in emissions compared to 1990 and 2019. But, when the previous year is compared there is a decrease that is recorded as 2.3% [3].

GHG emissions rate by sector

GHG emissions rate by gas N2O F-gases 8% 1%

Agricul Waste 3% ture Industri 14% al process es and product use 11%

CH4 12%

Energy 72%

(a)

CO2 79%

(b)

Fig. 2. Total greenhouse gas emissions in 2019 (a) by sector (b) by gas

To compare and predict greenhouse gas emissions machine learning algorithms are used in this study. Carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and

112

I. Ulku and E. E. Ulku

fluorinated gases (F-gases) are considered direct greenhouse gases named as CO2 equivalent (106 tonnes) as in the second column of Table 1. The descriptive statistics are represented in the dataset used in this study. Table 1. Descriptive statistics of the dataset.

Std. Error Median Std. Dev Kurtosis Skewness Range Minimum Maximum Observation

CO2 Year (106 tonnes)

Electricity generation (MWh)

# of road motor vehicles

Turkey population

Collected municipal waste (103/year)

18.05 326.09 98.89 –1.25 0.36 305.41 219.57 524.98 30

14,333,616.65 156,327,250.00 78,508,451.68 –1.17 0.29 247,258,885.00 57,543,000.00 304,801,885.00 30

1,121,304.42 10,691,091.50 6,141,637.26 –1.10 0.44 19,406,297.00 3,750,678.00 23,156,975.00 30

1,527,313.77 68,435,377.00 8,365,442.04 –1.13 0.07 28,034,997.00 55,120,000.00 83,154,997.00 30

796,855.73 25,205,500.00 4,364,558.58 –0.26 –0.11 14,567,472.38 17,757,000.00 32,324,472.38 30

1.61 2004.50 8.80 –1.20 0.00 29 1990 2019 30

As long as there are different units for each parameter, the data of each parameter must be scaled in order to obtain a normalized range that is between 0 and 1 as given in Eq. (1). X 1 is used as the actual data and the minimum and the maximum values in the dataset are given as X 1ðminÞ and X 1ðmaxÞ , respectively. The normalized value is represented with X 1ðnormalizedÞ which takes a value between 1 and 0. X 1ðnormalizedÞ ¼

1.20

X 1  X 1ðminÞ X 1ðmaxÞ  X 1ðminÞ

ð1Þ

Total Greenhouse gas emissions Year

Normalized Data

1.00

Electricity Generation (Mwh) Number of road motor vehicles

0.80

Turkey population Amount of municipal waste collected (tonnes/year)

0.60 0.40 0.20 0.00 1985

Years 1990

1995

2000

2005

2010

Fig. 3. Normalized values of each used dataset.

2015

2020

Forecasting Greenhouse Gas Emissions

113

Also, normalized values of each used dataset are represented in Fig. 3. There is a significant increase over the years in each parameter. In order to see if there is any linear relationship between variables correlation analysis is used as a statistical method. The correlation values of each dataset are represented in Table 2. When the coefficient is close to 1 there is a perfect correlation which means a change on the variable affects the other variable. According to the values in Table 2, these datasets are valid to be used in the machine learning algorithms to forecast CO2 emissions. Table 2. Correlation values of each attribute in the dataset. CO2 CO2 Year Electricity Generation Number of motor vehicles Population Municipal waste

2.1

Year

Electricity generation

Number motor vehicles

Population Municipal waste

1 0.982 1 0.996 0.993 1 0.995 0.985 0.997

1

0.983 0.999 0.994 0.885 0.916 0.902

0.988 0.895

1 0.920

1

Machine Learning Algorithms

Artificial Neural Network (ANN) Algorithm. The process of a human neural system is used to determine if there is any connection between inputs and outputs with the help of Artificial Neural Networks (ANN). Each connection means a neuron of hidden layers that is used with a proper weight. Model training consists of setting values. There are target values whose weights are optimized to match output estimates and targets [6]. This is accomplished by calculating the error between the forecasts and targets and then adapting the weights via learning methods [5]. In this study, a feed forward back propagation network type is used. As a training function gradient decent function is used that is commonly-used in order to train neural networks and machine learning models. There is a single layer that consists of 10 neurons in the model. Support Vector Machine (SVM) Algorithm. To find the distance between data points of distinct groups Support Vector Machines (SVM) are used. In this algorithm, the Kernel distribution is considered to optimize the distance [7]. In this study, the Gaussian radial technique is considered. In order to train the model to make proper predictions for new data, the kernel approximation classifiers are used in the model with several observations.

114

I. Ulku and E. E. Ulku

3 Numerical Results The data set was obtained from the Turkish Statistical Institute and the years are included between 1990 and 2019. All analyzes were performed using MATLAB version 2019b software. In forecasting models, R2 is used as the proportion of the variance in order to predict the output by using the input variables. The fit quality of the proposed model is measured with R2 [4] as represented by Eq. (2) where yi is the actual value and by i is the predicted value for all i from 1 to the m. Pm j¼1

ðyi  ^yi Þ2

i¼1

ðyi  yi Þ2

R ¼ 1  Pm 2

ð2Þ

The following Fig. 4 represents the variance of the predicted output by using the proposed model. The ANN model explains 99.58% of the variation when all predictors are considered.

Fig. 4. Training regression.

The results of the proposed model performance and the actual data are represented in Fig. 5.

1.2 1 0.8 0.6 0.4 0.2 0

Actual

115

ANN

Year

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Normalized Data

Forecasting Greenhouse Gas Emissions

Fig. 5. Forecasting of CO2 emissions.

The following Fig. 6 represents the plot to identify useful predictors for separating classes and to understand the relationships between features. The training data and misclassified points with dashed lines are illustrated on the parallel coordinates plot.

Fig. 6. Parallel coordinates plot for separating classes with predictors.

4 Conclusion In this paper, among several machine learning algorithms, artificial neural network and support vector machine are used to predict greenhouse gas emissions. Methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O), and fluorinated gases (F-gases) are considered direct greenhouse gases. Year, electricity generation (MWh), number of road

116

I. Ulku and E. E. Ulku

motor vehicles, Turkey’s population, and the amount of municipal waste collected tonnes per year are used to predict total greenhouse gas emissions. ANN model explains 99.58% of the variation when all predictors are considered in terms of forecasting the CO2 emissions. With the help of SVM, useful predictors can be identified for separate classes. Also, the relationships between features can be demonstrated clearly with the help of the algorithms. The results are satisfied for each machine learning algorithm in forecasting CO2 emissions. Machine learning algorithms represent convincing forecasting results. Therefore, in a future study, several machine learning algorithms will be studied and the performance success of the machine learning algorithms in the forecast will be evaluated.

References 1. Turkey Electricity Distribution Inc. (TEDAŞ), Energy Statistics. https://biruni.tuik.gov.tr/ medas/?kn=147&locale=tr. Accessed 24 March 2022 2. United Nations, Reporting from and review of Parties included in Annex I to the convention reports on national greenhouse gas inventory data from Parties included in Annex I to the Convention for 1990–2016, 1990–2017, 1990–2018 and 1990–2019, https://unfccc.int/sites/ default/files/resource/sbi2021_11E.pdf. Accessed 24 March 2022 3. Turkish Statistics Institution, Greenhouse Gas Emission Statistics, 1990–2019. https://data. tuik.gov.tr/Bulten/Index?p=Sera-Gazi-Emisyon-Istatistikleri-1990-2019-37196. Accessed 24 March 2022 4. Tao, Y., Yue, G., Wang, X.: Dual-attention network with multitask learning for multistep short-term speed prediction on expressways. Neural Comput. Appl. 33(12), 7103–7124 (2020). https://doi.org/10.1007/s00521-020-05478-2 5. Bakay, M., S., Agbulut, Ü.: Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms. J. Clean. Prod. 285, 125324 (2021) 6. Platon, R., Dehkordi, V., R., and Martel, J.: Hourly prediction of a building’s electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy Build 92, 10–18 (2015) 7. Lazos, D., Sproul, A.B., Kay, M.: Optimisation of energy management in commercial buildings with weather forecasting inputs: a review. Renew Sustain Energy Rev 39, 587–603 (2014) 8. Electricity Market Report, July 2021. https://iea.blob.core.windows.net/assets/01e1e9988611-45d7-acab-5564bc22575a/ElectricityMarketReportJuly2021.pdf. Accessed 24 March 2022 9. Cao, Y., Yin, K., Li, X., Zhai, C.: Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model. Appl. Soft Comput. 104, 107220 (2021)

The Computation Trend of Fuzzy Rules for Effective Decision Support Mechanism on Basis of Supervised Learning for Multiple Periods Bhupinder Singh(&) and Santosh Kumar Henge School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India [email protected], [email protected]

Abstract. This paper elaborates the attempt to measure the performance of neural fuzzy inference systems along with five different machine-learning algorithms for a 10-year dataset. Several models developed with machine learning concepts were evaluating on a short-term dataset that imparted a very limited values of prediction accuracy. Background study reveals the utilization of fuzzy inference systems using multiple rules forming a complex structure for computation with lagging issues in performance in terms of executive time. This paper has composed with ML based algorithmic techniques along with neural network and fuzzy inference system for stock market prediction. The methods involve the integration of fuzzy rules for the decision making process on the basis of technical indicators.Consequently, decision tree algorithms outperform with 86.6% accuracy for the prediction of future values for 10 Year Dataset and 87.5% for 15 Year Dataset. Meanwhile, it is very uncertain to refrain from the findings of concurrent studies on real–world investing strategies due to the lack of predictable results. The author has concluded that the Decision tree algorithm has the highest prediction accuracy of 89.4% in 20-year dataset compared to other machine learning algorithms and the simple architecture of the Neural Fuzzy Inference System performs with high precision and accuracy with low lagging issues. Keywords: Neural fuzzy inference system (NFIS)
Supervised learning (SL)
K-nearest neighbors algorithm (KNA)
Logistic regression (LR)
Random forest classification (RFC)
Naive Bayes Gaussian algorithms (NBGA)
Risk management (RM)

1 Introduction In present’s demand, successful investors place a distinction on high-quality data in order to make informed judgments in different request conditions. The fiscal stock investment contexture [2] inherits the experience of not just fund directors, but also experimenters in an operation- acquainted academic sector. The experimental results would be useful to businesses who seek to make prognostications grounded on vast © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 117–123, 2022. https://doi.org/10.1007/978-3-031-09176-6_14

118

B. Singh and S. K. Henge

data sets [9]. The stock request's time series data is veritably unpredictable andnonlinear. According to the size of the Portfolio, stock value vaticination [6, 10] offers a larger threat price. Abecedarian analysis and specialized analysis work together to help you make better opinions. The thing of this exploration is to show how the influence of a Neural Fuzzy determination System with Machine Learning Algorithms can be seen. The novelty of this exploration stems from the use of fuzzy sets to express knowledge for colorful input and affair signals. Eventually, we conduct an trial on a rapid-fire process operating system and describe our findings. Eventually, repeat the process until you get the stylish conjecture. Numerous experimenters have used the Support Vector Machine fashion on a short- term dataset, still it fails to perform on a 10- time dataset. We cannot calculate just on maps for delicacy. Likewise, it's generally a good idea to confirm the misdeeds and keep track of them for a long period. 1.1

Related Work

Dongdong et al. (2019) emphasized on data preprocessing of original data sets and therefore fastening on the life of stock value in unpredictable requests [1]. The author Park K et al. (2013) has initiated a new conception to simplify noisy fiscal series by studying the patterns, innovate that 4 chance and 7 chance of enhancement in delicacy in vaticination [5]. Sneh Kalra et al. (2019) make the model using literal data and news papers for vaticination of trends and stated that delicacy ranging from 65.30 to 91.2 achieved [9]. Wang et al. [6] worked on the manipulation discovery model and major beneficence is in the designing of a new RNN-grounded ensemble Learning grounded on demitasse stock request [8]. Kunal Pahwa Neha Agarwal (2019) stated that the unpredictable nature of the stock request makes it sensitive for time series models to prognosticate the movement and trend [7]. Chopra [12] illustrated the effects due to demonetization on the native grounded Stock request and used mean square error for retrogression analysis of neural networks [12].

2 Architecture of Anfis and Ml Algorithms The conception of data ingress, data constellation, Deep Learning issue working, and performance computation are all part of this exploration perpetration. All fine computations linked to processes [4] in Google Collab grounded on tensor inflow using Yahoo Finance API are performed using Python programming. A large quantum of training data aids in the consequence of a healthy and effective classifier [6], which improves overall issues [1]. The train and target inputs from the data set are used by the system to search for the exemplar complicacy that will be used to train the model. To appraise, the trained model saves. Fuzzy determination System is employing by one or further functional blocks. Originally, a functional block defined by a specific name. Likewise, each functional block is composed of multiple rule blocks, classes and multiple variables together with fuzzyfiers and defuzzifier [12]. Again, each rule block is formulating an antecedent part. Consequents are accumulated terms of rules, terms and classes. An Antecedent fete through a Rule grounded expression class (Fig. 1).

The Computation Trend of Fuzzy Rules for Effective Decision Support Mechanism

119

Fig. 1. Architecture of neural fuzzy inference system [3]

Trading prescriptions are prescribing conferring to fuzzy input variables with specialized indexes, indication Relative strength Indicator, and Plutocrat Flow Index.

3 Experiments and Results The neural networks and fuzzy determination systems work singly from each other [8]. The united NFIS uses the medium to learn all parameters from a fuzzy system. Emplace Machine memorizing algorithm on the given API data. As long as conditions started, additionally buy and vend conduct executes. Plot the graph in expressions of total earnings as per total investment. The inflow of divergence indicates the proximate fluctuation of the reward towards the divergence mode. 1. Calculate the raw price for a period RawPrice ¼

Low þ High þ Close 3

ð1Þ

2. Calculate the raw money flow RawMoneyFlow ¼ Volume  RawPrice

ð2Þ

3. Calculate the money ratio MoneyRatio ¼

14periodPositiveMoneyFlow 14periodNegativeMoneyFlow

ð3Þ

4. Calculate the Money Flow Index (MFI)


100 ðMFI Þ ¼ 100  1 þ moneyratio

 ð4Þ

120

B. Singh and S. K. Henge

5. Formula for Relative Strength Index 0 RSI ¼ 100  @

1 1þ



100 averagegain averageloss

A

ð5Þ

Table 1. Rules for computation: Bullish signal RSI MFI Trend if >70&75&60&70& 50&60&30&35&20&25&10&15&0.5 >=0.5 0, then the following operational laws are given

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 U 2 U 2 U 2 ~2 ¼ ~1  P ðlLP1 Þ2 þ ðlLP2 Þ2  ðlLP1 Þ2 ðlLP2 Þ2 ; ðlU P P1 Þ þ ðlP2 Þ  ðlP1 Þ ðlP2 Þ h

i U ; vLp1 vLp2 ; vU p1 Þðvp2 ð3Þ   U ~ 2 ¼ ½ lLP1 lLP2 ; lU ~1  P P P1 lP2 ;

q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 U 2 U 2 U 2 ðvLP1 Þ2 þ ðvLP2 Þ2  ðvLP1 Þ2 ðvLP2 Þ2 ; ðvU P1 Þ þ ðvP2 Þ  ðvP1 Þ ðvP2 Þ

ð4Þ

202

S. Seker

~k

P ¼

~¼ kP

" h

"rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffirffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi##

k

k 2 ; 1  1  ðvLp Þ2 ; 1  1  ðvU pÞ

ð5Þ

""rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffirffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi# #

k

k h i 2 L k U k U 2 L 1  1  ðlP Þ ; 1  1  ðlP Þ ; ðvp Þ ; ðvp Þ

ð6Þ

k ðlLP Þk ; ðlU PÞ

i

Definition 3. Interval-valued Pythagorean fuzzy weighted geometric operator (IVPFWG): An IVPFWG of measurement n is on behalf Pn of IVPFWG: Xn ! X, which identify the related vector as IVPFWGwð~ p1 ,~ p2 i¼1 wi ¼ 1; Qn  Qn L wi U wi ….~pn ) = ðl Þ ; ðl Þ , Pi Pi i¼1 i¼1  rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

wiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

wiffi Yn Yn 2 U 2 L ; 1  i¼1 1  ðvpi Þ 1  i¼1 1  ðvpi Þ

ð7Þ

pi ðj ¼ 1; 2; . . .; nÞ and wi 0: where w ¼ ðw1 ,w2 ; . . .:; wn ÞT be the weight vector of ~ 2.2

Interval-Valued Pythagorean Fuzzy (IVPF) AHP

The steps of IVPF-AHP suggested by Ilbahar et al. (2018) are shown in the following. This method is used to rank agile supplying factors. The stepwise of IVPF-AHP is given in the following: Step 1. Construct the pairwise comparison matrix R ¼ ðrik Þmxm between criteria associate with information obtained by experts’ evaluation. Saaty’s (1980) traditional consistency process is employed for each DMs’ pairwise comparison matrix performing the linguistic scale with respect to crisp values in classical AHP method as shown in Table 1. The ratio obtained should be smaller than 0.1 for a matrix to evaluate as consistent. Step 2. Establish the difference matrix D ¼ ðdik Þmxm between lower and upper values of the membership and non-membership functions on the basis of Eqs. (8–9). dikL ¼ l2ikL  v2ikU

ð8Þ

dikU ¼ l2ikU  v2ikL Step 3. Construct the interval multiplicative matrix S ¼ ðsik Þmxm using Eqs. (10–11). sikL ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1000dL

ð10Þ

sikU ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1000dU

ð11Þ

Step 4. Compute the determinacy value s ¼ ðsik Þmxm of the rik using Eq. (12):

Evaluation Model for Supply Chain Agility



 sik ¼ 1  l2ikU  l2ikL  v2ikU  v2ikL

203

ð12Þ

Step 5. To obtain the weights T ¼ ðtik Þmxm of the interval multiplicative matrix before normalization proses Eq. (13) is employed. tik ¼

s þ s ikL ikU sik 2

ð13Þ

Step 6. Calculate the normalized priority weights wi using Eq. (14). Pm t Pmik wi ¼ Pm k¼1 i¼1

ð14Þ

k¼1 tik

3 Case Study In this study, AHP is integrated using IVPF to evaluate agile factors for sustainable supply chain operations for a fuel oil supply company in Turkey. Data were collected through a questionnaire deliver to professionals in fuel oil industry. The most important agile factors were obtained from marketing and sales, information technology (IT), finance and R&D department’ managers to understand the critical agile factors of the fuel oil industry’ in supply chain operations. As a result, information was collected from managers with at least five years of experience in these departments. Agile supplying factors are determined as Delivery speed (C1), Cost (C2), Customer satisfaction (C3), Respond to changes in the market (C4), Delivery flexibility (C5), Innovation performance (C6), Flexibility (C7), Collaboration with partners (C8), and Information technology (C9). Once the consistency between evaluations are checked all evaluation matrix are aggregated using IVPFWG operator. The result pairwise matrix is obtained as in Table 2. Table 1. Linguistic terms and IVPFNs for ratings of agile factors. Pythagorean fuzzy number Linguistic Term lL Absolutely Low (AL) 0.00 Very Low (VL) 0.10 Low (L) 0.20 Medium Low (ML) 0.30 Exactly (EE) 0.50 Absolutely Medium (AM) 0.45 High (H) 0.55 Very High(VH) 0.65 Absolutely High (AH) 0.75

lU 0.15 0.25 0.35 0.45 0.50 0.60 0.70 0.80 0.90

vL 0.75 0.65 0.55 0.45 0.50 0.30 0.20 0.10 0.00

vU 0.90 0.80 0.70 0.60 0.50 0.45 0.35 0.25 0.15

204

S. Seker

The difference matrix between lower and upper values of the membership and nonmembership functions is obtained as in Eq. (8–9) and interval multiplicative matrix is constructed using Eq. (10–11). Interval multiplicative matrix based on agility factors is shown in Table 3. Table 2. Aggregated pairwise comparison.

Table 3. Interval Multiplicative matrix. Crit. C1 C1 1 C2 0.476 C3 1.34 C4 1.34 C5 0.752 C6 0.581 C7 1.569 C8 0.859 C9 0.592

1 1.239 2.576 2.576 1.532 0.994 3.993 2.206 1.547

C2 0.663 1 1.293 1.089 0.85 0.469 1.737 0.807 0.498

1.708 1 2.497 2.82 1.368 1.008 3.264 1.695 1.157

…. …. …. …. …. …. …. …. ….

C9 0.527 0.449 0.948 1.422 0.648 0.392 1.29 0.994 1

1.355 0.988 2.448 2.725 1.113 1.012 3.293 1.546 1

To obtain the weights, the interval multiplicative matrix before normalization proses Eq. (13) is employed and the result table is given in Table 4.

Evaluation Model for Supply Chain Agility

205

Table 4. The weights before normalization Crit. C1 C2 C3 C4 C5 C6 C7 C8 C9

C1 1.000 0.595 1.571 1.571 0.889 0.658 1.949 1.071 0.741

C2 0.827 1.000 1.520 1.371 0.950 0.561 2.044 0.969 0.609

C3 0.386 0.353 1.000 1.009 0.488 0.300 1.204 0.534 0.373

C4 0.347 0.303 0.595 1.000 0.334 0.229 0.889 0.449 0.176

C5 0.697 0.658 1.239 1.785 1.000 0.560 1.610 1.052 0.735

C6 1.102 1.009 1.949 1.544 1.170 1.000 2.667 1.520 1.071

C7 0.300 0.246 0.623 0.838 0.320 0.217 1.000 0.461 0.320

C8 0.488 0.380 0.830 1.355 0.474 0.353 1.349 1.000 0.658

C9 0.656 0.541 1.190 1.677 0.735 0.488 1.610 1.102 1.000

The importance weight of each agile factor for supply chain in fuel oil industry is obtained by performing Eq. (14) and the results are shown in Table 5. Table 5. Ranking of agile factors. C1 C2 C3 C4 C5 C6 C7 C8 C9 0.08 0.07 0.145 0.168 0.088 0.06 0.198 0.113 0.078

Flexibility has been achieved as the most important success factor as it significantly supports the responsiveness of the supply chain and increases customer satisfaction. The management of a dynamic agile supply network focuses on quick-to-market response. An agile supply chain requires the integration of business partners to acquire new competencies to respond to rapidly changing, ever-changing market needs. Customer satisfaction leads to repeat purchases, which positively affects revenue growth. Every business aiming at profit wants to reduce costs. In the agile supply chain, it is a strategic goal for businesses to reduce both internal and external costs that directly or indirectly affect the cost of the product. The time to deliver products directly increases the speed of the supply chain, contributing to the agile supply chain. Responding to changing competition requirements, being at the forefront in terms of speed or time, cost and quality provides agility against risks.

4 Conclusion Agility is identified as a strategic response to crises in any industry. In this sense, agility is necessary to ensure the sustainability of in an industry. In this study, to evaluate agility factors in any fuel oil company for supply chain operations IVPF-AHP method is used. For supply chain operations, it is important to respond quickly to changes in the market to ensure customer satisfaction, so flexibility has emerged as the most important agility factor for fuel oil supply companies. For this reason, fuel oil supply

206

S. Seker

companies that want to be successful in a dynamic environment focus on quick response to the market. In addition, the agile supply chain requires the integration of business partners to acquire new competencies to respond to rapidly changing, everchanging market needs. Although the number of of decision makers is accepted as a limitation in this study, it is aimed to repeat the study for companies that provide fuel supply by increasing the number of decision makers in future studies. In addition, the study can be applied to different sectors in the future.

References 1. Lou, P., Liu, Q., Zhou, Z., Wang, H.: Agile supply chain management over the internet of things. In: 2011 International Conference on Management and Service Science, pp. 1–4. IEEE, August 2011 2. Zhu, X.N., Peko, G., Sundaram, D., Piramuthu, S.: Blockchain-based agile supply chain framework with IoT. Inf. Syst. Front. 1–16 (2021) 3. Centobelli, P., Cerchione, R., Ertz, M.: Agile supply chain management: where did it come from and where will it go in the era of digital transformation? Ind. Mark. Manage. 90, 324– 345 (2020) 4. Oliveira-Dias, D., Maqueira, J.M., Moyano-Fuentes, J.: The link between information and digital technologies of industry 4.0 and agile supply chain: mapping current research and establishing new research avenues. Comput. Ind. Eng. 176, 108000 (2022) 5. Baramichai, M., Zimmers, E.W., Marangos, C.A.: Agile supply chain transformation matrix: an integrated tool for creating an agile enterprise. Supply Chain Manage. Int. J. 12, 334–348 (2007) 6. Wu, C., Barnes, D.: A literature review of decision-making models and approaches for partner selection in agile supply chains. J. Purchasing Supply Manage. 17(4), 256–274 (2011) 7. Gligor, D.M., Holcomb, M.C.: Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review. Supply Chain Manage. Int. J. 17(4), 438– 453 (2012) 8. Gunasekaran, A., Subramanian, N., Papadopoulos, T.: Information technology for competitive advantage within logistics and supply chains: a review. Transp. Res. Part E Logist. Transp. Rev. 99, 14–33 (2017) 9. Humdan, E.A., Shi, Y., Behnia, M.: Supply chain agility: a systematic review of definitions, enablers and performance implications. Int. J. Phys. Distrib. Logist. Manag. 50(2), 287–312 (2020) 10. Lee, H.L.. Aligning supply chain strategies with product uncertainties. Calif. Manag. Rev. 44, 105e119 (2002) 11. Hsu, C.H., Yu, R.Y., Chang, A.Y., Liu, W.L., Sun, A.C.: Applying integrated QFD-MCDM approach to strengthen supply chain agility for mitigating sustainable risks. Mathematics 10 (4), 552 (2022) 12. Mohammed, A., Harris, I., Soroka, A., Nujoom, R.: A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design. Comput. Ind. Eng. 127, 297–312 (2019) 13. Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–356 (1965) 14. Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986) 15. Atanassov, K., Gargov, G.: Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 31(3), 343–349 (1989). http://dx.doi.org/10.1016/0165-0114(89)90205-4

Evaluation Model for Supply Chain Agility

207

16. Yager, R.R., Abbasov, A.M.: Pythagorean membership grades, complex numbers, and decision making. Int. J. Intell. Syst. 28, 436–452 (2013) 17. Peng, X., Yang, Y.: Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators. Int. J. Intell. Syst. 31(5), 444–487 (2015) 18. Ilbahar, E., Karaşan, A., Cebi, S., Kahraman, C.: A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP and fuzzy inference system. Saf. Sci. 103, 124–136 (2018)

Efficiency Evaluation of Wastewater Treatment Plants Through Interval Data Envelopment Analysis: A Case Study in Turkey Selin Aksaç1(&) 1

and H. Ziya Ulukan2

TUBITAK Turkish Management Instıtute, Barış Mah. Koşuyolu Cad., No: 48 P.K., 41401 Gebze, Kocaeli, Turkey [email protected] 2 Department of Industrial Engineering, Galatasaray University, Çırağan Cad. No: 36, 34349 Istanbul, Ortaköy, Turkey

Abstract. Wastewater treatment plants (WWTPs) with the process of treatment, discharge under appropriate conditions, and recycling of the wastewater are strategic elements that directly serve the purpose of access to clean water and prevention of water pollution. In addition, the issue of the effective, efficient and sustainable operation of WWTPs has also gained importance. In this study, a three-stage methodology has been proposed to evaluate the effectiveness of WWTPs considering the uncertainties and some hesitations in the data. The case study has been carried out using the data of 88 WWTPs of the Istanbul Metropolitan Municipality. Firstly, expert opinions have been taken to determine the input and output variables for calculating efficiency values. These opinions have been evaluated with the extended-stepwise weight assessment ratio analysis (SWARA). Secondly, the data of the WWTPs have been converted into interval values to allow the evaluation of different year efficiencies at once and to eliminate the hesitations and uncertainties. Efficiency scores have been calculated using output-oriented DEA for the best-case, original, and worst-case scenarios that form the interval values. Finally, it has been examined whether there is a difference in the efficiency values of WWTPs based on age and type of facility. This study is an exemplary study for evaluating and developing the effectiveness of WWTPs in Turkey under uncertain conditions, considering different time intervals and expert opinions. Keywords: Data envelopment analysis
Uncertain conditions treatment plants
Efficiency evaluation
Interval efficiency


Wastewater

1 Introduction The global climate crisis that we are facing in the current century requires taking action on certain issues. One of the themes determined for the realization of these actions is “clean water and sanitation”. Within the scope of these actions, WWTPs have strategic importance in the treatment, removal, and reuse of urban and industrial waters without harming nature after their use. Industrialization and the increase in domestic pollutants © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 208–215, 2022. https://doi.org/10.1007/978-3-031-09176-6_25

Efficiency Evaluation of Wastewater Treatment Plants

209

brought about the increase in the number of WWTPs, and the increase in WWTPs brought along the problems related to the effective evaluation and management of their activities. Therefore, studies on the evaluation of the effectiveness of WWTPs due to these problems have contributed to the development of the international literature. The differences in size, type, and processes of WWTPs affect the actions to be taken to manage WWTPs effectively and efficiently. Treatment of wastewater refers to the removal of pollutants by physical, chemical, and/or biological processes [1]. In this study, it has been proposed that a three-stage methodology to measure and evaluate the effectiveness of WWTPs in different years, taking into account the uncertainties and some hesitations in the data. The case study has been carried out with Istanbul Metropolitan Municipality 85 WWTPs between the years 2019 and 2020 using the proposed methodology.

2 Methodology In this paper, it has been proposed a three-stage methodology for determining and evaluating the efficiency of WWTPs in Istanbul, Turkey. Firstly, the extended SWARA has been used to clarify the possible variables by taking expert opinions. Secondly, efficiency scores have been calculated by applying interval DEA and WWTPs have been ranked according to their efficiency scores. Finally, the relationship between the calculated efficiency scores of WWTPs and external factors has been examined. 2.1

Determination of the Input and Output Variables Using ExtendedSWARA

SWARA is a multi-criteria decision-making (MCDM) method that enables the determination of criterion weights by considering expert opinions. SWARA was firstly proposed by Keršulienė (2010) and Zolfani et al. (2018) extended SWARA to improve the quality of the MCDM process by incorporating the reliability evaluation of experts’ opinions of the criteria [2, 3]. 2.2

Data Envelopment Analysis

DEA is a basic linear programming methodology for evaluating the relative technical efficiency of decision-making units (DMUs) using multiple inputs and outputs was developed by Charnes et al. (1978) and extended by Banker et al. (1984) [4, 5]. P ur yrj maxEj0 ¼ Pr v x 0 i ij

0 r P ur yrj0 Pr  1; j ¼ 1; . . .; n vx

ð1Þ

r i ij0

ur ; vi  e [ 0; r ¼ 1; . . .; s; i ¼ 1; . . .; m n defines the number of DMU, and with varying amounts; m defines the number of input variables required to produce s different outputs. Ej0 defines the efficiency score

210

S. Aksaç and H. Z. Ulukan

of DMUj ; xij0 , yrj0 are the number of input i and output r for DMUj respectively. vi , ur are the weights of input i and output r for DMUj respectively. e is assumed to be a fairly small positive number (i.e., 106 ). Considering the operation of WWTPs and input-output variables for efficiency evaluation, it is considered that there is no or limited opportunity to intervene in the input variables. So, using an output-oriented DEA model has been deemed appropriate in this study. The linear forms of output-oriented basic general model Eq. 2 are shown below [6]: P minEj0 ¼ r vi xij0 P

r ur yrj0

s.t P ur yrj0 ¼ 1 Pr  r vi xij0  0; j ¼ 1; . . .; n

ð2Þ

ur ; vi  e [ 0; r ¼ 1; . . .; s; i ¼ 1; . . .; m Efficiency Assessment with Interval DEA Calculation of tolerance intervals is an important element to eliminate subjectivities and hesitations in DEA models with uncertainty. Sala-Garrido et al. (2012) handled the tolerance intervals as symmetrically as their distance from the mean [7]. rij and skj are the percentage of the deviation from the original values for the inputs and outputs. So, considering the defined tolerance values, the input and output variables are defined as intervals [8].           xij 2 xij 1  rij ; xij 1 þ rij ; ykj 2 ykj 1  skj ; ykj 1 þ skj

ð3Þ

It is not possible to calculate all combinations of values in this interval. Therefore, only extreme values and real values in these intervals of each input, and output variable are taken into account. Comparing and Ranking Interval Efficiencies of DMUs R1 and R2 efficiency indicators suggested by Bosca et al. (2011) are calculated to evaluate the interval efficiencies of the DMUs and to interpret them comparably [9]. R1j0 ¼ (S R2j0

¼

j0 ej0 sj0 ej0

0;

ej 0 sj 0

ð4Þ

; sj0 6¼ ej0 sj0 ¼ ej0

ð5Þ

j0 is defined as the order of DMU, ej0 is defined as the number of times that efficiency score is equal to one, sj0 is defined as the scenarios that are analyzed, Sj0 is defined as the sum of efficiency [8].

Efficiency Evaluation of Wastewater Treatment Plants

211

The R1 and R2 efficiency indicators take values between 0 and 1. The R1 value expresses the efficiency ratio of the relevant DMU in the scenarios examined. In the case of DMUs with the same R1 value, comparisons are made according to R2 values [7].

3 A Case Study: Efficiency Evaluation of WWTPs in Istanbul Data on WWTPs in Istanbul were obtained from the annual activity reports of the Istanbul Water and Sewerage Administration (ISKI), a subsidiary of Istanbul Metropolitan Municipality. Since the efficiency calculation will be made with two-year data, a newly opened WWTP in 2020 and two WWTPs whose activity data are specified as 0 in the reports will be excluded. So, 85 WWTPs will be evaluated within the scope of the study. According to ISKI annual reports and literature research; possible input variables are; I1: wastewater treatment capacity, I2: electricity consumption, I3: natural gas consumption, and possible output variables are; O1: the amount of wastewater treated, O2: the amount of wastewater recycled, O3: capacity utilization rate, O4: the amount of dried sludge, O5: the amount of trash held. 3.1

Determination of the Input and Output Variables Using ExtendedSWARA

Firstly, eight experts have been asked to rank the possible criteria according to their importance. When it is tested whether there is a consensus among the expert opinions on the importance of ranking values of the criteria with the hypotheses below; • H0: There is a consensus between independent expert opinions. • H1: There is no consensus between independent expert opinions Degrees of freedom (df) are v = 8 - 1 = 7, p = 0.05 at the significance level, 2 2 > Xtbl (32.67 > 14.1); H0 is rejected. There is no consensus between independent Xa;v expert opinions. Expert opinions on the ranking are acceptable, so it is possible to move on to the next steps in the methodology. Table 1 expresses the importance weights of each variable after expert opinion evaluations. Table 1. Calculation results of SWARA

I1 O1 I2 O2 O3 I3 O4 O5

Average  rank value tj

Comparative importance of   average value sj )

Coefficient  kj

Recalculated   weight qj

Weight   wj

1.38 2.50 4.00 4.50 4.75 5.63 6.38 6.88

0.55 0.63 0.89 0.95 0.84 0.88 0.93

1 1.55 1.63 1.89 1.95 1.84 1.88 1.93

1.00 0.65 0.40 0.21 0.11 0.06 0.03 0.02

0.41 0.26 0.16 0.09 0.04 0.02* 0.01* 0.01*

212

S. Aksaç and H. Z. Ulukan

According to the expert evaluation results “natural gas consumption”, “amount of dried sludge” and “amount of trash held” have the lowest importance weights for the model. It has been decided not to include these three variables, which have a total significance of 4% (*), in the next steps in the efficiency calculation methodology. 3.2

Efficiency Evaluation of WWTPs

In this study, while some data consist of precisely known and provided directly; some of them will be included in the calculations in an imprecise manner by inference from the available data. In addition to this, since the efficiency evaluation of 85 WWPTs in Istanbul will be carried out for two years between 2019 and 2020, no calculation will be made for each year, but a general efficiency evaluation will be made. So, interval DEA will be used in this study to eliminate the errors, uncertainties, and hesitations that may occur. Considering the data for 2019 and 2020, tolerance values have been calculated for each WWTP with the methodology applied by Sala-Garrido et al. (2012) and Gomez et al. (2018). Since “total wastewater treatment capacity” does not change from year to year, a tolerance value could not be calculated for this variable. The calculation of efficiency scores using Eq. 2 is evaluated 34(81) times for 85 WWTPs, with three different scenarios (best-case, worst-case, original) and four inputsoutputs (inputs for related DMU, outputs for related DMU, inputs for the remaining DMUs, outputs for the remaining DMUs). The efficiency values of 85 WWTPs analyzed for 81 scenarios considering interval values are given in Table 2. Table 2. Efficiency scores of WWTPs using interval DEA for 81 scenarios

WWTP WWTP … WWTP WWTP … WWTP WWTP Mean

Original

Mean

Max

Min

Amplitude (max-min) (%)

Amplitude (original-mean) (%)

1 2

0.427 0.359

0.428 0.360

0.494 0.415

0.368 0.311

12.6% 10.4%

−0.1% −0.1%

44 45

0.315 1.000

0.317 1.000

0.368 1.000

0.274 1.000

9.5% 0.0%

−0.1% 0.0%

84 85

0.780 1.000 0.507

0.781 0.761 0.503

0.892 1.000 0.570

0.681 0.256 0.444

21.0% 74.4% 12.6%

−0.1% 23.9% 0.4%

To rank the interval efficiency values of 85 WWTPs, in Table 3 R1j0 and R2j0 values have been calculated in Table 3.

Efficiency Evaluation of Wastewater Treatment Plants

213

Table 3. Ranking of WWTPs according to efficiency scores for 81 scenarios R1j0 WWTP WWTP … WWTP WWTP … WWTP WWTP

R2j0

10 1.000 45 1.000 58 0.691 0.966 68 0.679 0.961 43 0.000 0.115 47 0.000 0.113

WWTPs 10, 45, 55, 71, 76, 79, and 81 are effective for all scenarios. In addition, it was seen that WWTPs 58, 68, 85, 52, and 51 have been ranked more effectively than the remaining WWTPs. R2j0 values of the remaining WWTPs should be examined to rank their efficiencies. 3.3

Investigation of Linking Between the Efficiencies and Exogenous Factors

Finally, it has been examined the difference in the average efficiency scores calculated at intervals for each WWTPs according to their service year and type. This examination has been carried out using Kruskal-Wallis hypothesis testing. This test was firstly proposed by Kruskal-Wallis in 1952, as a nonparametric hypothesis testing whether samples originated from the same distribution [10]. It is an extension of the MannWhitney U test applicable for more than two groups. The data used in this study provide the necessary assumptions of the KruskalWallis test (the population does not have a normal distribution and the dependent variable must be at least ordinal). The hypothesis of Kruskal-Wallis is below and p = 0.05 at the significance level, If Asymp. Sig. < 0.05; H0 is rejected. • H0: There is no significant difference between the medians of all groups. • H1: There is a significant difference between the medians of at least two groups. If the age of the facility (year) is determined as a group variable, it is tested whether there is a significant difference between the median values of the efficiency scores; p = 0.05 at the significance level; since 0.114 > 0.05, the hypothesis H0 is not rejected. There is no significant difference between the medians of efficiencies according to the age of the facility. If the facility type is determined as a group variable, it is tested whether there is a significant difference between the median values of the efficiency scores; p = 0.05 at the significance level; since 0.003 < 0.05, the hypothesis (H0) is rejected. There is a significant difference between the medians of at least two groups’ efficiency scores according to the type of the facility. The median of at least one type of facility is

214

S. Aksaç and H. Z. Ulukan

different from the others. To find out which groups’ medians of rank values differ, the groups are compared in pairs and evaluated with hypothesis tests. If Asymp. Sig. < 0.05; H0 is rejected; • H0: There is no significant difference between the medians of the two groups. • H1: There is a significant difference between the medians of the two groups. If it is tested whether there is a significant difference between the median values of the efficiency scores at the significance level of p = 0.05 only physical WWTPs differ from the biological and advanced biological WWTPs: • Biological and advanced biological WWTPs; since 0.746 > 0.05, the hypothesis (H0) is not rejected. There is no significant difference between the medians of biological and advanced biological WWTPs’ efficiencies. • Advanced biological and physical WWTPs; since 0.005 < 0.05, the hypothesis (H0) is rejected. There is a significant difference between the medians of and physical and advanced biological WWTPs’ efficiencies. • Physical and biological WWTPs; since 0.001 < 0.05, the hypothesis (H0) is rejected. There is a significant difference between the medians of and physical and biological WWTPs’ efficiencies.

4 Conclusions In this study, a three-stage methodology has been proposed for evaluating the effectiveness of WWTPs with uncertainties in the data. By the proposed methodology, the efficiency evaluation of 85 WWTPs in Istanbul between the years 2019 and 2020 has been carried out. This is the first study carried out in Turkey in terms of the methodology used in this field. Firstly, expert opinions have been taken to evaluate the possible variables to be used in the efficiency calculation by using extended-SWARA. As a result of this stage, three variables with a weight of 4% in the total have been decided to be excluded. Secondly, to obtain a single evaluation result from the two-year data on the WWTPs and overcome the uncertainty conditions in the data, efficiency calculations have been made using interval DEA. Efficiency values have been calculated considering tolerance intervals. When the averages of the calculated values of each WWTP for 81 scenarios are evaluated; in general, the original, mean, maximum, and minimum efficiencies of 85 WWTPs are 50,7%, 50,3%, 57%, and 44,4% effective, respectively. A ranking has been created as a result of the evaluation of the efficiency values of each of these WWTPs. According to this ranking, it has been calculated that seven WWTPs are effective in all scenarios, and five WWTPs are effective in some scenarios. It is recommended that the authorities take action to increase the effectiveness of these five WWTPs in the short term and to make them effective continuously. In addition, it is recommended to evaluate the effectiveness of the remaining WWTPs one by one and analyze the mechanisms for the effective functioning of the WWTPs for all processes from the design of the WWTP to the commissioning. Finally, it has been examined the difference in the average efficiency scores calculated at the interval for each WWTPs

Efficiency Evaluation of Wastewater Treatment Plants

215

according to their service year and type. As a result of this examination, it has been that there is no significant difference in terms of the average efficiency scores of the WWTPs, considering the age of the facility. In addition, when the type of facility is considered, it has been observed that the average of the physical WWTPs differs in terms of average efficiency scores. For future research, the use of fuzzy techniques can be recommended for the criterion evaluation phase of this study. In addition, undesirable outputs can be included in the model in determining the output variables, and the interval calculation methodology can be applied.

References 1. Sala-Garrido, R., Molinos-Senante, M., Hernández-Sancho, F.: Comparing the efficiency of wastewater treatment technologies through a DEA metafrontier model. Chem. Eng. J. 173 (3), 766–772 (2011) 2. Keršuliene, V., Zavadskas, E.K., Turskis, Z.: Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). J. Bus. Econ. Manage. 11(2), 243–258 (2010) 3. Hashemkhani Zolfani, S., Yazdani, M., Zavadskas, E.K.: An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft. Comput. 22(22), 7399–7405 (2018). https://doi.org/10.1007/s00500-018-3092-2 4. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2(6), 429–444 (1978) 5. Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30(9), 1078–1092 (1984) 6. Cook, W.D., Seiford, L.M.: Data envelopment analysis (DEA)–Thirty years on. Eur. J. Oper. Res. 192(1), 1–17 (2009) 7. Sala-Garrido, R., Hernández-Sancho, F., Molinos-Senante, M.: Assessing the efficiency of wastewater treatment plants in an uncertain context: a DEA with tolerances approach. Environ. Sci. Policy 18, 34–44 (2012) 8. Gómez, T., et al.: Measuring the eco-efficiency of wastewater treatment plants under data uncertainty. J. Environ. Manage. 226, 484–492 (2018) 9. Bosca, J.E., et al.: Ranking decision making units by means of soft computing DEA models. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 19(01), 115–134 (2011) 10. Kruskal, W.H., Allen Wallis, W.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1952)

Feature Selection and Feature Extraction-Aided Classification Approaches for Disease Diagnosis Minglei Li1(B) , Xiang Li1 , Yuchen Jiang1 , Shen Yin2 , and Hao Luo1 1

Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China [email protected], [email protected] 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway

Abstract. In this paper, the application of machine learning approach in the construction of disease diagnosis system is introduced. So as to introduce machine learning approach into clinical medicine, and the performance of machine learning medical diagnosis model with different feature extraction or selection approaches are studied. The respective advantages of feature selection and feature extraction are employed to eliminate redundant information. Firstly, a feature extraction algorithm based on partial least squares is designed and proposed, and compared with the typical feature extraction approach principal component analysis. Then partial least square approach and recursive feature elimination are used to analyze the correlation of the original variable set, so as to analyze the possibility of the disease caused by the original disease variables collected. Finally, the proposed approach is verified on a cervical cancer data. Experimental results indicate that the proposed approach can achieve better feature extraction effect for cervical cancer diagnosis, which can improve the diagnosis accuracy on the premise of eliminating redundant information. In the aspect of correlation analysis of pathogenic factors, experimental results show that recursive feature elimination has better effect. Keywords: Partial least squares · Support vector machine diagnosis approach · Recursive feature elimination

1

· Disease

Introduction

Biomedicine has gradually entered the public’s vision because of the great potential in theoretical and technological research [5]. It is an interdisciplinary subject which integrates medicine, life science, and biology. Theorem model as a computer-aided diagnostic tool has emerged in various forms of medical diagnostic systems. These systems mainly store medical scholars’ clinical experience c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 216–224, 2022. https://doi.org/10.1007/978-3-031-09176-6_26

Feature Selection and Extraction-Aided Disease Diagnosis Approaches

217

and knowledge in a specific format in the processor, then establish a knowledge base and diagnose by means of symbolic reasoning. A complete medical diagnosis system consists of two main task modules: 1) medical data extraction and feature selection module; 2) data classification and prediction module. The feature space of high-dimensional data contains many redundant information and noise, which may reduce the accuracy of classification or clustering on the one hand, and greatly increase the time and space complexity of learning and training on the other hand. Both feature selection and feature extraction are the most important data dimension reduction approaches in the field of pattern recognition. Feature extraction refers to the transformation of the original feature space to obtain new features. Common feature extraction approaches such as principle component analysis (PCA) [2] can reduce the dimension of learning tasks, but the comprehensibility of features is poor, because even simple linear combination will make the constructed features difficult to understand and lack of cognitive significance. Feature selection is to select feature subsets from the initial feature to optimize some evaluation criteria, such as classification. Some features that independent with task are deleted through feature selection. Simplified datasets tend to get more accurate models, and feature subsets maintain the physical meaning of the original features and are easier to understand [7,8]. Machine learning (ML) can automatically learn, capture the hidden information and data distribution structure in medical data, and then improve the learning ability, which has been used widely in constructing data classification and prediction module for disease diagnosis [1,3,4,6,9]. Many approaches of machine learning have obvious advantages in processing medical data. When it comes to large-scale data, its advantage will be more obvious, because it can improve the speed of classification and regression on the premise of guaranteeing the accuracy. The objective of this work is to construct an effective disease diagnosis system based on ML, and the main contribution is to improve the performance of this system from the two dimensions of feature extraction and feature selection. Specifically, the partial least square (PLS) approach is designed to extract the primary features of the original features, which improves the diagnosis performance in the case of reducing the redundancy of the features. And we adopted recursive feature elimination (RFE) to analyze the correlation of original variables, which can give the key disease-related variables. Through the simulation test of cervical cancer data, the effectiveness of the proposed approaches are verified. This article is arranged as follows. Section 2 introduces the proposed feature selection and feature extraction-aided classification approaches. Section 3 gives experimental results and analysis of actual disease dataset. Section 4 summarizes this paper.

218

2 2.1

M. Li et al.

Proposed Approach PLS Feature Extraction

The steps of the PLS feature extraction approach are as follows: Step 1: The input variables and label outputs are standardized, and the standardization matrices are recorded as E0 and F0 respectively. Step 2: Extract the first principal component t1 from matrix E0 : t1 = E0 w1

(1) ⎡

⎤ r(x1 , y) ⎢ .... ⎥ ⎣ .. ⎦

where w1 =

ET F  0 0 E T F0  0

=

1 k 

r2 (xi , y)

(2)

r(xk , y)

i=1

The correlation coefficient between xi and y is presented as r(xi , y). The regression coefficient p1 of E0 to t1 is obtained: E0 = t 1

E0T t1

T

2

t1 

+ E1

(3)

Step 3: Repeat the step 1–2, replacing E0 , F0 with E1 , F1 , get it in the same way, since E1 does not satisfy the conditions of a standardized matrix, as follows: ⎤ ⎡ cov(E11 , y) E1T F0 1 ⎣ cov(E1i , y) ⎦   (4) w2 =  E T F0  = p  1 , y) cov(E 1p 2 cov (E1i , y) i=1

where cov(Eij , y) is the covariance of Eij and y. Step 4: Find the second component by the following: t2 = E1 w1

(5)

Step 5: Perform the regression of F1 to t2 F1 = t2 r2 + F2

(6)

2

where r2 = F1T t2 /t2  Step 6: Deduce the nth component, n < rank(X). Step 7: Derive the partial least squares model, and the least square regression equation of F0 for t1 ,t2 ,...,tn is Fˆ0 = r1 t1 + r2 t2 + · · · + rn tn

(7)

Feature Selection and Extraction-Aided Disease Diagnosis Approaches

2.2

219

SVM-RFE Feature Selection

Support vector machine (SVM) is adopted to combined with recursive feature elimination (RFE) as the feature selection approach. The core idea of RFE approach is to judge the impact on the objective function by evaluating the feature ranking criteria and deleting one feature at a time. This approach is a reverse feature elimination cycle iteration process, each iteration includes the following three steps: 1) to train classifier with existing feature set; 2) to calculate the ranking criteria of all features; 3) to delete the last feature in the sorting list from the feature set. In the iterative process of feature elimination, the smaller the feature ranking criteria is, the less the feature information it contains, and the less the contribution it makes to the detection of disease. Therefore, the feature with the smallest feature sorting standard will be eliminated first, and it will be in the last position in the feature sorting list. In the whole process, all the features of data after feature extraction are used as the initial conditions of recursion, one feature is eliminated at a time until the original feature set is empty. Table 1 shows the implementation steps of SVM-RFE approach. Table 1. SVM-RFE Algorithm Algorithml 1: SVM-RFE Inputs: Training samples{xi , yi }N i=1 Output: Ranked feature list R 1 2

3

2.3

Initialize the selected feature subset S = {1, 2, ..., D}, ranked feature list R = ∅ While S = ∅ do Restrict training samples to the selected feature subset; Train SVM to get α  Calculate the weight coefficient: w = n i=1 αi yi xi Compute the ranking criteria: ck = (wk )2 Identify the feature with the leastest ranking criterion: p = arg min ck Update R = [S(p), R] Update S = S(1 : p − 1, p + 1 : length(S)) end STDs:molluscum contagiosum

Classifier Model Construction

Following feature selection and feature extraction, the selected or extracted features are used as the input of SVM classifier. The classifier constructs the

220

M. Li et al.

corresponding decision rules through the limited training samples and the known samples, and then uses the rules to classify the independent test sets. To solve the nonlinear problem, SVM needs to introduce the kernel function K(xi , xj ), map the low-dimensional eigenvector to the high-dimensional eigenvector space, and construct a hyperplane to achieve the optimal classification in this eigenspace. The optimal classification function firstly map data to other Euclidean spaces, and the mapping is expressed as: Φ : Rd → H. The inner product operation maps data in the original space to a new multidimensional space, which are as follow: K(xi , xj ) = Φ(xi )Φ(xj ). And the SVM classifier is described by the following equation: max

n 

αi −

i=1

1 2

n n  

αi αj yi yj K(xi · xj )

i=1 j=1

s.t. 0 ≤ αi ≤ C, i = 1, 2, ..., n n  αi yi = 0

(8)

i=1

Select a positive component αi ∗ (0 ≤ αi ∗ ≤ C) in the optimal solution vector α = (α1∗ , ..., αn∗ ), the threshold can be obtained by formula: ∗

∗

b = yi −

n 

yi αi∗ K(xi · xj )

(9)

i=1

The decision function can be calculated: n  αi∗ yi K(x, xi ) + b∗ ) f (x) = sgn(

(10)

i=1

3 3.1

Experiments and Results Dataset

The dataset used in the experiment was generated by a Venezuelan hospital based on the medical records of patients and recorded in the machine learning database of University of California Irvine. Table 2 shows partial characteristic risk factors in the dataset. Because of the disease characteristics of cervical cancer, the data set gives the diagnosis results of different diagnosis approaches to improve the accuracy of diagnosis. The diagnosis approaches include: Hinselmann test with acetic acid for vaginoscope, biopsy of cervix, et al. In this work, the Hinselmann test is used as a label variable.

Feature Selection and Extraction-Aided Disease Diagnosis Approaches

221

Table 2. Partial features of cervical cancer No. Name

No. Name

1

Age

13

2

Number of sexual partners

14

STDs:syphilis

3

First sexual intercourse (age)

15

STDs:pelvic inflammatory disease

4

Number of pregnancies

16

STDs:genital herpes

5

Smokes

17

STDs:molluscum contagiosum

6

Smokes (years)

18

STDs:AIDS

7

Smokes (packs/year)

19

STDs:HIV

8

Hormonal Contraceptives

20

STDs:Hepatitis B

9

Hormonal Contraceptives (years)

21

STDs:HPV

10

IUD (intrauterine device)

22

STDs:Number of diagnosis

11

IUD (years)

23

STDs:Time since first diagnosis

12

STDs (sexually transmitted disease) 24

STDs:Time since last diagnosis

STD (number)

The data preprocessing of this work mainly includes data normalization and missing value processing steps. In view of the characteristics of the classic unbalanced data set of medical data, we also use the oversampling approach for the positive samples. 3.2

Feature Extraction Experiment

Table 3 summarizes the simulation results produced by SVM, SVM combined with PLS (PLS-SVM), and SVM combined with principal component analysis (PCA-SVM), which show these approaches can classify the cervical cancer similarly. When only the first three principal components are selected, both PCASVM and PLS-SVM can complete the basic prediction, but the total accuracy is not up to 90%, and specificity and precision at this time are also low. When six principal components are selected, the performance of PLS-SVM is similar to the original SVM: recall is 100%, which is the same as SVM. As for accuracy, specificity and precision, though it is lower than SVM, the average difference is only about 0.2%. When the number of principal components is up to 10, the performance of PLS-SVM is better than SVM. Specifically, the accuracy is 94.47%, the specificity and precision are 1 to 2% points higher than SVM. Moreover, even though PCA-SVM uses 11 principal components, its performance is still lower than SVM and PLS-SVM. The relationships between the number of features and classification accuracy of two feature extraction approaches are shown in Fig. 1, which indicates the proposed feature extraction approach based on PLS is better than PCA.

222

M. Li et al. Table 3. Feature extraction experiment SVM

PCA-SVM

Feature number 30 Accuracy (%)

3

5

PLS-SVM 11

3

6

10

93.38 84.95 90.61 93.06 87.66 93.20 94.47

Recall (%)

100

Specificity (%)

88.97 75.63 84.66 88.45 79.44 88.68 90.78

98.94 99.55 100

Precision (%)

85.71

72.88 81.06 85.14

100

100

100

73.98 85.40 86.11

95 93.2

94.5

90

Accuracy(%)

87.7

85

80

75 PCA−SVM PLS−SVM 70

3

5 6

10

15 20 Number of features

25

30

Fig. 1. Classification accuracies based on two feature extraction approaches

3.3

Feature Selection Expriment

In this part, two feature selection approaches (PLS and SVM-RFE) are used to analyze the impact of the original disease risk factors on the diagnosis results. Table 4 is the ranking table of correlation degree of top five variables obtained by these two approaches. We select variables for diagnosis according to this ranking table. Table 4. Top five features of cervical cancer SVM-RFE

PLS

9: Hormonal Contraceptives (years) 2: Number of sexual partners 4: Number of pregnancies

7: Smokes (packs/year)

2: Number of sexual partners

13: STD (number)

3: First sexual intercourse (age)

22: STDs:Number of diagnosis

13: STD (number)

3: First sexual intercourse (age)

Feature Selection and Extraction-Aided Disease Diagnosis Approaches

223

Table 5 shows the relationship between the performance of diagnosis algorithm and the number of variables. For SVM-RFE, when only the top three features are selected, the performance is unsatisfactory, it only gets 81.50% accuracy. The diagnosis performance of SVM-RFE can be regarded as the same as SVM when the risk factors increase to 13. Specifically, the accuracy, recall, specificity, and precision values of SVM-RFE are 92.54%, 100%, 87.57%, 83.91%, respectively. For PLS-SVM, the performance can be improved with the number of features gradually increases, while it is always lower than that SVM or SMV-RFE achieved. The relationships between the number of features and classification accuracy of two feature selection approaches are shown in Fig. 2, which indicates the proposed feature selection approach based on SVM-RFE is better than the PLS feature selection approach. Table 5. Feature selection experiment SVM Feature number 30

PLS-SVM 6

15

SVM-RFE 21

3

10

13

Accuracy (%)

93.38 79.99 88.94 91.88 81.50 89.82 92.54

Recall (%)

100

Specificity (%)

88.97 69.47 82.74 86.41 72.27 84.81 87.57

Precision (%)

85.71

95.75 97.99 100 67.40 78.92 83.04

95.30 97.32 100 69.38 80.86 83.91

100 92.5 89.8

90 81.5

Accuracy(%)

80

70

60

50 SVM−RFE PLS−SVM 40

3

5

10

13 15 20 Number of variables

25

30

Fig. 2. Classification accuracies based on two feature selection approaches

4

Conclusion

The problems in the disease diagnosis model, such as large signal noise interference are focused in this work. An improved disease detection approach based on

224

M. Li et al.

SVM is proposed. Specifically, the PLS feature extraction approach is employed to extract the effective information from original data and give useful information for further diagnosis; the feature selection approach, SVM-RFE can explain the physical meaning of the obtained principal components, which is not possible with feature extraction approach. Experimental results indicate the effectiveness of both the PLS feature extraction approach and the SVM-RFE feature selection approach in disease detection. In future work, we consider using the proposed method for more challenging disease diagnosis, such as neurodegenerative diseases, to validate the performance of this method.

References 1. Amin, M.S., Chiam, Y.K., Varathan, K.D.: Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform. 36, 82–93 (2019) 2. Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374(2065), 20150202 (2016) 3. Karule, P., Dudul, S.V.: PCA NN based classifier for liver diseases from ultrasonic liver images. In: 2009 Second International Conference on Emerging Trends in Engineering and Technology, pp. 76–80. IEEE (2009) 4. Li, X., Jiang, Y., Li, M., Yin, S.: Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans. Ind. Inform. 17(3), 1958–1967 (2020) 5. Li, X., Jiang, Y., Rodriguez-Andina, J.J., Luo, H., Yin, S., Kaynak, O.: When medical images meet generative adversarial network: recent development and research opportunities. Discov. Artif. Intell. 1(1), 1–20 (2021). https://doi.org/10.1007/ s44163-021-00006-0 6. Li, X., Jiang, Y., Zhang, J., Li, M., Luo, H., Yin, S.: Lesion-attention pyramid network for diabetic retinopathy grading. Artif. Intell. Med. 126, 102259 (2022) 7. Mao, K.: Identifying critical variables of principal components for unsupervised feature selection. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(2), 339–344 (2005) 8. Mitra, P., Murthy, C., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002) 9. Shahbakhti, M., Taherifar, D., Zareei, Z.: Combination of PCA and SVM for diagnosis of Parkinson’s disease. In: 2013 2nd International Conference on Advances in Biomedical Engineering, pp. 137–140. IEEE (2013)

Risk Assessment of WtE Plants by Using a Modified Fuzzy SCEA Approach Esra Ilbahar1(B) , Selcuk Cebi1 , and Cengiz Kahraman2 1

2

Yildiz Technical University, Industrial Engineering Department, Besiktas 34349, Istanbul, Turkey {eilbahar,scebi}@yildiz.edu.tr Istanbul Technical University, Industrial Engineering Department, Macka 34367, Istanbul, Turkey [email protected]

Abstract. With the increasing sustainability concerns, energy production from waste has also come to the fore as well as recycling and reuse. Since there are various materials processed by waste to energy (WtE) plants and several types of equipment used in different processes, there are different sources of risks in these plants. It is important to determine and prioritize these risks in order to effectively manage them by taking the necessary precautions. However, the risk evaluation process mostly depends on expert judgments which contain uncertainty. Therefore, in this study, after risks in a WtE plant are categorized, interval-valued intuitionistic fuzzy (IVIF) analytic hierarchy process (AHP) is used to obtain probability, severity, frequency, and detectability weights of these risks. Then, the risks are prioritized by using these weights in safety and critical effect analysis (SCEA). It was revealed that the most critical risk is “inappropriate storage of waste load”, followed by “heat generated by failing bearings on various plant items” and “the dust generated when processing and sizing waste material”. Keywords: Risk assessment · Waste to energy plant fuzzy sets · AHP · Rule-based system

1

· Intuitionistic

Introduction

As the amount of waste produced is constantly increasing with the increasing population and waste per capita, waste management become a significant component of sustainable development. There are different methods to handle waste according to the waste hierarchy such as reuse, recycling, recovery and disposal. Although recycling is preferred over recovery, economic and technical restrictions may cause some complications in the recycling of certain waste fractions, so it can be said that such waste is more suitable for either landfill or incineration. That is why, WtE has the potential of being an essential part of clean energy policies and sustainable waste management systems [1]. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 225–232, 2022. https://doi.org/10.1007/978-3-031-09176-6_27

226

E. Ilbahar et al.

As WtE facilities have become an important part of an effective waste management system with the ever-increasing waste generation, the risks at the facilities need to be identified and examined in order for these facilities to operate safely. However, the majority of studies in the literature address WtE-related risks from other perspectives. Wu et al. [2] and Luo et al. [3] focused on evaluating the risks of WtE incineration projects whereas Hu et al. [4] discussed the impact of mercury emission arising from WtE facilities on public health. Cangialosi et al. [5] and Titto and Savino [6] also investigated health risks of emissions from an incineration plant. Risks within a waste plant have been rarely studied in the literature [7,8]. That is why in this study, after risks in a WtE plant are categorized, IVIF AHP is used to obtain probability, severity, frequency and detectability weights of these risks. Then, by using these weights in a rule based system for SCEA, the risks are prioritized. The rest of this paper is organized as follows: fundamental knowledge on SCEA and the steps of the IVIF AHP are given in Sect. 2. The proposed risk assessment approach and its application are given Sect. 3. Lastly, concluding remarks are presented in Sect. 4.

2

Preliminaries

Since the proposed approach includes both IVIF AHP and a modified version of SCEA, the steps of IVIF AHP and the summary of SCEA are given in this section. 2.1

IVIF AHP

The steps of IVIF AHP, proposed by Wu et al. [9], are as follows: Step 1. Pairwise comparisons of criteria are obtained by using the linguistic terms and these linguistic terms are converted their corresponding intervalvalued intuitionistic fuzzy numbers (IVIFNs) according to the scale introduced by Wu et al. [9] to obtain interval-valued intuitionistic judgment matrix, ˜ = (˜ R rij )n×n . Step 1. From this pairwise comparison, score judgement matrix, s˜ij , is obtained by using Eq. 1. U U L ˜ij − ν˜ij = [μL ∀i, j = 1, 2, . . . , n (1) s˜ij = μ ij − νij , μij − νij ],  L U  L U where r˜ij = [μij , μij ], [νij , νij ] . ˜ is obtained by using Eq. 2. Step 2. Then, interval multiplicative matrix, A,

A˜ = 10s˜ij

(2)

Step 3. The priority vector of interval multiplicative matrix, w ˜i , is calculated as follows:  n  n n ˜ij ˜− ˜+ ij ij j=1 a j=1 a j=1 a w ˜i = n n = n n , n n (3) ˜ij ˜+ ˜− i=1 j=1 a ij ij i=1 j=1 a i=1 j=1 a

Risk Assessment of WtE Plants by Using a Modified Fuzzy SCEA Approach

227

Step 4. As w ˜i (i ∈ N ) are interval numbers, comparison of each w ˜i with others is made and possibility degree matrix, P = (pij )nxn , is constructed by using Eq. 4. Let a = [a− , a+ ] and b = [b− , b+ ] be interval numbers. The possibility degree of a  b is computed as follows: p(a ≥ b) =

min{La + Lb , max{a+ + b− , 0}} La + Lb

where La = a+ − a− and Lb = b+ − b− . Step 5. Final weights are obtained by using Eq. 5 [9]. ⎡ ⎤ n 1 n wi = ⎣ pij + − 1⎦ n j=1 2 2.2

(4)

(5)

Safety and Critical Effects Analysis

Some of the risk assessment methods in the literature such as fault tree analysis (FTA) and event tree analysis (ETA) calculate overall risk value based on probability parameter whereas some of them such as Hazard and Operability study (HAZOP) and matrix type risk-assessment technique (MRA) compute this value by using both severity and probability parameters [10]. In addition to these methods, some of the methods such as Fine Kinney Method and Failure Mode Effect Analysis (FMEA) calculate this risk value by using three parameters. Even though probability and severity are utilized by both Fine Kinney Method and FMEA, third parameters used in these methods are different. FMEA considers detectability whereas Fine Kinney method uses frequency as the third parameter in the analysis. Since both of these parameters have a huge role to identify final risk value, Karasan et al. [11] introduced SCEA to the literature to be able to compute risk value by considering probability, severity, detectability, and frequency parameters at the same time. When these parameters have equal weight, risk value/magnitude in SCEA is calculated as in Eq. 6. RM = p × s × f × d

(6)

where p, s, f, and d represent probability, severity, frequency, and detectability, respectively.

3

The Proposed Risk Assessment Approach and Its Application

The proposed approach consists of three phases as described in Fig. 1. Phase 1. Expert judgments are not always precise, and include uncertainty. This is why, the first phase of the proposed approach consists of forming risk hierarchy and the utilization of IVIF AHP to handle uncertainty arising from the experts’ indecisivenes. Before the implementation of IVIF AHP, the pairwise

228

E. Ilbahar et al.

comparisons of risks in terms of probability, severity, frequency and detectability are obtained in linguistic form. Then, IVIF AHP, proposed by Wu et al. [9], is implemented to obtain the weights of these parameters. Phase 2. After the obtained weights are normalized within each parameter by using minimum and maximum values, normalized parameter terms for risk parameters are identified as VL, L, M, H or VH. Phase 3. Terms obtained as a result of the normalization in Phase 2 are used as input for a rule based system given in Fig. 2 [11] to prioritize the risks. As a result of this rule based system, risks are prioritized based on probability, severity, frequency and detectability and classified as negligible risk, minor risk, major risk or critical risk.

Fig. 1. The framework of the proposed risk assessment approach

The proposed approach is applied to assess the risks in a WtE plant. There are various risks in WtE plants [7,8] and the main risks are classified and presented in Fig. 3.

Risk Assessment of WtE Plants by Using a Modified Fuzzy SCEA Approach

229

Fig. 2. The rule based system for risk assessment with four parameters [11]

Fig. 3. Categorization of WtE plant risks

After the risk categorization, pairwise comparisons of each category in terms of severity, probability, detectability and frequency are performed. Pairwise comparisons of risks are obtained from the experts by using linguistic terms and these

230

E. Ilbahar et al.

terms are converted to the corresponding IVIFNs. Then, IVIF AHP is utilized to obtain the severity, probability, detectability and frequency of incineration plant risks. The overall weights of severity, probability, detectability and frequency for these risks are presented in Table 1. Table 1. Overall weights of the severity, probability, detectability and frequency Risks Probability Severity Frequency Detectability R1.1

0.100

0.058

0.124

0.067

R1.2

0.194

0.084

0.163

0.105

R1.3

0.147

0.084

0.084

0.124

R2.1

0.061

0.094

0.062

0.071

R2.2

0.068

0.111

0.073

0.084

R2.3

0.061

0.073

0.073

0.056

R2.4

0.036

0.140

0.049

0.084

R3.1

0.053

0.119

0.060

0.124

R3.2

0.073

0.062

0.086

0.124

R3.3

0.097

0.094

0.113

0.095

R3.4

0.110

0.080

0.113

0.066

Risk weights in Table 1 are normalized by using minimum and maximum weights for each parameter and the five intervals are identified for each parameter as given in Table 2. Table 3 shows the normalized terms for each parameter and final categorization of all risks by using the rule based system given in Fig. 2. Table 2. Normalized evaluation of the severity, probability, detectability and frequency Normalized terms

Probability

Severity

Frequency

Detectability

VL

0.036 0.067 0.058 0.075 0.049 0.072 0.056 0.070

L

0.067 0.099 0.075 0.091 0.072 0.095 0.070 0.083

M

0.099 0.131 0.091 0.107 0.095 0.118 0.083 0.097

H

0.131 0.162 0.107 0.124 0.118 0.141 0.097 0.111

VH

0.162 0.194 0.124 0.140 0.141 0.163 0.111 0.124

As a result of the proposed approach, the most important risk is identified as R-1.3 inappropriate storage of waste load, followed by R-2.2 heat generated by failing bearings on various plant items and R3.1 the dust generated when processing and sizing waste material. Since these risks have a bigger potential for creating problems in WtE facilities, actions related to these risks should be

Risk Assessment of WtE Plants by Using a Modified Fuzzy SCEA Approach

231

Table 3. Prioritization of the risks based on the rule based system Risks Probability Severity Frequency Detectability Final categorization R-1.1 M

VH

H

VL

Ng

R-1.2 VH

L

VH

H

Mn

R-1.3 H

L

L

VH

Cr

R-2.1 VL

M

VL

L

Mn

R-2.2 L

H

L

M

Mj

R-2.3 VL

VL

L

VH

Mn

R-2.4 VH

VH

VH

M

Mn

R-3.1 VL

H

VL

VH

Mj

R-3.2 L

VL

L

VH

Mn

R-3.3 L

M

M

M

Mn

R-3.4 M

L

M

VL

Ng

reconsidered in detail and necessary measures should be taken accordingly. R-1.1 the heat of the sun on metal and R-3.4 spillage from the conveyor are identified as negligible risks. The rest of these risks are categorized as minor risks which means related operations should be monitored in the future.

4

Conclusion

Along with increasing sustainability concerns, energy production from waste has come to the fore as well as recycling and reuse. Therefore, the number of WtE facilities, which are components of an effective waste management system, is gradually increasing. While the contribution of WtE facilities to an effective waste management system is substantial, there are several sources of risk associated with the equipment used in the various processes or materials handled at these facilities. Therefore, in this study, these risks are identified and prioritized by using the proposed approach to be able to effectively manage them. By implementing the proposed approach based on IVIF AHP and the modified SCEA, it was found that the most crucial risks in a WtE facility are “inappropriate storage of waste load”, “heat generated by failing bearings on various plant items” and “the dust generated when processing and sizing waste material”. In future studies, the proposed risk assessment methodology can be used in risk analysis studies in other fields by updating the relevant hierarchy.

References 1. Pizarro-Alonso, A., Cimpan, C., S¨ oderman, M.L., Ravn, H., M¨ unster, M.: The economic value of imports of combustible waste in systems with high shares of district heating and variable renewable energy. Waste Manage. 79, 324–338 (2018)

232

E. Ilbahar et al.

2. Wu, Y., Xu, C., Li, L., Wang, Y., Chen, K., Xu, R.: A risk assessment framework of PPP waste-to-energy incineration projects in China under 2-dimension linguistic environment. J. Clean. Prod. 183, 602–617 (2018) 3. Luo, C., Ju, Y., Dong, P., Gonzalez, E.D.S., Wang, A.: Risk assessment for PPP waste-to-energy incineration plant projects in china based on hybrid weight methods and weighted multigranulation fuzzy rough sets. Sustain. Cities Soc. 74, 103120 (2021) 4. Hu, Y., Cheng, H., Tao, S.: The growing importance of waste-to-energy (WTE) incineration in China’s anthropogenic mercury emissions: emission inventories and reduction strategies. Renew. Sustain. Energy Rev. 97, 119–137 (2018) 5. Cangialosi, F., Intini, G., Liberti, L., Notarnicola, M., Stellacci, P.: Health risk assessment of air emissions from a municipal solid waste incineration plant-A case study. Waste Manage. 28(5), 885–895 (2008) 6. de Titto, E., Savino, A.: Environmental and health risks related to waste incineration. Waste Manage. Res. 37(10), 976–986 (2019) 7. Lavoie, J., Guertin, S.: Evaluation of health and safety risks in municipal solid waste recycling plants. J. Air Waste Manage. Assoc. 51(3), 352–360 (2001) 8. Industrial Monitoring and Control-Managing Risks Associated with Waste Management Facilities. https://www.imcontrol.com.au/assets/Uploads/WasteManagement-Risks.pdf. Accessed 6 Apr 2022 9. Wu, J., Huang, H.B., Cao, Q.W.: Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems. Appl. Math. Model. 37(24), 9898–9906 (2013) 10. Marhavilas, P.K., Koulouriotis, D., Gemeni, V.: Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000–2009. J. Loss Prev. Process Ind. 24(5), 477–523 (2011) 11. Karasan, A., Ilbahar, E., Cebi, S., Kahraman, C.: A new risk assessment approach: safety and critical effect analysis (SCEA) and its extension with Pythagorean fuzzy sets. Saf. Sci. 108, 173–187 (2018)

An Intelligent Retinal Fundus Image Label Sharing Method by Domain Transformation Technique Xiang Li1(B) , Minglei Li1 , Yuchen Jiang1 , Shen Yin2 , and Hao Luo1 1

Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China [email protected], [email protected] 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway

Abstract. Recent deep learning methods have shown excellent performance in retinal vessel image segmentation, but the well-trained model will no longer be effective when applied to a dataset that has a large difference from the training dataset. Moreover, only a few annotated datasets can be used for supervised training, considering that clinical processes produce a large number of un-annotated images with diverse styles. As a result, it has become a big challenge to design effective deep learning segmentation models to be practically applicable. In this paper, an unsupervised cycle retinal generative adversarial network is proposed. It can realize the mutual transformation between annotated datasets and un-annotated datasets. The transformed images still retain the original vessel structure. Only the image style has been changed, so that the annotated vessel label can be shared in two domains. Furthermore, the synthetic images and the shared vessel labels can be used to train the deep learning segmentation model. We conducted experiments on annotated datasets and un-annotated datasets. The experiment results show that the cross-domain synthetic images have authentic appearance, vessel structure is well maintained. Both domains have good segmentation results. Keywords: Retinal image synthesis Retinal image segmentation

1

· Cross domain transformation ·

Introduction

In recent years, supervised deep learning methods have achieved excellent performances in the medical image classification[6], detection [9], segmentation[7] and other aspects, but it heavily relies on annotated data [12]. The acquisition of medical image annotation is a time-consuming, expensive, and error-prone process, and the amount of annotated data is usually small. Another more practical c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 233–241, 2022. https://doi.org/10.1007/978-3-031-09176-6_28

234

X. Li et al.

Fig. 1. Different retinal fundus image datasets.

difficulty is that the collection process of medical images will involve different races, imaging equipment and collection environments, so there will be significant visual gaps between different datasets [3]. Good performance of supervised model only adapt to the training set, a model trained on one dataset maybe not applicable to other datasets. In the retinal vessel image segmentation, the existing annotated datasets have only 20 to 40 annotated images [10]. Most of the images have no annotations, for example, the Kaggle dataset has tens of thousands of un-annotated retinal fundus images. Figure 1 shows several retinal fundus image datasets, it can be seen that huge discrepancies in texture and style exist between different datasets. Therefore, segmentation models trained on dataset can not be applied to un-annotated Kaggle dataset. In previous studies on retinal fundus images segmentation [8,17], the focus is primarily on improving the segmentation performance (such as accuracy), without considering how to conduct supervised training on the dataset without annotations [11]. Images from the same dataset with similar visual properties can be regarded as a domain, which roughly satisfies the same data distribution [4]. The present situation is that only part domains have annotated vessel structures. If we can interconvert the styles between annotated images and un-annotated images, then supervised training on the un-annotated data can be conducted. In the existing image technology, the generative adversarial network has the potential to realize the domain transformation between images [2,16]. Inspired by CycleGAN [18], this paper constructs two symmetric and reciprocal generation networks, which implement the domain transformation between the annotated image domain and un-annotated image domain. In addition, the transformed image only changes in style, and the original vessel structure is still retained. In this way, retinal images and vessel structure images will exist in both image domains, and the supervised segmentation model can be trained in both domains. The rest paragraphs of this paper is as follows. Section 2 introduces the used datasets. Section 3 provides the proposed domain transformation technique. Section 4 presents the experiment results. Finally, Sect. 5 summarizes the conclusion of this paper.

2

Datasets and Preprocessing

There are two datasets to evaluate our domain transformation model: the DRIVE dataset [15] and the Kaggle dataset [1]. DRIVE dataset have annotated vessel structures, Kaggle dataset only has retinal fundus images and no annotated

An Intelligent Retinal Fundus Image Label Sharing Method

235

Fig. 2. An overview of the proposed approach.

images, so this paper mainly realizes the transformation between DRIVE and Kaggle. The generators proposed in this paper are all standard symmetric networks, so the purpose of image preprocessing is to maintain the same resolution in two domains. The DRIVE dataset contains 40 retinal fundus images with a resolution of 565 × 584. Images in the DRIVE dataset were first cropped to 548 × 548 and then resized to 512 × 512. Kaggle dataset is also preprocessed by cropping and reshape, which are consistent with the DRIVE dataset.

3

Method

The proposed method completes the transformation of different retinal fundus images styles, the transformed image can share the existing annotated vessel images, then supervised segmentation training can also be carried out on the un-annotated dataset. Figure 2 shows the data flow of entire process and our implementation steps. {(xtrain , ytrain )} and xtest are the data we already own, {(xtrain , ytrain )} are image pairs from an annotated dataset, such as the DRIVE dataset, xtest are the original images from an un-annotated dataset, such as the Kaggle dataset. {(xtrain , ytrain )} can be used to train supervised segmentation model, but the well-trained segmentation model is not applicable to xtest . Images of type xtrain constitute domain A, and images of type xtest constitute domain B, domain A and domain B have their styles and exist discrepancies [5]. The first step is to establish the mapping between domain A and domain B, which is done through GA and GB . GA and GB are two generators, they are identical in structure, but inversely in function. GA transforms xtrain in domain ˆtrain is the generated fake data, but it has both the A into x ˆtrain in domain B. x image style in domain B and the vessel structure of xtrain in domain A, which

236

X. Li et al.

Fig. 3. The proposed GAN training process.  is the starting point of data flow.

is very important. So x ˆtrain and ytrain could constitute image pairs, ytrain are shared in the two domains. Similarly, GB transforms xtest in domain B into x ˆtest in domain A, which has both the image style of domain A and the vessel structure of xtest . After the domain transformation is completed, the second step is to train the supervised segmentation model to segment the un-annotated dataset. In domain A, image pairs {(xtrain , ytrain )} are training set to segment xtrain , ytrain )} are training set to segment x ˆtest . In domain B, image pairs {(ˆ xtest . Finally, two groups of segmentation results of un-annotated images are obtained. The GAN structure used is shown in Fig. 3. It is important to note the transformation between two domains is mutual, to ensure that GA and GB are two inverse mappings, the data in each domain will be returned to the source domain after passing through GA and GB . Therefore, the data flow during the training process constitutes two completed closed cycles. The starting point for the first cycle is xtrain , the starting point for the second closed cycle is xtest . The two cycles can be expressed as: ˆtrain → GB → x ¯train xtrain → GA → x

(1)

ˆtest → GA → x ¯test xtest → GB → x

(2)

In each iteration, the generator trains alternately with the discriminator, xtrain is the real data in domain A, and x ˆtest is the fake data in domain A, both of which are used to train the discriminator DA . Similarly, x ˆtrain and xtest are used to train the discriminator DB .

4 4.1

Results Qualitative Analysis of Synthetic Images

The experiment results here mainly focus on the synthetic images in the DRIVE dataset and Kaggle dataset, we conduct a qualitative comparison of these images.

An Intelligent Retinal Fundus Image Label Sharing Method

237

Fig. 4. Transformed images between DRIVE dataset and Kaggle dataset. (a) and (b) are two different types in Kaggle dataset. Row1: original DRIVE images, row2: synthesized DRIVE images, row3: original Kaggle images, row4: synthesized Kaggle images.

The transformed images between the DRIVE dataset and the Kaggle dataset are shown in Fig. 4, (a) and (b) are two different types in Kaggle dataset. The first row is the real DRIVE images, the second row is the synthetic DRIVE images with the Kaggle image style, the third row is the real Kaggle image, and the fourth row is the synthetic Kaggle image with the DRIVE image style. The image synthesis process is that row1 → GA → row2, row3 → GB → row4. First of all, the synthetic images have high authenticity in terms of visual integrity. For example, the second and third images in the first column are both Kaggle style, but it is hard to distinguish which is true and which is false just visually. The visual effect of a retinal fundus image can be composed of blood vessels, textures, colors, and discs in general. It can be observed that the original image and the transformed image are in good agreement with the landmark objects such as vessel structure. Texture and color are also evenly distributed to meet the corresponding style. 4.2

Un-annotated Image Segmentation Results

There are many retinal segmentation methods based on deep learning in recent years [13], and these algorithms show advanced performance, usually achieving an accuracy of more than 95.5% in annotated datasets. Since the purpose of this paper is to explore the realizability of cross-domain segmentation, rather

238

X. Li et al.

than design a more advanced segmentation network to improve the evaluation index, we adopt one of the most advanced segmentation methods to realize the segmentation in two different domains. U-net [14] are the main structures often used in image segmentation based on deep learning, U-net is more suitable for medical image segmentation and requires a small number of samples. So the designed segmentation network takes a four-layer U-net as the main structure.

Fig. 5. Segmentation results of un-annotated dataset Kaggle. (a) original images and segmentation results. (b) the transformed and segmentation results.

Segmentation results of un-annotated Kaggle images are shown in Fig. 5. To illustrate the strong adaptability of the proposed cross-domain segmentation method, Fig. 5 shows four different styles of images. The images in (a) are the original images of Kaggle dataset. (b) are the transformed images. The second row is the segmentation results. It can be seen from the segmentation results, in addition to the main vessel structure being detected, many thin and tiny vessels are also marked. Generally speaking, the segmentation results in domain B are better than that in domain A, but it is not absolute, the fourth image in domain A is better than that in domain B, which shows more capillaries. The segmentation results between different domains are still influenced by style. 4.3

Quantitative Analysis of Synthetic Images

In our work, segmentation task can be regarded as the downstream task of domain transformation. The segmentation task not only achieves segmentation of un-annotated datasets, but also has opportunity to evaluate domain transformation. The previous focus is on the synthesis and segmentation of un-annotated dataset (Kaggle). But it should not be ignored that the annotated datasets (DRIVE) and their transformed images also appear in domain A and domain B. Therefore, it is completely possible to evaluate the synthetic images by using the segmentation results of annotated datasets. We completed the segmentation of annotated datasets in two domains. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under the ROC curve (AUC) were used to evaluate the segmentation results. Segmentation results are shown in Table 1.

An Intelligent Retinal Fundus Image Label Sharing Method

239

We first explored the situation that the training set and the test set are in the same domain, the result is the first two rows of Table 1. The segmentation result of domain A is slightly better than that of domain B, which is mainly reflected in Se, Acc and AUC. In general, when the training set and the test set are in the same domain, the segmentation results remain at a high level and close to the most advanced methods. It is also logical, because the training and testing are consistent. We want to emphasize that the segmentation result of domain B is only slightly lower than that of domain A, although domain B is a synthetic domain. This reaffirms that the synthetic domain can achieve a good segmentation effect. We also want to figure out how much the segmentation results would fall if the training set and the test set are in different domains. The result is the last two rows of Table 1. As expected there is a significant decline in the evaluation results. Table 1. Quantitative evaluation of domain A and domain B on the DRIVE dataset Trian set

Test set

DRIVE Se Sp

Acc

AUC

Domain A Domain A 0.8031 0.9728 0.9570 0.9740 Domain B Domain B 0.7755 0.9745 0.9486 0.9675 Domain A Domain B 0.7805 0.9600 0.9369 0.9561 Domain B Domain A 0.2985 0.9983 0.9084 0.9282

5

Conclusion

In this paper, a generative adversarial network that can realize the mutual transformation of two retinal fundus image datasets is proposed. The generator changes the style of the retinal fundus image without changing the vessel structure. The transformed images can constitute training image pairs in both domains. The un-annotated dataset can not only conduct supervised segmentation training, but also have segmentation results in both domains. Domain transformation experiments were carried out on multiple datasets. The synthetic images had real appearance, texture and color, and the vessel structure remained good. To quantitatively analyze the effects of domain transformation on segmentation results, annotated datasets and the corresponding transformed images were segmented. The segmentation results are good when the training set and the test set are in the same domain. Because the existing method take the domain transformation and image segmentation as a two -stage work, it’s a little bit complicated. In future work, we will integrated the domain transformation technique and image segmentation network as a one-stage work.

240

X. Li et al.

References 1. Diabetic retinopathy detection (2014). http://www.kaggle.com/c/diabetic-retino pathy-detection 2. BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE Trans. Med. Imaging 37(3), 792–802 (2017) 3. Bonaldi, L., Menti, E., Ballerini, L., Ruggeri, A., Trucco, E.: Automatic generation of synthetic retinal fundus images: vascular network. Procedia Comput. Sci. 90, 54–60 (2016) 4. Costa, P., Galdran, A., Meyer, M.I., Niemeijer, M., Abr` amoff, M., Mendon¸ca, A.M., Campilho, A.: End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imaging 37(3), 781–791 (2017) 5. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Maga. 35(1), 53–65 (2018) 6. Gao, Z., Wang, L., Zhou, L., Zhang, J.: Hep-2 cell image classification with deep convolutional neural networks. IEEE J. Biomed. Health Inf. 21(2), 416–428 (2016) 7. Girish, G., Thakur, B., Chowdhury, S.R., Kothari, A.R., Rajan, J.: Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model. IEEE J. Biomed. Health Inf. 23(1), 296–304 (2018) 8. Hu, K., Zhang, Z., Niu, X., Zhang, Y., Cao, C., Xiao, F., Gao, X.: Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309, 179–191 (2018) 9. Jiang, H., Ma, H., Qian, W., Gao, M., Li, Y.: An automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE J. Biomed. Health Inf. 22(4), 1227–1237 (2017) 10. K¨ ohler, T., Budai, A., Kraus, M.F., Odstrˇcilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 95–100. IEEE (2013) 11. Li, X., Jiang, Y., Li, M., Yin, S.: Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans. Ind. Inf. 17(3), 1958–1967 (2020) 12. Li, X., Jiang, Y., Liu, C., Liu, S., Luo, H., Yin, S.: Playing against deep neural network-based object detectors: a novel bidirectional adversarial attack approach. IEEE Trans. Artif. Intell. 3(1), 20–28 (2021) 13. Li, X., Jiang, Y., Zhang, J., Li, M., Luo, H., Yin, S.: Lesion-attention pyramid network for diabetic retinopathy grading. Artif. Intell. Med. 126, 102259 (2022) 14. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4 28 15. Staal, J., Abr` amoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004) 16. Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., Iˇsgum, I.: Deep MR to CT synthesis using unpaired data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 14–23. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68127-6 2

An Intelligent Retinal Fundus Image Label Sharing Method

241

17. Yan, Z., Yang, X., Cheng, K.T.: Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans. Biomed. Eng. 65(9), 1912– 1923 (2018) 18. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

The Effect of Trust and Price on Satisfaction and Intention to Online Group Buying Samira Baratian1(&), Abdul Sattar Safaei1, and Ajith Abraham2 1

Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran {S_baratian,s.safaei}@nit.ac.ir 2 Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259, Auburn, Washington 98071, USA [email protected]

Abstract. Online group buying, could be a framework that gives daily discounts for different administrations and items, be a system of showcasing at the intersection of advancement and estimating that had pulled in the consideration of both specialists and the scholarly community. The aim of this study is to provide a theoretical model to inspect the intention of online group buying and customer satisfaction by considering the variables of trust and price. The essential reason for this choice is that this method gives a win-win circumstance for system shareholders, companies, and customers. This model was tested using data collected from 252 consumers of a group buying website in Iran called Takhfifan website using AMOS24 and SPSS24 software. The results show that the variables are significantly related to each other, in fact, the price has a great impact on website satisfaction and purchase intention. After that, trust affects the satisfaction and intention to buy; and as expected, price is the most important variable in the customer acquisition debate. Keywords: Online group buying intention


Trust
Price
Satisfaction
Purchase

1 Introduction Online Group Buying (OGB) is a system that suggests daily discounts on several services and goods. It is a kind of advertising in the place of advertising and pricing that has been considered by academics, etc. The system was first successful when it started operating in the United States in 2008; and in just a short time, a large number of OGB websites have sprung up and spread all over the world. The main reason for this leap is that this method provides a win-win situation for system shareholders, companies, and customers [5]. Additionally, the real goal of any business is to achieve customer satisfaction; because meeting customer needs with their satisfaction can both make the organization more profitable and customers trust that organization. Also in today’s world, despite the busy schedule and the Coronavirus, customers’ desire to buy online has increased, and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 242–249, 2022. https://doi.org/10.1007/978-3-031-09176-6_29

The Effect of Trust and Price on Satisfaction

243

naturally, customers buy from websites they trust. As a result, studies on OGB behavior are considered essential because it relates to the development of e-commerce that builds customer relationships and marketing strategy [7]. Today, with the advent of e-commerce, OGB is on the rise; and because consumers are the main source of benefit for organizations, keeping them satisfied helps the company survive. As you know, several factors in OGB make customers want to buy; therefore, our focus has been on examining these factors; in previous research, several studies have focused more on economic variables such as cost, some on social variables such as trust. This research considered the variables of trust and cost to examine the customer’s intention to buy from OGB websites. In fact, the aim of this study is to provide a theoretical model to observe the intention of OGB and customer satisfaction by considering the variables of trust and price. From a practical point of view [4, 6], the effects of this research can be used to improve website purchasing and ways to attract customers to managers. In fact, by examining various factors, it is possible to identify the underlying variables of social and economic dimensions and pave the way for future research and focus on customeroriented managers. The structure of the study is as follows. OGB literature is provided in Sect. 2. Next, the research model is introduced in Sect. 3. Then, the results, and conclusion are given in Sects. 4 and 5, respectively. The section describes the future research directions.

2 Literature Review The intention to buy in various articles is also known as the desire to buy. Intention to buy refers to the motivation of a customer to buy a particular product and includes the customer’s mental planning for using the product soon; therefore, if we want to have an overview of the factors studied in OGB, we can point to the most important issue, namely the intention to buy [10]. In most cases, they have addressed factors such as trust [15] and social exchange theory [15], which are part of the social dimension. In other cases, researchers have addressed factors such as price justice [10] and price satisfaction [10], which are part of the economic dimension. One of the most commonly used topics in OGB is the intention to repurchase [7, 8] which is covered in most articles. Factors that have a social dimension such as trust [7, 8], popularity [16] and customer value [8] are more considered. Other factors such as habit [8], satisfaction [8], reputation [7], and perceived size can be mentioned in the following studies. Of course, it is also imperative to determine the factors that directly affect the intention to buy again. Another issue in OGB is the intention to participate [14]. In this context, many factors that play an important role in influencing participation can be examined. These include participation, trust, perceived value, interpersonal influence [14], social interaction, and sharing value [3]. These factors are also included in the social dimension. As can be seen in Table 1, according to previous research, the effect of the trust factor (the highest factor cited), which is the main variable we are discussing, and the findings show that trust has a direct effect on consumer satisfaction and intention to buy. The next category that is mentioned, is the price variable of the product or service,

244

S. Baratian et al.

the results of which show that a reasonable price can attract customers’ satisfaction and attention and make them want to buy. Table 1. Prior literature Authors

Independent variable (s)

Dependent variable (s)

Key findings

TR ! PI TR ! PI TR ! PI R ! CU, ST ! CU (Hsu, Chang et al. 2015) Trust (TR), satisfaction (ST) Repeat purchase intention (RPI) TR ! RPI, ST ! RPI, CV ! RPI (Chen, Wu et al. 2015) Trust (TR) Active participation (AP) TR ! AR (Che, Peng et al. 2015) Trust (TR), price (PR) Revisit intention (RI) TR ! PR, PR ! RI, TR ! RI (Hsu, Chang et al. 2014) Trust (TR), satisfaction (ST) Repurchase intention (RI) TR ! RI, ST ! RI (Shiau and Luo 2012) Trust (TR), satisfaction (ST) Intention to online group buying (IT) TR ! IT, ST ! IT (Erdoğmus and Çiçek 2011) Trust (TR) Purchase intention (PI) TR ! PI (Kauffman, Lai et al. 2010) Price (PR) Purchase intention (PI) PR ! PI (Hossain, Akter et al. 2021) (Hung, Cheng et al. 2021) (Sharma and Klein 2020) (Shi and Liao 2017)

Trust (TR) Trust (TR) Trust (TR) Satisfaction (ST), trust (TR)

Purchase intention (PI) Purchase intention (PI) Intention to participate in OGB (IP) Continuance use (CU)

3 Solving Method The investigation demonstrated was tested utilizing information collected from clients of the Takhfifan website in Iran (https://takhfifan.com/). Takhfifan website is the first and most popular online buying platform in Iran, which started in 2011 to provide distinctive services and create a good sense of excitement. Takhfifan is a platform for providing a variety of services and products used in everyday life, including restaurant reservations, train tickets, plane tickets, tour reservations, hotel reservations, discount pool ticket purchases, discount codes from online stores, and thousands of other jobs. An online questionnaire was used to target the respondents. This online survey was posted on several Takhfifan customers’ virtual pages. Experienced customers of OGB are invited to respond to this survey. Respondents are advised to answer all questions based on their buying experience with Takhfifan. The return questionnaires were first evaluated for usability and reliability. A whole of 310 questionnaires was gathered in this survey, which included 252 valid questionnaires. The respondents’ demographics show that most of the buyers were women and were between 19 and 30 years old. This research used a two-step method to perform data analysis. The first stage includes testing the measurement model, in the second stage, the structural relationships between the embedded structures were examined. Structural Equation Modeling (SEM) is a significant statistical technique in terms of performance and social quantification. As you can see in Fig. 1, SEM can evaluate the relationships between independent and dependent variables using various software such as Lisrel and AMOS.

The Effect of Trust and Price on Satisfaction

245

SPSS and AMOS were chosen as information preparing apparatuses in this research. Exploratory factor analysis was used to examine the validity of differences and convergence validity between various variables in this study. SPSS 24.0 software was used to analyze exploratory factors. Cronbach’s alpha coefficient was used to measure the interior steadiness of the scale. Cronbach’s alpha reliability was resolute for each of the research factors and according to the general principle, values > 0.7 are considered acceptable. Since the values are from 0.790 to 0.849 and all are above the recommended level; therefore, there is no need to change or delete a question in the research model and questionnaire. We then considered 5 hypotheses to inspect the significant effect of trust and price variables separately on online group satisfaction and intention to buy. By investigating the variables and studying previous research, the following hypotheses are formed: • Trust and satisfaction Trust is one of the factors of the social dimension, it arises from the feeling of security in social exchanges. However, satisfaction is the consumer’s post-purchase evaluation and expressive response to the overall shopping experience. Additionally, research shows that trust and social factors are highly associated with satisfaction [7]. Thus, the following hypothesis is presented. H1. Trust is positively related to satisfaction. • Price and satisfaction It is clear that the price plays an important role in an online trading process, and lower prices in online trading, of course, along with acceptable quality, play an important role in customer satisfaction after purchase [5]. Thus, customers may buy because of the reasonable price and satisfaction increases. Thus, the following hypothesis is presented: H2. Price is positively related to satisfaction. • Trust and intention to participate in OGB Trust has been revealed to play a vital role in the online trading process [7]. Lack of trust and certainty in sellers reduces the desire to continue operating in online transactions [11]. The positive effect of trust on purchase intention is evaluated by many studies [9], and the following hypothesis is presented: H3. Trust is positively related to intention to participate in OGB. • Price and intention to participate in OGB It has been proven that price perform an important role in online buying, as it can have a significant impact on the number of purchases. Moreover, lower price ensures continuing online buying [7]. The positive effect of price on buying intention has been experimentally proven by many studies [1, 2, 12]. Therefore, the following hypothesis is presented: H4. Price is positively related to the intention to participate in OGB.

246

S. Baratian et al.

• Satisfaction and intention to participate in OGB Previous studies have shown that customers’ intention to buy could be influenced by their level of satisfaction, and satisfied customers usually intend to buy more seriously. It is also shown empirical evidence for an association between satisfaction and buying intention [8]. Therefore, the following hypothesis is presented. H5. Satisfaction is positively related to the intention to participate in OGB.

Fig. 1. The structural equation model

4 Results With appropriate measurements, the suggested model and hypothetical relationships were assessed using 200 Bootstrap samples in AMOS. Figure 2 shows the estimated coefficients and their importance in the structural model. As anticipated, the positive relationship between the trust variable on OGB was confirmed by a coefficient of 0.25. The relationship between price and satisfaction was accepted with a coefficient of 0.37. The results also show that the positive effect of the trust variable on the intention of OGB with coefficients of 0.22 is accepted, and the significance of the price relationship on the intention of OGB was proved by a coefficient of 0.26. Satisfaction also has a coefficient of 0.29 on the intention to buy. According to the results, the highest coefficient is related to price, which affects satisfaction, followed by satisfaction on the intention to buy. Therefore, it is necessary to satisfy customers by providing appropriate services and discounts, followed having satisfaction is the most important factor in customer’s purchases; Of course, along with these variables, trust is the most important variable in any situation, because, without trust, customers are not willing to buy from fraudulent and anonymous websites. The results of this research afford suitable implications for e-commerce professionals and webmasters. For group webmasters who want to ease online trading, this study offers several strategies that they may use.

The Effect of Trust and Price on Satisfaction

247

The results also description that factors such as trust are very vital in simplifying consumers’ intention to buy. Because reputation is an imperative record of trust, webmasters need to growth their reputation through promoting and publicity. The reputation of the website can be established by carrying out the company’s social responsibility activities such as protecting the environment; and since they pay less attention to the environment in Iran, they can take measures to attract customers to preserve the environment. They can also attract customers by adding green services or products or even considering an environmental sign. In this way, it both serves its environment and attracts more customers with special advertisements. On the other hand, in today’s economic climate where people are looking for discounts and find better prices, they are eager to encourage customers to buy more by highlighting more discounts at times or introducing other friends for long-term discounts.

Fig. 2. SEM analysis of structural model

5 Conclusion The results of this study provide useful concepts for e-commerce professionals and webmasters. For managers who want to expand their online business, this study offers several solutions that they can use to promote their website. The results also show that factors such as trust are very important in the process of buying customers, because sufficient trust makes buying more serious. Since reputation itself is a factor in building trust, webmasters need to increase their reputation through proper advertising. The reputation of the website can be established by carrying out the company’s social responsibility activities such as protecting the environment [13]; and since customers pay less attention to the environment in Iran, OGB can take measures to attract them to preserve the environment. They can also attract customers by adding green services or products or even considering an environmental sign. In this way, it both serves its

248

S. Baratian et al.

environment and attracts more customers with special advertisements. On the other hand, in today’s economic climate where people are looking for discounts and better prices, they are eager to encourage customers to buy more by highlighting discounts more or introducing other friends for long-term discounts. Further research can use another perspective to examine the effect of trust and satisfaction on purchasing intent. Also, according to previous studies and the results of Hsu et al. (2014), trust and satisfaction in OGB can be divided into several types, such as trust in vendors and trust in websites. Third, future research may evaluate the findings of this study in different cultures and with different shopping habits in each country. Finally, more studies are needed to examine the factors that replicate customers’ intent to buy from different perspectives and situations.

References 1. Chen, C.-P., Tu, Y., Tung, Y.A.: A path analysis of online group buying: insights from Taiwan. Int. J. Appl. Manag. Theory Res. (IJAMTR) 4(1), 1–22 (2022) 2. Erdoğmus, I.E., Cicek, M.: Online group buying: what is there for the consumers. ProcediaSocial Behav. Sci. 24, 308–316 (2011) 3. Chen, Y.-C., Wu, J.-H., Peng, L., Yeh, R.C.: Consumer benefit creation in online group buying: the social capital and platform synergy effect and the mediating role of participation. Electron. Comm. Res. Appl. 14(6), 499–513 (2015) 4. Chouhan, V., Goodarzian, F., Esfandiari, M., Abraham, A.: Designing a new supply chain network considering transportation delays using meta-heuristics. In: Kahraman, Cengiz, Cebi, Selcuk, Onar, Sezi Cevik, Basar Oztaysi, A., Tolga, Cagri, Sari, Irem Ucal (eds.) INFUS 2021. LNNS, vol. 307, pp. 570–579. Springer, Cham (2022). https://doi.org/10. 1007/978-3-030-85626-7_67 5. Erdoğmus, I.E., Cicek, M.: Online group buying: what is there for the consumers. ProcediaSocial Behav. Sci. 24, 308–316 (2011) 6. Ghasemi, P., Goodarzian, F., Gunasekaran, A., Abraham, A.: A bi-level mathematical model for logistic management considering the evolutionary game with environmental feedbacks. Int. J. Logist. Manag. (2021) 7. Hsu, M.-H., Chang, C.-M., Chu, K.-K., Lee, Y.-J.: Determinants of repurchase intention in online group-buying: the perspectives of DeLone & McLean IS success model and trust. Comput. Hum. Behav. 36, 234–245 (2014) 8. Hsu, M.-H., Chang, C.-M., Chuang, L.-W.: Understanding the determinants of online repeat purchase intention and moderating role of habit: the case of online group-buying in Taiwan. Int. J. Inf. Manage. 35(1), 45–56 (2015) 9. Hung, S.-W., Cheng, M.-J., Lee, C.-J.: A new mechanism for purchasing through personal interactions: fairness, trust and social influence in online group buying. Inf. Technol. People (2021) 10. Kauffman, R.J., Lai, H., Ho, C.-T.: Incentive mechanisms, fairness and participation in online group-buying auctions. Electron. Commer. Res. Appl. 9(3), 249–262 (2010) 11. Li, W., Yuan, Y.: Purchase experience and involvement for risk perception in online group buying. Nankai Bus. Rev. Int. 9(4), 587–607 (2018). https://doi.org/10.1108/NBRI-11-20170064 12. Lim, W.M.: An equity theory perspective of online group buying. J. Retail. Cons. Serv. 54, 101729 (2020)

The Effect of Trust and Price on Satisfaction

249

13. Panda, T.K., et al.: Social and environmental sustainability model on consumers’ altruism, green purchase intention, green brand loyalty and evangelism. J. Cleaner prod. 243, 118575 (2020) 14. Sharma, V.M., Klein, A.: Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying. J. Retail. Consum. Serv. 52, 101946 (2020) 15. Shiau, W.-L., Luo, M.M.: Factors affecting online group buying intention and satisfaction: a social exchange theory perspective. Comput. Hum. Behav. 28(6), 2431–2444 (2012) 16. Zhang, Z., Zhang, Z., Wang, F., Law, R., Li, D.: Factors influencing the effectiveness of online group buying in the restaurant industry. Int. J. Hosp. Manag. 35, 237–245 (2013)

Kidney Transplantation and Allocation Decision Support Analysis Under COVID-19 Uncertainty Yaser Donyatalab1

and Fariba Farid2(&)

1

Industrial Engineering Department, University of Moghadas Ardabili, Ardabil, Iran 2 Department of Management, University of Nabi Akram, Tabriz, Iran [email protected]

Abstract. The coronavirus pandemic has significantly impacted all aspects of humankind's life. One of the major negative observations, based on the preliminary data, is the seriously decreased number of organ transplants. Due to chronic immunosuppressive treatment and other medical comorbidities organ transplantation recipients are at very high risk because of the COVID-19 virus. kidney transplantation naturally lies in a risky environment with high rates of complication and mortality. During the coronavirus pandemic, the risk attributable to COVID-19 infectious is incredibly increased and brings out a more uncertain environment. In such high uncertainty, some very critical questions regarding decision making to allocate a patient for transplant, or put into the wait-listed patient. In this chapter, it is considered a spherical fuzzy structure to evaluate the uncertainty around the kidney transplantation recipient and an allocation method in SFSs is proposed. To show the feasibility and applicability of the proposed algorithm is applied and solved a case study of renal transplantation allocation and the results are discussed in detail. Keywords: Kidney Transplantation (KT)
Solid-Organ Transplantation (SOT)
COVID-19 pandemic
Spherical Fuzzy Sets (SFSs)

1 Introduction and Literature Review Kidney Transplantation (KT) Solid-organ transplantation (SOT) brings significant advantages and many outstanding benefits like preventing death, longer survival, and high-quality life for patients with end-stage organ disease [1, 2]. SOT could be considered optimal treatment for those patients but the very few resources of organs for transplant and the steadily increasing number of organ demands during the last decades brings out a long waitlist of patients. Among different SOT types, the tremendous and increasing number of patients waiting for kidney transplants (KT) is considerable [3]. The number of active patients on the addition waitlist requiring KT from 35029 in 2011 in the United States increased to 42129 in 2021 [4]. But, surprisingly, waitlist KT patients in 2020 decreased to 37825, and this is just because of the COVID-19 pandemic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 250–262, 2022. https://doi.org/10.1007/978-3-031-09176-6_30

Kidney Transplantation and Allocation Decision Support Analysis

251

The KT process is facing many challenges including appropriate allocation because of its high demand and low donation rate. A significant issue regarding KT is a decisionmaking problem about choosing the most appropriate patient as a recipient [5]. In the literature generally, two types of allocations are applied: filtering and scoring [6]. In the filtering method, the most updated list of waiting patients will be filtered based on some predetermined kidney allocation parameters [6], at the end a KT expert will consider the filtered shortened list and select the patient to receive the kidney. This method is mostly under subjectivity conditions, and therefore it could carry unfairness and biases. The role of humankind in the process of filtering and specifically final choice is significant because of prevalent human errors and/or emotional-based parameters. On the other hand the scoring method by evaluating the patients based on different parameters which designed to feed forward the problem to catch optimality and strengthen the KT recipient decision process. The patient who reaches the highest value in comparison will be selected as the first alternative recipient [7, 8], and many developed countries that leading in the area of SOT is using the scoring methods like the United States [9], Spain [10], and the United Kingdom [11, 12]. This method is also carrying some serious problems related to the ambiguity of defined policies [13] and possible different interpretations of these standards. This issue is the vagueness of linguistic variables used in the scoring method and many researchers evaluated those in different manuscripts. For example, some parameters are defined in sharp intervals with different scores which is carrying fuzziness. It is obvious the uncertainty that such crisp intervals are bringing out in real-world problems [6]. So, it is needed a specialized knowledge-based system for the KT decision-making problem in such an uncertain environment that is directly related to human life and any faults could have extraordinary costs. KT Under the COVID-19 Uncertain Environment The respiratory disease coronavirus-2 which is also known as SARS-CoV-2 first detected as an acute respiratory syndrome in December 2019. Since that time COVID19 pandemic [14, 15] affected almost all aspects of our life, including healthcare organizations, medical systems, the global economy, and many routine lifestyles of humankind. Solid-organ transplantation (SOT) is among those which significantly affected and in some cases at the early stages of the pandemic completely disturbed and caused a substantial reduction in transplantation cases [16]. Some clinical features and diagnostic symptoms could refer to respiratory symptoms of cough, dyspnea, fever, chills, headache, muscle pain, sore throat, loss of smell and taste in some cases, and diarrhea which are common among KT recipients [17, 18]. Considering the novelty of coronavirus and the fact that we have little knowledge and information about it and its impacts on SOT patients [19–21], it is concluded that we are facing a huge uncertainty around this problem. There is a severe lack of knowledge about COVID-19 [22]. The lack of understanding of COVID-19 and its impacts in the SOT area is even more severe like ambiguities around the donor-derived infection, not appropriate diagnostic approaches related to the posttransplant period, high nosocomial together social outbreak risk, and the most dangerous one is observing severe illness in recipients and patients because of underlying conditions [23]. From similar experiences, SARS-CoV 2003 [24], and MERS-CoV 2015 [25], it is definitely

252

Y. Donyatalab and F. Farid

demonstrated that SOT recipients could suffer from high potential transmissibility, morbidity, and mortality risks [26, 27]. The COVID-19 outbreak’s early stages accompanied many suspending of living kidney transplant cases, especially cases that belong to societies with a high-speed infection pace and communities with a high death rate. The uncertainty umbrella encompasses SOT and KT, therefore different policies have been considered by transplant centers all over the world and in the US [28]. Also, an expert in the KT recipient election process will have an intuitive understanding of the whole explained conditions and the environment. So, to handle the aforementioned uncertainty of the recipient selection in the KT process, we propose to use 3D fuzzy logic model. Fuzzy logic is evaluated in many research items through a vast domain of health care and medical system problems [29], and it could be considered one of the best tools to handle complex SOT recipient allocation problems [30]. The fuzzy logic theory comprehensively evaluates the environment’s vagueness was introduced by Zadeh [31]. Also, it is progressively extended and many researchers introduced many novel generalizations [32]. The Spherical Fuzzy Set (SFS) is in the class of 3D fuzzy sets and is one of the latest extensions of fuzzy sets introduced by Gundogdu et al. [33]. SFSs are applied in many fields [34–40]. In [6] a Fuzzy Inference Systems (FIS) for kidney allocation was presented to integrate fuzzy expert systems with KT recipient allocation. An organ allocation algorithm based on equity and utility is developed in [41], and in [42] a multi-organ to multi-patient allocation algorithm is designed to optimize the sequential stochastic assignment problem. The fairness, efficiency, and flexibility metrics are considered in [43] for designing a national kidney allocation system. In [44] a kidney transplantation strategy to balance the renal supply and demand relation is introduced, and [45] used a Fuzzy Data Envelopment Analysis to increase the effectiveness of kidney allocation problem under uncertainty. For patient ranking-based allocation several methods are introduced, the Intuitionistic Fuzzy Analytical Hierarchy method (AHP) method [5], a hybrid TOPSIS-AHP method [46]. The linear assignment method (LAM) proposed by [47] is one of the conventional MCDM methods. Hesitant fuzzy linear assignment proposed in [48] to solve the GDM problems and to show the efficiency of the model and reliability of results it is compared with other methods. The linear assignment method was developed within the interval type-2 trapezoidal fuzzy numbers [49]. Pythagorean fuzzy linear assignment method was the aim of [50], and a new linear assignment approach was presented by [51] and [52] propped interval-valued Pythagorean fuzzy linear assignment method. In [53] spherical fuzzy linear assignment (SF-LAM) method is introduced, in [54] a biobjective linear assignment method integrated with cosine similarity measure is introduced in the spherical fuzzy environment. In [55] SF-LAM with an objective weighting method is applied to solve the sustainable supply chain of aviation fuel problem. This paper aims to deliver a clear introduction to the KT process, and the approach together with a detailed literature review. In this paper, we tried to define a robust relationship between the derived points from literature with what is proposed in this study. Our goal is to consider two types of uncertainty in the KT process and solve the problem while minimizing the biases of experts. So, we will solve the complicated decision-making problem about the selection of KT recipients by considering

Kidney Transplantation and Allocation Decision Support Analysis

253

uncertainty originating from the nature of the KT problem and the COVID-19 pandemic. Motivated by the above discussion, we first define the problem in the spherical fuzzy environment and then applied the novel spherical fuzzy linear assignment method in the framework of a proposed MAGDM algorithm. The rest of the chapter is designed as follows: in Sect. 2, the concept of spherical fuzzy sets is defined mathematically then in Sect. 3, the novel concept of spherical fuzzy linear assignment method is discussed and steps of the MAGDM algorithm are introduced. Section 4, discussed the Kidney Transplantation process and renal recipient selection. In Sect. 5, the results are summarized, and the chapter is concluded.

2 Methodology Spherical Fuzzy Sets (SFSs) In this section, the spherical fuzzy sets (SFS) will be introduced and basic operations discussed in detail. The SFS theory [56] is considering the following 4 to evaluate uncertainty the membership, non-membership, indeterminacy, and refusal degrees. ~ s of Definition 1 [57]. Let X be the universal set and xi 2 X; 8i ¼ 1; 2; . . .n, the SFS A ~ the universe discourse X is defined as xi as an element of As with membership, non~ s will be expressed mathematically in membership and hesitancy degree values, then A the form of: n o ~ s ¼ xi ; l ~ ðxi Þ; # ~ ðxi Þ; h ~ ðxi Þjxi 2 X ; A As As As

ð1Þ

where lA~ s ðxi Þ; #A~ s ðxi Þ; hA~ s ðxi Þ stands for membership, non-membership, and hesitancy degrees respectively, which belong to the interval ½0; 1 and satisfies the condition that the sum square of these values is between 0 and 1: SA~ s ðxi Þ ¼ l2A~ s ðxi Þ þ #2A~ s ðxi Þ þ h2A~ s ðxi Þ ! 0  SA~ s ðxi Þ  1;

ð2Þ

~ s will be as follows: then refusal degree RA~ s of u in the spherical fuzzy set A RA~ s ðxi Þ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  SA~ s ðxi Þ;

ð3Þ

Definition 2 [57]. Score (Sc) and accuracy (Ac) functions of sorting an SFN are defined as follows, respectively:   ~ ¼ Sc A

    h~ 2 h~ 2 lA~  A  #A~  A ; 2 2

  ~ ¼ l2~ þ #2~ þ h2~ ; Ac A A A A

ð4Þ ð5Þ

254

Y. Donyatalab and F. Farid

~ and B ~ B ~ are SFNs then A\ ~ if and only if: Remark 1. Note if A     ~ \Sc B ~ , i. Sc A or         ~ ¼ Sc B ~ \Ac B ~ and Ac A ~ . ii. Sc A ~¼ Definition 3 [56]. Let X be the universal set and xi 2 X; 8i ¼ 1; 2; . . .n then A   xi ; lA~ ðxi Þ; #A~ ðxi Þ; hA~ ðxi Þjxi 2 X be a spherical fuzzy set (SFS) with corresponding Pn weight vector w ¼ fw1 ; w2 ; . . .; wn g; wi 2 ½0; 1; i¼1 wi ¼ 1. Spherical Fuzzy Weighted Arithmetic Mean ðSFWAM Þ and Spherical Fuzzy Weighted Geometric Mean ðSFWGM Þ are defined as follows respectively:   ~ ¼ SFWAMw A



Yn 

1  l2A~ ðxi Þ i¼1 Yn  wi 0:5 2 2 1  l ð x Þ  h ð x Þ ; i i ~ ~ A A i¼1 1

Y

wi 0:5 Yn   w i Yn  wi ; # ; 1  l2A~ ðxi Þ  ~ ðxi Þ A i¼1 i¼1

ð6Þ

Yn  wi w 0:5 Yn  w ; 1  i¼1 1  #2A~ ðxi Þ i ; 1  #2A~ ðxi Þ i  i¼1 Yn  wi 0:5 2 2 1  # ð x Þ  h ð x Þ ; ~ i ~ i A A i¼1

  ~ ¼ SFWGMw A

n

i¼1



lA~ ðxi Þ

ð7Þ In the following, we will discuss an entropy measure for spherical fuzzy sets (SFSs), which are proposed by Aydogdu and Gul in [58]. ~¼ Definition 4 [58]. Let X be the universal set and xi 2 X; 8i ¼ 1; 2; . . .n then A   xi ; lA~ ðxi Þ; #A~ ðxi Þ; hA~ ðxi Þjxi 2 X be a spherical fuzzy set (SFS). Spherical Fuzzy   ~ is defined as: entropy measure ESFS A  

2

   1 Xn 4 

2 2

~ ¼ l ESFS A 1  ð x Þ  # ð x Þ þ h ð x Þ  0:25 ; ð8Þ i ~ i ~ i A A i¼1 n 5 A~

3 Spherical Fuzzy Linear Assignment (SF-LAM) Algorithm In the following section, first, we will introduce the spherical fuzzy linear assignment (SF-LAM) and then propose a multi-attribute group decision making (MAGDM) algorithm. In this way, we will be able to show the applicability of the proposed SFLAM method. The SF-LAM is composed of several steps which are discussed as follows. Table 1 presents the spherical fuzzy linguistic scales and their corresponding spherical fuzzy numbers.

Kidney Transplantation and Allocation Decision Support Analysis

255

Step 1. Collect the decision-makers’ judgments by using Table 1. Consider a group of d decision-makers, D ¼ fD1 ; D2 ; . . .; Dd g with corresponding weight vector sj ¼ Pd fs1 ; s2 ; . . .; sd g where j¼1 sj ¼ 1, sj  0, which participated in a group decisionmaking problem, where a finite set of alternatives, A ¼ fA1 ; A2 ; . . .; AM g are evaluated based on a finite set of criteria,P C ¼ fC1 ; C2 ; . . .; Cn g, with corresponding weight vector wi ¼ fw1 ; w2 ; . . .; wn g where ni¼1 wi ¼ 1, wi  0. Each class of criteria could be constructed of some sub-level criteria. So, we could have the set of criteria C ¼ fCi ji ¼ 1; 2; . . .; ng, set of sub-criteria Ci ¼ Cip jp : 1; 2; . . .; P and even a set of   sub-sub-criteria Cip ¼ Cipq jq : 1; 2; . . .; Q . Comments of decision-makers are stated by using linguistic terms introduced in Table 1. Each decision-maker d expresses his opinion about the performance of alternative Am regard to criterion Cn using SFSdmðipqÞ , so

the notation will be like this: SFSdmðipqÞ ¼ ldmðipqÞ ; #dmðipqÞ ; hdmðipqÞ ; 8m ¼ 1; 2; . . .; M: Step 2. Determining the weights of DMs is based on spherical fuzzy decision matrices by evaluating the entropy of DMs. We used the spherical fuzzy entropy measure to determine the weights for each expert by using Eq. (8, 9 and 10). DSFS ðDd Þ ¼ 1  ESFS ðDd Þ;

ð9Þ

Where, ESFS ðDd Þ is showing the spherical fuzzy entropy of Dd (dth decision maker) and DSFS ðDd Þ is divergency of Dd .. DSFS ðDd Þ WSFS ðDd Þ ¼ Pd ; j¼1 DSFS ðDd Þ

ð10Þ

Step 3. Aggregate the individual decision matrices by using aggregation operators to get the GDM matrix. Naturally, decision-makers have different judgments about each alternative in the decision process. Therefore, the aggregation operators aim to get the unified matrix. Step 4. Determining the weights of criteria by evaluating entropy, divergence, and weights of criteria in SF structure. As mentioned in the literature and the same in step 2, weights will be calculated for each level of criteria. Different weights for criteria that are calculated in step 4 mean a specific criterion could carry a different amount of significance by considering its role in spherical fuzzy aggregated decision making. Step 5. Aggregate the GDM matrix for each sub-level criteria by using determined weight vectors in the previous step. Hence, the result of this step will be the aggregated weighted GDM matrix. Step 6. Compute the elements of scored decision matrix by utilizing the spherical fuzzy score function to construct the score decision matrix.

256

Y. Donyatalab and F. Farid

Step 7. Establish the non-negative rank frequency matrix kmk with elements that represent the frequency that Am is ranked as the M th criterion-wise ranking. Step 8. Calculate and establish the weighted rank frequency matrix P, where the Pmk measures the contribution of Am to the overall ranking. Note that each entry Pmk . of the weighted rank frequency matrix, Pmk is a measure of the concordance among all criteria in the M th alternative and kth ranking. Pmk ¼ wi1 þ wi2 þ . . . þ wikMM

ð11Þ

Step 9. Define the permutation matrix P as a square ðm  mÞ matrix and set up the following linear assignment model according to the Pmk value. The linear assignment model can be written in the following linear programming format: XM

Max Subject to

XM Y

m¼1

XM k¼1

XM m¼1

k¼1

Pmk ¼ 1;

Pmk ¼ 1;

mk

:Pmk

ð12Þ

8m ¼ 1; 2; . . .; M;

8k ¼ 1; 2; . . .; M;

ð13Þ ð14Þ

Pmk ¼ 0 or 1 for all m and k:

ð15Þ

Step 10. Solve the linear assignment model, and obtain the optimal permutation matrix P for all m and k. Step 11. Calculate the internal multiplication of the matrix (P :A) and obtain the optimal order of alternatives. Table 1. Spherical fuzzy linguistic scales fs Spherical Fuzzy Linguistic Scales LS Absolutely High Possible (AHP) Very high Possible (VHP) High Possible (HP) Slightly High Possible (SHP) Equally Possible (EP) Slightly Low Possible (SLP) Low Possible (LP) Very Low Possible (VLP) Absolutely Low Possible (ALP)

SFNs ðl; #; hÞ (0.9, (0.8, (0.7, (0.6, (0.5, (0.4, (0.3, (0.2, (0.1,

0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,

0.1) 0.2) 0.3) 0.4) 0.5) 0.4) 0.3) 0.2) 0.1)

Kidney Transplantation and Allocation Decision Support Analysis

257

4 Application In this application, a KT recipient allocation will be evaluated under the ambiguity of the both KT process and the COVID-19 pandemic. The original problem is a priority decision-making problem and the selected recipient is the person who has the most satisfaction rate based on a multi-level criteria structure. In this problem, we designed a three-level criteria structure to minimize the biases of the process and at the same time consider the whole framework based on spherical fuzzy sets to be able to handle all aforementioned uncertainties. The first level criteria are categorized as two main areas of fairness and efficacy, and the sub-levels are defined and extracted from different references [5] so, the whole criteria structure is as follows:

In this problem, the recipient selection committee is including 4 KT experts who conducted the process. Also, there exist 9 patients as the active recipient on the waiting list who could be considered potential KT recipients. So, our problem size is including 4 different experts that are evaluating the alternative set of size 9 based on the whole 25 criteria. As mentioned before, many essential factors are needed to indicate the appropriate recipient. In Step 9, the linear assignment model is constructed as follows: X9 X9 Y Max Z ¼ :Pmk mk k¼1 8 P9 m¼1 > < m¼1 Pmk ¼ 1; 8k P9 S:t: Pmk ¼ 1; 8m > : k¼1 Pmk ¼ f0; 1g; 8m; k

258

Y. Donyatalab and F. Farid

For results part by solving the linear assignment model, and obtaining the optimal permutation matrix P for all m and k. It is used an Excel solver, Microsoft office 2019, to solve the above model and found the solution presented an optimal permutation matrix P as follows. Based on the optimal permutation matrix above, P51 ¼ 1, P62 ¼ 1, P93 ¼ 1, P44 ¼ 1, P35 ¼ 1, P26 ¼ 1, P87 ¼ 1, P18 ¼ 1 and P79 ¼ 1 together with optimal objective function value Z ¼ 5:3055. Also, optimal order is interpretable based on the result of the optimal solution of P , means that whenever an alternative gets 1 value in only and only one rank. So, based on P51 ¼ 1 it is clear that P5 stands in 1st rank priority for being selected as KT recipient. In any case that P5 couldn’t get the renal transplant the second priority belongs to P6 which stands in 2nd rank, and P9 stands in 3rd priority. But based on step 11, the resulted vector of alternatives shows the priority of patients, respectively. Thereby, it is evaluated that the optimal order is: P5 [ P6 [ P9 [ P4 [ P3 [ P2 [ P8 [ P1 [ P7

5 Conclusion In this paper, we applied the novel spherical fuzzy linear assignment algorithm (SFLAM) based on the objective weighting method to recipient allocation of Kidney Transplantation. The KT process in an uncertain and ambiguous environment is illustrated and defined, and to show the complexities of the process by its nature and the newly added uncertainties because of the COVID-19 pandemic, the spherical fuzzy environment is selected. An allocation method based on SF-LAM with the objective weighting method is proposed to show the advantages of providing resilience and a robust solution. The main objective of this paper is to minimize the biases of recipient allocation in the KT process and at the same time consider the whole problem in a realistic uncertain environment. Based on that, nine patients on the waiting list for KT are assumed as potential alternatives for renal transplantation. A committee of four KT process experts is selected and a total of 25 different criteria in the framework of a multi-level structure is designed for the allocation problem. The criteria selected from the literature part are based on two main categories, fairness and efficiency. The results have revealed the priority of all patients at this exact moment and shown which patients have the most satisfaction to be selected for KT. For future studies, we propose various applications in different fields of study like different aspects of SOT problems and also applications in other fields like financial, banking, social, network, health care, manufacturing, and transportation systems.

References 1. Dew, M., Switzer, G., Goycoolea, J.A.A.: Does transplantation produce quality of life benefits? A quantitative analysis of the literature1 (1997). undefined, journals.lww.com

Kidney Transplantation and Allocation Decision Support Analysis

259

2. Wolfe, R.A., et al.: Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N. Engl. J. Med. (1999). https://doi.org/10.1056/nejm199912023412303 3. Held, P.J., McCormick, F., Chertow, G.M., Peters, T.G., Roberts, J.P.: Would government compensation of living kidney donors exploit the poor? An empirical analysis. PLoS One. (2018). https://doi.org/10.1371/journal.pone.0205655 4. OPTN: Organ Procurement and Transplantation Network–OPTN. https://optn.transplant. hrsa.gov/ 5. Taherkhani, N., Sepehri, M.M., Shafaghi, S., Khatibi, T.: Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method. BMC Med. Inform. Decis. Mak. (2019). https://doi.org/10.1186/s12911-019-0892-y 6. Taherkhani, N., Sepehri, M.M., Khasha, R., Shafaghi, S.: Ranking patients on the kidney transplant waiting list based on fuzzy inference system. BMC Nephrol. 23 (2022). https:// doi.org/10.1186/S12882-022-02662-5 7. Yuan, Y., Feldhamer, S., Gafni, A., Fyfe, F., Ludwin, D.: An internet-based fuzzy logic expert system for organ transplantation assignment. Int. J. Healthc. Technol. Manag. (2001). https://doi.org/10.1504/ijhtm.2001.001118 8. Lee, D., Kanellis, J., Mulley, W.R.: Allocation of deceased donor kidneys: a review of international practices (2019) 9. Sethi, S., et al.: Allocation of the highest quality kidneys and transplant outcomes under the new kidney allocation system. Am. J. Kidney Dis. (2019). https://doi.org/10.1053/j.ajkd. 2018.12.036 10. Valentin, M., Vega, R., Transplantation, C.M.: The Spanish prioritization system for highly sensitized patients: a successful tool (2018). undefined, journals.lww.com 11. Gibbons, A., et al.: Patient preferences, knowledge and beliefs about kidney allocation: qualitative findings from the UK-wide ATTOM programme. BMJ Open (2017). https://doi. org/10.1136/bmjopen-2016-013896 12. Benaragama, S.K., et al.: Do we need a different organ allocation system for kidney transplants using donors after circulatory death? BMC Nephrol. (2014). https://doi.org/10. 1186/1471-2369-15-83 13. Policies–OPTN. https://optn.transplant.hrsa.gov/policies-bylaws/policies/ 14. Morens, D.M., Daszak, P., Taubenberger, J.K.: Escaping Pandora’s box—another novel Coronavirus. N. Engl. J. Med. (2020). https://doi.org/10.1056/nejmp2002106 15. Morens, D.M., Fauci, A.S.: Emerging pandemic diseases: how we got to COVID-19 (2020) 16. Azzi, Y., Bartash, R., Scalea, J., Loarte-Campos, P., Akalin, E.: COVID-19 and solid organ transplantation: a review article (2021) 17. Akalin, E., et al.: Covid-19 and kidney transplantation. N. Engl. J. Med. (2020). https://doi. org/10.1056/nejmc2011117 18. Crespo, M., et al.: Respiratory and gastrointestinal COVID-19 phenotypes in kidney transplant recipients. Transplantation (2020). https://doi.org/10.1097/TP.0000000000003413 19. Gandolfini, I., Delsante, M.E.F.-A.J.: COVID‐19 in kidney transplant recipients 20, 1941– 1943 (2020). undefined. ncbi.nlm.nih.gov. https://doi.org/10.1111/ajt.15891 20. Zhu, L., et al.: Successful recovery of COVID-19 pneumonia in a renal transplant recipient with long-term immunosuppression. Am. J. Transplant. (2020). https://doi.org/10.1111/ajt. 15869 21. Michaels, M.G., et al.: Coronavirus disease 2019: Implications of emerging infections for transplantation Am. J. Transplant. (2020). https://doi.org/10.1111/ajt.15832

260

Y. Donyatalab and F. Farid

22. Yang, X., et al.: Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study Lancet Respir. Med. (2020). https://doi.org/10.1016/S2213-2600(20)30079-5 23. Emami, A., Javanmardi, F., Pirbonyeh, N., Akbari, A.: Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Arch. Acad. Emerg. Med. (2020). https://doi.org/10.22037/aaem.v8i1.600 24. Kumar, D., Tellier, R., Draker, R., Levy, G., Humar, A.: Severe acute respiratory syndrome (SARS) in a liver transplant recipient and guidelines for donor SARS screening. Am. J. Transplant. (2003). https://doi.org/10.1034/j.1600-6143.2003.00197.x 25. Alghamdi, M., Mushtaq, F., Awn, N., Shalhoub, S.: MERS CoV infection in two renal transplant recipients: case report. Am. J. Transplant. (2015). https://doi.org/10.1111/ajt. 13085 26. Gandolfini, I.,et al.: COVID-19 in kidney transplant recipients (2020) 27. Fishman, J.A., Grossi, P.A.: Novel Coronavirus-19 (COVID-19) in the immunocompromised transplant recipient: #Flatteningthecurve (2020) 28. Boyarsky, B.J., et al.: Early impact of COVID-19 on transplant center practices and policies in the United States. Am. J. Transplant. (2020). https://doi.org/10.1111/ajt.15915 29. Seising, R.: From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis. Artif. Intell. Med. (2006). https://doi.org/10.1016/j.artmed.2006.06.004 30. Al-Ebbini, L., Oztekin, A., Chen, Y.: FLAS: fuzzy lung allocation system for US-based transplantations. Eur. J. Oper. Res. (2016). https://doi.org/10.1016/j.ejor.2015.08.001 31. Zadeh, L.A.: Fuzzy sets. Inf. Control. (1965). https://doi.org/10.1016/S0019-9958(65) 90241-X 32. Donyatalab, Y., Kutlu Gündoğdu, F., Farid, F., Seyfi-Shishavan, S.A., Farrokhizadeh, E., Kahraman, C.: Novel spherical fuzzy distance and similarity measures and their applications to medical diagnosis. Expert Syst. Appl. 191, 116330 (2022). https://doi.org/10.1016/J. ESWA.2021.116330 33. Kutlu Gündoğdu, F., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst. 36, 337–352 (2018). https://doi.org/10.3233/JIFS-181401 34. Gul, M., Yucesan, M.: Hospital preparedness assessment against COVID-19 pandemic: a case study in Turkish tertiary healthcare services. Math. Probl. Eng. 2021 (2021). https://doi. org/10.1155/2021/2931219 35. Seyfi-Shishavan, S.A., Donyatalab, Y., Farrokhizadeh, E., Satoglu, S.I.: A fuzzy optimization model for designing an efficient blood supply chain network under uncertainty and disruption. Ann. Oper. Res. (2021). https://doi.org/10.1007/s10479-021-04123-y 36. Kutlu Gündoğdu, F., Kahraman, C., Karaşan, A.: Spherical fuzzy VIKOR method and Its application to waste management. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2019. AISC, vol. 1029, pp. 997–1005. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-23756-1_118 37. Donyatalab, Y., Farid, F.: Spherical fuzzy inference systems (S-FIS) to control UAVs’ communication technologies. In: Kahraman, C., Aydın, S. (eds.) Intelligent and Fuzzy Techniques in Aviation 4.0. SSDC, vol. 372, pp. 459–496. Springer, Cham (2022). https:// doi.org/10.1007/978-3-030-75067-1_20 38. Farid, F., Donyatalab, Y.: Novel spherical fuzzy eco-holonic concept in sustainable supply chain of aviation fuel. In: Kahraman, C., Aydın, S. (eds.) Intelligent and Fuzzy Techniques in Aviation 4.0. SSDC, vol. 372, pp. 201–235. Springer, Cham (2022). https://doi.org/10. 1007/978-3-030-75067-1_9 39. Wei, G., Lu, M.A.O., Wu, J., Wei, C.U.N.: Similarity measures of spherical fuzzy sets based on cosine function and their applications. IEEE Access. 7, 159069–159080 (2019). https:// doi.org/10.1109/ACCESS.2019.2949296

Kidney Transplantation and Allocation Decision Support Analysis

261

40. Donyatalab, Y., Farid, F., Gündoğdu, F.K., Farrokhizadeh, E., Shishavan, S.A.S., Kahraman, C.: Distance and similarity measures under spherical fuzzy environment and their application to pattern recognition. J. Mult. Log. Soft Comput. (2021) 41. Girlanda, R.: Deceased organ donation for transplantation: challenges and opportunities. World J. Transplant. (2016). https://doi.org/10.5500/wjt.v6.i3.451 42. David, I., Yechiali, U.: Sequential assignment match processes with arrivals of candidates and offers. Probab. Eng. Inform. Sci. (1990). https://doi.org/10.1017/S0269964800001728 43. Bertsimas, D., Farias, V.F., Trichakis, N.: Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Oper. Res. (2013). https://doi.org/10.1287/opre.1120. 1138 44. Baskin-Bey, E.S., Nyberg, S.L.: Matching graft to recipient by predicted survival: can this be an acceptable strategy to improve utilization of deceased donor kidneys? Transplant. Rev. (2008). https://doi.org/10.1016/j.trre.2008.02.005 45. Ahmadvand, S., Pishvaee, M.S.: An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach. Health Care Manag. Sci. 21(4), 587–603 (2017). https://doi.org/10.1007/s10729-017-9414-6 46. Taherkhani, N., Research, R.K.-I.J.H.: A hybrid multi-criteria decision making model for kidney allocation (2018). undefined, ijhr.iums.ac.ir 47. Bernardo, J.J., Blin, J.M.: A programming model of consumer choice among multi-attributed brands. J. Consum. Res. 4, 111 (1977). https://doi.org/10.1086/208686 48. Razavi Hajiagha, S.H., Shahbazi, M., Amoozad Mahdiraji, H., Panahian, H.: A Bi-objective score-variance based linear assignment method for group decision making with hesitant fuzzy linguistic term sets. Technol. Econ. Dev. Econ. 24, 1125–1148 (2018). https://doi.org/ 10.3846/20294913.2016.1275878 49. Chen, T.Y.: A linear assignment method for multiple-criteria decision analysis with interval type-2 fuzzy sets. Appl. Soft Comput. J. 13, 2735–2748 (2013). https://doi.org/10.1016/j. asoc.2012.11.013 50. Liang, D., Darko, A.P., Xu, Z., Zhang, Y.: Partitioned fuzzy measure-based linear assignment method for Pythagorean fuzzy multi-criteria decision-making with a new likelihood. J. Oper. Res. Soc. 1–15 (2019). https://doi.org/10.1080/01605682.2019.1590133 51. Bashiri, M., Badri, H., Hejazi, T.H.: Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Appl. Math. Model. 35, 152–164 (2011). https://doi. org/10.1016/j.apm.2010.05.014 52. Liang, D., Darko, A.P., Xu, Z., Quan, W.: The linear assignment method for multicriteria group decision making based on interval-valued Pythagorean fuzzy Bonferroni mean. Int. J. Intell. Syst. 33, 2101–2138 (2018). https://doi.org/10.1002/int.22006 53. Donyatalab, Y., Seyfi-Shishavan, S.A., Farrokhizadeh, E., Kutlu Gündoğdu, F., Kahraman, C.: Spherical fuzzy linear assignment method for multiple criteria group decision-making problems. Informatica (2020). https://doi.org/10.15388/20-infor433 54. Seyfi-Shishavan, S.A., Kutlu Gündoǧdu, F., Donyatalab, Y., Farrokhizadeh, E., Kahraman, C.: A novel spherical fuzzy bi-objective linear assignment method and its application to insurance options selection. Int. J. Inf. Technol. Decis. Mak. 1–31 (2021). https://doi.org/10. 1142/S0219622021500073 55. Donyatalab, Y., Farid, F.: Spherical fuzzy linear assignment with objective weighting concept in the sustainable supply chain of aviation fuel. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 308, pp. 766– 776. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85577-2_90 56. Gündoǧdu, F.K., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst. (2019). https://doi.org/10.3233/JIFS-181401

262

Y. Donyatalab and F. Farid

57. Donyatalab, Y., Farrokhizadeh, E., Garmroodi, S.D.S., Shishavan, S.A.S.: Harmonic mean aggregation operators in spherical fuzzy environment and their group decision making applications. J. Mult. Log. Soft Comput. 33, 565–592 (2019) 58. Aydoğdu, A., Gül, S.: A novel entropy proposition for spherical fuzzy sets and its application in multiple attribute decision-making. Int. J. Intell. Syst. (2020). https://doi.org/ 10.1002/int.22256

A Forecasting Study of Covid-19 Epidemic: Turkey Case Omer Faruk Gurcan1(&)

, Omer Faruk Beyca2 and Orhan Er3

, Ugur Atici1

,

1 Sivas Cumhuriyet University, Sivas, Turkey {ofgurcan,uatici}@cumhuriyet.edu.tr 2 Istanbul Technical University, Istanbul, Turkey [email protected] 3 İzmir Bakırcay University, Bakırcay, Turkey [email protected]

Abstract. The Coronavirus (Covid-19) is an infectious disease and has spread over the 170 countries. The pandemic brings new challenges to the research community. Many measures are taken by countries and developed vaccines limit the spread of pandemic. Globally, there have been more than 490 million confirmed case of Covid-19, and 6.1 million deaths reported World Health Organization as of April 4, 2022. Disease modelling has critical policy impact on Covid-19. Forecasting is one of the key purposes of epidemic modelling. It will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. This study makes a forecasting of Covid-19 for Turkey. Specifically, a multi-step forecasting model is proposed. Additionally, the effect of some measures taken against Covid-19 are analyzed. The study period covers 11 March 2020 - 16 March 2022 and number of confirmed cases is selected as indicator. A summary information is given about the course of the pandemic in Turkey and the fight against Covid-19. Keywords: Covid-19


Forecasting
Random forest regression

1 Introduction The Covid-19 pandemic, which started in December 2019 and affected the world in a short time, caused 482.564.625 infected people and 6.149.714 deaths as of March 2022 [1]. The rapid transportation facilities of our age, the world population, and the crowdedness of cities have effectively transformed Covid-19 into a pandemic. Due to the economic integration of countries, the impact of the pandemic has been deeply felt not only in the health system but also in the national economies. Many sectors, from tourism to construction, have been adversely affected by the pandemic. Especially in China, which is a global production base, production has been affected due to the pandemic, and supply chain disruptions have been experienced on a global scale [2]. The increasing number of cases has caused an overload in the health system. Despite the increasing number of cases, the number of ventilators remained insufficient, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 263–271, 2022. https://doi.org/10.1007/978-3-031-09176-6_31

264

O. F. Gurcan et al.

health personnel were separated from their families for days and died due to patient contact. More severe pandemics than Covid-19 will likely occur in the future when global integration is increasing worldwide and cities are becoming more and more crowded day by day. It is necessary to estimate the number of cases and patients to reduce the pandemic’s impact on the health system and the country’s economy and take the necessary measures. The measures taken by states during the pandemic can be divided into two classes. The first of these is the measures taken to prevent the spread of the pandemic, while the other can be classified as the measures taken to reduce the social and economic effects of the pandemic on society [3]. The measures taken to prevent the spread of the epidemic are almost the same throughout the world. These measures can be summarized as individual hygiene, mask use, various restraint measures. As a result of the measures taken to prevent the spread of the epidemic, adverse economic and social consequences have emerged. Many additional measures have been taken to isolate the impact of negative results on society. The economic measures taken can be summarized as lowering interest rates, prohibiting dismissals, and direct and indirect financial aid. One of the most important issues to increase the effectiveness of the measures taken is to reveal the relationship between the measure taken and the spread of the pandemic. One of the most important lessons to be learned from the Covid-19 pandemic is the effect of the precaution taken against the pandemic on the spread of the disease. The most important issue in reducing the impact of possible future pandemics is accurately predicting the pandemic’s spread and development. Determining how the measures are taken affect the course of the pandemic is to determine how the course and development of the pandemic change according to the type of measure. Each different measure taken changes the course of the pandemic. It is relatively easy to predict the development of the pandemic without any action being taken. Our motivation behind this study is to determine how the course of the pandemic has changed in Turkey according to the measures taken and to shed light on the importance of the measures to be taken for possible pandemics in the future. Thus, it is to allow future generations to be more affected by possible pandemics. Turkey case is summarized in Sect. 2. Literature review is given in Sect. 3. Section 4 present case study and results and lastly, conclusion is given in Sect. 5.

2 Turkey Case The current data reported by the Republic of Turkey Ministry of Health from 3 Jan. 2020 to 3 April 2022 is as follow: the total number of case is 14.860.560, and the total number of deaths is 98.033. Ministry of Health reports the total number of applied vaccines is about 148 million and the second dose vaccination rate is 85.36% [4]. Figure 1 presents daily confirmed cases (top chart) of Covid-19 and daily deaths (bottom chart), reported to WHO [5]. According to charts, the daily confirmed cases peaked in Jan. 2022. On the other hand, daily deaths peaked in April 2021. It started to be used in Turkey on December 30, 2021.

A Forecasting Study of Covid-19 Epidemic: Turkey Case

265

Fig. 1. Daily confirmed cases and deaths of Covid-19 in Turkey [5].

Given the excessive Covid -19 mortality among high-risk groups and aged people, vaccine development plays a critical role. Recently, researchers agree on the Covid-19 vaccine’s efficacy [6]. The vaccination process in Turkey started on January 13, 2021, with CoronaVac. On April 12, 2021, the Pfizer-BioNTech vaccine started to be used. Turkovac is an inactive type Covid-19 vaccine developed by the Turkish Ministry of Health and Erciyes University [7]. The supplied Covid-19 vaccines are analyzed to check safety issues by relevant institutions of the Ministry of Health. Approved vaccines are distributed to related healthcare facilities through the vaccine tracking system (VTS), which is developed domestically. The system enables product safety in terms of evidence-based research. Up to an average of 2 million doses of vaccine per month are administered after the controls on the system. In the VTS, each dose is coded, vaccination records are monitored, vaccine and antiserum stock levels and temperatures are tracked in all stations and transport vehicles in real time. The system helps to maintain cold chain processes. It warns and guides users. It has a communication infrastructure that can communicate with approximately 70.000 healthcare personnel. Mobile software that can work in harmony with all devices is also used. Such as when a vaccine’s temperature goes out of limits or power-cut happens, VTS alerts related units. Barcode number links serial number of each vaccine with the citizenship number [7]. The system detects expired vaccines and prevents their usage. On the other hand, it helps to be taken immediate treatments against side effects of vaccine. The logistic management for vaccine-related subjects is made by General Directorate of Public Health (GDPH). Every province has at least one vaccine storage area in Turkey. There are more than 360 cold storage areas, 12.000 temperature monitors, 13.000 stock units (cabinet/cold storage). Table 1 summarizes some of the key events experienced during the Covid-19 pandemic in Turkey. This logistics is based on a cold chain which includes temperature-controlled processes of the supply chain [8]. The cold chain systems support immunization

266

O. F. Gurcan et al. Table 1. Some of the key events during the Covid-19 pandemic in Turkey [10].

Date 10.01.2020 24.01.2020 03.02.2020

29.02.2020 08.03.2020 11.03.2020 12.03.2020 15.03.2020 17.03.2020 19.03.2020 20.03.2020 21– 22.03.2020

23.03.2020 03.04.2020 04.05.2020 23.05.2020 01.06.2020 14.06.2020 01.07.2020 26.08.2020 17.11.2020 13.01.2021 03.02.2021 01.03.2021 12.04.2021 14.04.2021 29.04.2021 17.05.2021 23.06.2021

Event The Ministry of Health organized Scientific Advisory Board for Coronavirus Precautionary measures were taken in airports All fights from China were stopped. The Turkey-Iran border was closed, and flights were suspended (from or to Iran) The flights are stopped from and to South Korea, Iraq, Italy A great disinfection work in public areas was started Turkey’s first coronavirus case was announced Sports events will be played without spectators until the end of April, Schools and Universities will be closed for some time The citizens who returned from Umrah, were quarantined. Places of entertainment will be closed temporarily The first death from Covid-19 is announced Volleyball, football, handball, and basketball leagues were postponed Pandemic hospitals for coronavirus were announced. Cultural, scientific, and artistic activities were postponed until the end of April Organizations were ordered to allow workers to work remotely if possible and offer flexible schedules. A curfew was announced for old or chronically ill people. Flights to 46 more countries had stopped. Restriction of inter-city travel Favipiravir (drug) usage started. The distance education for schools started The curfew was extending-including young people The curfew was relaxed The curfew has started during the Eid holiday Domestic flights were resumed; Many public places were opened. The travel restriction between cities has been removed Pandemic regulation in central exams Theaters and cinemas opened under certain conditions Alternating, flexible and remote working allowed in public institutions and organizations A curfew was announced; Education will continue with distance education until the end of the year; Restaurants will only provide takeaway service CoronaVac vaccination has started South African and Brazilian variant were seen New normalization process was announced, the curfew has been relaxed; cafes and restaurants started to serve at 50% capacity Pfizer-BioNTech vaccine started to be used A two-week partial shutdown was announced Two weeks of full closure and a break from education Relaxation of curfews, continuation of restrictions for those who are not vaccinated The delta variant was seen (continued)

A Forecasting Study of Covid-19 Epidemic: Turkey Case

267

Table 1. (continued) Date 01.07.2021 06.09.2021 25.12.2021

Event Transition to gradual normalization, removal of restrictions Full-time education started Omicron variant was seen

programs in countries by providing the availability of potent and safe vaccines. In this way, decreases mortality caused by vaccine-preventable illnesses [9].

3 Literature Review Forecasting methods help to take productive decisions and develop better strategies. During the fight against the coronavirus pandemic, forecasting methods become necessary for efficient governmental decisions. On the other hand, forecasting the pandemic evolution is a complex task. The pandemic data is limited and the problem is multidimensional. The forecasting methods for pandemic researches can be categorized into agent based models, time-series methods, meta-population models, compartmental epidemiological models, approaches in metrology, deep learning and machine learning methods [11]. Many studies have been conducted to predict the cases of Covid-19 in Turkey using time series models. Eroglu [12] applied artificial neural network (ANN) and LSTM models for forecasting Covid-19 cases. Data is collected from WHO between 11 March 2020 - 15 July 2020. Author tested ANN models with several learning algorithms and LSTM models with different numbers of hidden layer and optimization algorithms. According to results, LSTM networks achieved better results than ANN models in new and cumulative cases of Turkey before 7-day. These models can help measures to be taken by government. Güzey [13] applied Susceptible Infected Recovered model which is often used in epidemiology research. The course of the epidemic during 91 days from the start of the epidemic was modeled with the number of cases in Turkey and the measures taken. The important events such as online education, curfews, relaxing the restrictions are considered in estimating the model parameters. The author gives estimation results of some cases related to measures. Akay and Akay [14] applied an ARIMA model to predict Covid-19 cases. The total number cases between March 11- August 24, 2020 is used, and prediction is made for following 2 weeks. Karcıoğlu et al. [15] compared ARIMA model and LSTM network performance on estimating Covid-19 daily number of cases, deaths, and recoveries in Turkey. As a result of the experimental studies carried out for the prediction of the next 15 days, it has been observed that the ARIMA model is estimated with higher accuracy in the number of daily cases and the number of daily deaths, and higher accuracy is made with the LSTM model in the daily recoveries. Kaya and Kaya [16] proposed a hybrid approach based on heuristic algorithms (Flower Pollination, Particle Swarm Optimization, Harmony Search) with neural network to make prediction of Covid-19 cases. Heuristic algorithms are used to optimize parameters of neural network. The dataset belongs to daily cases in Turkey between 1 April - 15 Sep. 2020. A time series

268

O. F. Gurcan et al.

is prepared with this dataset and nonlinear dynamic systems are applied in modelling of problem. The results reveal that Flower Pollination Algorithm generally performs better than the other two heuristic algorithms in predicting based on neural network. Ergül et al. [17] applied ANN to estimate the effect of Codid-19 case number, time period (days), and test numbers on recovery rates and deaths. A comparison study is made for China, USA, and Turkey. The proposed model shows that when precautions are followed strictly, case numbers will decrease after 160 days for Turkey. On the other hand, uncontrolled nature of problem can extent this period. Demir and Kirişci [18] applied NNAR, SARIMA and hybrid (NNAR-SARIMA) models for forecasting Covid-19 cases. Dataset comprised of daily deaths and confirmed case numbers in Turkey from 11 March 2020 to 22 Feb. 2021, is taken from Turkey Health Ministry. The NNAR model achieved the best results in estimating death numbers. Yenibertiz et al. [19] analyzed statistically the effects of restrictions (curfew for aged and young people; country-wide curfew for weekend; normalization) on number of inpatients in Turkey. According to findings, these restrictions don’t seem effect the numbers in early phase of pandemic but authors agree that these restrictions will have positive effect on severity and number of patients in long period.

4 Case Study and Results Dataset consists of confirmed case numbers of Covid-19 from 11.03.2020 to 16.03.2022. Data is taken from the official website of the World Health Organization for Turkey. In this study, Multi Output Regression strategy is applied, where one regressor is fitted per target. As an estimator, Random Forest Regression is selected. A random forest is a meta-estimator. Selected number of decision trees are fitted on several sub-samples of dataset. Averaging technique is used for improving accuracy [20]. Multiple models are used for multi-step forecasting model. For every k-period step ahead prediction a separate random forest model is constructed as follows: xt þ 1 ¼ f 1 ðxt ; xt1 ; . . .; xtl Þ xt þ 2 ¼ f 2 ðxt ; xt1 ; . . .; xtl Þ .. .

ð1Þ

xt þ k ¼ f k ðxt ; xt1 ; . . .; xtl Þ

where k is the farthest day forecasted from today and fk is the Random Forest model constructed for the k-step ahead prediction. Predictions are made using last l days’ observations. In this research, we take l as 30 and k as 15. The 15-day ahead prediction is presented in Fig. 2.

A Forecasting Study of Covid-19 Epidemic: Turkey Case

269

Fig. 2. The prediction of next 15 days.

For each precaution (fifteen events are selected from Table 1), we created dummy variables dit ; dit1 ; . . .; ditl where ditl is the effect of the precaution after l days. In order to analyze the effect of precautions on Covid-19 daily cases, we extracted feature importances from Random Forest Regressor. The selected precaution’s effect on daily confirmed case numbers for the next 30-day period is presented in Fig. 3. According to Fig. 3, the precautions taken on July 1, November 17 in 2020; January 13, February 3, March 1, April 12, April 14, April 29, May 17, July 1, and September 6 in 2021 have meaningful effect on daily case numbers in terms of obtained feature importance.

Fig. 3. The effect of the precautions on case numbers for the next 30 days.

5 Conclusion The Covid-19 outbreak has had negative effects in many areas around the world. Countries are taking many measures in the fight against the epidemic. Predicting the course of the Covid-19 plays an important role in the effectiveness of the measures taken and the new precautionary actions to be taken in the future.

270

O. F. Gurcan et al.

In this study Multi-output regression analysis is made to forecast coronavirus daily case numbers in Turkey. Additionally, in order to analyze the effect of precautions on case numbers, feature importances are obtained. This study has some limitations. The number of selected precautions can be expanded. In the analysis, daily confirmed case numbers are considered. Other indicators such as deaths, rate of vaccination can be added in model and different time series models can be applied. The data source is WHO. The data can have negligent reporting or false reporting.

References 1. Worldometers. https://www.worldometers.info/, Accessed 30 Mar 2022 2. Bicer, M., Dogan, O., Gurcan, O.F.: A detailed comparison of deep neural networks for diagnosis of COVID-19. In: Deep Learning in Biomedical and Health Informatics, pp. 79– 95. CRC Press, Boca Raton (2021) 3. Gurcan, O.F., Atici, U., Bicer, M.B., Dogan, O.: Diagnosis of COVID-19 using deep CNNs and particle swarm optimization. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 308, pp. 305–312. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85577-2_36 4. Covid-19 Turkey. https://covid19.saglik.gov.tr, Accessed 02 Apr 2022 5. WHO. https://covid19.who.int/region/euro/country/tr, Accessed 02 Apr 2022 6. Sadarangani, M., et al.: Importance of COVID-19 vaccine efficacy in older age groups. Vaccine 39(15), 2020–2023 (2021) 7. Asi. https://asi.saglik.gov.tr, Accessed 02 Apr 2022 8. Bamakan, S.M.H., Moghaddam, S.D., Manshadi, S.D.: Blockchain-enabled pharmaceutical cold chain: applications, key challenges, and future trends. J. Clean. Prod. 302, 127021 (2021) 9. Ashok, A., Brison, M., LeTallec, Y.: Improving cold chain systems: challenges and solutions. Vaccine 35(17), 2217–2223 (2017) 10. Wikipedia. https://wikipedia.org, Accessed 30 Mar 2022 11. Nikolopoulos, K., et al.: Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. Eur. J. Oper. Res. 290(1), 99– 115 (2021) 12. Eroglu, Y.: Forecasting models for COVID-19 cases of Turkey using artificial neural networks and deep learning. Endüstri Mühendisliği 31(3), 353–372 (2020) 13. Güzey, N.: Türkiye’deki COVID-19 yayılımının SIR temelli modellenmesinde RSS yöntemi ile parametre kestirimi. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi 11(3), 956– 963 (2021) 14. Akay, S., Akay, H.: Time series model for forecasting the number of COVID-19 cases in Turkey. Turk. J. Public Health 19(2), 140–145 (2021) 15. Karcıoğlu, A.A., Tanışman, S., Bulut, H.: Türkiye’de COVID-19 Bulaşısının ARIMA Modeli ve LSTM Ağı Kullanılarak Zaman Serisi Tahmini. Avrupa Bilim ve Teknoloji Dergisi 32, 288–297 (2021) 16. Kaya, C.B., Kaya, E.: A novel approach based to neural network and flower pollination algorithm to predict number of COVID-19 cases. Balkan J. Electr. Comput. Eng. 9(4), 327– 336 (2021) 17. Ergül, E., et al.: Modelling and prediction of Covid-19 epidemic in Turkey comparing with USA and China. J. Eng. Technol. Appl. Sci. 6(2), 111–126 (2021)

A Forecasting Study of Covid-19 Epidemic: Turkey Case

271

18. Demir, I., Kirisci, M.: Forecasting COVID-19 disease cases using the SARIMA-NNAR hybrid model. Univ. J. Math. Appl. 5(1), 15–23 (2022) 19. Yenibertiz, D., et al.: Effects of social restrictions on the outcomes of inpatients with coronavirus disease-19 (Covid-19) in Turkey. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Halk Sağlığı Dergisi 6(1), 11–21 (2021) 20. Scikit-learn. https://scikit-learn.org, Accessed 02 Apr 2022

Social Influence in Fuzzy Group Decision Making with Applications Amirah Nabilah Sedek Abu Bakar Sedek1 , Nor Hanimah Kamis1(B) , Norhidayah A Kadir1 , Daud Mohamad1 , and Francisco Chiclana2,3 1

Centre for Mathematics Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia {norhanimah,norhidayah,daud}@fskm.uitm.edu.my 2 Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, UK [email protected] 3 Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

Abstract. Group decision making (GDM) is a process of evaluating multi-expert preferences towards criteria or alternatives in order to solve problems in daily life. The previous GDM models often neglected the discussion on experts’ relationships and interactions between them. The incorporation of Social Network Analysis (SNA) in decision making context provides measurement of experts’ relations and interactions in the group. The experts normally have different background, status, position, level of knowledge, expertise, experience etc., in which the group decision might be changed or influenced. The influence notion in Social Influence Network (SIN) plays an important role in defining the influence element in decision making perspective. This new area of study is known as Social Influence Group Decision Making (SIGDM). This paper proposed the framework of the similarity-influence network based fuzzy group decision making model. The influence measure is utilized to identify the most influential expert in the network. For the purpose of aggregating all individual expert preferences into a collective one, the influence-based aggregation operator is used. This paper discusses on the possible applications and future works related to the SIGDM.

Keywords: Social influence network (SIN) (SNA) · Group decision making (GDM)

1

· Social network analysis

Introduction

Group decision making (GDM) allows experts to express their opinions and allows them to reflect their views in examining the problems, considers and Supported by GPK 2020, Universiti Teknologi MARA (UiTM), Malaysia. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 272–279, 2022. https://doi.org/10.1007/978-3-031-09176-6_32

Social Influence in Fuzzy Group Decision Making with Applications

273

evaluates possible alternatives or criteria and collectively choose the solution as a final decision. The use of social network in GDM problems allow us to explore, measure and develop models of experts’ relationship. This idea appears in the area of group decision making with integration of the elements in Social Network Analysis (SNA), known as Social Network Group Decision Making (SNGDM). Recent works in SNGDM were proposed by Li et al. [1], Gao and Zhang [2], Liu et al. [3], Wang et al. [4] and Zhang et al. [5]. Another promising area of study that is closely related to SNA and has broad potential to be explored in group decision making perspective is Social Influence Network (SIN). Social influence in group decision making context, known as Social Influence Group Decision Making (SIGDM) can be defined as a process of intrapersonal and interpersonal interactions, where it allows experts or users with different status or influence to revise and exchange their opinions or convince others towards achieving a final agreed decision. The interaction, discussion or opinion exchange involves in SIGDM might change the attitudes, thoughts, feelings, characteristics, or actions of the experts [6]. This study proposed the similarity-influence network based fuzzy group decision making framework and its applications. The utilization of influence measure in identifying the most influence expert in the network is discussed. This influence index is then used in the aggregation phase and the final ranking of alternatives is obtained. The procedure of this proposed work is slightly different from Kamis et al. [6], where consensus measure and feedback mechanism are excluded. We focus on the related applications to be implemented using this proposed model and possible future works. In this paper, the recent studies corresponding to the SIGDM are discussed in Sect. 2 and the related concepts of SIGDM are elaborated in Sect. 3. Section 4 presents the similarity-influence network group decision making model and Sect. 5 describes possible applications related to the proposed model. Conclusions and future works are drawn in Sect. 6.

2

State of the Art

The pioneer study on the Social Influence Network theory was done by French [7] and continued by other researchers, such as Friedkin and Johnsen [8,9]. Inspired by these works, influence notion has been introduced in group decision making models, where reformulations and new definitions have been proposed. Perez et al. [10] utilized the presented recursive definition by Friedkin and Johnsen [8,9] in order to estimate the evolution of the experts’ opinions based on the interpersonal influences. The interpersonal influence also has been introduced in Mahmood and Abbas [11] in order to improve the influence model and group decision process. Influence can also be related to trust, where the higher an expert trusts in another expert, the higher his/ her preference is influenced by that expert [12]. The similar idea of influence based on the truthfulness was given by Khalid and Beg [13].

274

A. N. S. Abu Bakar Sedek et al.

The influence model has been successfully implemented in consensus based GDM [14–19]. In the case of insufficient consensus level, the feedback mechanism or recommender system needs to be activated. Several studies have been presented in order to overcome this drawback, such as in Kamis et al. [6], Yin et al. [20] and Capuano et al. [21]. In social network, the influence model has been implemented to provide high quality of information to the most influential users and isolate those who act as a harmful user [22].

3

Influence Based Notions

In this section, we present the preliminary concepts related to SIGDM methodology.   A group of experts E = e1 , e2 , . . . , em can express their opinions over a finite set of feasible alternatives, A = {A1 , A2 , . . . , An } (n > 2), as a starting step for decision making. In this study, one of the promising preference representation formats is used, that is the reciprocal fuzzy preference relation (RFPR). Definition 1. A fuzzy binary relation μP : A × A −→ [0, 1] that associated each pair of alternatives (Ai , Aj ) a value μP (Ai , Aj ) = pij verifying the reciprocity property pij + pji = 1 (∀i, j) and the following interpretation: (i) pij = 0.5 if Ai and Aj are equally preferred (indifference) (ii) pij ∈ (0.5, 1) if Ai is slightly preferred to Aj (iii) pij = 1 if Ai is absolutely preferred to Aj . On a set of alternatives, the RFPR, A can be represented as P = (pij ), a matrix of dimension n × n. Based on the reciprocity property, Gonz´ alez-Arteaga et al. [23] defined the intensity preference vector (IPV) as follows: Definition 2. The intensity preference vector (IPV) , V ∈ Rn(n−1)/2 can be expressed as:     V = p12 , p13 , . . . , p1n , p23 , . . . , p2n , . . . , p(n−1)n = v1 , v2 , . . . , vr , . . . , vn(n−1)/2 .

The set of IPVs from  all individual experts E toward the set of alternatives A  is represented by V = V 1 , V 2 , . . . , V m . Based on the IPVs, the preference similarity measure [24] can be defined as: Definition 3. Given V as the set of experts’ IPVs toward the set of alternatives A. A preference similarity measure is a fuzzy subset of V × V with membership a a function  a bS : V × Vb →a [0, 1], verifies S (V , V ) = 1 (reflexive property) and ( symmetric property). S V ,V = S V ,V From Definition 3, the undirected weighted preference similarity network, N = E, T, S [24] on experts, E is developed. This undirected weighted prefer  ence similarity network involves T = t12 , t13 , . . . , t1n , t23 , . . . , t2m , . . . , t(m−1)m , the set of weights attached  to the set of links between experts’ nodes and S = S1 , S2 , . . . , Sm(m−1)/2 , the preference similarity degree between experts. For the purpose of achieving structural equivalent preference similarity network [24], the cosine similarity formula is used and formally defined as:

Social Influence in Fuzzy Group Decision Making with Applications

275

Definition 4. The cosine preference similarity measure of pair of experts, ea and eb is: n(n−1)/2





via · vib



  i=1  . S ab = S V a , V b =  n(n−1)/2 n(n−1)/2       2 2 vib (via ) · i=1

i=1

From preference similarity measure, the influence network can be formulated and named as Similarity Social Influence Network (SSIN) [8]. The SSIN is defined as follows: Definition 5. A Similarity Social Influence Network (SSIN) is an ordered 3tuple, G = E, L, Sη , consisting of a set of experts’ nodes (E), a set of links (L) or ordered pairs of experts in E, and a set of row normalized preference similarity weights (Sη ) attached to L. Based on SSIN and centrality concept, the influence measure is introduced by Kamis et al. [6]. This measure is called as σ-centrality. Definition 6. Given Sη be a set of row normalized preference similarity weights in SSIN G, σ be the relative importance of endogenous (network connections) versus exogenous (external) effects, and Z = (z)m×1 be a set of individual expert exogenous effect values. The influence index or σ-centrality of experts E, Y =   1 y , . . . , y m , is: −1  Y = I − σ STη Z. Z is formed as a unity vector (the vector with all components equal to 1) if the exogenous effect does not exist. The influence index is necessary to determine the most influential expert in the network. Thus, this value is utilized as  the order inducing variable of the experts’ preference evaluations, p1ij , . . . , pm in the IOWA-based aggregation ij operator [25]. The aggregation operator is known as σ-IOWA operator, formally defined as: Definition 7. The σ-IOWA operator of dimension m, ΦσW , is an IOWA-based operator with the order  inducing variable from the set of expert’s influence index in the network, Y = y 1 , . . . , y m . The influence index represents the influential status of each expert towards other group members in the network. Thus, the higher the influence score, the more powerful an expert has in controlling the decision making process. Indirectly, the higher the contribution (weight or importance) of that expert in the aggregation phase.

276

4

A. N. S. Abu Bakar Sedek et al.

Framework of the Proposed Model

In this study, the influence element is integrated in fuzzy group decision making procedure. The framework of the proposed model is depicted in Fig. 1.

Fig. 1. Framework of the social influence in fuzzy group decision making model

Generally, the proposed work can be categorized into 3 phases. At the beginning of Phase 1, a finite set of experts are involved in the decision making process. Regarding the identified problem, experts will discuss and give their preferences in terms of reciprocal fuzzy preference relations (RFPR), as defined in Definition 1. The RFPR is transformed into the intensity preference vectors (IPV) (Definition 2) and the similarity of experts’ preferences are then measured using Definition 4. In Phase 2, the Similarity Social Influence Network (SSIN) is constructed based on Definition 5. From this, the influence index (Definition 6) can be determined for each of expert. This value represents the expert’s influence status, which will be used to determine the most influential expert in the network. The aggregation of all individual experts’ preferences into a collective one occurs in Phase 3. The influence based aggregation operator, known as σ-IOWA operator (Definition 7) is utilized. Finally, the alternatives will be ranked based on the weightage from the influential status in the aggregation process.

Social Influence in Fuzzy Group Decision Making with Applications

5

277

Related Applications of the Proposed Model

The proposed decision making model can be utilized as an alternative procedure in solving selection problems or decision making. The possible applications might be implemented in: (i) Organization: The top management can use this model in the formation of team(s) based on employees’ expertise. The CEO, managers or supervisors will have different influence indexes depending on their positions. (ii) Competition: The judges from any competition can use this proposed work in judging the contestants. Judges from different categories might have different influence scores. (iii) Online/ offline reviews: The consumers allow to rate on the quality of products. The regular consumer(s) might have different influence status compared to the first timer. (iv) Education: The researchers, academicians or students can use, modify, extend or improve this proposed work, thus contributions to the knowledge are progressive.

6

Conclusion and Future Works

In traditional GDM models, it is usually assumed that experts are neutral and unrelated to other group members. The existence of any relations between them are ignored. Contrastly in real life, experts have potential of knowing each other, have different status, motivation, experience or expertise. These advantages allow experts to influence others to change their opinions over alternatives during decision making process. Inspired by this situation, we present the integration of influence element in fuzzy GDM model. The influence contributes to the identification of the most influential expert in the network and the aggregation of preferences based on influence status of each of them. The proposed framework can be used as an alternative procedure in solving related applications and has bright potential to be extended in the future. The existence of pioneer studies in SIGDM allows several issues that would be interesting to be explored further. This includes the reformulation of other notions, theories or concepts of SIN in group decision making perspective. Additionally, this proposed work can be extended to the development of new consensus procedure. Since influence element is related to the changes of opinions, thus new influence-based feedback mechanism or recommender system can be developed. This phase is necessary to be introduced when the consensus level of the group is insufficient. This situation might happen if experts are assumed that their individual opinions are not appropriately taken into considerations in the decision making process or disagree on the final decision.

278

A. N. S. Abu Bakar Sedek et al.

Acknowledgements. This contribution has been carried out thanks to the financial support of the Geran Penyelidikan Khas (600-RMC/GPK 5/3 (162/ 2020)), Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.The appreciation also goes to Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

References 1. Li, Y., Kou, G., Li, G., Wang, H.: Multi-attribute group decision making with opinion dynamics based on social trust network. Inf. Fusion 75, 102–115 (2021) 2. Gao, Y., Zhang, Z.: Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making. J. Oper. Res. Soc., 1–18 (2021) 3. Liu, B., Jiao, S., Shen, Y., Chen, Y., Wu, G., Chen, S.: A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment. Inf. Fusion 80, 266–281 (2022) 4. Wang, F., Zhang, H., Dong, Y.: A dynamic social network-driven consensus reaching model. Procedia Comput. Sci. 199, 1044–1051 (2022) 5. Zhang, Y., Chen, X., Gao, L., Dong, Y., Pedryczc, W.: Consensus reaching with trust evolution in social network group decision making. Expert Syst. Appl. 188, 116022 (2022) 6. Kamis, N.H., Chiclana, F., Levesley, J.: An influence-driven feedback system for preference similarity network clustering based consensus group decision making model. Inf. Fusion 52, 257–267 (2019) 7. French, J.R., Jr.: A formal theory of social power. Psychol. Rev. 63(3), 181 (1956) 8. Friedkin, N.E., Johnsen, E.C.: Social influence and opinions. J. Math. Sociol. 15(3– 4), 193–206 (1990) 9. Friedkin, N.E., Johnsen, E.C.: Influence networks and opinion change. Adv. Group Processes 16(1), 1–29 (1999) 10. P´erez, L.G., Mata, F., Chiclana, F., Kou, G., Herrera-Viedma, E.: Modelling influence in group decision making. Soft Comput. 20(4), 1653–1665 (2016). https:// doi.org/10.1007/s00500-015-2002-0 11. Mahmood, A., Abbas, M.: Influence model and doubly extended TOPSIS with TOPSIS based matrix of interpersonal influences. J. Intell. Fuzzy Syst. 39(5), 7537–7546 (2020) 12. Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., Loia, V.: Fuzzy group decision making with incomplete information guided by social influence. IEEE Trans. Fuzzy Syst. 26(3), 1704–1718 (2017) 13. Khalid, A., Beg, I.: Influence model of evasive decision makers. J. Intell. Fuzzy Syst. 37(2), 2539–2548 (2019) 14. Li, S., Wei, C.: Modeling the social influence in consensus reaching process with interval fuzzy preference relations. Int. J. Fuzzy Syst. 21(6), 1755–1770 (2019) 15. Li, S., Wei, C.: A two-stage dynamic influence model-achieving decision-making consensus within large scale groups operating with incomplete information. Knowl.Based Syst. 189, 105132 (2020) 16. Li, S., Rodriguez, R.M., Wei, C.: Two-stage consensus model based on opinion dynamics and evolution of social power in large-scale group decision making. Appl. Soft Comput. 111, 107615 (2021)

Social Influence in Fuzzy Group Decision Making with Applications

279

17. Yao, S., Gu, M.: An influence network-based consensus model for large-scale group decision making with linguistic information. Int. J. Comput. Intell. Syst. 15(1), 1–17 (2021). https://doi.org/10.1007/s44196-021-00058-1 18. Zhang, Y., Chen, X., Pedrycz, W., Dong, Y.: Consensus reaching based on social influence evolution in group decision making. IEEE Trans. Cybern., 1–14 (2022) 19. Liu, Z., Deng, Y., Yager, R.R.: Measure-based group decision making with principle-guided social interaction influence for incomplete information: a game theoretic perspective. IEEE Trans. Fuzzy Syst. 30(4), 1149–1163 (2021) 20. Yin, H., Wang, Q., Zheng, K., Li, Z., Yang, J., Zhou, X.: Social influence-based group representation learning for group recommendation. In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 566–577 (2019) 21. Capuano, N., Chiclana, F., Herrera-Viedma, E., Fujita, H., Loia, V.: Fuzzy group decision making for influence-aware recommendations. Comput. Human Behav. 101, 371–379 (2019) 22. Urena, R., Chiclana, F., Herrera-Viedma, E.: A new influence based network for opinion propagation in social network based scenarios. Procedia Comput. Sci. 139, 329–337 (2018) 23. Gonz´ alez-Arteaga, T., de Andr´es Calle, R., Chiclana, F.: A new measure of consensus with reciprocal preference relations: the correlation consensus degree. Knowl.Based Syst. 107, 104–116 (2016) 24. Kamis, N.H., Chiclana, F., Levesley, J.: Preference similarity network structural equivalence clustering based consensus group decision making model. Appl. Soft Comput. 67, 706–720 (2018) 25. Yager, R.R., Filev, D.: Operations for granular computing: mixing words and numbers. In: Proceedings of the 1998 IEEE World Congress on Computational Intelligence, vol. 1, pp. 123–128. IEEE (1998)

Predicting Firms’ Performances in Customer Complaint Management Using Machine Learning Techniques Serhat Peker(&) Department of Management Information Systems, Izmir Bakircay University, Izmir, Turkey [email protected]

Abstract. With the globalization and more intense increasing competition, customer relationship management (CRM) is an important issue in today’s business. In this manner, managing customer complaints which is a critical part of CRM presents firms with an is an opportunity to make long-lasting and profitable relationships with customers. In this context, the aim of this paper is to predict firms’ performances in online customer complaint management using machine learning algorithms. This study utilizes data obtained from Turkey’s largest and well-known third-party online complaint platform and employs three popular machine learning classifiers including decision tree (DT), random forests (RF) and support vector machines (SVM). The results show that the RF algorithm performed better in firms’ performance prediction compared to other ML algorithms. Keywords: Data mining
Machine learning analytics
Data-driven CRM


Business intelligence
CRM

1 Introduction With the raising competitiveness in today’s dynamic business environment, building good customer relationships has become very important. Customer satisfaction and customer loyalty are quite critical factors to keep long-term and closed customer relationship, since it contributes to the business profitability and positive word of mouth [1]. To improve customer satisfaction and promote customer loyalty, businesses need to implement effective customer relationship management (CRM) strategies. For this purpose, listening to customers and effectively responding to their complaints are among the major CRM components affecting customer satisfaction. A customer complaint is the situation where the expectation of the customer is not met in the face of the purchased product or service. In other words, it is the negative feedback of customers regarding their dissatisfaction. Customer complaints provide many strategic opportunities and are beneficial for companies to improve customer relationships [2, 3]. Firms can maintain customer loyalty by handling and managing the complaints of customers successfully [4]. As a result of this, companies are required to pay attention to their customers’ complaints in order to ensure the satisfaction of them, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 280–287, 2022. https://doi.org/10.1007/978-3-031-09176-6_33

Predicting Firms’ Performances in Customer Complaint Management

281

and thereby maintain their long-term relations with them. Further, thanks to today's information technologies, customer complaints are easily accessible online, and this can affect the behavior of other potential customers. Therefore, a good complaintmanagement strategy with the proper tools is essential for keeping regular customers as well as for attracting new ones [5]. Customer complaint management process consists of two main steps which are receiving and resolving the complaints respectively [6]. Customers can submit their complaints via various channels such as, written forms, telephone lines, face-to-face, etc. The rapid growth of the Internet and its associated technologies has enabled new flexible and easy to follow channels, such as firms’ websites, online chat applications, social media, and third-party internet platforms. Among these channels, third-party internet platforms enable customers to express their complaints to businesses in a virtual environment, find corrective actions to their problems. On the other hand, the presence of complaints in such platforms is critical for businesses. High number of complaints are likely to be accompanied by negative word of mouth communication and such cases damages the company's reputation [7]. Past research [8, 9] found the theory of negative effects which says that negative reviews affecting customers more strongly than positive ones. Customers also place greater emphasis on negative information on purchase decisions [10]. To prevent or reduce these causes, customer complaint management is very strategic for companies. To do so, they need to focus on the cause of customer complaints and offer solutions to these problems. This whole process is challenging, and customers finally evaluate this process in third-party internet platform. If the firm offers a corrective action to the problem of the customer, s/he is satisfied from this process and grades a high overall rating. Therefore, the firm gains the trust of its customers again and avoids the negative word of mouth communication as well. On the other hand, data mining is a useful tool to infer knowledge from large amount of data. The application of data mining tools are precisious for the development of different competitive CRM strategies [11]. Hence, it has drawn the attention of practitioners and academics, and numerous data mining techniques and machine learning algorithms have been widely implemented as solutions in CRM for different pruposes such as portfolio analysis or customer profiling [12–14], customer lifetime value (CLV) [15, 16], customer behavior prediction [17–20]. With such applications, compaines can extract knowledge from enormous customer databases, and make proactive, knowledge-driven CRM decisions. Data mining and machine learning algorithms can also create significant benefits for firms in governing customer complaints. These techniques help the CRM departments to analyze and handle complaints. Thus, a body of research studies utilized various data mining and machine learning techniques in the area of customer complaint analysis, and a summary of previous studies in this field is presented in Table 1.

282

S. Peker Table 1. Summary of related work

Article

Year Sector

Minaei-Bidgoli et al. [21]

Objective

2010 Government To discover the (municipality) relationship between different groups of citizens and different kinds of complainers Hsiao et al. [22] 2016 Restaurant To characterize customer complaints in aggregate manner

Features

Features affecting service quality in restaurants (e.g., physical facilities, equipment, and appearance of personnel, etc.) The complaints of Chugani et al. 2018 Banking To find similar [23] complaints are there credit reporting, mortgage, debt in relation to the same bank or service collection, consumer loan and banking or accounting product To extract information about complaints in certain regions of the country Customer services, Ghazzawi et al. 2019 Transportation To categorize [24] complaint records and details (subject according to their matter, subject detail, agencies and issue detail, agency)

Sann et al. [25] 2022 Hospitality

Method

Citizens’ Association rule demographic and call mining (Apriori data related to their algorithm) complaints Decision tree models (CHAID)

Cluster analysis (k-means and hierarchical clustering algorithms) Multi-linear Regression

Naïve Bayes, KNearest Neighbors (KNN), Random Forest Trees, Decision tree (ID3) To predict travelers’ Hotel-related features Decision tree models (C&R (e.g., hotel class, online complaint tree, QUEST, hotel facility, attributions for CHAID, C5.0) various hotel classes cleanliness, etc.)

As reported in Table 1, altough a wide range of data mining and machine learning techniques has been used in previous studies for the purpose of complaint management, there is a lack of research that examines firms’ performances in complaint management. This study fills this gap in the literature by proposing an approach to predict firms’ performances in complaint management. For this purpose, data obtained from Turkey’s largest and well-known third-party online complaint platform was utilized, and three popular machine learning classifiers including decision tree (DT), random forests (RF) and support vector machines (SVM) were employed. The proposed approach in this research can be helpful to firms in the evaluation of their effectiveness in managing complaints. Therefore, they can further improve the service quality, and enhance the process of handling customer complaints efficiently. The remainder of the paper is organized as follows. Section 2 describes the method used for predicting firms’ performances in complaint management. Results are given in

Predicting Firms’ Performances in Customer Complaint Management

283

Sect. 3, and in last section, the study is concluded by providing the contributions of this work and outlines directions for future research.

2 Method 2.1

Data Extraction

In this study, the dataset for prediction and analysis of firms’ performances in complaint management is obtained from Şikayetvar.com which is Turkey’s largest and well-known third-party online complaint platform. The website includes is a complaint profile for each company, and company profile pages contain numerical data related to company's complaints. To use in this research, data of 2710 companies that received 50 or more complaints on September 13, 2021 were retrieved using Uipath Studio which is one of the Robotic Process Automation tools. The attributes of the extracted dataset include the number of published complaints, the number of answered complaints, average response time, the number of complaints resolved in the last one year, and customer satisfaction score. 1465 companies that have not responded to any complaints or provided a solution were excluded from the dataset. Hence, data of 1245 firms were analyzed. 2.2

Data Preparation and Pre-processing

In the obtained dataset, average response time was not formatted, so values of this feature have been transformed into seconds. Moreover, response rate and solution rate features were generated for each company. Response rate was computed as the percentages of complaints that were answered, and solution rate was calculated as the percentages of complaints that were resolved. The target output dependent variable is the customer satisfaction score, and each company has a score between 1 and 5. Customer satisfaction score was incorporated into the predictive models as a categorical variable to utilize well-known classification techniques. Since satisfaction score has a symmetric distribution with no skew, equal width partitioning(binning) is one of the famous unsupervised techniques was used to divide this feature into a set of intervals. We set the number of intervals to five, and equal width partitioning technique creates the cut points based on the lowest and highest values of the attribute. Therefore, the customer satisfaction level feature was generated as a class variable, and each company were identified as either very poor, poor, average, good or excellent. A detailed description of features used in this research is given in Table 2. Table 2. Feature set Features Response rate Solution rate Average response time Satisfaction level

Type Numeric Numeric Numeric Multi-nominal

Range of values 0–100 0–100 60–257,280 1: very poor, 2: poor, 3: average, 4: good, 5: excellent

284

S. Peker

Before the implementation of SVM algorithms, all features except for class variable were standardized to the interval [0, 1] using min-max technique. 2.3

Machine Learning Algorithms

Several ML algorithms, each one with its own goals and capabilities, have been proposed for classification and this study consider the following ones: • Decision tree (DT): The DT is a branching structure that represents a set of rules that separate values in a hierarchical form. There are various DT algorithms, and this study uses C5.0 algorithm which is one of the most popular DT inducers. • Random forest (RF): RF is an ensemble of T unpruned DT, using random feature selection. It possibly improves the classification performance by creating more than one decision tree. • Support vector machines (SVM): SVM uses a hyperplane as a decision surface to optimize the margin of separation between positive and negative samples. It transforms non-linear input sample data into a higher dimensional space. 2.4

Experimental Setup and Performance Evaluation

All experiments in this study were conducted using R studio software. Selected ML algorithms were tested according to the class variable, satisfaction level and with three input attributes which are response rate, solution rate, and average response time. To conduct experiments, the dataset was split into two partitions, where 70% of the data was used for training to generate the predictive models, and 30% of the data was used to test these models. Performance of each ML classifier was measured in terms of wellknown performance measures including precision, recall, F-measure, and overall accuracy which are formulated based on the confusion matrix.

3 Results Table 3 reports prediction results with precision, recall, F-measure values for each satisfaction level.

Table 3. Prediction results for each satisfaction level DT RF Satisfaction level P R F-M P R Very Poor 0.75 0.98 0.85 0.90 0.81 Poor 0.77 0.79 0.78 0.76 0.84 Average 0.66 0.75 0.70 0.69 0.70 Good 0.61 0.63 0.62 0.61 0.70 Excellent 0.96 0.40 0.57 0.78 0.54 Note: P: Precision; R: Recall; F-M: F-measure

F-M 0.85 0.80 0.69 0.65 0.64

SVM P R 0.88 0.89 0.81 0.80 0.64 0.71 0.55 0.57 0.73 0.56

F-M 0.88 0.81 0.67 0.56 0.63

Predicting Firms’ Performances in Customer Complaint Management

285

As shown in Table 3, prediction results for companies having very poor satisfaction level are the best with highest F-measure scores (at least 0.8) by all classifiers, whereas prediction results for firms having good and excellent satisfaction levels are significantly lower than the other firms. Table 4 presents the comparative accuracy results of the applied ML algorithms in the prediction of firms’ performances in complaint management. Table 4. Overall prediction results Classifiers DT RF SVM

Accuracy (%) 71.05 72.12 70.24

The maximum classification accuracy was 72.12%, which was obtained using RF algorithm, while DT had the second-highest overall accuracy value (71.05%). Overall to predict firms’ performances in complaint management, the predictive model with RF algorithm yielded the best performance, followed by DT and SVM respectively.

4 Conclusion The rapid spread of the Internet has offered consumers to publish their complaints about products and services online. Publicly avaiable third-party complaint-handling platforms are common tools facilitating the resolution process between consumers and firms. Data Mining and machine learning techniques can also be integrated into complaint-handling process. Customer complaints management is a crucial component of CRM to make long-lasting and profitable relationships with customers. In this study, an approach for predicting companies’ performances in customer complaint management with the help of machine learning is proposed. For this purpose, customer satisfaction is selected as the target attribute, and companies’ response rate, solution rate and average response time as input attributes were considered. Experiments were conducted by individually implementing DT, RF, and SVM classification algorithms on the real-world dataset. The experimental results have shown that the performances of companies having very poor satisfaction level are more predictable. Moreover, the RF offers the highest performance compared to SVM and DT algorithms and therefore, it can be recommended for the development of prediction models for companies’ performances in customer complaint management. The contribution of this research is in the utilization of machine learning algorithms to forecast companies’ performances in customer complaint management. This study suffers from the following limitations. First, the problem is formulated as multi-class classification by converting numeric target variable (user satisfaction score) to nominal variable. In the future studies, the class feature can be kept as a numeric and problem can be modelled using multi-linear regression technique. Further,

286

S. Peker

predictive models were constructed in aggregate manner, but the proposed approach is applicable to individual firms. Future research could replicate this study using data pertaining to a company to ascertain whether the findings are consistent. Other future lines of work could be to test the proposed approach with different set of features and other machine learning classifiers.

References 1. Tseng, T.-L.B., Huang, C.C.: Rough set-based approach to feature selection in customer relationship management. Omega 35, 365–383 (2007) 2. Ferguson, J.L., Johnston, W.J.: Customer response to dissatisfaction: a synthesis of literature and conceptual framework. Ind. Mark. Manag. 40, 118–127 (2011) 3. Zairi, M.: Managing customer dissatisfaction through effective complaints management systems. TQM Mag. (2000) 4. Hoffman, K.D., Kelley, S.W., Rotalsky, H.M.: Tracking service failures and employee recovery efforts. J. Serv. Mark. 9, (1995) 5. Lee, C.H., Wang, Y.H., Trappey, A.J.C.: Ontology-based reasoning for the intelligent handling of customer complaints. Comput. Ind. Eng. 84, 144–155 (2015) 6. Yang, Y., Xu, D.L., Yang, J.B., Chen, Y.W.: An evidential reasoning-based decision support system for handling customer complaints in mobile telecommunications. Knowl.-Based Syst. 162, 202–210 (2018) 7. Luo, X.: Consumer negative voice and firm-idiosyncratic stock returns. J. Mark. 71, 75–88 (2007) 8. Kimmel, A.J., Kitchen, P.J.: WOM and social media: presaging future directions for research and practice. J. Mark. Commun. 20, 5–20 (2014) 9. Wu, P.F.: In search of negativity bias: an empirical study of perceived helpfulness of online reviews. Psychol. Mark. 30, 971–984 (2013) 10. Senecal, S., Nantel, J.: The influence of online product recommendations on consumers’ online choices. J. Retail. 80, 159–169 (2004) 11. Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36, 2592– 2602 (2009) 12. Dursun, A., Caber, M.: Using data mining techniques for profiling profitable hotel customers: an application of RFM analysis. Tour. Manag. Perspect. 18, 153–160 (2016) 13. Peker, S., Kocyigit, A., Eren, P.E.: LRFMP model for customer segmentation in the grocery retail industry: a case study. Mark. Intell. Plan. 35, 1–16 (2017) 14. Guney, S., Peker, S., Turhan, C.: A combined approach for customer profiling in video on demand services using clustering and association rule mining. IEEE Access. 8, 84326– 84335 (2020) 15. Safari, F., Safari, N., Montazer, G.A., Alejandro, T.B, Alejandro, B.T.: Customer lifetime value determination based on RFM model. Mark. Intell. Plan. 34 (2016) 16. Nikumanesh, E., Albadvi, A.: Customer’s life-time value using the RFM model in the banking industry: a case study. Int. J. Electron. Cust. Relatsh. Manag. 8, 15–30 (2014) 17. Peker, S., Kocyigit, A., Eren, P.E.: A hybrid approach for predicting customers’ individual purchase behavior. Kybernetes 46, 1614–1631 (2017) 18. Abbasimehr, H., Setak, M., Soroor, J.: A framework for identification of high-value customers by including social network based variables for churn prediction using neurofuzzy techniques. Int. J. Prod. Res. 51, 1279–1294 (2013)

Predicting Firms’ Performances in Customer Complaint Management

287

19. Chen, K., Hu, Y.-H., Hsieh, Y.-C.: Predicting customer churn from valuable B2B customers in the logistics industry: a case study. IseB 13(3), 475–494 (2014). https://doi.org/10.1007/ s10257-014-0264-1 20. Fathian, M., Hoseinpoor, Y., Minaei-Bidgoli, B.: Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods. Kybernetes 45, 732– 745 (2016) 21. Minaei-Bidgoli, B., Akhondzadeh, E.: A new approach of using association rule mining in customer complaint management. Int. J. Comput. Sci. Issues (IJCSI) 7, 142 (2010) 22. Hsiao, Y.H., Chen, L.F., Choy, Y.L., Su, C.T.: A novel framework for customer complaint management. Serv. Ind. J. 36, 675–698 (2016) 23. Chugani, S., Govinda, K., Ramasubbareddy, S.: Data analysis of consumer complaints in banking industry using hybrid clustering. In: Proceedings of the 2nd International Conference on Computing Methodologies and Communication, ICCMC 2018, pp. 74–78 (2018). https://doi.org/10.1109/ICCMC.2018.8487638 24. Ghazzawi, A., Alharbi, B.: Analysis of customer complaints data using data mining techniques. Procedia Comput. Sci. 16, 62–69 (2019) 25. Sann, R., Lai, P.-C., Liaw, S.-Y., Chen, C.-T.: Predicting online complaining behavior in the hospitality industry: application of big data analytics to online reviews. Sustainability. 14, 1800 (2022)

Hybrid Models for Vendor Selection Problem in Software Industry: A Pilot Case Study Servet Soygüder, Babak Daneshvar Rouyendegh, and Aylin Tan(&) Ankara Yıldırım Beyazıt University, Ankara, Turkey [email protected]

Abstract. Currently, real problems have more complexity. Therefore, solutions and topologies for these problems are also very complex, too. Providing a total solution for real life problems by one organization is almost impossible. At this point, working with the appropriate supplier for organizations are significant to make more agile, sustainable, and profitable products. In this study, alternatives are compared with considering twelve criteria. A combined approach of fuzzy and multi-criteria decision-making problems is handled, and a methodology is applied to find best speech to text supplier for a company from call center field. Because of the problem has uncertainty and subjectivity, the decision matrix is created with intuitionistic fuzzy methodology. Finally, solution is applied to a real-life problem. Keywords: Fuzzy sets Speech to text


Multi-criteria decision-making
Supplier selection


1 Introduction Technology is changing pretty faster day by day and it makes people’s live easier and better. Especially, after neural network algorithms were getting more popular for researchers, new methodologies were appeared in all fields. Bots and IVRs assist companies for raising customer satisfaction and loyalty and reducing functional expenses while running contact center operations. Today, chatbots, IVR’s and virtual assistants are utilized like never before to accomplish a more effective client experience in pretty much every area, like medical services, banking, government, protection. In banking industry, banks have smart assistants to run transactions of customers and to give right and fast information to customers when they need without going any branch office or even ATM. This is an opportunity for banking companies to reduce their operational cos and human resource while increasing customer experience and loyalty. Making transactions remotely has got more importance with COVID-19, because people want to decrease their works in person to keep out from disease. In this study, a buyer which is contact center business is chosen as case study. It has own IVR and Bots but, it needs a speech to text product to run its business. Speech to text products have significant role in the topology which buyer has, because it provides to transform audio to text and understand what user said. Also, apart from the quality, price, deployment availabilities, support process and flexibility are significant while selecting best speech to text vendor for the buyer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 288–295, 2022. https://doi.org/10.1007/978-3-031-09176-6_34

Hybrid Models for Vendor Selection Problem in Software Industry

289

2 Literature Review Vendor or supplier selection has long been a significant topic, and many different approaches for selecting the best supplier according to application area and data type have been proposed in the previous research. Integrated VIKOR and fuzzy AHP approach for the problem of multi-level supplier selection. In the study, criteria weights are defined with fuzzy AHP and supplier ratings are used with fuzzy VIKOR [1]. AHP, TOPSIS and Taguchi loss function are combined to solve supplier selection problem for heavy locomotive producer in India. Quality, lead, cost are selected as criteria in the problem [2]. AHP and TOPSIS methods are applied for supplier selection for steel industry in Iran. CO2 emissions, number of workers in the country of vendor, water consumption and distance are considered as main criteria [3]. Although, need for automation is popular topic because of COVID-19 and technology, number of various research with different disciplines is quite less. Generally, research in literature about IT contains only specific criteria which solution has. There is a gap in literature for supplier selection problem in IT industry with these criteria while research about supplier selection field is still increasing day by day. The study ranks five speech-totext engine vendors based on levels of accuracy, word error rates, and performance. Different types of audios are used in the study to make evaluations in a different type of audio format [4]. Various measurement techniques are being studied to classify the engines according to their performance. The study also considers different accents in a language as an issue [5]. Cloud based speech recognition engines are examined for analyzing human- machine interaction. In the study, accuracy of intent recognition and effect of background noise are considered [6]. Moreover, Bootstrap estimates are used for evaluating speech recognition performance. Word error rate is considered as criteria with using bootstrap for significance test [7]. In the literature, research is more about new algorithms or approaches to increase accuracy or applying speech recognition solutions to different industries. Although there is research for comparing different speech recognition engines for accuracy, there is a lack of research about comparing them with different criteria.

3 Structuring Problem According to previous research, generally quantitative measurement methods and algorithms are used for ranking speech-to-text providers. Most significant constraint in these methodologies, they cannot measure qualitative data and uncertainty. In this study, not only measurable criteria but also unmeasurable criteria are applied of supplier selection problem for speech to text product due to existing gap in the past studies. In this research, an integrated methodology is applied to handle constraints in current state of supplier selection problem for the product in software industry. Finding most appropriate MCDM approach could be useful to give a solution to the problem, previous studies are reviewed. Also, criteria are defined based on combining previous research of speech to text performance and rankings, and basic study of supplier selections. Goal of integrating this study is bringing new view to ranking of speech to text vendors by handling constraints in current research. As a result, word error rate

290

S. Soygüder et al.

(quality), leadership in technology, unit price, hardware requirements, support and maintenance, workflow tools and number of supported types of voice format are selected as criteria.

4 Constructing Decision Model The problem has not only quantitative data but also it has qualitative data collected from experts. It causes uncertain environment and lack of objectivity. Therefore, to eliminate subjectivity while ranking alternatives fuzzy AHP is used. 4.1

Fuzzy Sets

Fuzzy sets are obtained to eliminating uncertainty and lack of information. A fuzzy set represents being a member of a function. Then, triangular fuzzy number is obtained as membership function with defining smallest possible, average and largest possible values. Membership function is defined as below for TFN. 0 0; xb l Ax ¼ ab lx la

x[l x\b bxa axl

ð1Þ

Based on the study of (Rouyendegh, et al. 2018), basic concepts of intuitionistic fuzzy set is defined as A = {(r, lA (r), v(r)) ⋮ r 2 R}, while lA (r) is member of function, vA (r) is not member. In fuzzy set, summary of these functions should greater or equal to 0 and less than equal to 1. Also, is pA ðrÞthe intuitionistic fuzzy index of belonging to A. 4.2

Ahp

In methodology section MCDM is applied for supporting the decision makers in comparing suppliers among the alternatives. In the AHP process, firstly criteria and sub-criteria are defined. Then, making pairwise comparisons of each criterion to achieving aim and calculating weights of criteria are done. Next, according to previous step, build pairwise comparisons for suppliers and calculating corresponding weights. The last step is using step 2 and 4, calculating the weights in achieving the hierarchy goal.

5 Case Study In this research, real case supplier selection problem is solved with integrated method of fuzzy and AHP approaches. A company which runs in call center industry is selected as purchaser for speech- to-text product. The company needs the product to transcribing audio to text in their IVRs and Bots to provide that contact center operations to decrease operational cost and increase quality and customer satisfaction. While

Hybrid Models for Vendor Selection Problem in Software Industry

291

selecting the vendors, it was considered that to have similar success rates, similar market shares and similar type of solutions. As a first step, decision makers are selected according to their expertise and knowledge about speech-to-text and other contact center software. Team leader of software development team, speech-to-text’s product manager and director of research and development team are chosen for this real-life problem. Linguistic variables for pairwise comparison and triangular fuzzy numbers are shown in Table 1. In second step, Parkash’s fuzzy prioritization method is used for converting fuzzy numbers. Furthermore, data are collected from experts with making online and in person interviews. Answers from the experts are collected and applied to linguistic scale. Intuitionistic fuzzy scale is selected for data for transforming words to numbers. TFN provided to eliminating subjectivity and uncertainty of expert’s opinions.

Table 1. Linguistic variables for pairwise comparison Linguistic variables for pairwise comparison Preference numbers TFNs Equally important 1 (1, 1, 1) Weakly more important 3 (1, 3, 5) Strongly more important 5 (3, 5, 7) Very strong more important 7 (5, 7, 9) Absolutely more important 9 (7, 9, 11) Intermediate value 2 (1, 2, 4)

The linguistic terms are shown in Table 2. Table 2. Pairwise comparison of alternatives C1 C2 C3 C4 C5 C6 C7

C1 1 3 1/5 1/9 1/7 1/5 1/9

C2 1/3 1 1/5 1/9 1/7 1/5 1/9

C3 5 5 1 1/7 1/9 1/5 1/7

C4 9 9 7 1 3 5 1/3

C5 7 7 9 1/3 1 5 1/3

C6 5 5 5 1/5 1/5 1 1/7

C7 9 9 7 3 3 7 1

In third step, geometric mean fuzzy and weights of each criterion are calculated by following equation and shown in Table 3. In the equation k represents the criteria, l represents the first value of TFN, m second and u third.

292

S. Soygüder et al. Table 3. Geometric mean fuzzy and fuzzy weights of crtieria C1 C2 C3 C4 C5 C6 C7 Sum Rev INCR

ri 2.387 3.349 4.735 3.004 4.584 5.959 0.815 1.088 1.478 0.222 0.321 0.490 0.287 0.456 0.647 0.748 1.049 1.490 0.170 0.224 0.362 7.631 11.070 15.162 0.131 0.090 0.066 0.066 0.090 0.131

Wi 0.158 0.198 0.054 0.015 0.019 0.049 0.011

0.301 0.413 0.098 0.029 0.041 0.094 0.020

0.620 0.781 0.194 0.064 0.085 0.195 0.047

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi YK k Ri ¼ a k¼1 l;m;u

ð2Þ

Fourth, summation of ri and reverse of summations are calculated and ranked. In next step, average weights and normalized weights for each criterion are calculated by following equation. M ði Þ ¼ P

li; mi; ui P P li; mi; ui

Mi NðiÞ ¼ P Mi

ð4Þ

Table 4. Pairwise comparison of alternatives for each criterion C1 A1 A2 A3 A4 C4 A1 A2 A3 A4 C7 A1 A2 A3 A4

A1 1 3 1/5 1/7 1 1/3 1/5 1/7 1 3 1/5 1/7

A2 1/3 1 1/7 1/9 3 1 1/5 1/7 1/3 1 1/5 1/7

A3 5 7 1 1/3 5 5 1 1/3 5 5 1 1/5

A4 A1 7 C2 1 9 5 3 5 1 1/3 7 C5 1 7 1/3 3 1/5 1 1/7 7 7 5 1

A2 1/5 1 1/3 1/9 3 1 1/3 1/5

A3 1/5 3 1 1/7 5 3 1 1/3

ð3Þ

A4 A1 A2 A3 A4 3 C3 1 1/9 5 7 9 1/9 1 3 5 7 1/5 1/3 1 3 1 1/7 1/5 1/3 1 7 C6 1 1/5 1/3 5 5 5 1 5 7 3 3 1/5 1 5 1 1/5 3 1/5 1

Hybrid Models for Vendor Selection Problem in Software Industry

293

Table 5. Geometric mean fuzzy and fuzzy weights for each crtieria Cri-1 A1 A2 A3 A4 Cri-2 A1 A2 A3 A4 Cri-3 A1 A2 A3 A4 Cri-4 A1 A2 A3 A4 Cri-5 A1 A2 A3 A4 Cri-6 A1 A2 A3 A4 Cri-7 A1 A2 A3 A4

ri 1.316 2.432 0.355 0.212 0.378 2.141 1.136 0.226 3.201 0.723 0.411 0.237 1.968 1.316 0.378 0.223 1.968 0.880 0.411 0.237 0.541 2.590 0.809 0.378 1.316 1.968 0.497 0.205

1.848 3.708 0.541 0.270 0.589 3.409 1.495 0.293 4.213 1.136 0.669 0.312 3.201 1.848 0.589 0.287 3.201 1.495 0.669 0.312 0.760 3.637 1.316 0.589 1.848 3.201 0.669 0.253

2.817 4.717 0.760 0.411 0.863 4.430 1.884 0.467 5.131 1.495 1.136 0.508 4.213 2.817 0.863 0.447 4.213 2.432 1.136 0.508 1.236 4.583 1.848 0.863 2.817 4.213 0.939 0.340

Wi 0.151 0.280 0.041 0.024 0.050 0.280 0.149 0.030 0.387 0.087 0.050 0.029 0.236 0.158 0.045 0.027 0.238 0.106 0.050 0.029 0.063 0.303 0.095 0.044 0.158 0.236 0.060 0.025

0.290 0.582 0.085 0.042 0.102 0.590 0.259 0.051 0.666 0.180 0.106 0.049 0.541 0.312 0.099 0.049 0.563 0.263 0.118 0.055 0.121 0.578 0.209 0.094 0.309 0.535 0.112 0.042

0.654 1.094 0.176 0.095 0.223 1.143 0.486 0.121 1.124 0.327 0.249 0.111 1.083 0.724 0.222 0.115 1.205 0.696 0.325 0.145 0.287 1.063 0.429 0.200 0.707 1.057 0.236 0.085

MWi

NWi

0.365 0.652 0.101 0.054 0.125 0.671 0.298 0.067 0.726 0.198 0.135 0.063 0.620 0.398 0.122 0.063 0.669 0.355 0.164 0.076 0.157 0.648 0.244 0.113 0.391 0.609 0.136 0.051

0.312 0.556 0.086 0.046 0.107 0.578 0.257 0.058 0.647 0.177 0.120 0.056 0.515 0.331 0.102 0.053 0.529 0.281 0.130 0.060 0.135 0.558 0.210 0.097 0.330 0.513 0.114 0.043

Table 4 shows that pairwise comparison of the alternatives for each criterion. Then, average weights and normalized weights for each criterion are calculated by Eq. 3 and 4 for each of them and shown in Table 5. Lastly, score for each alternative is calculates by summation of multiplying weight of criteria I and normalized weight of alternative for ith criteria. X7 i¼1

Nci  Nai

ð5Þ

Nc represents the normalized weight of criteria while Na represents the normalized weight of alternative for each criterion (Table 6).

294

S. Soygüder et al. Table 6. Ranking alternatives Weight of criteria A1 A2 A3 A4 Word error rate 0.310 0.312 0.556 0.086 0.046 0.399 0.107 0.578 0.257 0.058 0.099 0.647 0.177 0.120 0.056 0.031 0.515 0.331 0.102 0.053 0.042 0.529 0.281 0.130 0.060 0.097 0.135 0.558 0.210 0.097 0.023 0.330 0.513 0.114 0.043 Sum * Weights 0.262 0.508 0.172 0.057

As a result, alternative 2 is found as best alternative for the problem with having highest ranking score.

6 Conclusion In today’s world, organizations need to increase their automation in the business processes due to that competitiveness is harder than ever. After COVID -19 there are many things are changed in people’s daily routines and tasks. For example, working in the office have been destroyed and people in many industries have started to work at home. Also, organizations maintaining a business in the client commitment industry is chosen because of its items that are bots, IVRs, and versatile applications, need Speechto-text innovation which the organization doesn’t exist. In the study, seven criteria are selected. Criteria are selected as word error rate, technological leadership, unit price, hardware cost, support and maintenance, workflow tools and number of supported voice formats. In the research, gap in the literature about comprising suppliers of speech to text tried to be closed with considering different type of criteria simultaneously. After using integration of methodologies, and ranking scores are calculated, alternative 2 is found as best alternative. Result is found as appropriate to reality by decision makers because the alternative has highest quality level and technology leadership based on most of their opinion. In future, problem can extend with adding security main criteria because security is getting more and more important every day due to reaching personal information easily. Keeping personal information in secure is very important and complex topic for all vendors.

References 1. Awasthi, A., Govindan, K., Gold, S.: Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int. J. Prod. Econ. 195, 106–117 (2018) 2. Kumar, R., Padhi, S.S., Sarkar, A.: Supplier selection of an Indian heavy locomotive manufacturer: an integrated approach using Taguchi loss function, TOPSIS and AHP. IIMB Manag. Rev. 31, 78–90 (2019)

Hybrid Models for Vendor Selection Problem in Software Industry

295

3. Azimifard, A., Moosavirad, S.H., Ariafar, S.: Selecting sustainable supplier countries for Iran’s steel industry at three levels by using AHP and TOPSIS methods. Resour. Policy 57, 30–44 (2018) 4. Kim, J.Y., et al.: A comparison of online automatic speech recognition systems and the nonverbal responses to unintelligible speech (2019) 5. Herchonvicz, A.L., Franco, C.R., Jasinski, M.G.: A comparison of cloud-based speech recognition engines. X Computer on the Beach, 366–375 (2019) 6. Deuerlein, C., Langer, M., Seßner, J., Heßa, P., Franke, J.: Human-robot-interaction using cloud-based speech recognition systems. Procedia CIRP 97, 130–135 (2021) 7. Bisani, M., Ney, H.: Bosstrap esimates for confidence intervals in ASR performance evaluation. In: IEEE International Conference on Acoustic, Speech, and Signal Processing, Montreal (2004) 8. Mullinera, E., Malys, N., Maliene, V.: Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega 59, 146–156 (2016) 9. Taherdoost, H., Brard, A.: Analyzing the process of supplier selection criteria and methods. Procedia Manuf. 32, 1024–1034 (2019) 10. Rostamzadeh, R., Ghorabaee, M.K., Kannan, G., Esmaeili, A., Nobar, H., Bogadhi, K.: Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSISCRITIC approach. J. Clean. Prod. 175, 651–669 (2015) 11. Rouyendegh, D.B.: Developing an integrated ANP and intuitionistic fuzzy TOPSIS model for supplier selection. J. Test. Eval. 43, 20130114 (2015) 12. Rouyendegh, D.B., Yıldızbaşı, A., Üstünyer, P.: Intuitionistic Fuzzy TOPSIS method for green supplier selection Problem. Soft Comput. 24, 1–14 (2019) 13. Rouyendegh, B.D., Topuz, K., Dag, A., Oztekin, A.: An AHP-IFT integrated model for performance evaluation of e-commerce web sites. Inf. Syst. Front. 21(6), 1345–1355 (2018). https://doi.org/10.1007/s10796-018-9825-z 14. Uygun, Ö., Dede, A.: Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega 59, 146–156 (2016)

Supply Chain Network (SCN) Resilient Pattern Recognition and Intelligent Strategy Recommender Approach for the Post-COVID-19 Era Yaser Donyatalab(&) Industrial Engineering Department, University of Moghadas Ardabili, Ardabil, Iran [email protected]

Abstract. The Coronavirus outbreak and its different variants have damaged the global supply chains and affected suppliers for both goods and service providers unprecedentedly. The post-COVID-19 era could be considered full of uncertainty based on many changes that have happened. Some new parameters are introduced because of the outbreak and bring out new circumstances. These new challenges consequently will increase the ambiguity around the supply chain networks. This study is designed to investigate and evaluate the vagueness of supply chain networks in the post-COVID-19 time. The paper aims to study the strength of the SCN systems and find the related disruption patterns for each of the SCNs and then recommend appropriate strategies to increase the resilience of SCN systems. In the literature review part, we reviewed many articles that categorized the challenges. To catch the goal of evaluating the resilience of supply chain networks, some significant challenges are identified based on the literature part. An algorithm consists of three stages, first defining the uncertainty, second pattern recognition of disruption patterns, and third strategy recommender system to increase SCN resilience is proposed based on the SFS aggregation operator and logarithmic f-similarity measure. An illustrative example of the SCN resilience problem is evaluated by the proposed algorithm under the spherical fuzzy structure to show the applicability and reliability of the proposed method. Finally, this paper provides guidelines and strategies for increasing the resilience of supply chain networks in the post-COVID-19 outbreak. Keywords: Supply Chain Network (SCN)
Spherical fuzzy sets pattern recognition
Resilient in SCN
Post-COVID-19 era

1 Introduction and Literature Background During the last couple of years, the unprecedented crisis of SARS-CoV2 has severely engulfed all aspects of human life especially those related to business activities all around the world [1]. Also, it has very serious and negative impacts on supply chain management (SCM) [2]. After the initial detected case in China, the COVID-19 pandemic spread all over the world in a very short time [3], so it has high potential capabilities in challenging the supply chains. Thereafter it spread at a high pace over © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 296–307, 2022. https://doi.org/10.1007/978-3-031-09176-6_35

Supply Chain Network (SCN) Resilient Pattern Recognition

297

different countries and remained no chance of strengthening those supply chain networks. Different authors all over the world investigated this problem and it is published many articles considering different aspects of supply chain problems under the effect of coronavirus during the past couple of years [4–6]. The COVID-19 outbreak has significant impacts on the SCM including the parts related to the demand, transportation, supply, and storage. Also, it has diverse impacts on different sectors [7], for example in some sectors, it caused a decrease in demand, and in some other parts, there was a huge increment in demand rate or transportation issues. Logistic procedures also encountered changes that caused unacceptable interruptions in delivery but in some other cases, it eased the delivery process. It could be concluded that the uncertainty of the COVID-19 pandemic caused many unexpected impressions on the Supply Chain Networks (SCN). The United Nations Sustainable Development Goals (SDGs) report [8], declared that the COVID-19 pandemic postpone and slowed down human beings from achieving some of the defined goals. However, this viral disease creates unexpected situations and postpones the goals, but at the same time, it highlighted the significance of progress and being more resilient for us. The lesson behind the COVID-19 pandemic that we should concentrate on, is to create a big difference between pre-COVID and post-COVID viewpoints over the SCN. The goal of this paper is to evaluate conditions before and during the pandemic to reach a comprehensive analysis of the circumstance. Then could be capable of suggesting some guidelines and appropriate strategies for having a more resilient SCN in the post-COVID era. Much potential vagueness existed in the parameters of the SCN by itself. But the incidence of COVID-19 disturbed many under control conditions and brings out an immense increase in uncertainty of the problem. Based on the discussed uncertainty in the SCN problem under the COVID-19 epidemic structured to first determine the challenges [9]. So, it is required to investigate the challenges of SCN due to the outbreak era. Researchers investigated many challenges of the SCN problem [10] and noticed low levels of resilience exist in our SCN in front of those challenges [11, 12]. Authors studied the barriers that faced the SCN during the COVID-19 and categorized challenges [13]. It is recognized the challenges in four main groups which are enlisted in Table 2. In this paper, we classified the challenges based on their similarities in the four classes. Those four main groups are including sub-level challenges, so could consider clusters of disruption challenges. Four main categories are the Upstream Network (UN), Downstream Network (DN), Management Network (MN), and Transportation Network (TN). The nature of the problem is carrying different possibilities from upstream and downstream sides. The vagueness of the problems is not limited to the supply and demand sides and many uncertainties have come from other managerial and transportation issues. Zadeh, in 1965 [14], introduced fuzzy logic to handle the uncertainty of real-world cases. Many different generations of fuzzy sets are introduced by different researchers. The Intuitionistic Fuzzy Sets (IFSs) [15], The Hesitant Fuzzy Sets (HFSs) [16], Pythagorean Fuzzy Sets (PyFSs) [17], Picture Fuzzy Sets (PFSs) [18] are next developed generations of fuzzy sets, which are also characterized based on membership, non-membership and hesitant values between 0 and 1. Then in 2017, the concept of ortho pair logic and the novel q-Rung Ortho Pair Fuzzy Sets (q-ROFS) are

298

Y. Donyatalab

introduced in [19]. Then in 2018, C. Kahraman and F. K. Gundogdu introduced the Spherical Fuzzy Sets (SFSs) [20]. In this paper, the resilience of the SCN systems will be evaluated based on determined clusters of disruption challenges. Then it is possible to determine the patterns of disruption in each of the SCN systems specifically. The determined patterns of each of the SCN systems will be compared with different strategies from the strategy pool. The most effective strategy or set of strategies will be revealed to satisfy the needed resilience for our SCN system. For this goal, we defined the SCN challenges and designed an SFSs framework to handle the uncertainties, and then we used some SFSs aggregation operators to combine the commented data and determine the patterns. Then for comparison applied the features of the novel SFS f-similarity (logarithmic function) based on Minkowski distance for each alternative SCN. The appropriate strategy or set of strategies that could satisfy the needed resilience based on the determined disruption pattern will the output of our problem. This manuscript tries to deliver a clear introduction to the SCN problem, challenges, and patterns of SCN disruptions, strategies proposed for the post-COVID environment, and the approach together with a detailed literature review. In this manuscript, we tried to define an intelligent algorithm to evaluate the relationship between disruption patterns and strategies to increase the resilience of the SCN systems for the post-COVID era. Motivated by the above discussion, we first define the problem in the SFSs environment and then proposed the algorithm based on Aggregation and fsimilarity measures to evaluate the problem. The rest of the chapter is designed as follows. In Sect. 2, some concepts of SFSs are discussed and the algorithm is proposed step by step. Section 3, represented the evaluation, results, and discussion around the SCN problem, and finally, in Sect. 4 all material is summarized and the paper is concluded.

2 Methodology Spherical Fuzzy Sets (SFSs) ~ s of Definition 1 [21]. Let X be the universal set and xi 2 X; 8i ¼ 1; 2; . . .n, the SFS A ~ the universe discourse X is defined as xi as an element of As with membership, non~ s will be expressed mathematically in membership and hesitancy degree values, then A the form of: n o ~ s ¼ hxi ; l ~ ðxi Þ; # ~ ðxi Þ; h ~ ðxi Þijxi 2 X ; A As As As

ð1Þ

where lA~ s ðxi Þ; #A~ s ðxi Þ; hA~ s ðxi Þ stands for membership, non-membership, and hesitancy degrees respectively, which belong to the interval ½0; 1 and satisfies the condition that the sum square of these values is between 0 and 1: SA~ s ðxi Þ ¼ l2A~ s ðxi Þ þ #2A~ s ðxi Þ þ h2A~ s ðxi Þ ! 0  SA~ s ðxi Þ  1;

ð2Þ

Supply Chain Network (SCN) Resilient Pattern Recognition

299

~ s will be as follows: then refusal degree RA~ s of u in the spherical fuzzy set A RA~ s ðxi Þ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  SA~ s ðxi Þ;

ð3Þ

Definition 2 [21]. Score (Sc) and accuracy (Ac) functions of sorting an SFN are defined as follows, respectively:   ~ ¼ Sc A

    hA~ 2 hA~ 2 lA~   #A~  ; 2 2

  ~ ¼ l2~ þ #2~ þ h2~ ; Ac A A A A

ð4Þ ð5Þ

~ and B ~ B ~ are SFNs then A\ ~ if and only if: Remark 1. Note if A     ~ \Sc B ~ , i. Sc A or         ~ ¼ Sc B ~ \Ac B ~ and Ac A ~ . ii. Sc A ~¼ Definition 3 [20]. Let X be the universal set and xi 2 X; 8i ¼ 1; 2; . . .n then A   hxi ; lA~ ðxi Þ; #A~ ðxi Þ; hA~ ðxi Þijxi 2 X be a spherical fuzzy set (SFS) with corresponding Pn weight vector w ¼ fw1 ; w2 ; . . .; wn g; wi 2 ½0; 1; i¼1 wi ¼ 1. Spherical Fuzzy Weighted Arithmetic Mean ðSFWAMÞ and Spherical Fuzzy Weighted Geometric Mean ðSFWGM Þ are defined as follows respectively: 

wi 0:5 Q  wl Qn 2 ; ni¼1 #A~ ðxi Þ ~ ðxi Þ i¼1 1  lA

wi Q

wi 0:5 Q n n 2 2 2  i¼1 1  lA~ ðxi Þ  hA~ ðxi Þ ~ ðxi Þ i¼1 1  lA   ~ ¼ SFWAMW ¼ A



1

wi 0:5  wi Q ; 1  ni¼1 1  #2A~ ðxi Þ ;

wi Q

wi 0:5 Q n 2  ni¼1 1  #2A~ ðxi Þ  h2A~ ðxi Þ ; ~ ðxi Þ i¼1 1  #A   ~ ¼ SFWGMW ¼ A

Qn  i¼1

lA~ ðxi Þ

ð6Þ

ð7Þ

Spherical Fuzzy f-Similarity Measure based on Minkowski Distance:  ~ s ¼ xi hl ~ ðxi Þ; # ~ ðxi Þ; Definition 4. Suppose that there are two sets of SFSs A A  A ~ s ¼ xi hlB~ ðxi Þ; #B~ ðxi Þ; hB~ ðxi Þijxi 2 X ; 8i ¼ hA~ ðxi Þijxi 2 Xg; 8i ¼ 1; 2; . . .n and B 1; 2; . . .n be two SFSs so that lðxi Þ, #ðxi Þ and hðxi Þ are membership, non-membership, and hesitancy degrees, respectively. Then, Spherical Fuzzy Similarity based on Minkowski distance is given as follows:

300

Y. Donyatalab

ð8Þ In definition 4, different spherical fuzzy distance measurements could be applied in Eq. (8), together with different linear and non-linear functions which will satisfy the needed features according to the investigated problem in our proposed spherical fuzzy f-similarity measures based on different distances. ~s Because the maximum and minimum distance between two spherical fuzzy sets A   ~ ~ ~ and Bs is 0  d SFS A; B  1, so Eq. (8) could be written as follows: ð9Þ In definition 4, by using different linear and non-linear functions, a vast variety of similarity measures could be obtained from Eq. (8). In this manuscript, we will apply the Logarithm function f ð xÞ ¼ log2 ð2  xÞ and then we will get spherical fuzzy distance-based logarithmic similarity measure as follows: ð10Þ  ~ s ¼ xi hl ~ ðxi Þ; # ~ ðxi Þ; Definition 5. Suppose that there are two sets of SFSs A A  A ~ s ¼ xi hlB~ ðxi Þ; #B~ ðxi Þ; hB~ ðxi Þijxi 2 X ; 8i ¼ hA~ ðxi Þijxi 2 Xg; 8i ¼ 1; 2; . . .n and B 1; 2; . . .n be twoPSFSs with corresponding weight vector w ¼ fw1 ; w2 ; . . .; wn g, where wi 2 ½0; 1 and ni¼1 wi ¼ 1; 8i ¼ 1; 2; . . .; n, so that lðxi Þ, #ðxi Þ and hðxi Þ are membership, non-membership, and hesitancy degrees, respectively. Then, Spherical Fuzzy Weighted Minkowski Distance-based f-Similarity (Logarithm function) given as follows:

ð11Þ

Where a  1. Remark 1. Spherical Fuzzy Weighted Hamming Distance-based f-Similarity (Logarithm function)

and Spherical Fuzzy Weighted Euclidean Distance based

f-Similarity (Logarithm function)

could result from Spherical Fuzzy

Weighted Minkowski Distance-based f-Similarity (Logarithm function) changing values for a in Eq. (11) as follows:

, by

Supply Chain Network (SCN) Resilient Pattern Recognition

301

i. If a ¼ 1 Spherical Fuzzy Weighted Minkowski Distance-based f-Similarity (Logarithm function)

will turn into Spherical Fuzzy Weighted Hamming

Distance-based f-Similarity (Logarithm function)

:

ð12Þ

ii. If a ¼ 2 Spherical Fuzzy Weighted Minkowski Distance-based f-Similarity (Logarithm function)

will turn into Spherical Fuzzy Weighted Eucli-

dean Distance based f-Similarity (Logarithm function)

: ð13Þ

Proposed Disruption Pattern Recognition and Resilient Strategy Recommender Algorithm for SCN: This section will provide the steps of the proposed algorithm for disruption pattern recognition in the SCN system and suggest strategies to increase resilience. The SCN is considered under the SFSs environment to evaluate the uncertainty of the COVID-19. The suggested algorithm is designed in 3 stages: The first stage defines the challenges together with classification of the challenges in the right cluster. The clusters constructed in this step are the disruption patterns of the SCN system. Then comments from experts will gather and transform into the SFSs data. In stage 2, it will be determined the most proper patterns for each SCN system will be determined by applying the SFSs aggregation operators and similarity measures. Stage 3, will cover the strategy recommender stage. In this stage, the similarity measures will be calculated, and by establishing the concordance matrices the strategies of the strategy pool will be compared for each alternative against related disruption patterns, and recommend the most appropriate strategy or set of strategies to provide the resilience of the SCN system. So, the stages and steps of the proposed algorithm are: Stage I. Define Challenge, Disruption Patterns, and Strategy Pool: Step 1. Collect the decision matrices of judgments. Consider a group of d decisionmakers, D ¼ fD1 ; D2 ; . . .; Dd g with corresponding weight vector sj ¼ fs1 ; s2 ; . . .; sd g P where dj¼1 sj ¼ 1, sj  0, which participated in a group decision-making problem, where a finite set of alternatives, A ¼ fA1 ; A2 ; . . .; AM g are evaluated based on a finite set of criteria, C ¼ fC1 ; C2 ; . . .; C N g, with corresponding weight vector wi ¼

302

Y. Donyatalab

PN fw1 ; w2 ; . . .; wN g where i¼1 wi ¼ 1, wi  0. Each class of criteria could be constructed of some sub-level criteria. So, we could have the set of criteria C ¼ fC i ji ¼  1; 2; . . .; N g which  are patterns of SCN disruptions, and a set of sub-criteria C i ¼ C ip jp : 1; 2; . . .; P that represents the challenges in different patterns. Comments of decision-makers are stated by using linguistic terms introduced in Table 1. Each decision-maker d expresses his opinion about the performance of alternative Am d d regard to challenge C ip using Z , so the notation will be like this: Z~ mðipÞ ¼ ld ; mðipÞ

mðipÞ

#dmðipÞ ; hdmðipÞ Þ, 8d; m; i; p: Step 2. Construct the strategy pool matrix based on the literature review part. In the pool, we will have at least one appropriate strategy for each pattern of disruption and then collect the judgment of each expert to construct the strategy pool matrix for each expert. The matrix will be the set of strategies S ¼ fSis ji ¼ 1; . . .; N ^ s ¼ 1; . . .; Sg which are stated based on linguistic terms in Table 1. Step 3. Transform all individual decision matrices of challenges and the strategy pool into SFNs by using values defined in Table 1. Stage II. Disruption Pattern Recognition: Step 4. Aggregate the individual decision matrices of different experts by using aggregation operators in Eq. (6) to get the GDM matrix. Step 5. Aggregate the GDM matrix based on each cluster of challenges to get the aggregated pattern GDM matrix, in this step will use the aggregation operator in Eq. (6). Step 6. Evaluate the score matrix by applying the SFSs score function in Eq. (4). Step 7. Establish the correspondence matrix K ¼ ½kmi MN which are showing which pattern is significantly describing the resilient disruption of the SCN alternatives. This step will rank the patterns for each alternative based on the score matrix. Stage III. Strategy RecommendaTable 1. SFS linguistic scales tion to Increase SCN Resilience:  ~ s SFNs (l; #; hÞ Step 8. Aggregate the strategy Spherical fuzzy linguistic scales LS pool matrices of experts by using Absolutely high possible (AHP) (0.9, 0.1, 0.1) aggregation operators in Eq. (6) to get Very high possible (VHP) (0.8, 0.2, 0.2) the aggregated strategy pool matrix. High possible (HP) (0.7, 0.3, 0.3) Step 9. Construct the concordance Slightly high possible (SHP) (0.6, 0.4, 0.4) matrix C for each disruption pattern Equally possible (EP) (0.5, 0.5, 0.5) between the strategies related to the Slightly low possible (SLP) (0.4, 0.6, 0.4) (0.3, 0.7, 0.3) pattern and the SCN alternatives. Low possible (LP) (0.2, 0.8, 0.2) Elements of the concordance matrix Very low possible (VLP) (0.1, 0.9, 0.1) are calculated by using the spherical Absolutely low possible (ALP) fuzzy weighted Minkowski distancebased f-Similarity (Logarithm function)

. So each element will describe the

similarity between strategies in the strategy pool of each alternative in the GDM matrix in comparison. Step 10. Rank the strategies for alternatives in concordance matrices and select appropriate strategies for each alternative based on the concordance matrix C and correspondence matrix K for each disruption pattern.

Supply Chain Network (SCN) Resilient Pattern Recognition

303

3 Evaluation, Results, and Discussion For the evaluation part, we assume the SCN problem with 3 different experts D ¼ fD1 ; D2 ; D3 g and related weight vector of f0:35; 0:25; 0:4g, then we have five different SCN alternatives. The four clusters of challenges and sub-level challenges which have all equal weights are introduced in Table 2. The experts’ comments for each alternative based on different challenges are gathered and constructed in the individual decision matrices. It will use the linguistic terms in Table 1 for constructing the individual decision matrices. Strategy pool matrices are established by experts commenting and for transforming data into the SFNs environment Table 1 is applied. Then, based on steps 4, 5 the spherical fuzzy arithmetic aggregation operator Eq. (6) is applied to combine the ideas, and then score values of the aggregated GDM matrix are evaluated based on Eq. (6). Based on step 7, correspondence matrix K is established among disruption patterns and SCN alternatives shown in Fig. 1a.   Table 2. SCN challenges Cip and patterns of disruption ðCi Þ Upstream Network (UN) 11 : Scarcity of Material (SoM) 12 : Scarcity of Labor (SL) 13 : Suboptimal Substitute Adoption (SSA) d. 14 : Inconsistency in Supply (IS) 2. 2 : Downstream Network (DN) a. 21 : Uncertainty in Demand Behavior (UDB) b. 22 : Uncertainty in Demand Quantity (UDQ) 1.

1:

a. b. c.

Management Network (MN) 31 : Constraint in Capacity (CC) 32 : Delay of Delivery Management (DDM) 33 : Suboptimal Manufacturing/Service (SM/S) 4. 4 : Transportation Network (TN) a. 41 : Transportation Unavailability and Delays (TUD) b. 42 : Last-Mile Delivery (LMD) Transportation Costs 43 : (TC) 3.

3:

a. b.

The strategies in Table 3 which construct the strategy pool matrices are combined based on comments of different 3 experts in step 8 by using the spherical fuzzy arithmetic aggregator, then based on step 9, we construct the concordance matrices C between strategies for each alternative by considering different patterns. In this step, the concordance matrices are constructed by using spherical fuzzy weighted Minkowski distance-based f-Similarity (Logarithm function)

relations. The pattern-

strategy relationship for the SCN alternatives based on the concordance matrix by using logarithmic f-similarity relations is shown in Fig. 1. b and Table 4.

304

Y. Donyatalab Table 3. Strategies Pool ðSis Þ

- S11 : Provide alternative material/labor - S12 : Plan to use contractual labor - S13 : Forming Umbrella Agreements - S14 : Labor welfare & insurance strategies - S15 : Business continuity plans - S16 : Additive and automated manufacturing - S17 : Network insight development - S18 : Supplier risk management - S21 : Excellent customer response & delight - S22 : Creating direct and safe channels for feed backward and data collecting from DN - S23 : Real-time visibility and tracking to customers - S24 : Real-time visibility and tracking of customers - S25 : Differential pricing to customers

- S31 : Develop local (onshore) vendors - S32 : Use AI and data analytics - S33 : Product mix optimization - S34 : Scenario planning techniques - S35 : Tracking and transparency of the delivery process - S36 : Omni-channel business model - S37 : Scenario planning techniques - S41 : Use of mobility solutions – IoT, Autonomous Vehicles & Drones - S42 : Partnering with third party logistic and warehousing providers - S43 : Supply chain control tower technology for real-time tracking and monitoring of transportation - S44 : Logistic fleet maintenance

Results are demonstrating the most significant approach to facing specific patterns and the priority of those strategies that should take into consideration. In this paper, based on priority level, we select three top-ordered strategies for each pattern and those selected significant strategies are the output of strategy recommender to the SCN for the post-COVID era. These strategies are the top approaches that could directly and effectively improve the negative effects of the pandemic on the SCN to prepare more resilience of any SCN system for post-COVID conditions.

4

4

3

3

4 3

2

2

2

1

1

1

3

1

0 Rank C1 Rank C2 Rank C3 Rank C4 A1 A2 A3 A4 A5

(a)

(b)

Fig. 1. (a): Correspondence Matrix K (b): Pattern-Strategy relationship for SCN alternatives and suggest strategies by applying logarithmic Minkowski distance-based similarity concordance

Supply Chain Network (SCN) Resilient Pattern Recognition

305

Table 4. Pattern-Strategy relationship to suggest appropriate strategies based on logarithmic concordance similarity for each SCN alternatives SCN alternatives

Patternstrategy

A1 A1 A1 A1 A1 A1 A1 A1 A1 A2 A2 A2 A2 A2 A2 A2 A2 A2 A3 A3 A3 A3 A3

C1-S15 C1-S13 C1-S17 C3-S32 C3-S37 C3-S33 C2-S21 C2-S23 C2-S25 C1-S15 C1-S13 C1-S17 C3-S32 C3-S37 C3-S33 C2-S21 C2-S23 C2-S25 C3-S32 C3-S37 C3-S33 C1-S15 C1-S13

Strategies (logarithmic concordance) 0.854583077 0.698399788 0.698399788 0.882956065 0.864221197 0.740931878 0.858923434 0.758470727 0.753110097 0.85618412 0.699354175 0.699354175 0.826056604 0.80590102 0.676784732 0.817822663 0.714375267 0.70699408 0.857489162 0.837088883 0.712478701 0.790873509 0.625384713

SCN alternatives

Patternstrategy

A3 A3 A3 A3 A4 A4 A4 A4 A4 A4 A4 A4 A4 A5 A5 A5 A5 A5 A5 A5 A5 A5

C1-S17 C4-S42 C4-S43 C4-S41 C1-S15 C1-S13 C1-S17 C3-S32 C3-S37 C3-S33 C4-S42 C4-S43 C4-S41 C4-S42 C4-S43 C4-S41 C1-S15 C1-S13 C1-S17 C3-S32 C3-S37 C3-S33

Strategies (logarithmic concordance) 0.625384713 0.576013174 0.57282274 0.544227903 0.885070868 0.733213055 0.733213055 0.907137518 0.887830047 0.768600073 0.644354945 0.641293583 0.613721004 0.616172367 0.613327724 0.584974666 0.824339737 0.664011207 0.664011207 0.800067398 0.779342236 0.64793649

4 Conclusion In this article, we discussed the challenges around the SCN problem under the impact of the COVID-19 pandemic and proposed an algorithm to handle the uncertainty of this complex problem based on the SFSs environment. Each of the SCN systems learns from the challenges and the understanding of SCN challenges will help to strategy recommender system to have the most appropriate guidelines for the post-COVID-19 era. The proposed algorithm is designed in 3 different stages. In the first stage, it is needed to define the problem, challenges, patterns of disruptions, and collecting the strategy pool then transform information into SFSs is in this stage. Then in the second stage, the expert's comments are combined by using the SFS arithmetic aggregation operator to reach a consensus combined decision matrix. Thereafter, it will be possible to determine which pattern of disruption is related to which SCN alternative. In the final stage after understanding the challenges and determining the pattern specifically

306

Y. Donyatalab

for each SCN, it would be reasonable to check among the solution to find the most appropriate ones. In this stage, the SFS logarithmic f-similarity measure based on Minkowski distance will be calculated between strategies and patterns for each alternative to constructing the concordance matrices. Then based on that we will suggest the most effective guidelines of strategies for establishing a more resilient SCN system for the post-COVID-19 environment. We showed an illustrative example of the proposed algorithm through the SCN problem including different 3 experts, 5 SCN alternatives, and 10 challenges of 4 different patterns. This evaluation and results represent the applicability of the proposed algorithm. For future studies, we propose various applications in different fields of study like different aspects of SCN problems and also real case study applications like real SCN in the fields of financial, banking, health care, and manufacturing systems.

References 1. Donthu, N., Gustafsson, A.: Effects of COVID-19 on business and research (2020) 2. Verma, S., Gustafsson, A.: Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach. J. Bus. Res. 118, 253–261 (2020). https://doi.org/10.1016/j.jbusres.2020.06.057 3. Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability: a position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58, 2904 (2020). https://doi.org/10.1080/00207543.2020.1750727 4. Govindan, K., Mina, H., Alavi, B.: A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: a case study of coronavirus disease 2019 (COVID-19). Transp. Res. Part E: Logist. Transp. Rev. 138, 101967 (2020). https://doi.org/10.1016/j.tre.2020.101967 5. Farid, F., Donyatalab, Y.: Novel spherical fuzzy eco-holonic concept in sustainable supply chain of aviation fuel. In: Kahraman, C., Aydın, S. (eds.) Intelligent and Fuzzy Techniques in Aviation 4.0. SSDC, vol. 372, pp. 201–235. Springer, Cham (2022). https://doi.org/10. 1007/978-3-030-75067-1_9 6. Lopes de Sousa Jabbour, A.B., Chiappetta Jabbour, C.J., Hingley, M., Vilalta-Perdomo, E. L., Ramsden, G., Twigg, D.: Sustainability of supply chains in the wake of the coronavirus (COVID-19/SARS-CoV-2) pandemic: lessons and trends. Mod. Supply Chain Res. Appl. 2, 117–122 (2020). https://doi.org/10.1108/mscra-05-2020-0011 7. Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E: Logist. Transp. Rev. 136, 101922 (2020). https://doi.org/10.1016/j.tre. 2020.101922 8. Filho, W.L., Brandli, L.L., Salvia, A.L., Rayman-Bacchus, L., Platje, J.: COVID-19 and the UN sustainable development goals: threat to solidarity or an opportunity? Sustain. 12, 5343 (2020). https://doi.org/10.3390/su12135343 9. Raj, A., Mukherjee, A.A., de Sousa Jabbour, A.B.L., Srivastava, S.K.: Supply chain management during and post-COVID-19 pandemic: mitigation strategies and practical lessons learned. J. Bus. Res. 142, 1125–1139 (2022). https://doi.org/10.1016/J.JBUSRES. 2022.01.037

Supply Chain Network (SCN) Resilient Pattern Recognition

307

10. Queiroz, M.M., Ivanov, D., Dolgui, A., Fosso Wamba, S.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-02003685-7 11. van Remko, H.: Research opportunities for a more resilient post-COVID-19 supply chain – closing the gap between research findings and industry practice. Int. J. Oper. Prod. Manag. 40, 341–355 (2020). https://doi.org/10.1108/IJOPM-03-2020-0165 12. Golan, M.S., Jernegan, L.H., Linkov, I.: Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic (2020) 13. Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B.L., Rajak, S.: Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. Int. J. Prod. Econ. 224, 10754 (2020). https://doi.org/10.1016/j. ijpe.2019.107546 14. Zadeh, L.A.: Fuzzy sets. Inf. Control. (1965). https://doi.org/10.1016/S0019-9958(65) 90241-X 15. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. (1986). https://doi.org/10.1016/ S0165-0114(86)80034-3 16. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. (2010). https://doi.org/10.1002/int.20418 17. Yager, R.R.: Pythagorean fuzzy subsets. In: 2013 Joint IFSA World Congress NAFIPS Annual Meeting, vol. 2, pp. 57–61 (2013). https://doi.org/10.1109/IFSA-NAFIPS.2013. 6608375 18. Cuong, B.C., Kreinovich, V.: Picture fuzzy sets - a new concept for computational intelligence problems. In: 2013 3rd World Congress on Information and Communication Technologies, WICT 2013 (2014) 19. Yager, R.R.: Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 25, 1222–1230 (2017) 20. Gündoǧdu, F.K., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst. (2019). https://doi.org/10.3233/JIFS-181401 21. Donyatalab, Y., Farrokhizadeh, E., Garmroodi, S.D.S., Shishavan, S.A.S.: Harmonic mean aggregation operators in spherical fuzzy environment and their group decision making applications. J. Mult. Log. Soft Comput. 33, 565–592 (2019)

Remote Access and Management of Plants Experience During Pandemics Time Across the World Nodirdek Yusupbekov1, Farukh Adilov2, Maksim Astafurov2(&), and Arsen Ivanyan2 1

Tashkent State Technical University, Uzbekistanskaya-2 prospect, 100095 Tashkent, Republic of Uzbekistan 2 XIMAVTOMATIKA Ltd., 2, Afrosiyob str., 1000311 Tashkent, Republic of Uzbekistan {Farukh.Adilov,Maksim.Astafurov,Arsen.Ivanyan} @himavtomatika.uz

Abstract. Considered experience in remote access, data acquisition, and control of plants worldwide based on the experience of multiple companies recognized world-class leaders in industrial automation, cyber security, SCADA & HMI development, Industrial Internet of Things, commissioning, and upgradation such as ABB, Belden, Honeywell, SIEMENS, Schneider Electric, etc. Keywords: Automated control
Remote access
SCADA
Remote control Process industry
Cyber security
Industrial internet of things




1 Introduction COVID-19 pandemic happened across the Globe and affected each of us, despite nationality, place of residence, position, or income. In one way or another, all countries applied travel bans, quarantines, and curfews to stop the spread of disease. The government urged people to stay in their homes if they can and not leave them except in case of emergency. In such conditions normal way to perform a job had become impossible. Many employees are forced to use remote access to a workplace arranged by their companies to decrease the number of contacts between people in offices, canteens, meeting rooms, etc., but what to do with critical plants that cannot be partially released or stopped? This question is actual for any facility regardless of size, complexity, or product being produced. So, in this article, we tried to give a breathy overview of existing or developing solutions that could help to mitigate the pandemic consequences.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 308–316, 2022. https://doi.org/10.1007/978-3-031-09176-6_36

Remote Access and Management of Plants Experience

309

2 Problem Statement There are several ways to avoid shutdown or unexpected conditions due to a reduced number of personnel on-site [1]: 1. Support the critical teams who are on site. 2. Bring in proven and secure technology. 3. Take advantage of virtual support. As an example, in one of the Honeywell’s plants - a plastics manufacturing facility in Orange, Texas - as coronavirus was starting to spread, management realized that having everyone on-site at the plant was impossible. Immediately the operating model has been changed - software for accessing the workplace remotely was quickly implemented, and the plant remain operational without interruption. This test on its manufacturing facility helps to do the same for others. By using our technology, easily identify how we can help our customers perform remote work while access to the workplace is limited. With a reduced number of personnel left on-site, it is mandatory to have round-the-clock online support from the rest of the team by using e.g., Digital Video Manager to control the situation in real-time. Physical equipment and devices can be monitored remotely with a secure application, such as Measurement IQ for Gas technology, or big facilities, it is Experion Elevate. [1, 2, 10]. The Latest Experion release enhances SCADA functionality by adding cloud computing and storage options, but it also elevates control capabilities for operations that need to get up and running quickly, with few people and little upfront capital expenditure. It is independent of the control center or backup center because having its own servers is not mandatory anymore - the cloud infrastructure can be leveraged by the service provider. Depending on a requirement a plant can be started with fewer assets and in the future, some more could be added or deleted. Software versions are always kept current. It is not mandatory anymore to have a full SCADA team - the one person, running SCADA is also providing cybersecurity, because Experion Elevate is by its nature secure – it is a proven solution, and it is always up to date. Another software product from Honeywell - Forge for Industrial can view at a plant and an asset level and see where there are risks or opportunities where they can increase production to improve profitability and operations [3]. All mentioned above can be used in combination with Collaboration Station - the specialized software package for industrial process industries, with uniform standardized data representation techniques for transferring values from field to enterprise-level, to help customers improve their strategy proactively reacting to changes. “From the control room to the boardroom, Collaboration Station drives culture change and breaks down silos across roles and geographies by integrating enterprise-wide data from business, process, assets, and people” [4–6] (Figs. 1 and 2).

310

N. Yusupbekov et al.

Fig. 1. Collaboration station - use on the process control network (PCN).

Fig. 2. Collaboration station - use on the business network (L4).

Caterpillar company also developed remote monitoring technology for its genset equipment to expand troubleshooting, data collection, and generation as well as analysis far beyond the basic SCADA monitoring (Fig. 3).

Fig. 3. Cat connect from caterpillar.

Remote Access and Management of Plants Experience

311

“With its Cat Connect technology, users can monitor an unlimited number of gensets at sites worldwide arranging data in a single dashboard view – the application can synthesize data across multiple sites and geographic areas and help users compare the performance of sites and individual assets”- Caterpillar says. As a general rule, the site SCADA system is isolated from the outside world as much as possible, thus the ability to extract data from outside, and monitor and compare generator sets at multiple sites is limited, however, if remote monitoring is used, this will give users the ability to aggregate retrieved information with business data for making prompt decisions that can on the fly adjusting the sequence of steps that remains to be performed to fulfill the planned objectives [7]. But not only running industrial processes have been affected by pandemic perturbations – SAT (site acceptance test) or FAT (factory acceptance test) activities as well need to be conducted remotely, and if a software or HMI part can be checked utilizing Remote Desktop or Microsoft Teams, Zoom applications, then hardware testing is required real-time video - Fieldstreaming system from Adtance (Support Module + Adtance Support software) can help with this (Fig. 4).

Fig. 4. Adtance support module.

Basically, the Fieldstreaming system comes as a portable case with any number and types of cameras (configuration depends on customer’s requirements) as well as a power supply unit, an internet cable, and connection cables for each camera – so it means a user can make any combination of cameras with different angles. By using remote control software - Adtance Support, the stationary cameras can be zoomed, and tilted allowing the technical or engineering Team to examine a machine or hardware part from various angles [8]. As mentioned above virtual support is one of the key factors that simplifies remote operation, so Honeywell designed and implemented Intelligent Wearables – hands-free,

312

N. Yusupbekov et al.

wearable connected technology solution to improve productivity and compliance with process procedures, capture the expertise of experienced workers and provide critical insights and information effectively to trainees or maintenance personnel in the field, it also reduces the need for site visits experts, empower the worker to continue learning, becoming their best and effectively share their knowledge with colleagues [9] (Fig. 5).

Fig. 5. Honeywell intelligent wearables.

Also, one of the important aspects mentioned above is security, in our case, it is cyber security: though most means of remote access to control systems use one-way flows of information from the system to the external device, the number of applications allowing some levels of remote control are increasing. These kinds of remote applications could be vulnerable to different attack types, such as man-in-the-middle (MiTM) attacks or through another malicious application that could be installed on the device. Awareness of the threats to such systems is critical to developing a countermeasure. The four main remote application threat types that manufacturers should address are: • • • •

Unauthorized physical or remote access to device or data stored. Communication channel compromise (MiTM). Application compromise. Directly/indirectly influencing an industrial process or industrial network infrastructure.

Leaking data via the Internet could give attackers a more thorough understanding of the industrial process, ICS infrastructure, network addressing schemes, etc., but remote access applications are even more dangerous – external devices can be connected through unsecure mobile broadband or public Wi-Fi access points allowing attackers to sniff, replay or alter communication data between the application and remote SCADA endpoint, especially for those which are using open data protocols like Modbus,

Remote Access and Management of Plants Experience

313

PROFIBUS, etc. Applications themselves could include various vulnerabilities on both the server-side and the client-side – for example, access control list issues/incorrect permission checking, remote code/command execution, etc. [11]. Multiple companies and non-governmental organizations across the world are doing their best to resolve the situation. ABB, Emerson, CyberX, MIT, and many others urge manufacturers and end-users to enforce measures against cyber-attacks. Safety practices are already more widely understood and ingrained than cybersecurity practices, so, safety comparisons help a lot with cybersecurity acceptance. Building efficient cybersecurity for industrial SCADA systems must be using a defense-in-depth strategy, similar to the layers-of-protection analysis for safety systems, this means that, like safety, cybersecurity must be in-built into control networks on the design stage [12] (Fig. 6).

Fig. 6. Cyber-safety - a four-step method developed by MIT researchers.

Based on the model above engineers can analyze their plant’s assets and build a model, which is identifying control actions that could be unsafe, disruptive, or damaging to determine new requirements that would prevent the worst possible outcomes. For example, CyberX company developed the CyberArk solution with a wide range of capabilities for securing privileged credentials and controlling remote access to critical assets such as SCADA workstations and Human Machine Interfaces (HMIs).

314

N. Yusupbekov et al.

It is allowing industrial organizations to immediately detect anomalous behavior indicating a potential breach of the OT network, continuously monitor and audit privileged user sessions, perform incident response, threat hunting and threat modeling around remote access, etc. [13]. Belden company in partnership with Forescout concentrated its attention on industrial network segmentation and threat detection to provide transparency and safeguarding from cyber-attacks, computer virus infection, or sabotage that can affect the enterprise infrastructure in whole or in part (Fig. 7).

Fig. 7. Application of switches and routers with Forescout.

“Network segmentation will be imperative to meet availability requirements for real-time, next-generation industrial automation networks, Belden and Forescout will allow operators to begin segmenting their networks today with existing infrastructure, while also providing a trajectory for additional controls as next-generation networks are deployed over time” [14]. As more operators move to support remote work, they are becoming more vulnerable to cybersecurity issues, so Honeywell also taking initiative-taking steps and has released the latest version of its Forge Cybersecurity Suite. This software solution can be used for industrial-grade remote access, cybersecurity risk monitoring, asset analysis and planning, mitigation without interruption, etc. The newest release addresses common weak points in operations technology domains, including [14]: • Safely moving and using operations data. • Strengthening endpoint and network security. • Improving cybersecurity compliance (Fig. 8).

Remote Access and Management of Plants Experience

315

Fig. 8. The honeywell forge cybersecurity platform.

Along with networks, Honeywell provides protection to USB devices, because initially, the network was the primary vector for an attack on a process control facility, so air gaps were put into place, along with firewalls, demilitarized zones, and other technologies to lock the network down tight. With the SMX solution, customers can check in their USB devices at an SMX Intelligence Gateway, usually centrally located within a facility. The ruggedized device analyzes the content of the USB device and either allows it for use or quarantines suspicious items in a separate folder. The SMX client software provides the other end of the enforcement since no USB device will work on a protected system until it has been scanned by the gateway. SMX has the possibility to connect to Honeywell’s the world’s first industrial threat intelligence cloud, the Advanced Threat Intelligence Exchange (ATIX), so when ATIX learns about a threat, it spreads that information across the network [15, 16].

3 Conclusion According to a recent study by LNS Research, approximately 50% of industrial transformation leaders have an autonomous plant initiative formalized, and an estimated 41% of these leaders are accelerating their autonomous plant efforts because of the pandemic. This growing trend may cause some challenges because different companies can achieve their goals at different times - industrial organizations need to develop and implement initiatives that align with their trust levels and risk management. As well one of the most interesting results of LNS Research’s study on autonomy and remote operations is the optimism among industrial company leaders that things will be returning to the “old normal” - the time before COVID-19 spread worldwide. A substantial 42% said they believed things would return to pre-pandemic conditions.

316

N. Yusupbekov et al.

Only time can reveal who was right, but the experience gained during this period will accelerate the development of a substantial number of industrial areas, software, and antivirus companies [17].

References 1. Tips to Run Operations Remotely. Honeywell International Inc. (2020) 2. Scalable, Current, Real-Time SCADA in the Cloud. Automation World magazine (2017) 3. Yusupbekov, N., Abdurasulov, F., Adilov, F., Ivanyan, A.: Improving the efficiency of industrial enterprise management based on the forge software-analytical platform. In: Intelligent Computing, LNNS, vol. 283, pp. 1107–1113 (2022). https://doi.org/10.1007/9783-030-80119-9_74 4. Honeywell introduces advanced technologies to shape the future of plant control rooms. News release. Honeywell International Inc. (2013) 5. Honeywell Experion Collaboration Station. Control Engineering (2013) 6. Honeywell Connected Plant. Collaboration Station Specification. Honeywell International Inc. (2018) 7. Remote Monitoring for Distributed Power Generation. Automation World magazine (2020) 8. Reinventing Remote Management. Automation World magazine (2021) 9. Honeywell helps transform Mexican petrochemical leaders’ operations. Automation World magazine (2019) 10. Honeywell launches a connected plant solution for easy health monitoring of midstream gas metering systems. Automation World magazine (2018) 11. Know Your Remote Access SCADA Vulnerabilities. Automation World magazine (2018) 12. Cybersecurity Lessons from Safety. Automation World magazine (2020) 13. CyberX Adds Secure Remote Access Integration for Critical Industrial Networks. Automation World magazine (2017) 14. Belden and Honeywell Expand Cybersecurity Protections. Automation World magazine (2020) 15. Take the Cyber Battle to the Clouds. Automation World magazine (2017) 16. Adilov, F.T., Astafurov, M.F.: Secure media exchange technology from honeywell as additional measures against industrial cyber threats. In: Conference “Modern Problems of Applied Mathematics and Information Technologies – Al-Khwarizmi 2021” (2021) 17. Autonomous and Remote Ops Trend Up as COVID-19 Changes the Landscape. Automation World magazine (2021)

An Autonomous UAV Based Rail Tracking and Sleeper Inspection with Light-Weight Line Segmentation Approach Ilhan Aydın(&), Erhan Akın, and Emre Güçlü Computer Engineering Department, Firat University, 23119 Elazig, Turkey {iaydin,eakin,eguclu}@firat.edu.tr

Abstract. With the development of technology in recent years, railway transportation is the most preferred transportation method in terms of comfort and safety. In railways, the sleeper component fixes the rail with ballast. Therefore, it is very important to determine the rail and sleeper problems that will affect the safety of the train during the operation of the railway system. In this study, an Unmanned Aerial Vehicle (UAV)-based rail tracking method is proposed for the control of railway track components and a method for inspecting the distance between sleepers by counting. The proposed method uses a lightweight deep learning-based line segmentation algorithm to detect rail and sleepers. By tracking the rail, sleeper counts are made from the images taken on the railway and the positions of the sleepers detected in the image are determined. Then, the distance between sequential sleepers is recorded as a time series, and anomalies in the time series and lost or shifted sleepers are detected. Keywords: Railways
Sleeper detection
Time series analysis


Light-weight line segmentation
Anomaly

1 Introduction The healthiness of rail, sleeper, and other components in railway transportation is very important to minimize transportation disruptions. Rails are an important component that ensures the safety of railway transportation for the movement of the train through the wheels. The fasteners fix the rail and the sleeper, preventing the rail from deviating [1]. The sleeper is used to prevent the road from escaping from the axis and to provide electrical insulation between two steel rails. If a fault in the rail components is not detected and corrected, more serious problems will inevitably occur and even fatal accidents will occur [2]. The main method in the inspection of railways is made by the railway workers with the naked eye. The inspection done in this way is not preferred because of low sensitivity, subjective evaluation, and inefficiency [3]. Automatic and non-destructive checking of rail problems is very useful. The automatic inspection systems include ultrasonic sensing [4], eddy current [5], and computer vision techniques. The ultrasonic detection works with the pulse-echo technique and is suitable for detecting rail defects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 317–324, 2022. https://doi.org/10.1007/978-3-031-09176-6_37

318

I. Aydın et al.

However, the implementation cost is high. The eddy currents are not useful because they are mostly affected by environmental noises [6]. With the developments in computer vision techniques in recent years, its usability for automatic and real-time inspection of railway lines has increased even more. Problems on railway tracks can be classified as track surface defects, slippage of sleepers, and missing or breakage of fasteners. Different rail defects are classified by combining image processing and deep learning-based features [7]. Aydin et al. [8] combined features from MobileNet and SqueezeNet to classify different rail surface defects. They also proposed methods for rail detection and image enhancement. An approach that combines two deep learning models is presented to detect rail surface defects in a limited number of samples dataset [3]. With the proposed model, rail surface defects are determined by labeling at the line level. In addition, segmentationbased image processing techniques have also been proposed for the detection of rail surface defects [9–11]. However, these methods only determined whether there was a defect on the rail surface rather than the defect type. Object detection-based deep learning and image processing techniques were compared for the detection of defects in fasteners, which is another rail component [12]. Partially lost or completely missed fasteners were detected with the probabilistic subject modeling technique [13]. A segmentation and image matching-based approach is proposed to identify defects in rails and fasteners [14]. A conversion process has been done so that the proposed method can work in real-time on an embedded GPU card. Since there are fewer defective fasteners than healthy fasteners, a duplication process was applied to increase the number of defective fasteners. [15, 16]. In a study for the detection of sleeper defects, edge detection and entropy-based methods were applied after applying the Haar transform [17]. In all of these methods, defects in rails and components are detected with images taken from cameras placed under a measurement train. The studies on the detection of sleeper defects with a measurement vehicle are limited. In addition, the images taken with an autonomous UAV determine the defects in the rail and its components [18]. A contrast enhancement and segmentation-based approach are presented for the detection of rail defects with images taken from the UAV [19]. With the segmentation-based deep learning and contour finding-based image processing method, the rail defects were determined by the images taken from the UAV [20]. No study has been found in the literature on counting sleepers, detecting slip and missing sleepers according to the distances between them. In this study, an approach that includes deep learning and image processing techniques is presented to identify problems in sleepers of railways. The proposed method uses a lightweight deep learning-based line segmentation method for rail tracking with autonomous UAVs. During the rail tracks, the sleepers are also detected and the distance between sequential sleepers is recorded. The anomalies in the distances between the sleepers are detected from the recorded time series. Section 2 includes automatic railway tracking and sleeper inspection using a UAV. Section 3 includes experimental results on the detection of rails and sleepers, rail tracking by UAV, and the detection of anomalies in sleepers.

An Autonomous UAV Based Rail Tracking and Sleeper Inspection

319

2 UAV Based Automatic Rail Tracking and Sleeper Inspection In this study, a vision-based approach is proposed for rail tracking and anomaly detection in sleeper components on a railway line with an autonomous UAV. The proposed approach first provides a deep learning-based light-weight line segmentation method for rail tracks. The distance between the sequential sleepers is calculated from the images obtained while tracking the rail with the autonomous UAV and recorded as a time series. The anomaly changes occurring in sleepers are determined by analyzing the time series. Figure 1 shows the block diagram of the proposed method.

UAV Railway image

Rail Segment detector

UAV control and rail tracking

Save distances of sequential sleepers

Anomaly detection

Fig. 1. The scheme of the proposed method for sleeper condition

In Fig. 1, firstly, horizontal and vertical lines are determined by using a line segmentation algorithm on images taken from the bottom view camera of the UAV. Then, the UAV can be controlled with the PID control algorithm for tracking the rails. For this purpose, the difference between the UAV center and the midpoint of the two rails is should be minimum. At the same time, the positions of successive sleeper components are recorded as a time series, and anomalies occurring during the flight are obtained from the time series. 2.1

Rail and Sleeper Detection with Deep Learning-Based Light-Weight Line Segmentation

A lightweight line segmentation method was used for autonomous path tracking and sleeper detection. The backbone of this method consists of a lightweight MobileNetv2 and a reduced line prediction system [20]. The proposed mobileNetv2 system is based on the encoder-decoder architecture. Figure 2 shows the architecture of the proposed line segmentation network. The decoder blocks also use the bottleneck layers of MobileNetV2. With the addition of these layers, the number of parameters in the network becomes 0.56 million. Then line segment maps are created. In this study, the lines obtained for both the sleeper and the rail are used for two different purposes. The lines obtained for the rail are used for rail tracks. Many lines can be detected for both rails. To obtain a single line for the right and left rails from these lines, the found lines are averaged separately on the x-axis. For the sleeper, one of the very close lines is eliminated. In the next stage, PID-based rail tracking is performed.

320

I. Aydın et al. 11 layers of MobileNetV2

Length map Displacement map Degree map Center map

Segments of Lines Maps

Feature extraction

Line generation

Fig. 2. The light-weight line segmentation method

2.2

Autonomous Rail Tracking with UAV Control

A PID-based system has been proposed for autonomous rail tracking with UAVs. The UAV camera is placed facing the rail at a 90° right angle. The center of the rail is the midpoint of the two rails. The aim is to minimize the difference between the rail center and the UAV center along the x-axis. The instantaneous position of the UAV is the midpoint of the acquired image on the x-axis. The desired position is calculated as the midpoint of the two detected rails. Therefore, the aim is to minimize the distance between the instantaneous position and the desired position while the UAV is moving at a constant height and speed. For this purpose, a PID controller is designed. In the recommended method, first, the previous error value (p_error) is taken as zero, and P and I values are given. For each received frame, the actual position of the UAV and the midpoint of the two rails found by line segmentation are calculated. If the value found is different from zero, the yaw_velocity value is calculated with the PID and the calculated error is assigned to the previous error value. This process is repeated for new frames. 2.3

The Detection of Sleeper Problems

To detect problems in sleepers, the distance between consecutive sleepers is recorded as a time series. Then, the changes in this time series are modeled and the problems occurring in the sleepers are taken as an anomaly and modeled.

Sleeper i

Sleeper i+1

The sequential sleepers

Distance calculation

Time series constrcution

Fig. 3. The time series construction from the position of sequential sleepers

An Autonomous UAV Based Rail Tracking and Sleeper Inspection

321

In Fig. 3, the midpoint of the x-axis is determined for each sleeper. The distance along the y-axis between these points is then recorded as a point in the time series. After the time series is obtained, it is analyzed and anomaly points are determined. The problems that will occur in sleepers can be given as the inability to detect the sleeper due to being covered with ballast, the failure to detect the sleeper due to sleeper slips, or sleeper fractures.

3 Experimental Results In this study, a Parrot Anafi drone was used for rail tracking and data collection with an autonomous UAV. This UAV can be programmed with the Python programming language and autonomous flights can be done both with the real UAV and via Olympe in the Gazebo simulation environment. In Fig. 4, testing of the UAV in the real region and an image taken is given.

(a) The testing of the UAV on the railway region

(b) A sample image

Fig. 4. Autonomous UAV field tests

During the field tests in Fig. 4, access to flight information can be provided. In addition, the location information of the images obtained via GPS is also recorded. Data were collected in an area of 4km on the field. Flights were generally made at altitudes lower than 5 m. During this test, after making a UAV flight from a certain height, the command to return home was given and returned to the point of departure. Therefore, in the middle of the first minute, the UAV rose to a height of 30 m and returned home.

Fig. 5. Detection of rails and sleepers

322

I. Aydın et al.

A large lightweight line segmentation method was used in this study. In the study, it is seen that the rails are not detected when the tiny line segmentation model is used. When the large model is used, it is seen that the rail positions are shown with more than one line. For tracking, the right and left rails must be shown with a single line. Therefore, the average of the lines to the right of the center of the image concerning the x-axis is averaged to show the right rail and the ones to the left to show the left rail. The same situation is obtained for the sleepers that are close to each other, and the upper and lower edges of the crossbar are shown. Figure 5 shows the left and right rail components and sleeper components. By finding the midpoint along the x-axis of the lines obtained for the right and left rails in Fig. 5, the difference between the position of the UAV and the center of the two rails will be minimized. For this purpose, the UAV will be provided to follow the rail with the PID system. PID control system result is given in Fig. 6. In Fig. 6, the distance between the center of the image and the midpoint of the left and right rail is tried to be minimized by using PID control. The distance between the sleepers is recorded as a time series and the change in distances is represented as an anomaly. For this purpose, the isolation forest anomaly detection method was used. In Fig. 7, the detection result for an anomaly occurring in the time series obtained for 45 frames is shown.

Fig. 6. Position control of the UAV with the PID control system

Fig. 7. Anomaly detection in sleepers

As seen in Fig. 7, the isolation forest method gives the value of 1 for healthy data, while it produces a -1 value in the case of anomaly data. Although studies on sleeper detection have been carried out in the literature, studies on shifts in sleepers and counting sleepers are very limited. In Table 1, the results of comparison for the sleeper detection rate of the proposed method according to the literature are given.

An Autonomous UAV Based Rail Tracking and Sleeper Inspection

323

When the obtained results given in Table 1 are analyzed, it is seen that the proposed method is better in terms of both sleeper detection rate and detection time. In [21], the detection of sleepers from UAV images was performed with Yolo V4. Their method is not suitable for real-time work. In [22], a study was conducted to determine the sleeper position in the images taken from under the train.

Table 1. Comparison results of detection rate for sleepers The reference

Method

Detection rate

[21] [22]

Yolo V4 based object detection Edge detection and classical image processing Lightweight line segment detection

92.0 59.0

Detection time (in seconds) 12 0.52

97.77

0.25

This study

4 Conclusions In this study, an approach is presented for data collection and analysis of sleepers with an autonomous UAV on railways. The proposed approach offers a lightweight line segmentation method for rail and sleeper detection. After the rail detection is made, PIDbased control is provided for the movement of the UAV between the two rails for autonomous flight. In the proposed approach, rails and sleepers are detected without being affected by the problems in light and imaging systems in the classical image processing approach. The distance is recorded as a time series and the anomalies in the time series are determined by the isolation forest method. Although there are studies on the detection of sleepers and the detection of defects in sleepers in the literature, as far as we know, it is the first study on the detection of sleeper slips with an autonomous UAV. Acknowledgments. This work was supported by the TUBITAK (The Scientific and Technological Research Council of Turkey) under Grant No: 120E097.

References 1. Gibert, X., Patel, V.M., Chellappa, R.: Deep multitask learning for railway track inspection. IEEE Trans. Intell. Transp. Syst. 18(1), 153–164 (2016) 2. Li, Y., Trinh, H., Haas, N., Otto, C., Pankanti, S.: Rail component detection, optimization, and assessment for automatic rail track inspection. IEEE Trans. Intell. Transp. Syst. 15(2), 760–770 (2013) 3. Zhang, D., Song, K., Wang, Q., He, Y., Wen, X., Yan, Y.: Two Deep Learning Networks for Rail Surface Defect Inspection of Limited Samples with Line-Level Label. IEEE Trans. Industr. Inform. 17(10), 6731–6741 (2020)

324

I. Aydın et al.

4. Campos-Castellanos, C., Gharaibeh, Y., Mudge, P., Kappatos, V.: The application of longrange ultrasonic testing (LRUT) for the examination of hard-to-access areas on railway tracks (2011) 5. Zhu, J., et al.: Characterization of Rolling Contact Fatigue Cracks in Rails by Eddy Current Pulsed Thermography. IEEE Trans. Industr. Inf. 17(4), 2307–2315 (2020) 6. Rajamäki, J., Vippola, M., Nurmikolu, A., Viitala, T.: Limitations of eddy current inspection in railway rail evaluation. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232(1), 121–129 (2018). https://doi.org/10.1177/ 0954409716657848 7. Zhuang, L., Qi, H., Zhang, Z.: The automatic rail surface multi-flaw identification based on a deep learning powered framework. IEEE Trans. Intell. Transp. Syst. (2021). 8. Aydin, I., Akin, E., Karakose, M.: Defect classification is based on deep features for railway tracks in sustainable transportation. Appl. Soft Comput. 111, 107706 (2021) 9. Ni, X., Liu, H., Ma, Z., Wang, C., Liu, J.: Detection for rail surface defects via partitioned edge feature. IEEE Trans. Intell. Transp. Syst. (2021) 10. Nieniewski, M.: Morphological detection and extraction of rail surface defects. IEEE Trans. Instrum. Meas. 69(9), 6870–6879 (2020) 11. Gan, J., Li, Q., Wang, J., Yu, H.: A hierarchical extractor-based visual rail surface inspection system. IEEE Sens. J. 17(23), 7935–7944 (2017) 12. Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., Li, Y.: Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study. Eng. Appl. Artif. Intell. 80, 66–81 (2019) 13. Feng, H., Jiang, Z., Xie, F., Yang, P., Shi, J., Chen, L.: Automatic fastener classification and defect detection in vision-based railway inspection systems. IEEE Trans. Instrum. Meas. 63 (4), 877–888 (2013) 14. Tu, Z., Wu, S., Kang, G., Lin, J.: Real-Time Defect Detection of Track Components: Considering Class Imbalance and Subtle Difference Between Classes. IEEE Trans. Instrum. Meas. 70, 1–12 (2021) 15. Liu, J., Ma, Z., Qiu, Y., Ni, X., Shi, B., Liu, H.: Four discriminators cycle-consistent adversarial networks for improving railway defective fastener inspection. IEEE Trans. Intell. Transp. Syst. (2021). 16. Liu, J., Teng, Y., Ni, X., Liu, H.: A Fastener Inspection Method Based on Defective Sample Generation and Deep Convolutional Neural Network. IEEE Sens. J. 21(10), 12179–12188 (2021) 17. Franca, A.S., Vassallo, R.F.: A method of classifying railway sleepers and surface defects in a real environment. IEEE Sens. J. 21(10), 11301–11309 (2020) 18. Güçlü, E., Aydın, İ., Akın, E.: Fuzzy PID based autonomous UAV design for railway tracking. In: 2021 International Conference on Information Technology (ICIT), pp. 456–461. IEEE (2021) 19. Wu, Y., Qin, Y., Wang, Z., Jia, L.: A UAV-based visual inspection method for rail surface defects. Appl. Sci. 8(7), 1028 (2018) 20. Gu, G., Ko, B., Go, S., Lee, S. H., Lee, J., Shin, M.: Towards real-time and light-weight line segment detection. arXiv preprint https://arxiv.org/abs/2106.00186 (2021) 21. Singh, A.K., Dwivedi, A. ., Nahar, N., Singh, D.: Railway track sleeper detection in low altitude uav imagery using deep convolutional neural network. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 355–358. IEEE (2021) 22. Franca, A.S., Vassallo, R.F.: A method of classifying railway sleepers and surface defects in the real environment. IEEE Sens. J. 21(10), 11301–11309 (2020)

Modeling Urban Human Mobility and Predicting Planning Transportation Facilities Using K-Means Clustering Algorithm Mwizerwa Maurice1(&) and Hanyurwimfura Damien2 1

2

African Centre of Excellence in Data Science, University of Rwanda, P.O. Box 4285, Kigali, Rwanda [email protected] African Centre of Excellence in the Internet of Things, University of Rwanda, P.O. Box 4285, Kigali, Rwanda

Abstract. Today, the highest percentage of the world population lives in the cities, and most of the employment opportunities are found in the cities. This brings the challenge to the decision-makers for transportation planning due to the human mobility behavior in the cities. This study aims to analyze urban human mobility patterns through call details records (CDR) data for the development of the KMeans clustering algorithm for planning public transportation facilities in the cities and evaluated the performance of the developed model. This paper used k-means, a machine learning model to cluster the human mobility based on the geographical features from the CDR data and the results of the model showed that the urban human mobility in the city was clustered in three clusters. The results also revealed that there is a need for public transport facilities, especially in the two periods of peak hours identified in the city which is the evening time where it was found that 80 locations of the city, need a high number of public transports means. On the other side of peak hour of the morning which starts, it was found that in 50 locations of the city need a high number of public transports means. The results will enable decision-makers to have an insight into transportation planning in the city for improving the living standards and assuring the quality and sustainability of transport facilities in the city and ensuring speedy exchange of goods and services. Keywords: Human Mobility


Mobile phone data
Transportation planning

1 Introduction 1.1

Background

Today, most of the world’s population lives in the cities, a proportion that is expected to increase from 55% today to 68% by 2050 [1]. Cities worldwide are attempting to transform themselves into smart cities. The key factor in this transformation is the use of urban big data from stakeholders and physical objects in cities. The use of this big data contributes to transportation optimization, and the creation of helpful content for citizens, visitors, local government, and companies. It is believed that by pursuing the urbanization agenda worldwide, governments are preparing to be able to provide access to safe, affordable, accessible, and sustainable transport systems © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 325–335, 2022. https://doi.org/10.1007/978-3-031-09176-6_38

326

M. Maurice and H. Damien

for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons by 2030 [2]. It is with the above challenges/needs that this research area was chosen for developing a new model (algorithm) to be used for addressing the challenge of public transportation planning in the city based on the urban mobility for improving the living standards and assuring the quality and sustainability of transport facilities to ensure speedy exchange of goods and services [3]. This paper is organized into four parts: (i) introduction, (ii) Related works, (iii) methodology, (iv) Results discussion, and conclusion.

2 Related Works 2.1

Activity-Based Patterns of Human Mobility

In 2017, research carried out by Shan, Ferreira, Jr., and Gonzalez with Singapore as an example demonstrated the use of call detail records in extracting the human movement against the activity-based approach. The CDR data used were collected by one mobile network operator (MNO) for two consecutive weeks on 3.17 million of anonymized phone users were captured. To interpret the dynamism of human mobility behavior in the city and planning of transportation, Shan et al. (2017) synthesized methods in previous research [5] 015, Schneider, Belik, Couronne, Smoreda, & Gonzalez [8] and come up with an algorithm that reads mobile raw data (CDR) to estimate the work and home locations of the mobile users, process users and identify samples representing the work and home place of the users. The algorithm was also able to extract the daily mobility connections of the mobile user and derive the expansion factors for the processed calls using CDR and surveyed data (see Fig. 1).

Fig. 1. Model estimating population mobility pattern using raw CDR data (source: [4])

Modeling Urban Human Mobility and Predicting Planning Transportation Facilities

327

They used data mining techniques for analyzing mobility features using mobile CDR data and develop the algorithm for translating CDR data to spatial human mobility features for transportation planning purposes (see Fig. 2).

Fig. 2. Average population-expansion factor distribution at the tower level

From the processed mobile phone users based on their phone activity frequency, they mined good mobility features using their recorded data and considering the data as travel survey. The expansion factors were applied to the user-day observations and combine the daily travels and mobility patterns for the residents (above 10 years old) at the municipality level and infer human mobility patterns for Singapore.

3 Inference Spatiotemporal Pattern of Human Mobility in Maputo Using Data Mining Technique To have a good understanding of human mobility and travel behavior of persons call detail record data analytics was proven to be useful [6]. In the inference of human spatiotemporal mobility, [7] used the opportunities offered by mobile phone (CDR) data in Mozambique. They proposed a method of retrieving users’ trips in Maputo based on proven techniques and previous studies [8] from the raw mobile phone (CDR) data after the adoption of some techniques used by the other researchers. The method was administered to 3.4 million phone users collected for about two weeks from the main mobile network operator (MNO) in Mozambique. The data were analyzed to find the mobility of persons living in Maputo for the different time periods (range). In adopting the techniques used and proven studies [8] the CDR was combined with the high-resolution settlement layer (HRSL), proposed by the Connectivity Lab of Facebook and the Center for International Earth Science Information Network (CIESIN). The HRSL was used, and the CDR user sample was expanded to the actual

328

M. Maurice and H. Damien

population. Moreover, they used OpenStreetMap to incorporate geographic information, and they determined the spatiotemporal distribution of the mobile phone users in Maputo on weekends and weekdays. Raw CDR data were used to retrieve the trips as per their origin-destination (OD). These were scaled to represent the mobility of the mobile users of Maputo on weekends and weekdays. The proposed method incorporated proven techniques from previous research [9]. They consisted of: (1) partitioning of the study area into the closest area, (2) Approximation of the home location of mobile users for explaining users’ travel and mobility behavior [4]. The researcher found that 1,279,291 mobile users stay in Maputo which covers 37% of mobile phone users of Mozambique as it appears in Figure below (Fig. 3),

Fig. 3. Subscriber distribution in the study area. Source: [7].

(3) Examining the valid user-days of the sample for retrieving individual mobility patterns from raw CDR data which was considered as statistically consistent [8] compared with that of conventional survey data given that a certain threshold of daily mobile use met by users where the filtering rules gave 797,329 mobile phone users with at least one valid user-day records (62%) as it appears on Fig. 4 below,

Modeling Urban Human Mobility and Predicting Planning Transportation Facilities

329

Fig. 4. Fraction of users as per valid user-days. Source: [7].

(4) retrieval of origin-destination (OD) trips of the examined sample where an approach similar to Shan et al. [4], where the origin-destination retrieval process was divided into 2 main phases: (a) estimation of stay locations possible for each mobile user, and (b) retrieval of trip parts between the different stay locations, and (5) the magnification factor of scale up the mobile phone user sample to represent the actual persons in each zone, and normalization of mobile user sample to one record day were applied. The proposed method after applying the OD matrix has come up with the classification of the travel generated and attraction maps as shown in Fig. 5 below. The results of this proposed method were useful for urban modeling and transportation planning practice as they provide the explanation of which area has to be considered or given priorities for the development or improvement of the new or existing public transportation planning.

Fig. 5. Trip generation and attraction maps, Source: [7]

330

M. Maurice and H. Damien

4 Methodology 4.1

Introduction

This part, explains the methodology followed when analyzing, processing the information on human mobility in urban locations for addressing the problem related to transport planning and distribution. Today, geolocation and information technology applications in the management of transportation are widely used by local governments. 4.2

Data Collection Method

To do this research paper, we preferred to use the secondary data collection method where data were obtained from another source, the data collected and used were open data shared by Shanghai Telecom a Chinese mobile network operator, obtained from http://sguangwang.com/TelecomDataset.html. The CDR dataset provided by the shanghai mobile network operator was collected in the first 15 days of June 2014 (Fig. 6).

Fig. 6. First 28 of 611,506 entries of Mobile phone data (CDR) were provided by the Shanghai mobile network operator.

4.3

Data Processing and Analysis

To understand or analyze the urban human mobility through the CDR data in the City for providing the directions of public transport distribution and planning and extracting patterns that show the home and workplace of the mobile phone user, an exploratory data analysis was done and it was found that in the dataset there are some missing data

Modeling Urban Human Mobility and Predicting Planning Transportation Facilities

331

in the important feature (variable) of location and it was found also that to drop the rows of missing values on the location variable, the remaining data were sufficient for analysis because the missing location was taking 7.78% of locations in the dataset. The home and work location estimation of mobile phone users was also done, the researcher excluded the users having more than one homeplace or more than one workplace, and those who work at night in this study. Another assumption was that the home and workplace of the users were not changed during the period of our study. The users of well-defined and distinct work and home place geocoordinates were considered for selecting users for modeling urban mobility in the steps below. The first step is to map the phone activities recorded on the network (calls or SMS). The location of the activity is inferred from information available on the location of the cell towers as shown in Fig. 7 below. The second step consists of performing a spatiotemporal aggregation to convert the record of phone activities into aggregated data matching a predetermined definition. This consists of defining aggregation units to produce statistical indicators. Finally, the home and workplace indicators were estimated, based on the following assumption: The home place of the user was assumed to be the cellular tower (Location) in which the highest number of call activities was registered during the hours that the user is making calls between 21:00 and 6:00. The workplace of the user was assumed to be the cellular tower (Location) in which the highest number of call activities was registered during the hours that the user is making calls between 7:00 AM and 5:00 PM.

Fig. 7. Processing of mobile phone data for home and workplace estimation

From the processed call details records of our dataset, all call activities registered in the respect of date and time meaning from Monday to Sunday of June 2014, the records registered from every Monday to Friday of June 2014 were extracted using the Date Time module of python to obtain users that have call activity on weekdays. To determine and identify the peak hours, the mobile phone users made any phone activity (call, called, sent and receive the message) during the workdays in the morning, during working hours, and evening time and night were extracted and grouped into four different according to the time of the events and found that the peak hours for this study are morning and evening hours where public transportation facilities are needed.

332

M. Maurice and H. Damien

To develop the clustering model that gives the directions for the planning of public transport facilities in the City, the K-means clustering algorithm was adopted and used as shown in Fig. 8 below. The basic idea of clustering urban human mobility in the City was to define the number of clusters (k) such that the total within-cluster sum of squares (WSS) is minimized so that the insights/directions of planning public transport facilities can be gained by the City management and stakeholders in the sake of ensuring steady public transport in the City according to human mobility distribution. The right value of K was determined using the elbow method [10] which consists of optimizing the total within-cluster sums of squares (WSS). The elbow method was used in this research to cluster the geolocation data of our dataset processed for determining the optimal value of k to specify the optimal number of clusters into which our data were clustered in. Based on mobility patterns of the data given, the value of K was chosen from 1 to 10 clusters after performing the following steps: (1) Compute clustering algorithm for different values of k by varying k from 1 to 10 clusters, (2) for each value of k, compute the total within-cluster sum of square (wss), (2) plot and visualize the curve of wss according to the number of clusters k, (3) the location of a knee (bend) in the plot is specifically accepted as an indicator of the appropriate number of clusters as shown in Fig. 8 during below.

Fig. 8. From the left, the K-Means model was used for clustering human mobility in the city and the optimum number of clusters using the Inertia of Elbow Method at the right.

Modeling Urban Human Mobility and Predicting Planning Transportation Facilities

333

Step 1: On the starting point, the CDR extracted data for the peak hours (5:00 AM to 8:00 AM and 5:00 PM to 9:00 PM) of the working days was fed to the above model developed. Step 2: The right value of K selected using the elbow method was used to specify the number of clusters to allow the model to continue. Step 3: Randomly the number of centroids in the clusters were selected by the model itself. Step 4; Measure the distance between data points of initial centroids. Here the distance was calculated using Euclidean distance was measured using the mathematical model (2) below to minimize the sum of distances between the data points of the CDR dataset and their corresponding clusters for influencing the shape of clusters to be formed. d ð p; qÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðp1  q1 Þ2 þ ðp2  q2 Þ2 þ . . . þ ðpi  qi Þ2 þ . . . þ ðpn  qn Þ2

ð1Þ

where p is Geolocation point one called Latitude in the selected cluster from the centroid and q is Geolocation point one called Latitude in the nearest cluster from the centroids of the selected cluster of P. Step 5: Assign the 1st data point to the nearest cluster, where each data point found that it has a small distance to the centroid defined for making a cluster. Steps 6 and 7: calculate the mean distance of each cluster and create a cluster based on the smallest distance. Step 8: Data points move to their clusters; this was done to reposition clusters and their data points. From step 8 the model once found that there are some adjustments needed in the clusters for giving good directions in planning public transport, it forces the model to go back to steps 3 up to 7 until the final reposition of data points in clusters is achieved. To evaluate/measure the performance of the urban human mobility clustering algorithm (model) developed, the Silhouette score was chosen because it considers the intra-cluster distance between the sample and other data points within the same cluster (a) and the inter-cluster distance between the sample and the next nearest cluster (b). The silhouette score falls within the range [−1, 1].

5 Results Discussions and Conclusion This research study analyzed urban human mobility patterns through the mobile phone data called Call Details Records in short CDR collected for the sake of developing an algorithm to be used by the decision-makers and stakeholders of the City in planning the public transport in the City. Through the analysis and training of mobile phone data, the results obtained showed that the human mobility patterns are the basic elements that show how human mobility in the City are distributed from the morning, working hours and after work in the City by using k-means the unsupervised machine learning algorithm where the distribution was clustered in three groups and at this point, the objectives of the study were achieved as shown in Fig. 9 and 10.

334

M. Maurice and H. Damien

Fig. 9. Clusters of human mobility in the city

Fig. 10. Locations (Parts) of the City which need high number of public facilities in the evening hours.

The results showed that the groups of human mobility in the clusters give clear directions for the planning of public transport where it was shown that in the evening time from 5:00 PM to 9:00 PM the 80 parts of the City among those clusters that the public transport is very much needed a shown in Fig. 7 above and results showed that

Modeling Urban Human Mobility and Predicting Planning Transportation Facilities

335

another time where public transport is much needed in the four clusters of the City is the morning hours from 5:00 AM to 8:00 Am where 50 regions were identified. The elbow method [10] was used for optimizing the total within-cluster sums of squares (WSS) that helped to find the right number and best clusters to be used to group the urban human mobility that have similar characteristics in the City, the model was evaluated using the silhouette metric method of the sklearn and elbow metric method was also used to find the suitable number of clusters that can be used where the average of the squared distances from the cluster centers of the respective clusters was calculated and the sum of squared distances of samples to their closest cluster center was also calculated and found that the clustering model is performing to the level 96.1% when the urban human mobility is clustered in three groups (clusters) and allowed the researcher to achieve the specific objective of evaluating the performance of the algorithm developed. The researcher is interested to continue working with mobile phone data in the area of public transport and urbanization where the Real-time monitoring of urban human mobility in the City will be the researcher’s main focus as future work.

References 1. UN: 2018-revision-of-world-urbanization-prospects.html (2018) 2. The United Nations: Housing and Sustainable Urban Development. UN global summit on urbanization. The United Nations, Quito (2016) 3. Etienne Thuillier, L.M.: Clustering weekly patterns of human mobility through mobile phone data. IEEE Trans. Mobile Comput. 17, 1–3 (2017) 4. Shan Jiang, J.F.: Activity-based human mobility patterns inferred from mobile phone data. IEEE Trans. Big Data 3, 4–8 (2017) 5. Lauren Alexander, S.J.: Origin–destination trips by purpose and time of day inferred from mobile phone data. Transp. Res. C Emerg. Technol. 58, 240–250 (2015) 6. González, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008) 7. Batran, M., et al.: Inferencing human spatiotemporal mobility in greater Maputo via mobile phone big data mining. ISPRS Int. J. Geo-Inf. 7(7), 259 (2018) 8. Schneider, C., Belik, V., Couronne, T., Smoreda, Z., Gonzalez, M.: Unraveling daily human mobility motifs. J. Royal Soc. Interf. 10(84), 20130246 (2013) 9. Zin, T., Lwin, K., Sekimoto, Y.: Estimation of originating-destination trips in Yangon by using big data source. JSTAGE 13, 6–13 (2018) 10. Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A.: NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61, 1–36 (2014)

Air Cargo Competition with Modern Layout Methods Ahmed Oudah Abbood1 1

and Gözde Ulutagay2(&)

Institute of Graduate Studies, Istanbul Gedik University, Istanbul, Turkey [email protected] 2 Department of Industrial Engineering, Istanbul Gedik University, Istanbul, Turkey [email protected]

Abstract. The global demand for air transport is rising in both current and emerging markets for the transport of goods. The air transport industry has become more dependent on technology, so it requires the adoption of modern layouts that keep pace with this development, because we are not far from our global surroundings. Therefore, leading companies are working on developing and evaluating their marketing strategy for survival, and this is done by keeping pace with the rapid changes in the world of air transport because it is a dynamic and competitive environment. That is why companies realize the complexity of the external and internal competitive situation. Therefore, air ports are considered an economic power, therefore, countries seek to improve and develop services and facilitate procedures in all joints of their airports, including air cargo. In the case of the presented paper, we worked on preparing the appropriate ground by developing proposals and recommendations, and then working on proposing internal and external layouts for the air cargo area. The aim, that to clarify the aspects of competition in air freight and develop its joints through modern layouts and designs that reduce global warming through green designs that give comfort in the work environment. This is done through the use of ENVI-met software, which provides solutions by providing 2D and 3D designs. One of the advantages of this program is addresses the problem, of global warming during the long daylight hours of concrete structures, roads, especially in the summer, which the heat load increases. And the heat emissions are reflected at sunset, this reflected increase the global warming in this area. These modern layouts and the ground services provided, that well be environmentally friendly are considered a strength factor in the competitive environment. Through practical experience, improving air freight in the case of the Baghdad International Airport does not depend only on the use of modern layouts. There are several factors are involved that contribute to developing and improving the performance of air freight and making it a pivotal center for transporting goods between countries of the world. One of the most important factors is the financial aspect, the planning aspect, the creative human resources that implement the plan, and the method of exploiting the available land spaces that help planners implement the modern development vision to improve performance. Keywords: Air cargo
Modern layouts ENVI-met
Green design


Competitive
Development


© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 336–347, 2022. https://doi.org/10.1007/978-3-031-09176-6_39

Air Cargo Competition with Modern Layout Methods

337

1 Introduction In ancient times, animals were used for land transportation between local areas, cities, and countries for trade. Then the transport process was developed by horse-drawn carriages to increase the transport capacity (people or goods). The industrial revolution and the discovery of the steam engine in 1775 AD accelerated the speed of technological progress in road travel by developing railway networks and operating trains on them to transport people and goods. This process increased the trade exchange and speeded the transfer of expertise between local and regional regions. This was reflected in competitiveness to develop the engines of vehicles, trains, and ships, down to jet engines. With the invention of aircraft, the world began a new stage in transportation: transportation by air. Therefore, the stage of accelerating the technology transfer process, the factor that changes human life, and competition between companies to provide the best in all fields, including the usual transport (people, goods) process. The increase in trade exchange between the world’s countries led up to increased demand for the transportation of express shipments worldwide that consist of airmail, air cargo, and express mail. Indicator of increase in air transport is matched by the need to have adequate systems and protocols to organize the transport process efficiently, provide the best services, and reduce the cost of transportation to attract the most significant number of customers. For this, the logistics systems used in this field have been developed by specialists to make continuous improvements. It started from receiving the goods and shipping them to the consignee through the supply chains. 1.1

Supply Chains

In 1991 A.D., the Logistic Board of Directors in the United States of America defined logistics as the process, planning, executing, controlling the flow, and the necessary storage of goods, services, and various information from the origin point to consumer point to achieve consumer satisfaction with the goods or services provided. One of its goals is to improve the physical flow from the source to the downstream. Logistics is part of a broad term for supply chains, and as strategic planning matured, the term “supply chain management” entered the public domain of supply chains. The term was first used by Keith Oliver, a consultant at Booz Allen Hamilton, in an interview with the Financial Times in 1982 Groover (2007). After publishing a series of articles and books on the subject in the mid-nineties of the previous century, this term grew in prominence. It became a common word for operations management after a sluggish spread. They have used it in their addresses regularly Shingo (1985). Therefore, we need layouts designed to facilitate handling between the different lines for (Product or line layout, Process or functional layout, Location layout or fixed status, Group layout). In order to adopt the mechanism of action, for which the strategy followed in management, decision-making, marketing, the most important of which are the characteristics of competition. 1.2

Competitiveness

Previously, traditional airlines focused solely on transporting passengers, but due to the high demand for transportation, the importance and profitability of the air freight sector

338

A. O. Abbood and G. Ulutagay

in the field of transporting goods were recognized. Today, air freight is regarded as one of the most important modes of transportation globally for various reasons, including increased global trade and high demand for on-time deliveries. As a result, competition in all areas of the work environment, including competition in the field of air cargo. Consequently, the competitor and its tools, as well as customer analysis, are understood. We usually begin by identifying current and potential competitors when analyzing competitors. However, identifying potential competitors is difficult for various reasons. The most important is that they are invisible competitors. There are two primary methods of engagement (Gerhardt 2002). As customers are drawn to a better service or a brand change, the first method is to monitor customers who are likely to switch brands or choose between competitors. The second method is to classify competitors and group them according to their competitive strategy to understand and ensure they remain competitive. Therefore, because of the constant change, the air cargo industry is competitive and dynamic. Monitoring and categorization make it possible to benefit from the rival’s mistakes to outperform it, or ideas, processes, or innovation that the competitor has worked on to earn the customer’s trust. Within this framework, it is critical to analyze the competition based on organization, recognizing competitors, and understanding their strengths and shortcomings, because these metrics may help us create our marketing plan to attract customers. Some elements influence the behavior or actions of competitors among them (profitability and growth, current and past strategies, goals and commitment, strength and weakness, the cost, culture and organization.). These aspects may be examined indirectly and efficiently by identifying and categorizing competitors. Some groupings share a competitive strategy, such as distribution channels, delivery strategy, pricing, location, brand, capacity, logistics, and R& D (Gerhardt 2002). According to the information provided above, learning about competitors necessitates additional research and data collection through available sources, such as the competitor’s official website, which is considered a reliable source for obtaining brief information about the competitor or an outline about it, due to the lack of detailed information about the competitor, which is difficult to obtain. The importance of information in the study cannot be overstated. Because the business’s environment is a set of elements and variables that surround the firm and directly or indirectly impact the efficiency of work within the organization, either favorably or adversely, corporate environments are often split into two types: internal environments and external environments. Because the firm may control these activities, the internal environment comprises sources of strength and weakness and encompasses the administrative structure, organizational culture, human resources, marketing resources, and information systems within the company. The external environment is the firm’s exterior perimeter, its dealers, and those engaging with it, such as suppliers and people with the capacity to change. As a result, to make the best option, we need to conduct an environmental assessment of the internal and exterior structure. In our topic, we need unconventional ideas in the field of competition, including the use of sustainable development and thermal comfort when roaming in air freight joints, to attract suppliers, shippers and companies working in this field. This is done through the use of modern layout methods.

Air Cargo Competition with Modern Layout Methods

1.3

339

ENVI-met Software Program

The ENVI-met program is a program for designing cities from an environmental point of view and considers the factors of climate influence (temperature, humidity, wind speed, and direction, etc.) and vegetation cover (green spaces and trees) on structures, buildings, bridges. One of the advantages of this program is that it addresses the problem of global warming during the long daylight hours of concrete structures, roads, and corridors, especially in the summer. Furthermore, the effect of sunlight on these buildings and the amount of heat absorbed by those buildings. The heat load increases and the heat emissions are reflected at sunset, which is reflected in the increase in global warming. The ENVI-met simulation program specifies the design options for the user in terms of dimensions and distances between buildings, and the ability to choose the appropriate material to cover the walls and surfaces of buildings, corridors, and main roads, in addition to giving an option that allows choosing the vegetation cover (Fig. 1).

Fig. 1. A figure explain sub-models of ENVI-met

This program simulates ENVI-met local climate dynamics based on atmospheric physics and heat transfer principles. The non-hydrostatic and incompressible NavierStokes equations calculate the three-dimensional wind gusts with the Bousinessq approximation. The minimal turbulence closure model plays an essential role in the mean flow simulation, as Katul et al. (2004) demonstrated. The most common engineering applications options for closure models are k-e models, which are computational models. Therefore, choosing a model to calculate the potential moisture and temperature distributions is through the advection and diffusion equations, adjusted according to the existing moisture and temperature sources and sinks for the chosen model. For the simulations to be compatible with atmospheric processes, the ideal time to start the simulation would be night or sunrise. Entering data into the ENVI-met simulation program requires defining the three-dimension input area for the selected area (buildings, soil, plants, and receivers). Key input information for the simulation includes weather conditions, vegetation characteristics, and material properties for the

340

A. O. Abbood and G. Ulutagay

urban and engineering area. The maximum number of grid cells is minimal, so it cannot simulate an entire region’s microclimate. Huttner (2008) also pointed out that ENVI met. Vanessa says no energy inputs are a significant source in areas with a high urban metabolism, e.g., city centers because ENVI-met does not include a man-made thermal component. ENVI-met program consists of a mesh structure with dimensions of (250 * 250 * 30) cells as a maximum of the grid. The model is suitable for small- to local-scale analyses because the horizontal resolution ranges from 0.5 m to 10 m. This makes the model suitable for small-scale to local-scale analyzes. 1.4

Layouts

In general, the layout impacts the cost, time, and productivity of material handling and all phases of total production and plant efficiency (Apple 1977). This paper will offer an overview of the cargo facility construction process and methods to guarantee that airport property is efficiently used for air freight facilities. The expansion operation covers two regions: the land used and the utilities. As a result of the industry’s development and changing technologies, the air cargo industry’s dynamics drive the cargo facilities’ plan to be more creative. It presupposes that the air freight system will be planned and designed using present conditions to prove a static, non-existent environment. Change must be considered in all steps of the process The freight facility consists of a warehouse office structure, aircraft parking space, waiting area for trucks, and dedicated car parks for employees and customers (Fig. 2).

Fig. 2. Systematic Layout Planning procedure (Tompkins 2010)

Air Cargo Competition with Modern Layout Methods

341

Richard Mother invented the Systematic Layout Planning (S.L.P.) design approach in 1965. This systematic approach is still used for planning numerous facilities, such as warehouses, hospitals, and manufacturing plants. It is a sequential approach that allows planners to describe and visualize linkages between activities and present other options to planned design (Tompkins 2010). The diagram below depicts ten steps that outline the procedure and its execution.

2 Problem Definition This work aims to understand better how competition choices affect layouts by comparing old air cargo areas and new designs. As well as how to share types of shading and vegetation to reduce the UHI and facilitate movement between joints of the facility. The conventional approach is to increase exchange trade. Also, how do you conduct business in a competitive environment? Moreover, what can we glean from the options for using ENVI-met program? In conclusions and recommendations, we will provide how to improve the cargo transport planning and strategy, reduce UHI, and promote comfortable movement between the facility’s joints. Moreover, design measures can be used from environmentally-friendly equipment and vegetation to address internal and external thermal comfort the problems in hot and dry climates.

3 Literature Review According to the numerous readings, the worldwide trend is toward increased usage of air cargo and the growth of the global logistics services business (Alkaabi and Debbage 2011). The recent increase in air traffic has created numerous new challenges for airports and the air freight sector and a new role for airports in providing services. It is becoming increasingly vital, particularly in the air cargo business and related logistical issues, to efficiently plan and manage air freight at airports and look at aviation difficulties from a new perspective (Boloukian and Siegmann 2016). As a result of the findings, it is clear that airport areas will significantly influence economies and more extensive regional regions (Walcott and Zhang 2017). In light of considerable advancements in the air freight sector and the diverse environment in which air freight services are performed (Merkert et al. 2017). As a result, we will approach the aiming step by step, which will lead us to enhance the work environment via current practical approaches. 3.1

Airport Major Facilities

An airport is an extraordinary functional unit, a limited setting, and a web of interconnected services. Airport side buildings include the runway, taxiway, parking lot, waiting area, air traffic control tower, lighting systems, rescue and fire services, aircraft maintenance, and fuel pumping systems, security barriers, and control gates. The

342

A. O. Abbood and G. Ulutagay

airport buildings’ structures are the terminal building, the yard, and the cargo area terminal (Civil Aviation Department Hong Kong 2008). It serves airlines, importers, and exporters of products; therefore, the airport is not isolated. 3.2

Analysis of Air Cargo Facility

The objective of evaluating air cargo facilities is to identify weaknesses and strengths that impact the development and improvement of those facilities, define those implications, and make recommendations for modifications to facility construction requirements. 3.3

Air Cargo Definition

Except for mail and passenger luggage, air freight/cargo refers to transporting all items by air. Freighters and passenger airplanes with belly capacity are the two cargocarrying choices. For the total shipments transported worldwide, air freight represents 1% in weight, and of the total return from freight, it represents 35%. As mentioned above, it is clear that high-value goods are shipped by air, and low-value goods are shipped by truck, ship, or rail. Many experts focus on the value per ton component when assessing the air freight prospects for a particular product or geographical market. As a result, we note that the changing relationship is essential because it affects everything, including freight demand forecasting (Bañez n.d.). 3.4

Air Cargo Market

Because air cargo markets have higher pricing than land transport and marine markets, demand for air freight is governed by cost, and the cost of shipping a kilo is typically 4 to 5 times that of road transport and 12–16 times that of sea transport. Therefore, to gain a competitive advantage in the air freight market compared to other transport markets, administrative procedures must be streamlined, goods must be cleared from the customs campus in record time, and the logistics network for transporting goods must be improved (Moshe n.d). Airfreight markets are managed based on demand, or in other words, on global supply chain operations. Also affected by global trade are airline supply operations and network development: airport location, freedom, restriction removal, and airport management. The first three factors are external factors that are either low or not controlled through airport management, whereas airport management is an internal factor. The following supply and demand indicators can be used to analyze the market: international trade (for example, imports and exports of goods in terms of monetary value and type of goods), air freight volume, frequency of flights, and network development (for example, connectivity) (Burghouwt et al. 2009). The conceptual framework for airport competitiveness in the freight market is depicted in Fig. 3. Merchandise markets were classified into two categories: merchandise type and geographic region. There are three types of goods delivery services: traditional transport companies, freight forwarding companies, and integrated freight forwarding companies.

Air Cargo Competition with Modern Layout Methods

343

Fig. 3. Competitiveness of airports in freight markets and its conceptual framework (Wong 2016)

4 ENVI-met Program Here we discuss which contributes to reducing the impact of (UHI) the purpose of that, to making external thermal comfort better for the working environment and pedestrians. The simulation program, after entering the required data, proposes a set of external materials involved in the construction process of the targeted buildings in the simulation process. For the purpose of giving the best materials used in construction and planning of roads and corridors that are used to reduce temperatures and increase green spaces. Since we are not far from our global surroundings, and the transportation industry is becoming more dependent on technology, it requires significant efforts to achieve sustainability in all facilities as it is known that the global demand for air transport is rising in both mature and emerging markets. In terms of freight transport, the China-North American and European Free Trade Association (EFTA) trade relations are expected to increase the demand for traffic over the coming years. In case international Baghdad airport, air cargo section. In general, there are areas of land surrounding the field of air freight that is not invested, and some airports lack possession of these areas. This feature allows the creation of green designs and layouts, giving flexibility in moving between the joints of the sections within the work environment. With human resources, this feature helps planners implement the modern development vision that the company seeks to improve performance, but these human resources need training and contact with experts in this field and directing them of the right way. 4.1

Input Data for the Simulation Model

The data of (maximum and minimum temperatures), (wind speed), (relative humidity), and (Wind direction) for the city of Baghdad were received from the Iraqi General Authority for Meteorology and Seismic Monitoring based in Baghdad. The simulation period was for twenty-four hours. The air cargo area’s highest temperature, lowest

344

A. O. Abbood and G. Ulutagay

temperature, wind speed, and relative humidity were used with dimensions (120 m * 95 m) of two years (2019–2020). The distribution of cells on the x-axis was 40 cells, and on the y axis was 20 cells. The site was rotated 45° northward according to the location of the buildings, and the height of the grid was dz = 2 m, dx = 4 m, dy = 4 m. As shown in Fig. 4, determination the simulation location and rotation angle. When designing in the hot, dry regions of the simulation model, require rotating the layouts of building towards the northeast was required to obtain the highest temperature and prevailing winds in that region (Table 1). Table 1. The model region and rotation angle that was selected

5 The Simulation Results of the Model Eight receptors were placed in the simulation program in different places of the air cargo area, as shown in Fig. 4, to obtain the temperature in the work site, corridors, roads, wind speed and direction, relative humidity, and well as the average radiant temperature. Moreover, Fig. 5 shows the top view of the air cargo layout.

Fig. 4. The receptor’s locations in the simulation program

Air Cargo Competition with Modern Layout Methods

345

Fig. 5. Top view of the cargo layouts

Fig. 6. Clarify analyze method

Figure 6 shows the method of analyzing and filtering the data to announce the results. Figure 7 shows the results of the simulation program outputs for air temperature and humidity at 1.5 m above the construction floor level, which correspond to the temperature data received from the General Authority for Meteorology and Seismic Monitoring, We notice at six in the morning that the minimum temperature is 24 °C, and at four in the evening we notice that the maximum temperature is 51.8 °C through the readings of the receptors. The reason is that the air cargo area is not surrounded by vegetation. Moreover, Fig. 8 shows the sun’s radiation online (33.26N/44.23E).

346

A. O. Abbood and G. Ulutagay

Fig. 7. Show the air temperature and humidity for 2020

Fig. 8. Shows the sun’s radiation

6 Conclusion and Future Work Global warming is a global phenomenon, and reducing global warming requires our work environment to feeling thermal comfort. This is done according to the work environment, and in the field of our subject, to address the problem of global warming during the long daylight hours of concrete structures and roads, especially in the summer when the heat load increases. Heat emissions are reflected at sunset, and this is reflected in the increase in global warming in this region, and the machines, machines, and equipment used in transportation and logistical support operations, in addition to aircraft engine exhaust, are considered a source of pollution that increases global warming. Therefore, the use of modern layouts and ground services provided, which are environmentally friendly, is a strong factor in the competitive environment, among the strengths available in the field of work. Using the simulation program Envi-met after entering the required data provides us with several options that reduce heat emissions in

Air Cargo Competition with Modern Layout Methods

347

the work environment, and is reflected in the thermal comfort of employees and the rest of the people in that space. In addition to use of layouts for movement and cargo transportation. For this, we note through practical experience that the use of simulation software gives many options of designs, allowing us to choose the best design according to the request of the organization.

References Groover, M.P.: Work Systems: The Methods, Measurement & Management of Work. Prentice Hall, Upper Saddle River (2007). ISBN 978-0-13-140650-6 Shigeo, S.: A revolution in Manufacturing: The SMED System. Productivity Press, Cambridge (1985). ISBN 0-915299-03-8 Gerhardt, P.L.: A paper presented in partial fulfillment of the requirements of OM 814 marketing strategy and practice. J. Serv. Market. 20(8), 150–160 (2002) Apple, J.M.: Plant Layout and Material Handling. Wiley, Michigan (1977) Hitt, M.A., Ireland, R.D., Hoskisson, R.E.: Strategic Management Cases: Competitiveness and Globalization. South-Western Pub, Columbia (2012) Alkaabi, K.A., Debbage, K.G.: The geography of air freight: connections to U.S. metropolitan economies. J. Transp. Geogr. 19, 1517–1529 (2011) Boloukian, R., Siegmann, J.: Urban logistics; a key for the airport-centric development – a review on development approaches and the role of urban logistics in comprehensive airportcentric planning. Transp. Res. Procedia 12, 800–811 (2016). https://doi.org/10.1016/j.trpro. 2016.02.033 Merkert, R., Van de Voorde, E., De Wit, J.: Making or breaking – key success factors in the air cargo market. J. Air Transp. Manag. 61(1), 1–122 (2017). https://doi.org/10.1016/j.jairtraman. 2017.02.001 Civil Aviation Department Hong Kong: The Major Facilities in Airport and the Demand Forecast (2008) Satre, Mead & Hunt: Associates, Oregon Department of Aviation, Oregon Airport Land Use Compatibility Guidebook (2003) Burghouwt, G., de Wit, J., Veldhuis, J., Matsumoto, H.: Air network performance and hub competitive position: evaluation of primary airports in East and South-East Asia. J. Airpt. Manag. 3, 384–400 (2009) Katul, G.G., Mahrt, L., Poggi, D., Sanz, C.: One and two-equation models for canopy turbulence. Bound.-Layer Meteorol. 113, 81–109 (2004) Huttner, S., Bruse, M., Dostal, P.: Using ENVI-met to simulate the impact of global warming on the microclimate in central European cities. Paper presented at 5th Japanese-German Meeting on Urban Climatology. Berichte des Meteorologischen Instituts der Albert-LudwigsUniversität of Freiburg, Germany (2008) Tompkins, J.J.: Facilities Planning, 4th edn. Wiley, New York (2010) Walcott, M., Zhang, F.: Comparison of major air freight network hubs in the U.S. and China. J. Air Transp. Manag. 61, 64–72 (2017) Wong, J.-T., Chung, Y.-S., Hsu, P.-Y.: Cargo market competition among Asia Pacific’s major airports. J. Air Transp. Manag. 56(Part B), 91–98 (2016)

Performance of Simultaneous Perturbation Stochastic Approximation for Feature Selection Ramazan Algin(&), Ali Fuat Alkaya, and Mustafa Agaoglu Marmara University, Istanbul, Turkey [email protected]

Abstract. Feature Selection (FS) is an important process in the field of machine learning where complex and large-size datasets are available. By extracting unnecessary properties from the datasets, FS reduces the size of datasets and evaluation time of algorithms and also improves the performance of classification algorithms. The main purpose of the FS is achieving a minimal feature subset from the initial features of the given problem dataset where the minimal feature subset should show an acceptable performance in representing the original dataset. In this study, to generate subsets we used simultaneous perturbation stochastic approximation (SPSA), migrating birds optimization and simulated annealing algorithms. Subsets generated by the algorithms are evaluated by using correlation-based FS and performance of the algorithms is measured by using decision tree (C4.5) as a classifier. To our knowledge, SPSA algorithm is applied to the FS problem as a filter approach for the first time. We present the computational experiments conducted on the 15 datasets taken from UCI machine learning repository. Our results show that SPSA algorithm outperforms other algorithms in terms of accuracy values. Another point is that, all algorithms reduce the number of features by more than 50%. Keywords: Feature selection


SPSA
Meta-heuristics

1 Introduction The size of digital data created and stored is increasing tremendously day by day. As the size increases, irrelevant or redundant information in these datasets also increases and it becomes harder to extract useful information from the datasets. Even as the number of features increases linearly, the number of candidate subsets increase exponentially. As a result, it causes to curse of dimensionality problem. At that point, feature selection (FS) undertakes a very important role in the field of machine learning and data science to obtain crucial features from complex and large-size datasets and to solve curse of dimensionality problem. By extracting unnecessary properties from the datasets, FS reduces the size of datasets and evaluation time of algorithms and also improves the performance of classification algorithms. The main purpose of the FS is achieving a minimal feature subset from the initial features of the given problem dataset where the minimal feature subset should show an acceptable performance in representing the original dataset [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 348–354, 2022. https://doi.org/10.1007/978-3-031-09176-6_40

Performance of Simultaneous Perturbation Stochastic Approximation

349

FS is in the set of NP-hard problems. In general, solution techniques applied to the FS are categorized as wrapper approaches, filter approaches and embedded approaches. Classification/learner algorithms are used in the wrapper approaches for the evaluation process [2]. In wrapper approaches, classification algorithm provides a feedback about the candidate subset and according to this feedback a new subset is generated. On the other hand, in the filter approaches, the feedback is provided by subset evaluators. There is no need to generate a model in the subset evaluators; therefore, they are much faster than the classification algorithms. In the literature, there are many subset evaluators; such as, probabilistic consistency-based FS [3], correlation-based FS (CFS) [4] and mutual information-based FS [5]. On the other hand, embedded approaches perform the FS process during the training phase of the model. In this study, we tackle the FS problem and specifically focus on the filter approaches where we used simultaneous perturbation stochastic approximation (SPSA), migrating birds optimization (MBO) and simulated annealing (SA) algorithms as search algorithms. To our knowledge, SPSA algorithm is applied to the FS problem as a filter approach for the first time. Search algorithms generate feature subsets according to the feedback obtained from the subset evaluators and accuracy value is obtained by using the classifier algorithms. In our study, as a subset evaluator, we use correlation-based FS (CFS) and as a classifier algorithm we use decision tree (C4.5) [6] which are shown as the best evaluator and classifier in [7]. Performance of the search algorithms is compared on the 15 datasets taken from the UCI machine learning repository [8]. The paper is organized as follows. The brief definition of algorithms exploited for solving the FS problem and their literature review are given in Sect. 2. Details about application of algorithms to the FS problem are explained in Sect. 3. Results of computational experiments and their discussions are reported in Sect. 4. Section 5 gives the concluding remarks together with some possible future work.

2 Tackled Algorithms and Literature Review In this section, we briefly explain the tackled algorithms with their related literature survey. 2.1

Simultaneous Perturbation Stochastic Approximation

Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is one of the techniques that uses only loss-of-function measurements and has recently received great attention for multifunctional problems. SPSA algorithm has been added to the literature by Spall [9]. SPSA is based on a highly efficient and easy-to-apply “simultaneous perturbation” approach to the gradient: this gradient approach uses only two loss-function measurements, regardless of the number of parameters optimized. The goal in the algorithm is to minimize the loss function. SPSA is a powerful algorithm used for optimization in complex systems. Discrete version of the SPSA algorithm has been brought to the literature by Wang and Spall [10]. Binary SPSA (BSPSA) is a special form of discrete SPSA and the pseudo-gradient descent calculation feature was

350

R. Algin et al.

used in its design [11]. The loss function takes only {0, 1} values in BSPSA and the algorithm starts with a random solution vector and then it moves towards the best-fit vector by repeated iterations. As it moves towards the best solution vector, individual components of the current solution vector are perturbed simultaneously with random offsets. Since the binary SPSA algorithm has recently been brought to the literature, there is not much work done on it. In [11], the binary SPSA algorithm has been applied to the FS problem as a wrapper approach. 2.2

Migrating Birds Optimization

Migrating birds optimization (MBO) algorithm is inspired from the migration birds in the nature and it mimics the behaviors of migrating birds [12]. It is a population based metaheuristic. MBO algorithm starts by placing solutions (birds) in hypothetically V shape search space. A solution is selected as leader solution and remaining solutions are split into two lines behind the leader. Firstly, leader solution tries to improve itself by generating neighbor solutions. If any solution in the neighbor set is better than the leader solution then it is replaced with leader solution. After that, best unused neighbor solutions belongs to leader are transferred to the solution behind the leader. Next solution is also generates its neighbor solutions and among the new set of neighbor solution (solutions come from other bird and newly generated neighbors) best one is replaced with current solution if it is better than the current solution. When all birds do this process then a tour is completed and leader is replaced by the solution at the end of the tail to start another loop. The algorithm stops when the stopping criteria are met. In the literature, MBO algorithm is compared with other metaheuristics for the FS problem and shown that its performance is better than others [7, 13]. 2.3

Simulated Annealing

Simulated annealing (SA) is one of the oldest metaheuristics in the literature [14]. It mimics the annealing technique in metallurgy which heats and cools metals over and over for reaching the best quality. Due to its stochastic structure, SA avoids to stuck in local optima which makes it very popular in many research fields. SA starts with an initial solution and the initial solution changes, regards to temperature parameter, at each iteration. The solution is not always moves to better solution, with a low probability it is possible that it may move to worse solutions. The algorithm stops when the stopping criteria are met. Due to its popularity, there are many studies that SA is applied to the FS problem [7, 15].

3 Application of Algorithms to the FS Problem In this study, we focus on the filter approaches where we use SPSA, MBO and SA algorithms as search algorithms. Search algorithms are used to generate feature subsets and their performance measured by subset evaluators. After that, final subset performance measured by using classifier algorithms. As a subset evaluator, we use correlation-based FS (CFS) and as a classifier algorithm we use decision tree (C4.5).

Performance of Simultaneous Perturbation Stochastic Approximation

351

While applying algorithms to a problem successfully, how a solution is represented and how new solutions are generated are very important. Similar to [7] we define a solution as a weight vector where weights are in the range of [0, 1]. However, in our study, initialization of solutions is different from [7] where we prefer to set initial weights to 0.5 rather than random values. By doing this, at the beginning, we give all features an equal chance of being selected. Neighbor generation for the MBO and SA is the same as in [7] where future weights are mutated by using small incremantations/ decrementations. For SPSA algorithm we use similar approach as in [11] but their work is wrapper approach and ours is filter, therefore, in our implementations we calculated loss function by using the subset evaluator score. Performance of SPSA, MBO and SA are compared on the 15 datasets taken from UCI machine learning repository [8]. Table 1 shows the details of the datasets where range of features and classes change from 4 to 275 and 2 to 29, respectively. In Table 1, accuracy value found by C4.5 classifier algorithm is given as percentages. This accuracy value is calculated by using all features. Table 1. UCI datasets. Dataset # of features abalone 8 arrhythmia 275 iris 4 letter 16 libras 90 lymphography 18 muskv1 166 optdigits 64 promoters 57 sonar 60 spect 22 splice 60 vehicle 18 waveform 21 wine 13

# of classes # of instances 29 4177 16 452 3 150 26 20000 15 360 4 148 2 476 10 5620 2 106 2 208 2 267 3 3190 4 846 3 5000 3 178

Accuracy (C4.5) 20.49 60.40 96.00 87.92 69.72 75.00 84.87 90.68 81.13 71.15 80.90 92.76 72.58 75.98 93.82

4 Computational Experiments and Discussion In the first set of experiments we conduct fine tuning experiments to decide which parameter value set is the best for our algorithms. The set of parameter values and the best performing parameter values can be seen in Table 2. The parameters of our algorithms are as follows:

352

R. Algin et al.

SPSA: a nonnegative iteration gain sequence (a), stability constant (A), nonnegative gradient gain sequence (c) and pre-defined parameter used in the calculation of iteration gain sequence (a). MBO: # of birds (b), # of neighbors (n), # of flapping (f), overlap factor (o) and radius (r). SA: initial temperature (T), # of iterations in each temperature (R), decrease ratio of temperature (d), increase ration of R (i) and radius (r). As recommended in [9] the stability constant should be 10% (or less) of the maximum number of iterations and the optimum value of a is 0.6, which we also found that they work well in our experiments. Table 2. Parameters and their values used in the fine tune experiments. (Bold ones are the best) SPSA Param. a A c a

MBO Values Param. 0.5, 0.75, 1, 1.5 b 500, 1000 n 0.02, 0.05, 0.1 f 0.3, 0.6, 0.8 o r

SA Values Param. 11, 21, 51 T 3, 5, 7 R 5, 10 d 1, 2, 3 i 0.01, 0.02,0.05 r

Values 100, 1000 5, 20 1.1, 1.5 1.1, 1.5 0.01, 0.02, 0.05

After parameters of our algorithms are fine-tuned, we conducted another set of experiments to measure the performance of search algorithms. As mentioned in previous section, 15 dataset are used in our experiments. Iteration number for all search algorithms is set to 10000 and all experiments are repeated five times. Average accuracy values found by the algorithms are given in Table 3 as percentages. Results show that SPSA algorithm is better than MBO and SA in terms of number of winning cases. Among 15 datasets; SPSA has 8, MBO has 6 and SA has 3 winning cases. When we check the average accuracy of 15 datasets, again SPSA performs better than other two. As seen in Table 3, SPSA gives better performance especially on the large-sized datasets. Also note that average accuracy of C4.5 is 76.89% where SPSA has 77.38%, MBO has 77.10% and SA has 76.58%. SPSA and MBO have slightly better performance than the C4.5. Remember that in the FS problem the aim is reducing the number of features by keeping the original accuracy value. When we check the number of features found by our algorithms, we observe a significant decrease in the number of features. As an average of 15 datasets, SPSA performs 51.8%, MBO performs 53.7% and SA performs 50.7% improvements in the number of features. We can conclude that we successfully eliminate more than half of the features and also increase the accuracy value.

Performance of Simultaneous Perturbation Stochastic Approximation

353

Table 3. Accuracy values of algorithms (given as percentages) Dataset abalone arrhythmia iris letter libras lymphography muskv1 optdigits promoters sonar spect splice vehicle waveform wine Average

SPSA 20.24 61.28 96.00 87.08 71.00 77.57 82.14 90.67 84.34 74.81 82.02 93.20 69.76 76.62 93.93 77.38

MBO 19.34 59.34 96.00 87.28 70.28 77.97 81.51 90.44 83.96 75.96 82.55 92.71 68.32 76.98 93.82 77.10

SA 19.34 59.87 96.00 86.81 68.94 76.89 81.81 88.79 82.08 74.42 80.97 91.16 70.99 76.65 94.04 76.58

5 Conclusion In this study, SPSA algorithm is applied to the feature selection problem as a filter approach and its performance is compared on the 15 datasets taken from the UCI machine learning repository. To our knowledge, SPSA algorithm is applied to the FS problem as a filter approach for the first time. As a subset evaluator, correlation-based FS (CFS) and as a classifier algorithm, decision tree (C4.5) are used. SPSA algorithm is also compared with other widely used metaheuristics, namely, MBO and SA algorithms. Results show that SPSA algorithm outperforms MBO and SA algorithms. Also note that the number of features is decreased significantly despite the slight increase in the accuracy value. As a future work, performance of SPSA can be tested by using different subset evaluators, classifiers and/or larger datasets. Another possible future work is hybridization of SPSA algorithm with other algorithms.

References 1. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997) 2. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273– 324 (1997) 3. Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151(1–2), 155–176 (2003) 4. Hall, M.A.: Correlation-based feature selection for machine learning (1999) 5. Hoque, N., Bhattacharyya, D.K., Kalita, J.K.: MIFS-ND: a mutual information-based feature selection method. Expert Syst. Appl. 41(14), 6371–6385 (2014)

354

R. Algin et al.

6. Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Amsterdam (2014) 7. Algin, R., Alkaya, A.F., Agaoglu, M.: Feature selection via computational intelligence techniques. J. Intell. Fuzzy Syst. 39(5), 6205–6216 (2020) 8. Dua, D., Graff, C.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. University of California, School of Information and Computer Science, Irvine (2019) 9. Spall, J.C.: Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. Aerosp. Electron. Syst. 34(3), 817–823 (1998) 10. Wang, Q., Spall, J.C.: Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements. In: Proceedings of the 2011 American Control Conference, pp. 4520–4525. IEEE, June 2011 11. Aksakalli, V., Malekipirbazari, M.: Feature selection via binary simultaneous perturbation stochastic approximation. Pattern Recogn. Lett. 75, 41–47 (2016) 12. Duman, E., Uysal, M., Alkaya, A.F.: Migrating Birds Optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf. Sci. 217, 65–77 (2012) 13. Tarakçi Kalayci, G., Alkaya, A.F., Algin, R.: Exploitation and comparison of computational intelligence techniques on the feature selection problem. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds.) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. AISC, vol. 1029, pp. 1243–1249. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-23756-1_146 14. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983) 15. Abdel-Basset, M., Ding, W., El-Shahat, D.: A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artif. Intell. Rev. 54(1), 593–637 (2021)

Intelligent Container Repositioning with Depot Pricing Policies Ceyhun Güven1

, Erdinç Öner1(&)

, and Uğur Eliiyi2

1

2

Department of Industrial Engineering, Yaşar University, Izmir, Turkey [email protected] Department of Business, Faculty of Economics and Administrative Sciences, Izmir Bakırçay University, Izmir, Turkey [email protected]

Abstract. Empty container management has always been a crucial issue in the logistics sector. Specifically, the repositioning of empty containers plays an important role in the industry of maritime shipping. Not only has an economic impact on the stakeholders affiliated with the container logistics chains but also has an effect on the society in terms of environment and sustainability as the reduction in the movement of empty containers will also reduce fuel consumption. The main objective of this paper is to minimize the total cost. This total cost includes the cost required for transportation of the empty containers to their depots and the storage cost of these containers in the assigned depots. The types of costs involved in empty container repositioning are defined via the review of the related literature and industrial practices. In this study, a mixedinteger linear programming model is developed that minimizes the total cost require in the repositioning of empty containers. The proposed model determines the storage depot of each empty container considering the depot pricing policies and distances between the port terminals and container depots. Computational results indicate that the proposed model can identify the best alternatives for empty container storage with minimum total cost. Keywords: Empty container repositioning
Mixed integer programming
OR in maritime industry

1 Introduction The management of empty containers has always been a major issue in the logistics sector, which can be improved. The process of loading and unloading containers takes place in a way that when the vessel is berthed at the port, the containers that need to be imported are removed from the vessel, and the containers that need to be exported are loaded on the vessel. For an order that needs to be exported, an empty container is transported to the location of the warehouse of the customer where it is filled with the product (that needs to be exported). After that, the full export container is then delivered to the departure port. Whereas for the orders to be imported, the transportation flow of containers is generally reverse, i.e., a full import container can be delivered from the arrival terminal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 355–363, 2022. https://doi.org/10.1007/978-3-031-09176-6_41

356

C. Güven et al.

to the location of the warehouse of the customer. Or it can also be unstuffed at the port and goods need to be transported to the warehouses of customers. If the customer receives the full import container at the warehouse, then that container needs to be unstuffed at the customer’s address. Once the full import container is unstuffed and becomes an empty status, it should be transported to one of the available container depots and stored there for a new suitable booking request. Based on the types and the number of containers that are stored, Liner companies made different contract agreements, resulting in different pricing policies for each depot. Moreover, these container depots can either be located within the container terminal boundaries. Other than the terminal, these containers can also be located at container warehouses which are placed outside the container terminal. Hence, the cost of transportation of the empty containers varies depending on which depot the container is going to be stored in. To the best knowledge of the authors, this study is the first study of container assignment modeling through different pricing policies of the warehouses. The remainder of the paper is organized as follows. Section 2 presents a literature review on empty container repositioning. Problem definition is defined in Sect. 3 whereas Sect. 4 presents our proposed solution methodology. In Sect. 5, the result of the paper is explained. Finally, a conclusion is made in Sect. 6.

2 Literature Review The problem of empty container repositioning (ECR) is attractive for researchers since the beginning of containerization. There are many studies in the literature about the ECR. Song and Dong in [1] provided a thorough literature review on the ECR problem from the network scope. Focusing on the ECR, [2] developed a deterministic mathematical model to recognize the factors that affect the movement of empty containers and quantify the scale of ECR in shipping routes. The reduction in the movement of empty containers has a direct positive impact on fuel consumption, congestion, and emission of greenhouse gases. Different types of containers may affect the ECR process, but the main obstacle here is uncertainty in demand, handling, and transportation. Epstein in [3] developed the decision support system to deal with the problem associated with the uncertainties in containers’ demand and the stocking of empty containers according to available spaces. This stoking needs to be done based on the capacity of vessels. In order to minimize the total relevant costs, involve within the scope of planning joint cargo routing and the repositioning of empty containers, [4] developed the mathematical model. Two-stage shortest-path and two-stage heuristic-rules-based integer programming methods were proposed to solve the optimization problem. For demonstrating the transshipment operations of empty and full containers, [5] proposed a mathematical model for the network where nodes symbolized companies, ports, and warehouses, whereas the arc were representing the route of transportation. In the model, the flow of full containers has been integrated with the empty containers.

Intelligent Container Repositioning with Depot Pricing Policies

357

They integrated the flow of full containers with the flow of empty containers in their model. The repositioning costs of foldable containers and standard containers have been compared by [6]. For this, a mathematical model was proposed with the objective of minimizing the total relevant cost that included costs related to inventory, container purchasing, repositioning, etc. Heuristic algorithms were proposed to solve that mathematical model. According to [7] the design of container liner shipping service networks explicitly considers ECR. They propose to optimize the whole problem by designing the network with consideration of both full and empty container traffic. A genetic algorithm is used as a heuristic approach. [8] suggested a multi-product model, which makes a straightforward dissimilarity between flows of damaged and non-damaged containers. In their article, the model is tailored for the repositioning of empty containers in the sample layout of a network of out-port empty depots, marine terminals, and internal terminals. Moreover, the total costs could be optimized by preventing damaging containers to cause significant additional cost minimization. For studying the topic in detail, the author of the paper suggests the reader study [90] which is provided an extensive review of the transportation of ocean containers. The other issues regarding operation management and different planning like strategic and tactical have also been discussed.

3 Problem Definition Currently, liner shipping companies prefer to send empty containers to the depots according to the decision of agencies. Repositioning of containers from one container terminal/depot to another is generally not required as it imposes unnecessary costs on liner shipping companies. In order to minimize the distance/time involved in the transportation of empty containers, agencies prefer to transport the empty containers to the closest containers warehouses. However, as a result, some container depots have an excess of empty containers while others have a shortage of containers to meet the demand of customers. In addition to this, the assignment of the containers to the closest warehouses will lead to higher storage costs for the liner shipping companies. To minimize the storage and transportation of empty containers, containers should be assigned to the warehouses systematically. In this problem, the objective is to minimize the total cost, including the transportation cost of the empty containers to the container depots and the storage cost of these containers in the container depots. ECR plays a key role in the liner shipping industry. Not only has an economic impact on the stakeholders affiliated with the container logistics chains but also has an effect on the society in terms of environment and sustainability as the reduction in the movement of empty containers will also reduce fuel consumption. There are many different pricing policies at the stage of determining the storage prices of the containers. The examples of the most used alternative pricing policies of warehouses are listed in Table 1. Every pricing policy has a gate cost per container. For

358

C. Güven et al.

the first four of the pricing policies, the price for the gate cost is 15$. For the fifth pricing policy, the gate cost is 30$. Table 1. An example for pricing policies

Pricing policy 1 Pricing policy 2 Pricing policy 3 Pricing policy 4

Pricing policy 5

Gate cost (in $) $15

Daily storage cost (in $) $2

Free pool (in TEU) 100 TEU

Free days (in a day) N/A

$15

$2

N/A

7

$15

$2

100 TEU

7

$15

$4 $3 $2 $1 $0

100 TEU

N/A

N/A

N/A

$30

(0–7 days), (7–14 days), (14–21 days), (21+ days)

Daily storage cost is the cost that every container needs to be paid for storing containers in a warehouse on daily basis. For the first three pricing policies, the price is 2$ per day. For the fourth pricing policy, the daily storage cost is 2$ for 0–7 days, 2.5$ for 8–14 days, 3$ for 15–21 days, and 4$ for 22 and more days, respectively. The free pool defines the number of containers that can be stored free of charge if the number of assigned containers to the warehouse does not exceed the number of free pools. For the pricing policy 1, 3, and 4, the free pool is available with the restriction of up to 100 pieces of 20 TEUs or 50 pieces of 40 TEUs. If the number of free days does not exceed the number of free stock days level assigned, that warehouse does not charge the daily storage cost. The free days policy is valid for the first three pricing policies, and it is limited to up to 7 days.

4 Solution Methodology In this section, the introduction of the proposed mixed-integer linear programming model is made. The proposed model determines the storage warehouse of each empty container considering the warehouse pricing policies and distances to reach the related warehouses from the current ports. Assumptions that have been taken into account for developing the mathematical model are explained below: • Only empty containers are taken into consideration. • Concerning the type of container, warehouses have the capacity level. • The total number of days a container stays in the related warehouse is assumed to be deterministic.

Intelligent Container Repositioning with Depot Pricing Policies

359

The mathematical model formulation is presented below along with the definitions of indices, decision variables, and the problem parameters. Mathematical Model: C: Set of containers W: Set of warehouses P: Set of ports gw: Gate cost for warehouse w; w 2 W tpw: transportation cost from port p to warehouse w; w 2 W and p 2 P dpw: distance between port p and warehouse w; w 2 W and p 2 P dw: Daily storage cost for warehouse w; w 2 W freedaysw: Free stock day level for each warehouse w; w 2 W freepoolw: Container limit for free storage of warehouse w in TEU; w 2 W capacityw: Capacity of warehouse w in TEU; w 2 W ccw: Calculated daily storage cost for container c on warehouse w; c 2 C; w 2 W
TEU c ¼

1; if container c0 s TEU is 20 2; if container c0 s TEU is 40

Decision Variables:
xcw ¼

1; if container c is assigned to warehouse w 0; otherwise

Objective Function: Minimize Z = X

X c2C

X

X

t d x þ w2W pw pw cw

c2C

g x þ w2W w cw

X

X c2C

w2W

ccw xcw

ð1Þ

Subject to X w2W

X

xcw ¼ 1; 8c 2 C

x TEUc c2C cw

 capacityw ; 8w 2 W

xcw  0

ð2Þ ð3Þ ð4Þ

The first term of the objective function (1) deals with the transportation cost for containers that are transported from the port to container warehouses. The second component of the objective function (1) indicates the gate-in cost of the related warehouses, and the third term of the objective function (1) indicates the total storage cost of the container in the related warehouse.

360

C. Güven et al.

Constraint (2) represents that each container can only be assigned to a single warehouse. Constraint (3) represents the containers that are assigned to the specific warehouses cannot exceed the capacity of this warehouse. Constraint (4) represents the nonnegativity constraint. First, the parameters are prepared for the mathematical model. The data are preprocessed before the optimization model is run by using IBM ILOG [10]. The estimated storage costs of each container in the related warehouses are calculated in preprocessing step.

5 Experimental Results In this paper, 11396 containers that are available on Aliağa, Evyap, Gemlik, İzmir, Kocaeli, Mersin, and Marport ports are assigned to the warehouses using the model presented in Sect. 4. The details of the warehouses in Turkey are listed in Table 2. Table 2. Warehouse details Warehouse Capacity (in TEU) Bursa 3000 Gemlik Gaziantep 3000 İskenderun 3500 Gebze 1500 Hadımköy 2000

Free days (in day) 0

Free pool (in TEU) 100

Daily storage cost (in $) 3

Gate cost Is fixed daily (in $) storage 15 Yes

7 7 0 0

100 500 500 0

15 15 30 15

Yes Yes Yes No

Aliağa Alsancak Sütçüler İzmit Kayseri

3000 3000 3500 1500 2000

0 7 7 0 0

100 100 500 500 0

15 15 15 15 15

Yes Yes Yes Yes No

Konya Mersin Yenice

3000 3000 3500

0 7 7

100 100 500

3 3 3 4 (0–7 days), 3 (7–14 days), 2 (14– 21 days), 1 (21+ days) 3 3 3 3 4 (0–7 days), 3 (7–14 days), 2 (14– 21 days), 1 (21+ days) 3 3 3

15 15 15

Yes Yes Yes

Gate cost of the warehouses is a cost item for storing the empty containers in the related warehouses for each container. The Gate Cost column of Table 2 indicates the gate cost of the related warehouses.

Intelligent Container Repositioning with Depot Pricing Policies

361

The distances between ports and warehouses in our example are also determined and explained in Table 3 as it is stated above. It is assumed the unit transportation cost per container is 1$/km. The transportation cost between ports and warehouses will be calculated accordingly. Table 3. Distance between warehouses and ports (in km)

Bursa Gemlik Gaziantep İskenderun Gebze Hadımköy Aliağa Alsancak Sütçüler İzmit Kayseri Konya Mersin Yenice

Aliağa port 326

Evyap port 87

Gemlik port 12

İzmir port 364

Kocaeli port 87

Mersin port 863

Marport port 165

1161 1080 104 486 5 63 92 406 797 614 968 936

1052 970 30 123 407 439 410 8 679 562 850 819

1052 1055 73 174 331 362 334 94 687 514 859 827

1102 1021 423 524 61 3 30 443 830 555 909 878

1050 960 27 121 405 437 408 6 677 560 848 817

289 208 887 975 966 913 880 853 293 362 6 44

1168 1087 101 24 486 517 489 128 797 669 969 937

The mathematical model is checking details of containers, warehouses, ports, and pricing policies of the related warehouses. The objective is to minimize the total cost of empty containers in the related warehouses including transportation costs, gate costs, and total storage costs. The number of containers that are assigned to the container warehouses using the model presented in Sect. 4 results in Table 4. Table 4. Number of assigned containers to the related warehouses Warehouse

Bursa Gemlik 1836

Gaziantep İskenderun Gebze Hadımköy Aliağa Alsancak

Assigned 0 0 868 1311 containers Warehouse Sütçüler İzmit Kayseri Konya Mersin Yenice Assigned containers 1443 1106 0 0 1952 493

1745

642

Currently, agencies may prefer to transport the empty containers to the closest container port terminals to minimize the distance/time of the transportation of empty containers. Table 5 indicates the total cost of the assignments of both the proposed

362

C. Güven et al.

model and the current assignment. The proposed model outperforms the current assignment method when the overall average transportation and storage costs are compared.

Table 5. Comparison of the proposed model and current assignment methodology Port

Vessel

Aliağa Vessel A Aliağa Vessel B Aliağa Vessel C Aliağa Vessel D Aliağa Vessel E OVERALL

Arrival date Departure date

03.02.2022 01:00 06.02.2022 01:00 07.02.2022 23:00 10.02.2022 14:00 14.02.2022 07:00

03.02.2022 15:00 06.02.2022 12:00 09.02.2022 02:00 11.02.2022 17:00 14.02.2022 18:00

Proposed model objective function (in $) 3160

Closest distance objective function (in $) 10819

Improvement (in percentage)

11793

15812

25,42%

12096

13005

6,99%

3280

16528

80,15%

3700

17305

78,62%

34029

73469

53,68%

70,79%

6 Conclusion Today, empty containers management has an important place in the logistics sector. The main objective of this paper is to minimize the total cost. This total cost includes the cost required in the transportation of the empty containers to their depots and the storage cost of these containers in the assigned depots using an intelligent optimization procedure. A mixed-integer linear programming model is proposed to assign the containers related warehouses based on the pricing policies of the warehouses and transportation costs between ports and warehouses. Computational results indicate that the proposed model outperforms the current assignment approach of the agencies by approximately 54% when the overall average transportation and storage costs are compared. For further research, different methods (random assignment, closest depot assignment, minimum storage cost assignment) can be implemented for this problem and can be compared with the proposed model.

Intelligent Container Repositioning with Depot Pricing Policies

363

References 1. Song, D.P., Dong, J.X.: Empty container repositioning. In: Lee, C.-Y., Meng, Q. (eds.) Handbook of Ocean Container Transport Logistics, pp. 163–208. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11891-8 2. Song, D.P., Carter, J.: Empty container repositioning in liner shipping. Marit. Policy Manag. 36(4), 291–307 (2009) 3. Epstein, R., et al.: A strategic empty container logistics optimization in a major shipping company. Interfaces 42(1), 5–16 (2012) 4. Song, D.P., Dong, J.X.: Cargo routing and empty container repositioning in multiple shipping service routes. Transp. Res. B Methodol. 46(10), 1556–1575 (2012) 5. Bandeira, D.L., Becker, J.L., Borenstein, D.: A DSS for integrated distribution of empty and full containers. Decis. Support Syst. 47(4), 383–397 (2009) 6. Moon, I., Do Ngoc, A.D., Konings, R.: Foldable and standard containers in empty container repositioning. Transp. Res. E Logist. Transp. Rev. 49(1), 107–124 (2013) 7. Shintani, K., Imai, A., Nishimura, E., Papadimitriou, S.: The container shipping network design problem with empty container repositioning. Transp. Res. E Logist. Transp. Rev. 43 (1), 39–59 (2007) 8. Hjortnaes, T., Wiegmans, B., Negenborn, R.R., Zuidwijk, R.A., Klijnhout, R.: Minimizing cost of empty container repositioning in port hinterlands, while taking repair operations into account. J. Transp. Geogr. 58, 209–219 (2017) 9. Lee, C.Y., Song, D.P.: Ocean container transport in global supply chains: overview and research opportunities. Transp. Res. B Methodol. 95, 442–474 (2017) 10. IBM ILOG User Manual. https://www.ibm.com/docs/en/SSSA5P_12.8.0/ilog.odms.studio. help/pdf/usrcplex.pdf. Accessed 27 Mar 2022

Improving the Performance of a Network of Signalized Roundabouts via Microscopic Traffic Simulation Tool Syed Shah Sultan Mohiuddin Qadri(&) , Mahmut Ali Gökçe and Erdinç Öner

,

Department of Industrial Engineering, Yaşar University, Izmir, Turkey [email protected], {ali.gokce,erdinc.oner}@yasar.edu.tr

Abstract. Roundabouts are effective intersection designs, which are rapidly gaining attention and popularity among traffic engineers. This is due to the roundabout’s capacity to handle the mobility of a substantial number of vehicles. The ever increasing demand for more traffic capacity can be satisfied by either significant capital investment in infrastructure or creating more capacity by intelligent signalization. The appropriate traffic signal timing is critical in smoothing traffic flow. Inappropriate traffic signal timing not only causes delays and inconvenience to drivers but also increases environmental pollution. Thus, it is important to investigate different signal timings to ensure that implemented plan will have a positive impact on the network’s performance. The optimization of roundabouts’ signal timing is relatively a new area of research. The problem is difficult to model realistically and computationally challenging. Due to the flow nature of the traffic problem, wider areas of traffic must be regulated simultaneously in a network. This can be achieved via either field-testing or using a reliable simulation tool. Microscopic simulation allows a safer and cheaper evaluation of many more alternative signal timings compared to fieldtesting. Although development, calibration, and validation of simulation models for traffic networks are challenging. We present a model to evaluate the performance of a network of signalized roundabouts, with which the combination of different traffic volume and cycle length scenarios can be intelligently studied. We also provide information on the development, calibration, and validation of the model, as well as a real-life implementation on a network of roundabouts in Izmir/Turkey. Keywords: Microsimulation
Traffic signal timing
Traffic signal control
Stochastic simulation model
Network of signalized roundabouts
Cycle length

1 Introduction Traffic congestion is a growing concern for metropolitan cities. With the increase in urban population, the number of vehicles is also increasing, leading to a worsening of the traffic in urban areas. Since the space availability prevents the construction of additional capacity, it is important to look for more viable, cost-effective, and adaptable © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 364–371, 2022. https://doi.org/10.1007/978-3-031-09176-6_42

Improving the Performance of a Network of Signalized Roundabouts

365

schemes. One of the practical ways to reduce these issues is to install traffic lights at intersections [1]. The development of microsimulation models for efficient Traffic Signal Timing (TST) for traffic networks of signalized roundabouts is a new area, for which not many references exist. So the aim of this study is two-fold. First, we provide the methodology to the traffic engineers for the development of a realistic model through the microscopic simulator SUMO. Secondly, we also provide the implementation and results of this methodology for a real-life example of a network of roundabouts in Izmir, Turkey. The implementation involves the performance evaluation of a network of connected signalized roundabouts through SUMO by comparing different scenarios. These scenarios are consisting of a combination of different traffic volumes and cycle lengths. To the best knowledge of the authors, this study is the first such real-life implementation of the connected signalized roundabouts case using a microsimulation tool. The remainder of this study is structured as follows. Section 2 sheds light on a review of the related literature on the problem. Section 3 comprises the flow overview of different modules of SUMO implemented in this study. The detail of the scenarios for the performance evaluation of the network has been explained in Sect. 4. Section 5 incorporates computational results of the methodology implemented in this study, whereas the last section i.e., Sect. 6, concludes the study with the future research directions.

2 Literature Review Signalized intersections are the type of intersections in which the flow of vehicles and pedestrians at the intersection is regulated by traffic lights. According to [2], the signalized intersections are effective in reducing accidents, minimizing vehicle delays, optimizing road capacity, and calming traffic flow. There is one more type of signalized intersection that is being adopted by different countries like the U.K, France, Sweden, Turkey, etc. [3]. In this form of intersection, vehicles travel counterclockwise (in the right-hand traffic countries) around a central island and where the inbound traffic has to become part of the circulating traffic to get an exit from the desired outbound [4]. These types of intersections are described as signalized roundabouts. It has been found that these types of intersections can be more effective in increasing traffic capacity, decreasing greenhouse gas emissions, and decreasing fuel consumption by up to 50, 65, and 28% respectively than the traditional four-way intersections [5]. Finding the best combination of TST is a complex issue, especially for intersections where the flow of vehicles is controlled by multiple signal heads. The history of this topic goes back to the 1960s when Webster [6] came up with the guidelines for traffic signal control settings and from then it is being consistently under study. A decade later, the Transport Research Laboratory developed a simulation tool called TRANSYT [7] that was able to determine the optimal TST for fixed-time traffic signal controllers and evaluate the performance of the signalized network. Following these researches, Allsop [8] and Akcelik [9] developed the equations for estimating the capacity and average delay per vehicle at the signalized intersection. These researches opened up the

366

S. S. S. M. Qadri et al.

gate for the idea of developing microscopic simulation tools to support the evaluation of optimizing TST problems. For example, a commercial simulation package PTV VISSIM [10] developed in 1992 is still quite popular [11]. Alternatively, an opensource activity-based Multi-Agent Transport Simulation (MATSim) [12] is freely available. Furthermore, another open-source microsimulation Simulation for Urban MObility (SUMO) [13] published under Eclipse Licence is also available. We would refer interested readers to [11] to understand the characteristics and differences between the most commonly used simulation tools. In recent years, signalized roundabouts have been proven by several studies to improve the operational performance of some congested roundabouts [14, 15] as well as safety performance [3]. However, the TST of the signalized roundabouts needs to be carefully timed to ensure the proper flow of vehicles and to avoid congestion at the roundabout. The signalized roundabouts in a region may prove to be effective in solving access and congestion issues at roundabouts, but still, the TST of these signalized roundabouts needs to be optimized according to the traffic demand. We have come across a few studies dealing with the signalized roundabout [16–21] and no study has been found for the network of the connected signalized roundabout under real-life scenarios. Among all the above-mentioned studies, [18, 20] are solely simulation-based studies.

3 Flow Overview of SUMO’s Modules SUMO is a traffic simulation package that generates urban traffic scenarios based on experimental data. The package comes with various modules that help in adapting the simulation model to the real-life traffic scenario. An overview of the flow diagram of the modules that have been implemented in the study is shown in Fig. 1.

Fig. 1. Flow diagram of SUMO modules used

Improving the Performance of a Network of Signalized Roundabouts

367

The user input on Sumo is a set of text files that the user creates according to the details of the desired traffic scenario. OpenStreetMap (OSM), origin/destination (O/D) matrix, and SUMO configuration text file are major files that are used to define the desired traffic scenario. The operation stage of SUMO has many modules. Netconvert converts the road network imported from OSM into a format that can be used within the operation stage. Netedit is a visual network editor that can be used to edit all aspects of the network or to create a network from scratch. Detectors are used to calculate the duration in which the vehicle passes through the detector area and measure queues of jammed vehicles. Traffic Assignment Zones (TAZ) is the list of O/D edges described by an arbitrary id. In total there are 32 TAZ in the network. These TAZs are created in Netedit by drawing polygons which were further processed by edgesInDistricts.py (provided python code). Odtrips is a SUMO-provided python file that has been used to generate a set of trips of vehicles from the O/D matrix for a given network. Duarouter converts the trips generated by odtrips.py into a route. The SUMO output provides a vast variety of simulated traffic data for the experimental analysis in terms of edge/lane/trip and most of them are a part of this study.

4 Performance Evaluation of a Network of Signalized Roundabouts Under Different Scenarios This study is a predictive analysis of the network’s performance of signalized roundabouts. For that, a simulation model of a real-life network is indispensable to project complex traffic scenarios. A network of four connected signalized roundabouts located at Bayraklı/İzmir, Turkey has been modeled on SUMO. Figure 2 is showing the simulation model and the aerial image of the area of the network.

Fig. 2. Aerial image and simulation model of the network’s area

368

S. S. S. M. Qadri et al.

The scenarios tested under this study are composed of the combinations of five different traffic volumes and four different cycle lengths. Table 1 shows different traffic volumes that have been used for the evaluation of the network. The data for base traffic volume (BTV) were collected manually during the morning peak hours (8:30 am–9:30 am) of the working day for four consecutive weeks in March 2021. The four different cycle lengths that are taken for this study are from 30 s to 75 s with a gap of 15 s, of which the cycle length of 45 s is currently being used. As far as the total number of scenarios is concerned, there are 20 in total. This means that with each above-specified cycle length, the performance of the network is evaluated under five different traffic volume data (see Table 1). Under each cycle length, several combinations of green TST are possible. Among these, 50 different combinations of green signal timing are uniformly generated in each cycle length. These green TST are generated through Python (programming Language) code in a form of a chromosome that was integrated into the simulation model. Each gene in the chromosome is representing the duration of the green phase timing of the particular signal head. The value of each gene’s green time is an integer that lies within the range of [7, Cycle Length Total Phase Change Duration (i.e. 10 s)]. For controlling the simulation model in SUMO through Python’s interface, TraCI (Traffic Control Interface) was used. To deal with the stochasticity and randomness, every scenario has been tested for 5 replications of the same TST setting and the average of these replications has been considered as a result of that particular TST setting. These results have been evaluated by considering the average trip duration and the average time loss. Table 1. Traffic volumes Traffic volumes’ cases 20% less than BTV 10% less than BTV BTV (base traffic volume) 10% more than BTV 20% more than BTV

Vehicle per hour (veh/hr) 3408 veh/hr 3834 veh/hr 4260 veh/hr 4686 veh/hr 5112 veh/hr

5 Computational Results In this section, the relation between cycle lengths and traffic volumes is being evaluated to understand the performance of the network. For calibration and validation of the simulation model, real-time traffic flow data was used for comparison. The performance measures of every TST were recorded during each simulation run which was set to 4800 s, among which, an initial 1800 s were kept for the warm-up period. It means that data was collected for the remaining 3600 s after this period had elapsed. Table 2 is showing the best results for average trip duration, which has been achieved by testing various TST settings of different cycle lengths under different traffic volumes.

Improving the Performance of a Network of Signalized Roundabouts

369

Table 2. Average trip duration (seconds) Cycle Lengths 30 seconds 45 seconds (current) 60 seconds 75 seconds

20 % less than BTV 108.51

10 % less than BTV 1136.97

1406.55

10 % more than BTV 1422.28

20 % more than BTV 1770.41

110.01

107.54

167.30

178.96

1780.00

115.27 245.18

1503.16 245.76

142.32 896.48

136.96 602.91

1809.30 1889.05

BTV

The cells highlighted in each column represent the best average trip duration (in seconds) obtained for this particular traffic volume case concerning cycle lengths. It can be observed that for improving the traffic conditions, as the traffic volume increases, the cycle lengths also need to be increased up to a certain limit. After this limit, a further increase in the cycle length causes the traffic situation to deteriorate again. As far as the minimum average time loss (in seconds) is concerned, the same trend can be observed in the average trips duration case. Table 3 is showing the minimum results of the average trip duration that has been achieved for testing. Table 3. Average time loss (seconds) Cycle Lengths 30 seconds 45 seconds (current) 60 seconds 75 seconds

20 % less than BTV

10 % less than BTV

BTV

10 % more than BTV

20 % more than BTV

54.60

1098.53

1370.1 1

1385.92

1736.70

56.14

53.71

113.86

125.56

1746.94

61.61 192.62

1465.94 194.11

88.83 852.85

83.42 555.56

1775.22 1856.22

Figure 3 demonstrates the results of the average journey duration and average time, obtained by testing different cycle lengths with BTV. The clear disparity among the results of the current cycle length (45 s) and the best possible one (60 s) can be seen and instant improvement could be achieved by switching the cycle length from 45 s to 60 s.

370

S. S. S. M. Qadri et al.

1600

Average Trip Duration (BTV)

Duration in Seconds

1400

Average Time Loss (BTV)

1200 1000 800 600 400 200 0 30 seconds

45 seconds (current) 60 seconds Cycle Lengths Under Testing

75 seconds

Fig. 3. Comparison of minimum average trip duration and average time loss for BTV under different cycle lengths.

6 Conclusions and Future Work In the first part of this study, the methodology for developing the simulation model of the urban road network through SUMO is explained. After that, results from the implementation of the methodology for a real-life example of a network of roundabouts from Izmir, Turkey are provided. The performance of the network of connected signalized roundabouts has been evaluated by comparing different scenarios consisting of different traffic volumes and cycle lengths to improve urban mobility on the road network. Average trip duration and average time loss have been considered as the performance measures for evaluating the combination consisting of four different cycle lengths and five different traffic volumes. In total, twenty scenarios have been tested in this study. Through the use of the SUMO model, it has been found that the cycle length of 60 s shows significantly better results in terms of trip duration and time loss compared to the base scenario. The work on the underlying problem can further be extended by applying an efficient simulation-based optimization in which the findings from the simulation model are integrated with an efficient optimization algorithm in order to find the optimal green signal timings of the signal heads. The challenge here would be dealing with the immense computational time requirement that comes with both the optimization and detailed simulation of the network.

References 1. Tan, M.K., Chuo, H.S.E., Chin, R.K.Y., Yeo, K.B., Teo, K.T.K.: Optimization of urban traffic network signalization using genetic algorithm. In: ICOS 2016 - 2016 IEEE Conference on Open System, pp. 87–92 (2017). https://doi.org/10.1109/ICOS.2016.7881994 2. Demir, H.G., Demir, Y.K.: A comparison of traffic flow performance of roundabouts and signalized intersections: a case study in Nigde. Open Transp. J. 14, 120–132 (2020). https:// doi.org/10.2174/1874447802014010120 3. Azhar, A.M., Svante, B.: Signal control of roundabouts. Procedia Soc. Behav. Sci. 16, 729– 738 (2011). https://doi.org/10.1016/j.sbspro.2011.04.492

Improving the Performance of a Network of Signalized Roundabouts

371

4. Rodegerdts, L., Bansen, J., Tiesler, C., Knudsen, J., Myers, E.: Roundabouts: An Informational Guide. Transportation Research Board (2010) 5. Valdez, M., Cheu, R.L., Duran, C.: Operations of modern roundabout with unbalanced approach volumes. Transp. Res. Rec., 234–243 (2011). https://doi.org/10.3141/2265-26 6. Webster, F.V.: Traffic signal setting. Road Res. Lab Tech Pap./UK/ 39, 1–44 (1958) 7. Robertson, D.I.: TRANSYT: A traffic network study tool. https://trid.trb.org/view/115048. Accessed 17 Feb 2022 8. Allsop, R.E.: Delay at a fixed time traffic signal–1. theoretical analysis. Transp. Sci. 6, 260– 285 (1972). https://doi.org/10.1287/trsc.6.3.260 9. Akcelik, R.: Traffic signals: capacity and timing analysis. Australian Road Research Board, ARR, Melbourne, Australia (123) (1981) 10. Fellendorf, M.: VISSIM: a microscopic simulation tool to evaluate actuated signal control including bus priority. In: 64th Institute of Transportation Engineers Annual Meeting, pp. 1–9 (1994) 11. Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. Eur. Transp. Res. Rev. 12(1), 1–23 (2020). https://doi.org/10. 1186/s12544-020-00439-1 12. Horni, A., Nagel, K., Axhausen, K.W.: The Multi-Agent Transport Simulation Title of Book: The Multi-Agent Transport Simulation MATSim (2016) 13. Lopez, P.A., et al.: Microscopic traffic simulation using SUMO. In: IEEE Conference on Intelligent Transportation Systems Proceedings, ITSC, November 2018, pp. 2575– 2582 (2018). https://doi.org/10.1109/ITSC.2018.8569938 14. Fortuijn, L.G.H.: Robustness of roundabout metering systems (RMS). In: Proceedings of the TRB 2014 4th International Roundabout Conference (2014) 15. Austroads: Improving the Performance of Safe System Infrastructure: Final report (2015) 16. Tarek, Z., Al-rahmawy, M., Tolba, A.: Fog computing for optimized traffic control strategy. J. Intell. Fuzzy Syst. (2018). https://doi.org/10.3233/JIFS-18077 17. Gökçe, M.A., Öner, E., Işık, G.: Traffic signal optimization with Particle Swarm Optimization for signalized roundabouts. SIMULATION 91, 456–466 (2015). https://doi. org/10.1177/0037549715581473 18. Qadri, S.S.S.M., Ali Gokçe, M., Öner, E., Gokçe, E.G.: Analysis of various scenarios to mitigate congestion at a signalized roundabout using microsimulation. In: Proceedings 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 (2019). https://doi.org/10.1109/ASYU48272.2019.8946339 19. Al-Obaidi, M.K.J.: Improvement of the traffic management of deactivated Al-Faris Al-Arabi signalized roundabout in Baghdad City. IOP Conf. Ser. Mater. Sci. Eng. 518 (2019). https:// doi.org/10.1088/1757-899X/518/2/022016 20. Shah, R., Khan, T.N., Ullah, N., Khan, M.T.: 67. Traffic analysis evaluate and signalize the existing Roundabout using PTV VISSIM Software. In: 1st International Conference on Recent Advances in Civil and Earthquake Engineering (ICCEE 2021), p. 361 (2021) 21. Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: Traffic signal timing optimization for signalized Roundabout using GA. Int. J. Adv. Res. Eng. Technol. 11, 1888–1897 (2020). https://doi. org/10.34218/IJARET.11.11.2020.176

Decarbonization of Turkey: Text Mining Based Topic Modeling for the Literature Selin Yilmaz(&), Ercem Yeşil, and Tolga Kaya Istanbul Technical University, 34467 Maslak, Sarıyer/Istanbul, Turkey {yilmazseli17,yesile17,kayatolga}@itu.edu.tr

Abstract. The European Green Deal and Carbon Border Adjustment Mechanism, which both presented at the Paris Climate Conference in 2019, impose new costs on Energy-Intensive and Trade-Exposed sectors in countries that want to export to the European Union and force them into transformation. In this study, to provide a perspective for Turkey, which is one of the largest exporters to the European Union, the use cases realized in the European Union on decarbonization in energy-intensive and trade-exposed sectors are examined with the use of various text mining algorithms and machine learning techniques. A text corpus consisting of 100 different studies on the topic of concern is used for topic modeling with the aid of text mining algorithms such as term frequency and inverse document frequency, hierarchical clustering, and Latent Dirichlet Allocation methodologies for 5-topic generation towards topic-document association procedure. The study provides the fundamental literature-based topic modeling concerning the provided literature of industry-specific decarbonization studies, essential for guiding the future research towards the extracted key points for the policy development of a country under the context. Keywords: Green deal
CBAM Allocation
Text mining


Decarbonization
Latent Dirichlet

1 Introduction The greenhouse gas emissions resulting from the actions taken since the industrial revolution have been seriously damaging the biosphere for more than two centuries. In 2019, China, the US, EU-27 countries, India, and Russia together contributed to more than two-thirds of global CO2 emissions [1]. As one of the four countries/regions with the largest emissions, the EU has put forward a set of policy roadmaps and action plans to take accountable and transparent steps to be the pioneer in emission reductions. Countries wishing to export to the EU will be subject to even more stringent controls due to the European Green Deal (EGD) and the Carbon Border Adjustment Mechanism (CBAM) that was introduced within the scope of the decarbonization goal. Being the EU’s one of the largest CBAM exporters, Turkey needs to adapt to global targets and take concrete steps. In this study, it is aimed to analyze the published studies on the technology and action plans available in the EU on decarbonization in related sectors with the help of text mining and associated methodologies for the purpose of topic modeling in an unsupervised manner for classification of studies according to these © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 372–379, 2022. https://doi.org/10.1007/978-3-031-09176-6_43

Decarbonization of Turkey

373

generated topics. The main objective of the study is to provide a model for guiding the user to topic-document association which will aid to the instant access for the most similar studies to the interest of the user. The next sections are organized to present the literature review for the concept of the green deal in Sect. 2, followed with methodology in Sect. 3, data preparation in Sect. 4, findings of this study in Sect. 5, and conclusions in Sect. 6.

2 Literature Review Paris Climate Agreement was signed in 2016 to reach a consensus on actions that can be taken to limit the global average temperature rise to an increase of 2 °C from preindustrial levels. Within the scope of this agreement, which Turkey officially ratified in October 2021, the signatory countries began to demonstrate their determination by stating their targets for emissions. In this context, the European Commission announced the EGD in December 2019, which is a policy roadmap for the EU towards a more sustainable economy. CBAM is one of the actions that will be taken for reducing emissions within the scope of EGD. With this mechanism, the scope of the EU’s emission trading system (EU ETS), where direct GHG emissions of EU manufacturers operating in energy-intensive and trade-exposed (EITE) sectors such as cement, electricity, aluminum, fertilizer, iron and steel are priced, has been expanded [2]. By CBAM, it is aimed to apply a cost equivalent to the emission costs of goods produced in EITE sectors within the EU borders to goods imported into the EU [3]. At this point, it is important that Turkey, one of the EU’s largest exporters of CBAM products, is aligned with the relevant action steps. The establishment of a working group that consists of public and private sector stakeholders by the Ministry of Commerce of Turkey is one of the initiatives taken at this point [4]. It is important to underline that Turkey is still in the early stages of planning its national transformation process, should examine the EU’s best practices on emission reduction strategies in EITE sectors and design its transformation. Looking at the emission reduction strategies implemented in EU member states in EITE sectors, prominent practices in the cement industry such as increasing crosssectoral material synergies, such as the substitution of the by-products in the cement production process without performance degradation are observed [5]. The use of biogenic waste fuels as an energy source in the cement industry, where hightemperature applications are often practiced in production processes, is another decarbonization good practice that stands out [5]. To extend the sample analysis of the selected major industries under the concept of Green Deal and CBAM, the iron and steel industry is investigated. The industry and operations are conceptually simple even though their carbon footprint is globally prominent, which is approximately 7% of all global CO2 emissions [6]. The most common approach for decarbonization in the industry is transforming to hydrogenbased manufacturing as listed in studies [6–8]. Similarly, for the aluminum industry decarbonization, the literature review indicated that the renewal of the electricity industry or recycling of existing resources for energy consumption reduction is the main objective for the aluminum industry [9].

374

S. Yilmaz et al.

The next examined EITE sector is fertilizers in the chemicals industry. By import figures, N-type fertilizers form one of the most significant percentages of the carbon footprint in this category. Some studies suggest that using a different form of nitrogenous fertilizers, called stabilized nitrogen-based fertilizers, is the key to reducing carbon emissions by up to 15% [10]. Lastly, for the literature review of the major industries, the electricity industry is investigated. According to EU Green Deal, the use of coal and natural gasses should diminish by 2040 with a sharp transition towards reduction of costs and carbon emissions by 2030 [11]. Studies suggest that decarbonized energy generation techniques can satisfy the carbon footprint requirements while sustaining a low-cost architecture in the long term [12], and policy actions towards this goal should become stricter in the following decade. All in all, the literature shows that there are technically and economically feasible opportunities that can significantly reduce GHG emissions from EITE sectors. Furthermore, by economies of scale, emission reduction technologies such as wind and solar energy are becoming cheaper every year [13]. In this regard, it is crucial to take ad-vantage of proven mitigation options to significantly reduce emissions. Following this point, the study loads the fundamental role of performing the literature-based topic modeling for the context of decarbonization and EGD which becomes a crucial tool for enlightening future studies to focus on specific areas of concern within the given list of industries.

3 Methodology This section will cover the most frequent approaches for knowledge discovery from the constructed, structured research paper text corpus. 3.1

Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is one of the unsupervised learning approaches used for topic modeling that allows the disclosure of observed words by latent topics/groups. This method assumes that the comment/document is a composition of a small number of topics, and the presence of each word in the comment can be attributed to one of the topics of the document. In addition, each latent subject in the LDA model is also represented as a probabilistic distribution over words and the word distributions also share a common Dirichlet prior [14]. The general structure of the LDA formula is as follows [14]: pðDja; bÞ ¼

M Z Y d¼1

pðhd jaÞ

Nd X Y n¼1 zdn

! pðzdn jhd Þpðwdn jzdn ; bÞ dhd

ð1Þ

Decarbonization of Turkey

3.2

375

K-Means

K-means is an unsupervised learning approach that enables one to cluster documents into multiple categories based on tags, topics, and the content of the document. The k parameter in k-means determines the number of clusters. After determining the k value, the algorithm randomly selects k centroids and assigns the data to a cluster according to the nearest center point by calculating the distance between each data and the centroid. Then, another centroid is selected again for each cluster, and clustering is performed according to the new centroids. This procedure continues until the system becomes stable [15]. 3.3

KNN

K-nearest neighbor (k-NN) is one of the algorithms used for classification and regression in supervised learning. The algorithm asserts that the documents belonging to the same class are more likely to be close to each other based on similarity measures, such as the cosine similarity function [16], which will be discussed in Sect. 4. Therefore, a document is assigned to the class that is most common among its closest neighbors. To exemplify, if the relevant document has a single neighbor (k = 1), the document is simply assigned to the class of that neighbor. 3.4

Other Methodologies

Additional to these discussed approaches, other methods such as support vector machines, Naïve Bayes, decision tree classifiers, and many other different schemes might be adopted for the model development. This study will select the best-suited method for topic generation out of these models. Additionally, the data preparation section will utilize the advantage of using the term-frequency – inverse document frequency methodology (TF-IDF). The generic equation for this calculation is given as follows: TFIDF ðtx ; d x Þ ¼

f ðtx ; d x Þ NðDÞ 
log  P  NðDÞ NðT x Þ t 2 Di ¼ 1 1 þ i¼1

ð2Þ

x

In the given equation, t and d stand for the term and document whereas capital versions indicate an array of terms and documents respectively. Lastly, the cosine similarity function is as follows: Sc ðTFIDF 1 ; TFIDF 2 Þ ¼

TFIDF 1
TFIDF 2 kTFIDF 1 k  kTFIDF 2 k

ð3Þ

The cosine similarity equation is the tool to extract a similarity figure between the documents using the TF-IDF calculations. The results of these methodologies are the main foundations of the following analyses given under the findings section. Following this section, text corpus preparation is discussed in Sect. 4.

376

S. Yilmaz et al.

4 Data Preparation Section 3 covered the list of methods as alternatives to the problem of concern although these methods demand the text data in a pre-processed manner. These pre-processing steps are listed under the topic of text mining which includes tokenization - for vectorizing each sentence to a vector of individual words, filtering – eliminating nonessential information, lemmatization – for the morphological analysis of the information in these vectors, and stemming – for obtaining the core of the words [16]. Using open-access online databases, such as Web of Science, a list of studies relevant to decarbonization, green deal, EU ETS, and CBAM are filtered, resulting in an unstructured text corpus. To create the bag-of-words and assign weights to these terms, the Term Frequency – Inverse Document Frequency (TF-IDF) methodology is used. Similarities between these documents are obtained using the cosine similarity equation. Following this section, topic modeling findings of the utilized models using the constructed text corpus are presented.

5 Findings Section 4 covered the basic data collection and pre-processing steps which included the creation of bag-of-words as well as the TF-IDF methodology. As an example, for the obtained “bag-of-words”, the following word clouds for 2 different studies are gen-

Fig. 1. A sample from the bag-of-words generation results for 100 different research papers.

erated (Fig. 1). Following this point, the first generated unsupervised model is the LDA methodology. Obtained term-document matrix is sampled with the Gibbs method for 500 iterations for a 5-topic generation. The resulting topics with their associated keywords are as follows:

Decarbonization of Turkey

377

Table 1. LDA topic modeling results with associated keywords. Topic 1 Emissions Renewable Production European CBAM

Topic 2 Carbon Emissions Policy Trade Climate

Topic 3 Hydrogen Steel Energy Production Iron

Topic 4 Energy Electricity Power Emissions Generation

Topic 5 Energy Policy Green Climate Market

Concerning Table 1, topics 3 and 4 are examples of the previously discussed areas of concern of the literature review as topic 3 is linked with the iron and steel industry, topic 4 is with electricity generation. Topic 2 and 5 are the general concept of reducing carbon emissions and the new policies towards the green ecosystem. Even though the sample size is small, the model can generate meaningful topics. The following figure presents the topic probability distributions for a sample of 10 studies (Fig. 2).

Fig. 2. Topic – document association for a sample of 10 studies.

The second approach for document clustering is the use of the hierarchical clustering methodology. After model creation and clustering, the results of the clusters and the associated documents are as follows:

378

S. Yilmaz et al. Table 2. Hierarchical clustering results.

Cluster

Document number

A B C

97 14 1,2,3,4,5,11,13,20,22,25,26,27,28,29,58,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81, 82,84,85,86,87,88,89,91,94,95,96,98,100 12,21,23,24,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,49,50,51,52,53,54,55,56,59,83,90,92,93,99 6,7,8,9,10,15,16,17,18,19,30,32,48,57,60,61

D E

According to the results of Table 2 and LDA, hierarchical clustering cluster 55 documents the same as LDA when cluster A is taken as topic 1, B as 3, C as 2, D as 4, and E as 5. Although this method can perform clustering, topic modeling is not achieved. Thus, due to the better consistency and ability to model topics, LDA outperforms hierarchical clustering. Paralleling to this conclusion, other methodologies which were previously discussed in the methodology section fail to challenge the success of LDA towards the given task of topic modeling.

6 Conclusion In this study, the general topic of Green Deal, CBAM, decarbonization, and movement towards new policies in different sectors such as iron and steel, aluminum, fertilizers, and electricity are investigated. The study provided a comprehensive overview of the literature which is followed by the methodological approach to the problem of topic modeling. After model development using LDA and hierarchical clustering approaches, generated topics parallel to the intrinsic structure of the literature and main problematic sectors. The study offers the opportunity for the user to match a specific topic with documents related to the topic of decarbonization and Green Deal policies as well as the contrary, document-to-topic matching. For the future of the study, a more comprehensive paper database with a higher number of samples is recommended to generate a greater order of topics. This action will result in broader coverage of the list of industries, countries as well as various studies with a higher relative significance based on the country of concern.

References 1. Ritchie, H., Roser, M.: CO2 emissions. Our World in Data (2020). https://ourworldindata. org/co2-emissions 2. International Carbon Action Partnership: EU Emissions Trading System (EU ETS) (2021). https://icapcarbonaction.com/en/?option=com_etsmap&tan/presssk=export&format= pdf&layout=list&systems%5B%5D=43 3. Aşıcı, A.A.: Türkiye Ekonomisinin Sera Gazı Kırılganlığı [The EU’s Carbon Border Adjustment Mechanism and the Turkish Economy] (Policy brief) (2021). https://ipc. sabanciuniv.edu/Content/Images/CKeditorImages/20210812-20083115.pdf

Decarbonization of Turkey

379

4. Yeşil Mutabakat Çalışma Grubu Oluşturuldu. BloombergHT (2021). https://www. bloomberght.com/yesil-mutabakat-eylem-plani-genelgesi-yayimlandi-2284375 5. IEA: Energy Technology Perspectives 2017: Catalyzing Energy Technology Transformations. International Energy Agency (IEA), Paris, France (2017). 443 pp. 6. Bhaskar, A., Assadi, M., Nikpey Somehsaraei, H.: Decarbonization of the iron and steel industry with direct reduction of iron ore with green hydrogen. Energies 13(3), 758 (2020) 7. Ren, M., et al.: Decarbonizing China’s iron and steel industry from the supply and demand sides for carbon neutrality. Appl. Energy 298, 117209 (2021) 8. Gielen, D., Saygin, D., Taibi, E., Birat, J.P.: Renewables-based decarbonization and relocation of iron and steel making: a case study. J. Ind. Ecol. 24(5), 1113–1125 (2020) 9. Liu, G., Bangs, C.E., Müller, D.B.: Unearthing potentials for decarbonizing the US aluminum cycle. Environ. Sci. Technol. 45(22), 9515–9522 (2011) 10. Kabiri, S.: Carbon footprint of fertilizer imports to the East African Bloc and policy recommendations for decarbonization. AAS Open Res. 3, 21 (2020) 11. Chojnacka, K., et al.: Carbon footprint of fertilizer technologies. J. Environ. Manage. 231, 962–967 (2019) 12. Gerbaulet, C., von Hirschhausen, C., Kemfert, C., Lorenz, C., Oei, P.Y.: European electricity sector decarbonization under different levels of foresight. Renew. Energy 141, 973–987 (2019) 13. Roser, M.: Why did renewables become so cheap so fast? Our World in Data (2021). https:// ourworldindata.org/cheap-renewables-growth 14. Jelodar, H., et al.: Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools Appl. 78(11), 15169–15211 (2018). https://doi. org/10.1007/s11042-018-6894-4 15. Na, S., Xumin, L., Yong, G.: Research on k-means clustering algorithm: an improved kmeans clustering algorithm. In: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 63–67 (2010) 16. Allahyari, M., et al.: A brief survey of text mining: classification, clustering and extraction techniques. arXiv preprint arXiv:1707.02919 (2017)

The STAMP€ IT Platform: Digitalisation for Modelers Jérôme Henry1(&) and Alexandre Januário2 1

Macroprudential Policy and Financial Stability, European Central Bank, Frankfurt, Germany [email protected] 2 Information Systems, European Central Bank, Frankfurt, Germany

Abstract. Stamp€ IT is a data science platform providing a “model management” cooperative tool. Users can create, change and run models and analyse simulations, all in a shared digital environment. There is always a single “Stamp-ed” version of the model, as validated via an embedded governance process. Users can be “basic” who run pre-defined simulations, “advanced” who may change some of the parameters governing the pre-defined simulations, or “developers” who have full freedom to amend model and simulation designs. The original IT set-up combines notebooks to run simulations, a master and variant code repository, and a tool to compare simulation results (across varying parameters, sample, time horizon etc.). These three tasks are respectively conducted by using Jupyter, Bitbucket and MLFlow, all integrated in one place. Screenshots document the step-by-step process involved. We finally provide a use case, by conducting a complex and computation-intensive “reverse stresstest” for a sample of euro area banks, requiring multiple model runs and specific visualisations. We specify the concept of reverse stress test for multiple institutions considered jointly. We introduce an efficient way of running simulations as an alternative to Monte Carlo simulations. Results are provided using a multiplicity of scenarios, covering a range of underlying probabilities. Keywords: IT system


Modelling
Reverse stress test

1 Introduction 1.1

Main Objectives and Contribution

A stress-test is an exercise whereby a bank or system is assumed to be subject to a set of generally macrofinancial adverse shocks, in order to assess the impact thereby checking the resilience of the bank or set thereof [1]. At the origin of the STAMP€-IT project was the fact that stress testing-for a system of banks builds on large datasets from banks and financial markets and requires a whole team of modelers to be working closely together, employing a set of complex non-linear models [2]. The platform responds to the need for an ad hoc, suitable and original, IT environment that would be commensurate to such computational-intensive ambitious but regular activity. The associated needs were threefold, namely the capacities for a given team to adjust their models, to share and run them, all that within a given institution or even across © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 380–390, 2022. https://doi.org/10.1007/978-3-031-09176-6_44

The STAMP€ IT Platform: Digitalisation for Modelers

381

institutions in the event where cooperation exist – e.g. across ECB and EBA for the latter’s stress tests. This objective called for a novel dedicated IT infrastructure, that would allow users in particular to run multiple simulations quickly and securely, also with a view to capturing uncertainty in scenarios and banks’ behaviour – for example, conducting Worse Case Scenario exercises or even Reverse Stress Tests. The latter is tantamount to inverting a large and complex non-linear multivariate model, hence ever more computationally intensive than standard stress-tests. [3] and [4] are most relevant surveys, and [5] provides an updated and comprehensive review of financial sector applications. The reflection on the platform design had to account for the generic approach followed by any modeler, so as to ensure usability and relevance for just any model. This means mapping an input - output (transducer) type of business logic, specifically taking data and economic or else assumptions as input, then using the models to compute the output, i.e. simulation results conditional on given inputs and transducer. The user requirements also are aligned with modelers’ standard practices. The ideal platform should handle alternative configurations for data sources or vintages; scenarios or macro financial variables; parameters in behavioural or regulatory equations; also proposing a range of format for post-simulation summary charts and tables as well as standardised and customisable comparisons across execution runs. 1.2

Approach Taken

In the remainder of the paper, we will show how the platform can achieve these aims, First we will elaborate on the rationale and functions of the specific IT architecture that was put in place for that specific model-management purpose. We will then introduce a specific application that illustrates the effectiveness and the value of the tool, taking worst case scenario and associated literature on reverse stress testing as a reference for the use case that we document. Finally, a conclusion section also introduces further plans for developing follow-up future IT solutions, inter alia using the Cloud.

2 An Original IT Infrastructure for the Specific Purpose of Model Management 2.1

Overview of the Objectives and Main Functionalities

Stamp€ IT is a data science platform to develop, train and share models in other words, a “model management” tool – it should offer an end-to-end life-cycle collaboration platform for developers and users of a given model (i.e. a given set of equations to address a specific problem) and deliver a set of results for pre-defined sets of variables. The platform project took three types of characteristics as objectives, namely: Objective 1: On the technical IT front, the platform should be scalable and secure; provide a Single Entry Point; permit both on-premises or Cloud runs. Confidentiality and security were essential, especially for supervisory or individual data. Objective 2: On modelling tasks, the platform should distinguish between development and execution activities (changing the model vs. using a fixed model) plus

382

J. Henry and A. Januário

allow multiple models to be hosted, developed or run, as well as multiple languages to be supported (Python, R, Matlab, …). Objective 3: On the needed environment, there should be a central model repository for the codes to be stored and validated, “Stamp-ed” – with model versioning and execution tracking, i.e. which equations were used to generate which set of results from which data. There should also some “press-the-button” execution facility, embedding the above-mentioned tracking and also allowing users to conduct standardised comparisons across runs of various types. In particular, results of simulations within a team need to be reproducible by any team member, which is facilitated by versioning and tracking of executions. The system should be flexible, i.e. modular and easily extensible to cater for changes in the models used or in the set of models as such landscape rarely remains frozen for long. 2.2

Required Capabilities to Be Covered

Figure 1 top part reflects the modelers’ perspective, with from left to right the 3-stage typical workflow: load data as input, then develop models and run them, finally getting, analysing and disseminating results.

Fig. 1. The capabilities map of the platform

There is a crucial need to manage model versions, codes and datasets to render the whole chain of actions both feasible and controllable. The lower yellow part shows governance aspects which are also key to ensure cooperation is smooth and secure – in particular model approval and publication, Stamping the new code meant to become the reference one for users or subsequent developers.

The STAMP€ IT Platform: Digitalisation for Modelers

383

The bottom grey part recalls what is needed operationally for a given model to be created and run – namely data collection processes and routines, development and execution IT environment, access rights and security, IT task scheduling and monitoring. 2.3

The IT Solution: Three Set-Ups for Three User Types

Given its initial objectives, the platform valuably consider three different types of users (developer/advanced user/basic user). Basic users can only run a given available model, as interested only in simulation results conditional on a given input. An advanced user instead will experiment with some changes to the code or key equation parameters within an ex ante limited scope. Finally a developer can do anything. There is a last but key role, namely that of the approver, i.e. the one who eventually Stamp-s the model, so that a new version created by developers is allowed to become the official master one, now to be used by advanced or basic users as their reference. Each user needs a specific IT environment depicted on Fig. 2 – from a very simple one for the basic users to a rich and complex one for the developers, reflecting that they have many more potential tasks to complete. In particular developers initiate the sharing of the model version that they have arrived at with other users, via a number of repositories (model – model documentation – data – codes and “notebooks”) again subject to the authorisation workflow in the hands of the model owner or responsible manager. A critical element is the use of notebooks, i.e. a structured pre-defined set of instructions that allow a full run of the model to be carried out, without any other intervention. The notebook script embeds all needed steps, i.e. the sequence data loading, code reading, model running and eventually results collection (Jupyter being the tool).

Fig. 2. A threefold IT set-up for three types of users

384

2.4

J. Henry and A. Januário

A Bunch of Software All in One Place

The introduction of specific building blocks and corresponding software is done incrementally (see Fig. 3). The first release of the platform put together the various pieces of codes, so as to run them jointly in a structured and efficient manner, dispensing with any manual intervention (e.g. to store or read data in spreadsheets). Matlab, Python as well as Excel routines and codes had to be brought in a single Python script – with a web-based solution to share and execute these modified and harmonised codes. A Virtual Machine approach was employed, with users accessing only the platform and specific data feeds without connection to any external environment or internet. The second release went much further by adding an File Transfer Service, a Matlab Server, but also two separate execution areas (for test and production), both with dedicated notebooks. The collaboration function also required the use of complementary pieces, such as Jenkins or Mlflow. The notebook can be adapted to trigger whatever format for the Mlflow output. The latter allows the user to compare results in a pre-defined manner across a larger number of simulations (with e.g. differing model specifications or parameters, different sample of time or individuals, various scenarios…). Below the software box all connected datasets are listed, with the right-hand side bar showing connections to in-house shared services for e.g. data or documents.

Fig. 3. Software phasing-in for subsequent releases of the platform

The STAMP€ IT Platform: Digitalisation for Modelers

385

3 A Use Case Illustration: Macro Reverse Stress Testing 3.1

An Illustration of How the Platform Functions

A typical step-by-step process involves five actions in sequence that echoes a standard model execution run ie data load - code load - run code - store results. Action 1: Enter the Virtual Machine space (in a test or production environment); Action 2: Open links to dedicated URLs: Bitbucket, Jupyter Hub, MLFlow; Action 3: Enter workspace (in a given structure: data/codes/results/”personal”); Action 4: Check-out for the latest STAMP-ed environment (i.e. load newest codes); Action 5: Open and run one of the standardised multi-step notebooks. The user then wait for simulation(s) to be over, after which he can alternatively: Amend notebooks (set of banks, time horizon, scenarios, parameters…), re-run If allowed, change codes and eventually submit to approver via Bitbucket Process results – see direct outcomes in datafiles or compare across using MLFlow (Figs. 4, 5 and 6).

Fig. 4. The Jupyter directory environment for a given user (after check-out)

386

J. Henry and A. Januário

Fig. 5. An illustrative run launched with a pre-defined standard notebook

Fig. 6. How Mlflow can provide summary comparisons across many model runs

The STAMP€ IT Platform: Digitalisation for Modelers

3.2

387

Reverse Stress Test: Definition and Conduct for a System of Banks

The user once equipped with the just described toolkit, i.e. in possession of the model and associated software pieces, can then readily experiment with as many scenarios as wished. A wide range of outcomes can be simulated, e.g. reflecting how the banking system would be impacted by a variety of alternative scenarios. This opens the route to Reverse Stress Test or RST. [6] depicts such a macrofinancial application and [7] provide details on the methodological background and illustrative results for a macro RST. RST can be defined as a generic inversion of a given stress test model, as in Eq. (1): Output ¼ ST ½Input 

ð1Þ

Solving this RST problem means for the user to find {Inputs} such that Eq. (1) holds for a given Output. The solution when existing is generally not unique, to the extent that e.g. a high unemployment as a single shock can be equally damaging as a shock only to say long-term interest rates, or as any suitable combination of these two shocks. 3.3

A Specific Scenario Design

Implementing an RST requires significant departures from the otherwise standard approach to scenario design, i.e. how to shape the Input to a stress test, as recalled in [8]. The RST version of scenario design would require: (i) skipping the otherwise standard Risk Identification (RI) step, or only using it to derive a shortlist of risk factors, (ii) ignoring severity in the calibration of underlying exogenous shocks and (iii) running the scenario generation and impact analysis models to compile a (host of) results for a (finite) variety of shocks, instead only for a single or a couple of scenarios. All simulation results eventually need to be screened to identify eg a Worst Case Scenario or to derive combinations of shocks consistent with the inversion of the ST model, i.e. the set of simulations delivering the outcome as specified in Eq. (1) above. This process as straightforward as it might sound still raises a number of methodological issues. There is e.g. a need to select the reference Outcome metrics or to pick the scenarios of interest among the likely many solutions to the problem stated in Eq. (1). In practice the selection end-decision reflects a mix of ex ante and ex post choices (look at specific risks, pick the most stressful or most likely scenario, etc.). 3.4

Computational Avenues to Solve the RST Problem

Running an RST for a system of banks is clearly a complex task, for which [7] identified three ways of running the extensive and complex simulations as anyhow needed. Way 1: take a given scenario post-RI and push to extreme by multiplying shocks– a homothetic/scalar approach (on a single risk factor or group thereof), as in [6]. Pros – uses a risk narrative linked to standard exercises, remains trackable Cons – not capturing the unknowns (for events, correlations, probabilities)

388

J. Henry and A. Januário

Way 2: Experiment with no ex ante identified risks, carrying out many (e.g. Monte Carlo) simulations, as no priors, as in [9]. Pros – has no priors nor gaps, uses unknown unknowns (a bounded list still) Cons – need to identify/select drivers ex post, a heavy modelling tasks Way 3: Mixed strategy, i.e. combines groups of factors with a variety of shocks at alternative probability levels, as in [7]. Pros – relates to risk narrative, but not bound by it, has room for unknowns Cons – still needs probabilistic considerations to ex post select scenario(s) The third approach is taken in [7], reporting results from simulations carried out with the STAMP€ IT platform and model [2]. Macrofinancial risk factors are grouped in 3 subsets (domestic, external, interest rates). The exogenous shocks to all variables in the same subset are set to a given multiple of their respective standard error sigma. Shocks range in magnitude from 0 to 4 sigma, thereby covering a wide array of input probabilities. The latter are identical for all risk factors in the same subset for any simulation. Considering the capital ratio of the system after the stress has been applied, the surface shown on Fig. 7 based on [7] obtains, with limit points seen for shocks set at 0 or at the maximum of 4 sigma. Scaling up the severity domestic shocks has a quicker and stronger impact than applying a similar change to the other shocks – i.e. for a given same probability loss, a domestic shock is worse for banks. By design, the point in the very middle of the surface of outcomes correspond to a 5% probability for all shocks. Taking instead the post-stress capital ratio as a threshold or bar to be passed by banks, Fig. 8 also based on [7] reports the share of banks below bar. The resulting surface is no longer smooth, since the variable of interest is now a discrete, binary one. Having most banks with a capital ratio below bar would require both financial shocks and real ones to go far beyond a 5% level (outcomes on the left end of the green area).

Fig. 7. Mapping scenario severity factors with the banking system capital ratio

The STAMP€ IT Platform: Digitalisation for Modelers

389

Fig. 8. Mapping scenario severity factors with the share of banks below bar

4 Conclusions and Future IT Systems The currently available STAMP€ IT platform has rendered model computations and management far easier and extended the scope for demanding simulations, including for very complex and computationally intensive tasks such as Reverse Stress Testing. At the same time, a number of additional features are required for the tool to fully benefit from innovations in the digital space (as illustrated in Fig. 9). Connections with the Cloud could e.g. be established via the ECB HyHop initiative.

Fig. 9. Possible future enhancements and connections to the Cloud

390

J. Henry and A. Januário

Also a broader range of kernels could be made available for model coding and execution beyond Python and Matlab as already available. The model development environment and functionalities could finally be substantially enhanced by resorting to Kubernetes. Disclaimer and Acknowledgments. The views expressed in the paper are those of the authors and do not necessarily reflect those of the ECB. The authors would like to thank M. Frantzi, D. Laliotis, M. Gabriele, A. Bousquet, M. Weigand, P. Maccury, M. Bayona, T. Dubiel, C. Murphy and Y. Bhagat for their key contributions to the project over time.

References 1. Baudino, et al.: Stress testing banks: a comparative analysis, Financial Stability Institute Insights #12. BIS (2018) 2. STAMP€: Stress Test Analytics for Macroprudential Purposes in the Euro area. ECB (2017) 3. Cihak: Stress-testing: a review of key concepts, Research and Policy Notes, 2004/02. Czech National Bank (2004) 4. Breuer, T., et al.: How to find plausible, severe, and useful stress scenarios. Int. J. Cent. Bank. 5(3) (2009) 5. Bellini, et al. (eds.): Reverse Stress Testing in Banking. De Gruyter (2021) 6. Bank of England: UK banking sector resilience and Covid-19, Interim Financial Stability Report, May (2020) 7. Henry, J.: Reflections on macroprudential reverse stress testing. In: Bellini et al. (eds.) Reverse Stress Testing in Banking. De Gruyter (2021) 8. Gross, M., et al.: Macrofinancial scenario for system-wide stress tests, for banks and beyond. In: Farmer, J.D., Kleinnijenhuis, A.M., Schuermann, T., Wetzer, T. (eds.) Handbook of Financial Stress Testing. CUP (2021) 9. Glassermann, P., et al.: Stress scenario selection by empirical likelihood. J. Quant. Finance 15 (1), 25–41 (2014)

Development of Secure Platform for Innovative Processes Implementation in Scientific and Industrial Cluster by VPN Network Segment Differentiation Artur Zaenchkovski1 , Alexey Lazarev1(&) and Sergey Moiseev2

,

1 Department of Information Technology in Economics and Management, National Research University ‘Moscow Power Engineering Institute’ (Branch) in Smolensk, Energy Passage 1, 214013 Smolensk, Russia [email protected], [email protected] 2 LLC «Massa», Volochanovskoe Highway, 6A, Shakhovskaya RP, 143700 Moscow, Russia

Abstract. Nowadays, the priority role of innovations in modern industrial systems is emphasizing. At the same time such innovation processes can’t be realized alone. For remote connection of several industrial and science organizations in clusters for innovative processes implementation, common software and analytical solution is implementation of VPN protocol in network LAN segment. The installation of this solution often allows solving the generalized problem of remote access to industrial enterprises and scientific organizations network. However, the lack of means to distinguish segmentation of VPN clients does not allow using this solution for effective and secure cooperation work. This paper was aimed to develop software for solve this problem. The proposed solution is based on an intelligent system for detecting topological cluster divisions for subsequent segmentation of VPN tunnel based on deep learning neural networks. Original features of authors’ software are: ability to dynamically replace customer fingerprints and secure information interaction system for innovative process implementation in scientific and industrial cluster. Among other things, the use of the implemented primary encryption approach based on the Elliptic Curve signature and symmetric HMAC and FERNET encryption methods made it possible to provide dynamic generation of signatures to ensure the required level of security. Keywords: VPN tunnelling
Scientific and industrial cooperation
Distributed computing
Intelligent data processing
Neural data collecting

1 Introduction The development of data transmission at the present stage focuses on issues of security ensuring these processes at all stages from generation, pre-processing of transmitted information by the end user. Considering interactions between scientific organizations and industrial enterprises in the framework of innovative projects joint implementation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 391–399, 2022. https://doi.org/10.1007/978-3-031-09176-6_45

392

A. Zaenchkovski et al.

and a number of information exchange features can be identified. Among such features in the field of information exchange are issues of information distribution between cluster participants, requests for extraction and parallel processing of the targeted information flow [1, 2]. Existing software and analytical solutions for localized management of a remote segment for such segment allow end clients connecting to a server point by installing a tunnel using VPN protocols, and even taking into account security through unique certificates in IPSec/OpenVPN solutions [3]. They don’t solve the problem of distributed access in the presence of multiple control points in the segments of one scientific and industrial cluster for the organization of centralized management with the support of secure information exchange [4]. To solve identified problems, in this article is proposed to develop a unique algorithm for the intelligent separation of network routes into separate segments, access to which will be implemented by modifying the application packages of OpenVPN/IPsec servers. The topology of the neural network fuzzy model will be taken as the basis of an algorithm for identifying and predicting segments of VPN servers with the possibility of subsequent security through the registration module of network participants local fingerprints. Cognitive fuzzy logic modeling methods for innovation processes management [1, 2] could’t be easily and effectively adapted for network models. In [3] there were identified VPN protocol vulnerabilities and offered few software solutions. Publication [4] also presented some adapted software tools to existing vulnerabilities. The classification of network notifications [5] made it possible to adapt the MLP model to work with network topologies. In [6–10], the main possibilities of nmap effective use to prevent reverse scanning are given. Publication [11] cite atypical models of neural networks for adaptation to various fields of software work. The paper was structured for several chapters: firstly, algorithmic implementation, then detailed scheme of network segmentation, next part – neural network data forecasting model based on MLP and finally – a multi-factor authentication algorithm to ensure the security of implemented software. In conclusion, the main results and prospects for the development of software are presented.

2 Implementation of the Algorithmic Component The standard representation of a VPN server is described as isolation of the main connection into a virtual tunnel with a dedicated static IP segment of the network and random IP addresses of clients. This algorithm is proposed to be restructured by using a neural network model and network analysis with system calls from proprietary software to determine the primary structure and partition of the network segment [5]. So, as input layers of the neural network model, it is proposed to submit groups of IP address segments for subsequent forecasting of changes (see Fig. 1). Taking into account the identified requirements for work of devices interaction of cluster main subjects, the following modular system of interconnected elements was developed – for example, the modules ‘primary construction of segmented network sections and extraction of parameters of the neural network model’ and ‘smart subnetting for network segments’ allow to perform an initial polling of network segments

Development of Secure Platform for Innovative Processes Implementation

393

and setting subnets for each of them [6]. Modules ‘registration of fingerprints of cluster members’ and ‘polling changes and decision-making’ allow to ensure security by generating a root digital fingerprint of the device and using methods to track changes in the network segment.

Fig. 1. The algorithm of modules system interaction of VPN segment secure neural network differentiation platform in scientific and industrial cluster.

2.1

Cluster Analysis and Segmentation

The standard representation of network analysis systems is based on the use of proprietary and network packets, the output of which is represented by a terminal log. For the primary analysis of the network segment, it is assumed to use iptraf, nmap and their analogs (arp-scan) packets – so, the initial scanning of a network segment using nmap allows to detect a number of hosts with possible names, which can then be exported to xml format for processing [7, 8]. Due to the use of these methods, the initial scanning and splitting of network segment into independent sections is carried out at the planning stage, while each subsequent segment is determined by a separate DHCP server using the ‘isc-dhcp-server’ package [9]. It should also be noted that due to the low consumption of hardware resources, this package can combine the capabilities of client and server in one device, which will allow the implementation of the so-called ‘flexible cluster’, in which each device acts simultaneously in these two states. The proposed package can be changed in configuration mode using a separate configuration file dhcpd.conf, where can dynamically set network segments by varying the parameters. As a result, at this stage of algorithm, the comparison of DHCP network segments with VPN server is implemented using the example of an OpenVPN server. This operation provides the possibility of clients flexible IP addressing – both using an integrated DHCP server in the OVPN server, and setting own configuration. Taking

394

A. Zaenchkovski et al.

into account the fact that the implemented system plans to use its own configuration, in which the client of OpenVPN server receives an IP address from the OVPN-DHCP segment and has feedback through bridge to the integrated DHCP server, the implementation option is shown in Fig. 2.

Fig. 2. Network segmentation in accordance with the implemented software.

As can be seen from Fig. 2, the implementation of such topology has a number of advantages. For example, flexibility in remote network segment placement and the need for access to a device with an Eth0 adapter IP address attached. It is organized by adding virtual tap adapters and their configuration is performed automatically by adjusting the configuration file. Among the main advantages is that it makes possible to highlight the possibility of static nodes installation in the local network of scientific and industrial cluster participants, for example, if the location of the server 10.6.5.10 changes, static access to it will remain at this IP address [10]. The technology under consideration can be implemented as follows: 1. The ‘dev tap#’ option initializes the operation of the virtual adapter of the installed number #= No. X. 2. A similar virtual adapter is initialized on the client side and the configuration of the client’s IP address is added – ‘ifconfig 10.5.0.5 255.255.255.0’. 3. Virtual adapters tun, bridge (via modprop & –mktun –dev) are initialized on the server side, then the bridge is installed using ‘brctl addif’, while the number of virtual tap adapters is equal to the number of network segments that are remote from each other. 4. Network configuration of iptables rules for accepting and redirecting ‘tap, br’ virtual adapters is also performed. Through the implementation of above-described procedures, IP addresses are assigned to individual scientific and industrial cluster participants, including the

Development of Secure Platform for Innovative Processes Implementation

395

‘distribution’ of IP addresses to virtual interfaces for the implementation of a bridge between clients of network segments and feedback support. The process of assigning IP subnets for allocated IP address pools is based on the partial use of the primary MAC address identification module of the device. So, based on the priority MAC address of the initialized hardware device node, the first 5 bytes are converted into a unique identifier, according to which the subnet address and the corresponding pool of node addresses are determined (See Eq. 1). ½AB : AF : B1 ) 14:16:2:1

ð1Þ

where [AB: AF: B1] is cells of the hardware MAC address of the initialized device; 14.16.2.1 is primary address of the initialized device. So, the implementation of this module will allow, after the initial scan, to set a pool of VPN addresses for possible subnets within the innovative ecosystem of the region. 2.2

Building a Neural Network Model

Due to the parameters of the devices identified at the previous stage the prediction of possible changes in the network topologies was carried out. Existing approaches and time series forecasting tools allow to perform training both independently and with a teacher [11]. To solve this problem, a supervised learning model based on Multilayer Perceptron network (MLP) is used, where the forecasting process is reduced to a nonlinear comparison of data with a direct connection of connected weights (See Fig. 3). Among other things, MLP has an error back propagation function, where differentiable weights are combined in the model. Together with these factors, the prospects for using an MLP network are determined by significantly lower consumption of hardware resources, resistance to noise, the use of multidimensional input vectors, as well as the possibility of multi-step forecasting.

Fig. 3. Integration of the MLP model into the implemented system.

The algorithmic implementation of the Multilayer Perceptron Model, shown in Fig. 3, demonstrates the learning process of a neural network model, where the input layers are datasets from variable network segments identifying a hardware device

396

A. Zaenchkovski et al.

(IP/MAC Addr), where step-by-step prediction of possible changes in the input rows is performed. That is, an analysis of offsets is performed – adding or removing devices in a network segment. So, the vectors of the initial inputs xi in Eq. 2 include N linear combinations and are defined as x-the number of vector inputs. At the next stage, each activation function is transformed into a nonlinear function, for example, a sigmoid. In this solution, a modification of the ReLU function – LeakyReLU, defined by Eq. 3, is set as an activation function. The choice of this function is isolated by the absence of ReLU problems deactivation after passing due to the possibility of shifting the range of the standardized function by a certain value. zi ¼

XD i¼0

wji xi ; j ¼ 1; 2; ::; M;

ð2Þ

where zi is activation functions; j, i is weights of neurons; M is number of linear combinations of D-inputs.  f ðxÞ ¼

ax f or x \ 0 ; x f or x  0

ð3Þ

where f(x) is the LeakyReLU activation function; a is the gradient constant (by default a = 0.01); x is the target vector of the function. The software implementation of the predicted model is carried out using the PyTorch library. The input dataset for neural network training is a set of network segments describing changes in the participants of scientific and industrial cluster. To process regression calculations, a linear function is used at the final stage; the Adam model performed to a gradient descent, the value of categorical cross-entropy is set as a loss function, and the root-mean-square error is used for regression. The training of the model was carried out using 210 epochs, the losses amounted to 0.0028 units. 2.3

Multi-factor Security of Transmitted Data

To ensure the security of the transmitted data and prevent possible leakage, a mod-ular dynamic multi-factor verification system of unique device fingerprints based on use HMAC, FERNET, and Elliptic Curve encryption methods. As can be seen from Fig. 4, the primary bundle of several participants nodes of the scientific and industrial cluster is carried out by generating open and closed signatures of the EC protocol, the input of which is supplied with a constant variable of the cluster generated using the pwgen package. Then, after initializing the primary connection, data verification and validation is carried out using HMAC and FERNET methods. Consideration of the HMAC fingerprint generation block includes dynamically the process of generating a time value used to determine the validity of a digital fingerprint in a time interval (See Eq. 4).    T1  T0 FINGERPRINT ðSIGNATURE Þ ¼
TIMEVER ; T

ð4Þ

Development of Secure Platform for Innovative Processes Implementation

397

where FINGERPRINT is a digital temporary fingerprint, which is the input node of the secret key in HMAC; T1 is the current UTC time value; T0 is the starting point of UTC time; TIMEVER is the value of the validity time of the digital fingerprint.

Fig. 4. The algorithm for ensuring security and primary linking of devices.

Among other things, the parameter set by Eq. 5 and 6 has a dynamic structure and the ability to set the period of validity of the interval, for example 1 h. HMACK ðK; mÞ ¼ H ððK 0  opadÞkHððK 0  ipadÞkmÞÞ;  HðKÞ K0 ¼ ; K

ð5Þ ð6Þ

where H is a cryptographic function; m is a target encrypted message; K is a secret key; K′ is a block key with the dimension of the original key K (also identified by filling with zeros up to the block size/equal to the block size); opad is an external filling with a cyclic offset of 05c; ipad is an external filling with a cyclic offset of 036. The process of performing asymmetric FERNET encryption is based on the input of a dynamic HMAC fingerprint from Eq. 5 and 6 in base64 encoding and the data to be encrypted. The software implementation in Python is carried out using the Fernet library and the corresponding key generation and encryption processing modules. So, considering this set of algorithms, can say that the developed proposal will allow both the primary identification of devices in the network and the process of data transmission between the participants of the scientific and industrial cluster.

3 Conclusion Software tool proposed in the paper will allow implementing an IP-addressing distribution system into the network topology for innovative processes implementation in scientific and industrial clusters with the possibility of using VPN tunnels based on OpenVPN or similar solutions. The proposed solution based on the standard package “isc-dhcp-server” made it possible to create a dynamic system for generating IP addresses with the ability to operate a single instance of both the client and the server. Flexibility in remote access to a network segment is expressed by automated address assignment to a virtual tap adapter. The uniqueness of the generated segment of IP

398

A. Zaenchkovski et al.

addresses is confirmed by the dependence on the distinctive MAC address of the central device. The implemented approach will also provide the necessary security level through the use of HMAC and FERNET encryption algorithms modifications. To enhance security, a system of dynamic generation of unique prints with a variable choice of time interval is used. The process of forecasting changes in the network structure of the scientific and industrial cluster was modified based on the use of MLP neural network model, which allowed determining changes in the segment with subsequent output to the decision-making module. The use of the LeakyReLU activation function also allowed optimizing the training model and reducing the training time. This paper can be used for farther investigations in possibilities of building adaptive IP address distribution systems/VPN servers and it will contribute to the implementation of dynamic authentication platforms. The development of this technology can also contribute to the improvement of the IPv6 protocol. Also, it can be helpful in implementation of various network scientific and industrial cooperation topologies. Acknowledgments. The research was supported by RSF (project No. 22-21-00487).

References 1. Kirillova, E.A., Lazarev, A.I.: Software-analytical tool for forecasting and evaluating the implementation of innovative processes in integration formations. Econ. Environ. Manage. 2021(3), 47–57 (2021) 2. Zaenchkovski, A.E., Kirillova, E.A., Golovinskaya, M.V., Sazonova, E.A., Borisova, V.L.: Cognitive fuzzy-logic modeling tools to develop innovative process management procedures for scientific-industrial clusters. In: Bogoviz, A.V., Suglobov, A.E., Maloletko, A.N., Kaurova, O.V., Lobova, S.V. (eds.) Frontier Information Technology and Systems Research in Cooperative Economics. SSDC, vol. 316, pp. 209–221. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-57831-2_22 3. Sawalmeh, H., Malayshi, M., Ahmad, Award, A.: VPN remote access OSPF-based VPN security vulnerabilities and counter measurements. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 311– 314. IEEE, Manhattan (2021) 4. Erokhin, V.V., Pritchina, L.S.: Analysis and improvement of methods for detecting shellcodes in computer systems. J. Appl. Inform. 16(92), 103–122 (2021) 5. Sun, Y., Esaki, H., Ochiai, H.: Detection and classification of network events in LAN using CNN. In: Proceedings of the 2019 4th International Conference on Information Technology (InCIT), Piscataway, New Jersey, pp. 203–207 (2019) 6. Calderon, P.: Nmap Network Exploration and Security Auditing Cookbook, 3rd edn. Van Haren Publishing, ‘s-Hertogenbosch (2021) 7. Sharma, H.: Kali Linux – An Ethical Hacker’s Cookbook: Practical Recipes That Combine Strategies, Attacks, and Tools for Advanced Penetration Testing, 2nd edn. Packt Publishing, Birmingham (2019) 8. Ghosh, J.: Corporate networking with advance routing, switching and security. In: Proceedings of the 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), pp. 311–314. IEEE, Kolkata (2020)

Development of Secure Platform for Innovative Processes Implementation

399

9. Diatta, B., Deme, C.B., Basse, A., Ouya, S.: Framework for automatic VPN access to remotely discovered resources. Internet Things Infrastruct. Mobile Appl. 2020(9), 327–336 (2020) 10. Solomon, M.G., Kim, D.: Fundamentals of Communications and Networking (Issa: Information Systems Security & Assurance), 3rd edn. Jones & Bartlett Learning, Burlington (2021) 11. Dli, M.I., Putchkov, A.Y., Lobaneva, E.I.: Analysis of the influence of the architecture of input layers of convolution and subsampling of a deep neural network on the quality of image recognition. J. Appl. Inform. 1(85), 113–122 (2020)

Performance Measurement of Healthcare: A Case Study for Pilot Hospital Babak Daneshvar Rouyendegh Erdebilli1(&) and Yavuz Selim Özdemir2 1

Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), 06010 Ankara, Turkey [email protected] 2 Department of Industrial Engineering, Ankara Science University, 06200 Ankara, Turkey [email protected]

Abstract. Hospitals are more critical than ever in the current pandemic phase. Performance measurement and implementation of associated measures have a substantial influence on service quality in a hospital. The purpose of this research is to evaluate the performance of a pilot case study by combining FuzzyTechnique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) and Data Envelopment Analysis (DEA). Additionally, the suggested methodology incorporates the group decision-making (GDM) approach. The paper’s application section illustrates how to use the integrated F-TOPSIS - DEA technique in a real-world healthcare management challenge. The suggested model is used to determine and compare hospital unit efficiency. Keywords: Performance measurement


Healthcare
F-TOPSIS
DEA

1 Introduction Healthcare in Turkey is improving daily, Total health expenditure increased by 24.3% in 2020, compared to the previous year, and reached $33.32 billion in total. General government health spending increased by 26.3% and reached $26.4 billion. Private sector health expenditure was estimated at $6.9 billion with an increase of 17.3% [1]. In order to compete effectively with competitors, hospitals must measure performance and improve service quality based on calculated inputs and outputs. The healthcare industry must be able to eliminate as much supposition and human error as possible from the equation. The study of healthcare performance measurement is critical because many stakeholders are interested in improving the current situation. Because policymakers are the citizens’ voice, it is their responsibility to ensure that our healthcare costs are on a sustainable path. They must understand where the current inefficiency exists to

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 400–408, 2022. https://doi.org/10.1007/978-3-031-09176-6_46

Performance Measurement of Healthcare

401

develop effective policies to assist the healthcare sector. The healthcare sector needs to know whether its resources are being used efficiently and what aspects can be improved. In this study, the case study is presented as a combination of F-TOPSIS and DEA methods for analysis and solving a pilot hospital units’ performance measurements. A combination of the F-TOPSIS and DEA methods evaluates both actual inputs and outputs. In the application, 16 units of the pilot Hospital are analyzed based on 2 inputs and 5 outputs. In addition, a group decision approach is used to determine the weights used in F-TOPSIS.

2 Literature Review The most commonly used approaches are known as Charnes, Cooper, Rhodes (CCR) and Banker, Charnes, Cooper (BCC) [2]. To solve that DEA model is the unique solution with more than one input and output factor, the success of the increase in the efficiency of the alternative is ranked on the ratio of output towards input. Friedman and Sinuany-Stern show how different outputs and inputs can be added to the DEA model to calculate efficiency, with the sum of the outputs divided by the inputs [3]. Tanbour uses labor and beds as input factors and three surgical processes and one doctor’s appointment as four output factors to assume the productivity level of a surgical procedure in Sweden, using the amount of waiting time during the operations. The DEA-based Malmquist models (DBMM) have been performed here for 6 years, starting from 1988 to 1993. In this article, they find that progressions inefficiency is the main factor against technological advancements. However, there is no DBMM procedure research found in the non-profit medical facility studies [4]. Linna measured hospitals’ financial efficiency and productivity in Finland. The author used multi DEA methods and the Malmquist Productivity index [5]. Hollingsworth et al. present a review of 91 healthcare articles about DEA applications in related facilities [6]. Bhat et al. investigate the effectiveness of district-level government hospitals. The overall efficiency levels of government facilities are higher than those of district-level government hospitals [7]. Kontodimopoulos and Niakas conduct a 12-year study of 73 dialysis facilities in Greece [8]. According to DEA healthcare models, as mentioned above, beds, medical staff, nurses, overall staff, supplies, equipment/infrastructure, and total costs are used as input variables, whereas outpatients, surgery, services, performance, and revenue are outputs. In recent years, the number of DEA-based articles especially in the healthcare area is constantly increasing. According to Sebastian et al., publication year, the number of input/outputs, model, and decision-making units (DMUs) are given in Table 1 [9].

402

B. D. R. Erdebilli and Y. S. Özdemir Table 1. The examples of DEA-based healthcare articles in recent years [9]

Authors

Year

Model used

2014

#Input / output 6/4

Chowdhury et al. Herwartz et al. Allin et al.

2014 2015

5/1 8/1

Cheng et al.

2015

3/2

Dotoli et al. Mahate et al. Musiunas et al. Rouyendegh et al. Flokou et al.

2015 2016 2016 2016

4/3 6/3 7/1 2/3

2-stage Malmquist (VRS-I), SFA 2-stage not-specified DEA, stepwise regression M index-CRS-I, VRS-I, Tobit regression Cross efficiency, fuzzy DEA CRS-O, VRS-O 2-stage: stratified CRS-I, ANN 2-stage: DEA-FAHP

2017

3/3

CRS-I, VRS-I, NDRS-I

2-stage MI-DEA, KDE

DMUs 113 1600 84 114 15 96 12744 7 71

3 Material and Method In this part, F-TOPSIS and DEA methods are defined separately. Firstly, the F-TOPSIS method is described. Hwang and Yoon [10, 11] created the TOPSIS approach. TOPSIS is a MultiCriteria Decision-Making (MCDM) approach based on the positive-ideal solution (PIS) and negative-ideal solution (NIS). Whereas the PIS optimizes benefits while minimizing costs, the NIS does the opposite [12, 13]. Since Zadeh introduced fuzzy sets in 1965, a great deal of research has been undertaken on different expansions [14]. Fuzzy sets and their variants are commonly used to communicate imprecise and ambiguous preference information [15]. As a result, fuzzy variants of the TOPSIS approach are widely used. DEA is a non-parametric, deterministic, and linear solver that does not require any assumptions about its mode of operation. A large number of articles focus on the subject of DEA by demonstrating various variations of examples and expansions of the well-known method [16]. These articles may be the right path to increasing healthcare managers’ abilities to respond to the above-mentioned problems, such as using the maximum bed capacity of hospitals and achieving staff efficiency under the relevant criteria.

Performance Measurement of Healthcare

3.1

403

F-TOPSIS

Here m units, A = A1, … , Am, each evaluating on n decision criteria. C1… Cn Generally, each option is assessed against the n criterion [17]. Let C be the criterion. The FTOPSIS technique is broken down into eleven phases: Step 1: A decision-making group is determined. Step 2: Decision makers calculate the importance of fuzzy weights of the decision criteria with linguistic variables given in Table 2.

Table 2. Linguistic representations for decision-makers Linguistic scale Very Low Importance (VLI) Low Importance (LI) Medium Importance (MI) High Importance (HI) Very High Importance (VHI)

Fuzzy numbers (1, 1, 1) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 9)

Step 3: Criteria weights are evaluated by using the GDM approach [18]. Step 4: Alternative scores are normalized by utilizing the normalization formula (1). xij rij ¼ max k xij

ð1Þ

Step 5: Normalized alternatives are converted into a fuzzy evaluation matrix by using Table 3. As a result, an initial fuzzy decision matrix is obtained. Table 3. Linguistic representations of fuzzy numbers Normalized score range Linguistic scale (0, 0–0, 2] Very Bad (VB) (0,2–0,4] Bad (B) (0,4–0,6] Average (A) (0,6–0,8] Good (G) (0,8–1,0] Very Good (VG)

Step Step Step Step

6: 7: 8: 9:

Fuzzy numbers (1, 1, 1) (1, 3, 5) (3, 5, 7) (5, 7, 9) (7, 9, 9)

Normalized Fuzzy decision matrix is calculated. Weighted and normalized fuzzy decision matrix is created. Weighted and normalized fuzzy decision matrix is defuzzified [19]. Determine a PIS and NIS.

404

B. D. R. Erdebilli and Y. S. Özdemir

Step 10: Calculation of the separation for each alternative. Now, the separation of each alternative is applied to determine the measure Si and S i and of each of the units is given in Eq. (2). Si

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 Xn  Xn     and S ¼ v  v ¼ v  v ij ij j j i j¼1 j¼1

ð2Þ

Step 11: Determine relative closeness to the ideal solution (3). The relative closeness of alternatives is given as; Ci ¼

3.2

S  i  ; where 0  Ci  1 S i þ Si

ð3Þ

DEA

DEA has been used in different alternative evaluations and different practices such as healthcare, education, agriculture, military, business, banking, public services, evaluating cultural institutions and many other sectors [16, 20–22]. Consider n units with s outputs denoted by yrk , r ¼ 1; . . .; s and m inputs denoted by xik , I = 1, … , m. Model (4), often referred to as the CCR model, can be formulated to measure the relative efficiency of the kth DMU [7]: P ek ¼ max sr¼1 ur yrk s:t: Pm vi xik ¼ 1 i¼1P P max sr¼1 ur yrk  m i¼1 vi xik  0 vi  0 ur  0

k ¼ 1; . . .; n i ¼ 1; . . .; m r ¼ 1; . . .; s

ð4Þ

The above CCR model is solved for each DMU, and the DEA results can categorize them as efficient or inefficient. DMUk is said to be efficient if ek ¼ 1 and otherwise it is inefficient. While it is likely to result in many efficient units, the original DEA fails to provide more information about these units, and this problem as the lack of discrimination in DEA has been frequently argued in the literature [23].

4 Hospital Performance Evaluation The pilot hospital presently provides healthcare services to 16 training branches and 35 healthcare service branches. The number of beds available has been raised to 600. The proposed F-TOPSIS – DEA algorithm’s purpose is to determine which units are the most efficient. There are many factors influencing the performance of the DMUs. Through interviews with the hospital administration, along with the relevant literature, we decide to define the following data given in Table 4.

Performance Measurement of Healthcare

405

Table 4. Input and output measures INPUTS/OUTPUTS I1=# Doctors, Nurses, and others l2= # beds OUTPUTS O1=# Outpatient O2=# Inpatient O3=# Discharged O4=# Dead O5=Bed Usage Rate

INPUTS

The inputs and outputs have been calculated based on a literature review and viewpoint of the heads of the hospital. In this part, five outputs and two inputs of the performance measurement problem are given in Table 5. Table 5. Input and output measures for pilot hospital DMUs O1 DMU-01 17180 DMU-02 4752 DMU-03 112064 DMU-04 18929 DMU-05 103216 DMU-06 2834 DMU-07 55994 DMU-08 24550 DMU-09 31390 DMU-10 17104 DMU-11 47644 DMU-12 14363 DMU-13 56624 DMU-14 9948 DMU-15 22299 DMU-16 26793

O2 553 747 789 543 477 135 909 1006 581 451 1324 102 2289 1994 413 1556

O3 554 757 744 537 4637 124 901 951 551 436 1312 102 2221 1976 422 1533

O4 0 0 21 3 10 0 0 10 1 0 1 0 4 0 1 1

O5 0,063 0,065 0,067 0,070 0,087 0,043 0,052 0,102 0,068 0,050 0,071 0,011 0,076 0,065 0,050 0,082

I1 67 70 140 165 393 21 79 128 67 24 114 143 183 25 403 152

I2 17 22 20 19 88 4 8 31 16 7 19 10 44 14 45 20

The hospital also serves every day a week with Cancer Patient School and Cancer Continuous Education Centre to increase health literacy in the cancer field. Diagnosis, treatment, and care services are provided in the framework of international quality standards by qualified physicians and qualified health personnel who are equipped with contemporary technology and are supported by reliable, timely laboratory outputs and scientific competence at a high level.

406

B. D. R. Erdebilli and Y. S. Özdemir

In the application part, four decision-makers were selected for the decision-making group. The experts evaluated the criteria separately given in Table 2. Group decisions were calculated as given in F-TOPSIS Step 3. As a result, the evaluated group decision criteria weights are given in Table 6.

Table 6. Fuzzy weights of criteria Criteria I1 I2 O1 O2 O3 O4 O5

l 0,1145476 0,1145476 0,0689391 0,08520396 0,0308305 0,0689391 0,07498901

m 0,18469633 0,18469633 0,12928136 0,14974559 0,08463452 0,12928136 0,13766452

u 0,28709836 0,28709836 0,25319696 0,26961534 0,18872187 0,25319696 0,25319696

The CCR DEA model is investigated using the Eq. (4). Table 7 shows the outcomes. Table 7. Determine relative closeness to the ideal solution DMUs DMU-01 DMU-02 DMU-03 DMU-04 DMU-05 DMU-06 DMU-07 DMU-08 DMU-09 DMU-10 DMU-11 DMU-12 DMU-13 DMU-14 DMU-15 DMU-16

DAE Score 0,4870 0,4160 1,0000 0,5030 0,4630 1,0000 1,0000 0,7420 0,6410 1,0000 0,6360 0,2050 0,4640 1,0000 0,1410 0,6880

F-TOPSIS 0,6544 0,6381 0,6842 0,5831 0,2903 0,6716 0,6399 0,6693 0,6487 0,6716 0,5675 0,6711 0,3816 0,5754 0,4718 0,5551

Result 0,3187 0,2655 0,6842 0,2933 0,1344 0,6716 0,6399 0,4967 0,4158 0,6716 0,3609 0,1376 0,1771 0,5754 0,0665 0,3819

As shown in Table 7, higher scores indicate better outcomes. Out of the 16 units, DMU3 has the highest score (0,6842). The DMU has the best efficiency and performance. DMU15 has the lowest score (0,0665). The results show the efficiency score of units for the selected inputs and outputs.

Performance Measurement of Healthcare

407

5 Conclusion This paper applies the F-TOPSIS - DEA hybrid methodology to study certain aspects of Turkey’s healthcare sector. The aim of this paper is to fully-ranking of the pilot hospital measurement. The F-TOPSIS & DEA hybrid methodology is applied to analyze the efficiency and fully ranking units and show potential outcome improvements. The combination of the F-TOPSIS and DEA model, on the other hand, would integrate the necessary pieces of the two models while avoiding the drawbacks of each model. This study’s findings have relevance for healthcare settings. An efficient framework to comprehensively evaluate and grade multivariate DMUs is devised to evaluate the effectiveness of Turkish pilot healthcare institutions. This study proposes a two-stage methodology. An F-TOPSIS is an initial stage. DMU priorities were assessed. Second, a DEA model was used to assess effectiveness. The study used a hospital unit model to calculate inputs and outputs to rank the units’ efficiency. Future studies may use MCDM techniques like Fuzzy DEA or IF-TOPSIS combination. However, the recommended approach may be used in other areas, and contract negotiation.

References 1. TUIK: Health Spending Statistics (2020). https://data.tuik.gov.tr/Bulten/Index?p=SaglikHarcamalari-Istatistikleri-2020-37192 2. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision-making units. Eur. J. Oper. Res. (1978). https://doi.org/10.1016/0377-2217(78)90138-8 3. Friedman, L., Sinuany-Stern, Z.: Scaling units via the canonical correlation analysis in the DEA context. Eur. J. Oper. Res. (1997). https://doi.org/10.1016/S0377-2217(97)84108-2 4. Tambour, M.: The impact of health care policy initiatives on productivity. Health Econ. (1997). https://doi.org/10.1002/(sici)1099-1050(199701)6:13.3.co;2-r 5. Linna, M.: Measuring hospital cost efficiency with panel data models. Health Econ. (1998). https://doi.org/10.1002/(SICI)1099-1050(199808)7:53.0.CO;2-9 6. Hollingsworth, B., Dawson, P.J., Maniadakis, N.: Efficiency measurement of health care: a review of non-parametric methods and applications. Health Care Manag. Sci. (1999). https:// doi.org/10.1023/A:1019087828488 7. Bhat, R., Verma, B.B., Reuben, E.: Data envelopment analysis (DEA). J. Health Manag. (2001). https://doi.org/10.1177/097206340100300207 8. Kontodimopoulos, N., Niakas, D.: A 12-year analysis of Malmquist total factor productivity in dialysis facilities. J. Med. Syst. (2006). https://doi.org/10.1007/s10916-005-9005-9 9. Kohl, S., Schoenfelder, J., Fügener, A., Brunner, J.O.: The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Manag. Sci. 22(2), 245– 286 (2018). https://doi.org/10.1007/s10729-018-9436-8 10. Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey, pp. 58– 191 (1981) 11. Wang, Y.M., Elhag, T.M.S.: Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Syst. Appl. (2006). https://doi.org/10.1016/j. eswa.2005.09.040

408

B. D. R. Erdebilli and Y. S. Özdemir

12. Daneshvar Rouyendegh, B., Erol, S.: Selecting the best project using the fuzzy ELECTRE method. Math. Probl. Eng. (2012). https://doi.org/10.1155/2012/790142 13. Carlsson, C., Fullér, R.: Fuzzy multiple criteria decision making: recent developments. Fuzzy Sets Syst. (1996). https://doi.org/10.1016/0165-0114(95)00165-4 14. Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965). https://doi.org/10.1109/2.53 15. Shaout, A., Yousif, M.K.: Performance evaluation – methods and techniques survey. Int. J. Comput. Inf. Technol. 3, 966–979 (2014) 16. Emrouznejad, A., Yang, G.L.: A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socioecon. Plann. Sci. (2018). https://doi.org/10.1016/j.seps. 2017.01.008 17. Emrouznejad, A.: Advances in data envelopment analysis. Ann. Oper. Res. 214(1), 1–4 (2014). https://doi.org/10.1007/s10479-014-1535-4 18. Boran, F.E., Genç, S., Kurt, M., Akay, D.: A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst. Appl. 36, 11363– 11368 (2009). https://doi.org/10.1016/j.eswa.2009.03.039 19. Eraslan, E., Iç, Y.T.: A multi-criteria approach for determination of investment regions: Turkish case. Ind. Manag. Data Syst. (2011). https://doi.org/10.1108/02635571111144964 20. Shang, J., Sueyoshi, T.: A unified framework for the selection of a Flexible Manufacturing System. Eur. J. Oper. Res. (1995). https://doi.org/10.1016/0377-2217(94)00041-A 21. Taheri, H., Ansari, S.: Measuring the relative efficiency of cultural-historical museums in Tehran: DEA approach. J. Cult. Herit. (2013). https://doi.org/10.1016/j.culher.2012.10.006 22. Public sector efficiency measurement applications of data envelopment analysis. Eur. J. Oper. Res. (1993). https://doi.org/10.1016/0377-2217(93)90104-u 23. Rouyendegh, B.D., Oztekin, A., Ekong, J., Dag, A.: Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach. Ann. Oper. Res. 278(1–2), 361–378 (2016). https://doi. org/10.1007/s10479-016-2330-1

Store Segmentation in Retail Industry Using Clustering Algorithms Ayşegül Ünal, Merve Önal(&), Tolga Kaya, and Tuncay Özcan Department of Management Engineering, Istanbul Technical University, 34367 Istanbul, Turkey {unala17,onal17,kayatolga,tozcan}@itu.edu.tr

Abstract. In today’s digital age, the development of technology has made it easier for customers to reach everything. Store segmentation, which is one of the new methods, can be done in order to survive in the competitive environment due to the increase in retail companies. By doing this, they can gain an advantage by developing target marketing strategies specific to each segment instead of a whole marketing strategy. In this study, the data of 101 stores of a retail company were segmented according to 9 variables. These variables include the location of the stores, income levels, invoice numbers, inventory turnover, etc. has. Fuzzy C-means and K-means clustering algorithms were used for this study. Optimal cluster numbers were determined as 8 for Fuzzy C-means in terms of Dunn index and 7 for K-Means in terms of Silhouette index, which measure the effectiveness of clustering study. Keywords: Store segmentation
Retailing
Clustering
K-means
Fuzzy Cmeans
Silhouette
Dunn

1 Introduction The rapid development of technology has profoundly affected many things in the way business is done. Concepts such as artificial intelligence are now widely used in the business world. At the same time, the rapid increase in the world population has led to the growth of all industries. This changes and developments have affected retail as well as all sectors. The increase in the number of retail companies in the same sector and the increase in options have increased competition [4]. For this reason, the advancement of data science can help provide competitive advantage by doing different analysis in the retail industry. For a retailer with a large number of stores, after grouping these stores, it provides more effective management by making strategic decisions on the groups [6]. The aim of this study is to suggest a store segmentation framework using clustering techniques. To do this we compared the performances of two different clustering algorithms using the data received from a retail company. Correct clustering is important so that stores can make strategic decisions that can provide competitive advantage.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 409–416, 2022. https://doi.org/10.1007/978-3-031-09176-6_47

410

A. Ünal et al.

The rest of the paper is organized as follows: in the second section, there will be a literature study. The methodology to be used in the third section will be given. In the fourth section, the data preparation method will be shown. Part fifth will contain the findings from the study, and finally, a conclusion and recommendations for future work in sixth section.

2 Literature Review As the number of stores of retail companies increases, the quality of service offered decreases gradually. The decrease in the service offered causes problems with the customers and therefore a decrease in sales. It is not supported today for companies to use the same mass marketing strategy for all their stores. For example, by performing store segmentation, strategies suitable for each segment can be developed [6]. For companies, preparing a target strategy specific to each segment both provides a competitive advantage within the sector and increases financial performance to a great extent [9]. Store segmentation is costly and time consuming to segment each store’s thousands of customers, so instead it should be based on store features, features of the region, demographics of customers, etc. Store segmentation groups stores by considering many variables. Although customer/market segmentation approaches have been widespread in retail in the last fifty years, the number of studies on store segmentation, which is a new approach, is less [4]. Some studies have been carried out in the field of store segmentation, based on different variables and using different methods. Some of these studies are summarized below. Kargari and Sepehri [11] made a store segmentation on the stores of a retail chain selling automotive spare parts, using the K-means algorithm and Association Rules principles. As a result of this study, the company’s part distribution and transportation costs decreased by 32%. Another similar study was Hernandez, Bermingham and Clarke [3] using data from a Canadian firm, segmented retail stores using the MIRSA program, which clusters with the K-means technique. Another study with this technique, Kusrini [13] carried out a store segmentation study with the K-means algorithm, using the Citramart Minimarket sales data of Stmik Amikom. Their aim was to design a system that could minimize stock and profit margin. Another study was Clarke, Mackaness and Ball [7] conducted a store segmentation study using the MIRSA program prepared for retail stores. Clustering in MIRSA program is done with K-means technique. In the other study, Han, Ye, Fu and Chen using fuzzy K-means, separating Chinese convenience store chains into four categories. That was effective for making long-term decisions which were providing competitive advantage [8]. The study carried out with a different approach, Bilgiç et al., they have divided 175 stores into five segments to minimize logistics and inventory costs and better customer service, with Rule-Based classification data mining method [4].

Store Segmentation in Retail Industry Using Clustering Algorithms

411

Studies have also been carried out with different approaches. Kumar and Karende [12] divided 646 grocery stores into four segments based on their socioeconomic characteristics using Ward’s hierarchical clustering method. They found that each segment differs in store performance due to the social environment. In a similar study, Bilgic, Kantardzic and Cakir [5] conducted a store segmentation study for 73 retail stores in Istanbul. In this study, Ward’s hierarchical clustering and Association Rules principles were used. In some studies, alternative methods have been used together. Agarwal, Jain, and Rajnayak [1] segmented a global retailer’s stores on its stores in four European markets. In this study, hierarchical clustering, Self-Organizing Maps (SOM), Gaussian Mixture Matrix (GMM) and Fuzzy C-means methods were used, respectively. As a result, they decided that they could not generalize as this method is the best for store segmentation. Finally, Mendes and Cardoso [15] conducted a store segmentation study on a supermarket chain in Portugal using three alternative methods. These methods are respectively: Ward’s hierarchy algorithm, regression tree technique and sequential hierarchical clustering technique.

3 Methodology K-means. The K-means algorithm is one of the most widely used clustering methods. One of the main purposes of K-means is to ensure that the similarities within the cluster are high, while minimizing the variation within the cluster. The application steps of the K-means algorithm are as follows; First of all, the number of clusters (k) is determined, then all the observations are randomly distributed to these clusters. Second, the centers of gravity of each cluster (Mi) are determined. The distances of each data from these centers are calculated. Finally, each data is placed in the cluster with the shortest distance (dM2i ) from the centers of gravity of the clusters [10]. E¼

XK XC i¼1

p€{i

dðM i ; xÞ2

ð1Þ

Fuzzy C-means. Fuzzy C-means algorithm is one of the widely used clustering techniques. In this algorithm, each data point is assigned membership degrees, varying between 1 and 0 in all possible sets. In this case, data points can belong to more than one cluster at the same time. The belonging to the cluster is fuzzy, so an object has more than one degree of membership indicating which cluster it belongs to. When a part of a data point belongs to a cluster, the other part outside the cluster is placed in the closest cluster with the highest probability [2]. Jm ¼

XN XC i¼1

um kxi j¼1 ij

 cj k2 ; 1\m\1

ð2Þ

The U membership matrix is randomly assigned, and the center vectors are calculated, and the algorithm is started.

412

A. Ünal et al.

PN i¼1

c j ¼ PN

um ij xi

i¼1

um ij

ð3Þ

According to the calculated cluster centers, the U matrix is recalculated with Eq. 3. The old U matrix and the new U matrix are compared. The steps are repeated until the difference between them is less than the stopping criterion. uij ¼ P n

1

kxi ci k2=m1 i¼1 kxi ck k

ð4Þ

Silhouette. After clustering study is done, cluster validity indexes are used. One of the popular of these indexes is the Silhouette index. The purpose of using these indexes is to determine the perfection of parts of the data. In other words, it measures whether data is assigned to the correct clusters. It is an index based on the silhouette width formula seen in the formula 5 [16]. Sð x Þ ¼

bð xÞ  aðxÞ maxðað xÞ; bð xÞÞ

ð5Þ

Dunn. In the Dunn index, which takes values between 0 and infinity, compares the minimum distance between two clusters to the maximum of the cluster diameter. It predicts the diameter of the clusters to be small and the distance between the two clusters to be large. The greater the value, the better. as seen in Eq. 6. d (i, j) indicates the distance between two clusters and d′(k) indicates the diameter of the cluster [14]. D¼

min1\i\j\n dði; jÞ : 0 max1\k\n d ðkÞ

ð6Þ

4 Data This study was carried out using the data of a retail company’s chain stores. Store segmentation clustering is done by using these data. There are 101 store records and 9 variables in the data set. These variables, which will be used in segmentation, include the structure of your store environment and some store accounts. The columns of this data set consist of invoice number, average invoice amount, geographic region code, location code, income status, store area, inventory turnover, GMROI, inventory cost (Table 1).

Store Segmentation in Retail Industry Using Clustering Algorithms

413

Table 1. Dataset variables Variable Store

Type Character

bill_amount bill_amount_avg geo_region_code

Numeric Numeric Categorical

location_code

Categorical

income_status

Categorical

store_area stock_tr

Numeric Numeric

GMROI stock_goods

Numeric Numeric

Description The values given for the Store column are assigned from 1 to 101. Each number represents a different store, and each row contains attributes for a different store The number of invoices issued in the store Average of invoices issued in the store It represents the geographical region where the store is located in 3 groups. 1 Istanbul, 2 big cities in Anatolia and 3 small cities in Anatolia It shows the locations where the stores are located.1 represents “very large AVM” interior, 2 “large AVM” interior, 3 “medium sized AVM” interior, 4 “small AVM” interior, 5 “street/street” area It shows the income level of the store customers. It shows the income level from 1 (highest) to 5 (lowest) It shows the area covered by the store in m2 Inventory turnover is the cost of goods sold (COGS) in a given time period divided by the average inventory cost for that time period Gross Margin Return on Inventory Investment Stock cost

5 Findings 5.1

K-means and Fuzzy C-means Clustering

With the K-means clustering algorithm, it is desired to maximize the inter-cluster distribution while minimizing the intra-cluster distribution. When using the K-means algorithm, different values were used when determining K. First, the ratio values of the distance between clusters / (between clusters + within clusters) to the distance value were examined. This ratio shows that the sum of x% times-errors is reduced. When K = 10 is taken, 94.5%, 88.1% for F = 5, 79.4% for F = 3. The purpose of this algorithm is to minimize the frame-error ratio. Because clusters are very different from each other in optimal clustering, most of the total variance is explained by the variance between groups. In summary, the goal is to maximize the between_ss/total_ss value. Considering these ratios, the most optimal clustering ratio is obtained when K = 10. But indexes should be considered. Considering the Fuzzy C-means cluster membership value, it assigns the observations to the cluster with the highest membership value. Assignments were made by creating different cluster numbers between 3 and 10. At the same time, cluster centers were obtained for our variables. While obtaining the results in this study, R programming language was used.

414

5.2

A. Ünal et al.

Comparison of Methods with Silhouette Index and Dunn Index

The values of Silhouette and Dunn indexes used while testing K-means and Fuzzy Cmeans algorithms are in Table 2. While obtaining the values in Table 2, the algorithms were repeated 20 times and the average was taken. The silhouette index was used to measure the reliability of the algorithm after the clustering study for store segmentation. Although its values are in the range of −1 to 1, its reliability increases as the values get closer to 1 for an optimal result. Dunn value takes values ranging from 0 to infinity. Also, higher value is preferred. Table 2. Silhouette and Dunn index scores for K-means and Fuzzy C-means K 3 4 5 6 7 8 9 10

K-means (Silhouette) 0.4400 0.4300 0.4400 0.4200 0.4500 0.4100 0.4300 0.4200

Fuzzy C-means (Silhouette) 0.4400 0.4300 0.4200 0.3800 0.4300 0.4400 0.4300 0.3800

K-means (Dunn) 0.0973 0.1318 0.1184 0.0607 0.1602 0.0671 0.1212 0.1467

Fuzzy C-means (Dunn) 0.0821 0.1051 0.0664 0.1063 0.0698 0.1756 0.0632 0.0905

When Silhouette index is considered, K = 7 for K-means is selected as optimal clustering. In addition, looking at the Dunn index, K = 8 for Fuzzy C-means is optimal clustering. The clustering obtained for the specified optimal results is included in Fig. 1 and Fig. 2.

Fig. 1. K = 8 for Fuzzy C-means

Fig. 2. K = 7 for K-means

Store Segmentation in Retail Industry Using Clustering Algorithms

415

6 Conclusion As a result of this study, by using the data of a retail store, store segmentation was done using K-means and Fuzzy C-means algorithms. Along with these algorithms, Silhouette index was used to decide the reliability of the algorithms and how many clusters should be. The outputs and silhouette outputs of both algorithms were examined, and inferences were made. Clustering was done by using 9 different variables belonging to the stores. The purpose of this store segmentation is to increase the sales by developing a target marketing strategy suitable for each segment, instead of using the general marketing strategy of the retailer. As a suggestion for future studies, the study can be extended with K-means and Fuzzy C-means as well as different clustering methods. Calinski, Elbow, etc. indexes can be used with Silhouette and Dunn indexes which are used to evaluate clustering algorithms. When the methods used in studies are increased, the probability of obtaining accurate results can be enhanced.

References 1. Agarwal, K., Jain, P., Rajnayak, M.: Comparative analysis of store clustering techniques in the retail industry. In: DATA 2019 - 8th International Conference on Data Science, Technology and Applications, pp. 65–73 (2019) 2. Baykasoğlu, A., Gölcük, İ, Özsoydan, F.: Improving fuzzy c-means clustering via quantumenhanced weighted superposition attraction algorithm. Hacettepe J. Math. Stat. 48(3), 859– 882 (2018) 3. Bermingham, P., Hernandez, T., Clarke, I.: Network planning and retail store segmentation: a spatial clustering approach. Int. J. Appl. Geospat. Res. 4(1), 67–79 (2013) 4. Bilgic, E., Caki, O., Kantardzic, M., Duan, Y., Guangming: Retail analytics: store segmentation using rule-based purchasing behavior analysis. Int. Rev. Retail Distrib. Consum. Res. 31(4), 457–480 (2021) 5. Bilgic, E., Kantardzic, M., Cakir, O.: Retail store segmentation for target marketing. In: Perner, P. (ed.) ICDM 2015. LNCS (LNAI), vol. 9165, pp. 32–44. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20910-4_3 6. Bilgiç, E., Çakır, Ö.: Sosyoekonomik yaklaşımla zincir perakende mağazalarının segmentasyonu [Store segmentation of retail chains via socioeconomic approach]. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi 41(2), 338–363 (2019) 7. Clarke, I., Mackaness, W., Ball, B.: Modelling intuition in retail site assessment (MIRSA): making sense of retail location using retailers’ intuitive judgements as a support for decisionmaking. Int. Rev. Retail Distrib. Consum. Res. 13(2), 175–193 (2003) 8. Han, S., Ye, Y., Fu, X., Chen, Z.: Category role aided market segmentation approach to convenience store chain category management. Decis. Support Syst. 57, 296–308 (2014) 9. Hunt, S., Arnett, D.: Market segmentation strategy, competitive advantage, and public policy: grounding segmentation strategy in resource-advantage theory. Australas. Mark. J. 12(1), 7–25 (2004) 10. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning, vol. 112, p. 18. Springer, New York (2013) 11. Kargari, M., Sepehri, M.M.: Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs. Expert Syst. Appl. 39(5), 4740–4748 (2012)

416

A. Ünal et al.

12. Kumar, V., Karande, K.: The effect of retail store environment on retailer performance. J. Bus. Res. 49(2), 167–181 (2000) 13. Kusrini, K.: Grouping of retail items by using k-means clustering. Procedia Comput. Sci. 72, 495–502 (2015) 14. Legany, C., Juhasz, S., Babos, A.: Cluster validity measurement techniques. In: Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 388–393. World Scientific and Engineering Academy and Society (WSEAS) Stevens, Point, Wisconsin, February 2006 15. Mendes, A.B., Cardoso, M.G.: Clustering supermarkets: the role of experts. J. Retail. Consum. Serv. 13, 231–247 (2006) 16. Starczewski, A., Krzyżak, A.: Performance evaluation of the Silhouette index. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J. M. (eds.) ICAISC 2015, Part II. LNCS (LNAI), vol. 9120, pp. 49–58. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19369-4_5

A Comparative Study of Artificial Intelligence Based Methods for Abnormal Pattern Identification in SPC Umut Avci

, Önder Bulut(&)

, and Ayhan Özgür Toy

Engineering Faculty, Yaşar University, Bornova/İzmir, Turkey [email protected]

Abstract. Statistical process control techniques have been used to detect any assignable cause that may result in a lower quality. Among these techniques is the identification of any abnormal patterns that may indicate the presence of an assignable cause. These abnormal patterns may be in the form of steady movement in one direction, i.e., trends; an instantaneous change in the process mean, i.e., sudden shift; a series of high observations followed by a series of low observations, i.e., cycles. As long as we can classify the observed data the decision maker can decide on actions to be performed to ensure quality standards and planning for interventions. In identification of these abnormal patterns, rather than relying on human element, intelligent tools have been proposed in the literature. We attempt to provide a comparative study of various classification algorithms used for pattern identification in statistical process control. We specifically consider six different types of patterns to classify. These different types are: (1) Normal, (2) Upward trend, (3) Downward trend, (4) Upward shift, (5) Downward shift, (6) Cyclic. A recent trend in classification is to use deep neural networks (DNNs). However, due to the design complexity of DNNs, alternative classification methods should also be considered. Our focus on this study is to compare traditional classification methods to a recent DNN solution in the literature in terms of their efficiencies. Our numerical study indicates that basic classification algorithms perform relatively well in addition to their structural advantages. Keywords: Statistical process control network


Pattern classification
Deep neural

1 Introduction and Literature Review In statistical process control variations in an attribute may be due to either random cause or assignable cause. Quality Control Charts are frequently used to detect any abnormalities in the process. There is a variety of control charts but the logic behind is monitoring a sample metric and deciding when the process output is nonconforming as soon as possible. An alarm for possible nonconformity is triggered when either (i) a sample falls beyond the acceptable limits, or (ii) a consecutive series of samples displays an unusual pattern [1]. Our focus in this work is the latter, i.e., the unusual patterns. Patterns in control charts may indicate abnormalities. Therefore, when control © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 417–425, 2022. https://doi.org/10.1007/978-3-031-09176-6_48

418

U. Avci et al.

chart patterns (CCPs) are unnatural manufacturing process may be out-of-control, producing substandard products. Unnatural CPPs may be in the form of trends, shifts or cycles. Trend patterns may be due to changing physical condition of the tool ware; shift patterns may be due to operator or material; cyclic patterns may be due to alternations in external factors (see [2] and [3]). Alarms raised for possible nonconformity in the process are not always true. One would like to avoid any false alarm while detecting the nonconformity as soon as possible. Therefore, methods in identifying unnatural patterns must be reliable for not raising many false alarms. This yielded research on the employment of intelligent techniques for control chart pattern recognition (CCPR). For earlier work on unnatural pattern detection, we refer the reader to [4–9]. Along with the increase in the computational power expert system and machine learning methods have been employed for CCPR. In this work, we aim to extend performance comparison studies of CPP recognition methods by incorporating a wider range of classifiers. In order to identify CCP, we test performances of 45 WEKA classifiers against a one-dimensional convolutional neural network (1D-CNN) proposed in [10]. In this regard, we start with introducing possible control chart patterns and generate random samples for each CPP. Since comparison of 45 classifiers with their optimized parameters requires an extensive study, we conduct the study in three stages. In the first stage, we choose the leading classifiers in terms their performances with their default parameters and proceed to the second stage with these leading classifiers. In the second stage, we optimize parameters of selected classifier and conduct a comparison of their performances with those optimized values. And lastly, in the third stage we provide performance comparison of the best classifier to 1D-CNN which has been previously studied in [10]. In the rest of the paper we introduce our experimental design in Sect. 2, our results in Sect. 3. We conclude the paper in Sect. 4.

2 Experimental Design In this section we present the dataset and the classifiers we used in the experimental study for the control chart pattern recognition. 2.1

Dataset

In general, when we plot sample points on the control charts we observe six types of patterns: (1) Normal Pattern, (2) Upward Shift Pattern, (3) Downward Shift Pattern, (4) Upward Trend Pattern, (5) Downward Trend Pattern, and (6) Cycle Pattern. These patterns are depicted in Fig. 1. Among these patterns all but the Normal Pattern are of unnatural type.

A Comparative Study of Artificial Intelligence

419

Fig. 1. Control chart patterns

We generate data sets for each pattern using a similar approach to the literature [10]. The baseline will be the parameter values when the system is in-control, i.e. the mean, l, and the standart deviation, r. The datum value at t, yðtÞ, comprises a noise component, xðtÞ  Nð0; rÞ, and a pattern specific disturbance component dðtÞ. Then, yðtÞ ¼ l þ xðtÞ þ dðtÞ

ð1Þ

with 8 0 > > > v  s > > < v  s d ðt Þ ¼ vmt > > > > v  mt > : v  a  sinð2pt=xÞ

for Normal Pattern for Upward Shift Pattern for Downward Shift Pattern for Upward Trend Pattern for Downward Trend Pattern for Cycle Pattern

where v is the indicator for shift/trend transition, taking value 0 up to shift/trend transition instance and 1 afterwards; s is the magnitude of the shift; m is the slope of the trend line; a is the amplitude in a cycle; x is the period of a cycle. We have created 2,000 samples for each CPP with a data length of 25 time instances randomly. Specifically, random generation with the following: l ¼ 30, r ¼ 0:05, v  Uniform½4; 9, s  Uniform½1:5r; 3r, m  Uniform½0:1r; 0:3r; a  Uniform½1:5r; 4r, x 2 f4; 5; 6; 7; 8g. Hence, we created a total of 12,000 different samples. These samples are randomly divided into two parts, of which 9600 samples were used to train classification models, and the rest was used for testing.

420

U. Avci et al.

2.2

Classifiers

Classification in WEKA In this study, we used WEKA toolkit for building classification models. The toolkit offers seven categories of classifiers [11]: • • • • • • •

bayes: contains bayesian classifiers, e.g., NaiveBayes functions: e.g., Support Vector Machines, regression algorithms, neural nets lazy: “learning” is performed at prediction time, e.g., k-nearest neighbor (k-NN) meta: meta-classifiers that use a base one or more classifiers as input, e.g., bagging misc: various classifiers that don’t fit in any another category rules: rule-based classifiers, e.g., ZeroR trees: tree classifiers, like decision trees with J48 The following table shows specific algorithms implemented for each category.

Table 1. List of classification algorithms Category Bayes Functions Lazy Meta

Misc Rules Trees

Classification algorithms BayesNet, NaiveBayes, NaiveBayesMultinominal, NaiveBayesMultinominalUpdateable, NaiveBayesUpdateable SVM, Logistic, MLPClassifier, MultilayerPerceptron, RBFClassifier, RBFNetwork, SimpleLogistic, SMO IBk, Kstar, LWL IterativeClassifierOptimizer, AdaBoostM1, Bagging, ClassificationViaRegression, FilteredClassification, LogitBoost, MultiBoostAB, MultiClassClassifier, MultiClassClassifierUpdateable, MultiScheme, RandomCommittee, RandomizableFilteredClassifier, RandomSubSpace, Stacking, Vote, WeigthedInstancesHandlerWrapper InputMappedClassifier DecisionTable, Jrip, OneR, PART, ZeroR DecisionStump, HoeffdingTree, J48, LMT, RandomForest, RandomTree, RepTree

WEKA offers four methods for evaluating the built classification model: use training set, supplied test set, cross-validation, and percentage split. In this study, we have used the second option, i.e. supplied test set, and provided 2400 samples generated in the data preparation step as the test set. WEKA provides a variety of metrics for measuring the success of classification including but not limited to Correctly Classified Instances, Incorrectly Classified Instances, Mean absolute error, and Root mean squared error. All the results presented in this paper show the percentage of Correctly Classified Instances (pCCI) for each classifier.

A Comparative Study of Artificial Intelligence

421

Deep Neural Network Recently, Deep Neural Networks (DNNs) have taken the place of traditional machine learning techniques because of their success in many fields. A Deep Neural Network simulates the structure and function of the brain as an Artificial Neural Network (ANN) does. DNNs are differentiated from ANNs by the number of hidden layers in between the input and output layers. A neural network is qualified as deep when it has more than three layers including the input and output ones. Thanks to its mutli-level layering, DNNs eliminate the need of manual feature extraction phase. DNNs take as input the raw data and learn a distinct set of features at each layer. The more the data advance into the network, the more the network structure learns complex features. There are different types of Deep Neural Networks in the literature such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In this study, we have adopted a CNN structure that has previously been used in the same problem [10] since building a CNN model that successfully works in a given domain is itself a problem. The study in question has determined the building blocks of the 1D-CNN, i.e. the number of layers, their types, ordering of the layers and the activation functions. The paper has also optimized the number of filters, kernel size and pool size used in the layers. The network structure used in the paper is presented in Fig. 2. Interested readers are encouraged to refer to the relevant paper for detailed information.

Fig. 2. The structure of the 1D-CNN [10]

3 Results We have conducted three separate sets of experiments on the generated data. In the first set of the experiments, we trained the standard WEKA classifiers with default parameters. In the second set, we selected two of the classifiers with the best performance in each category from the first experimental set and re-trained them by tuning their parameters. In the last set, the classification was performed by the 1D-CNN introduced in the previous section.

422

U. Avci et al.

In the first stage, i.e., the first set of experiments, we ran all 45 algorithms given in Table 1 with the default parameters defined in the WEKA toolkit. Thirteen of those algorithms produced low classification rates: five with pCCI around 40% and eight with pCCI around 20%. We omit those algorithms with the low rates and show the classification rates of the remaining 32 algorithms in Fig. 3. Performance of the WEKA classifiers as the percentage of CCI. As can be seen from the figure, the majority of the algorithms classified at least 80% of the instances correctly. The algorithms in the “Functions” and “Trees” categories can be said to outperform the algorithms in other categories. The “Rules” category, on the other hand, contains the worst performing algorithms among those presented.

Fig. 3. Performance of the WEKA classifiers as the percentage of CCI

The results obtained in the first stage can be improved since the algorithms have been run with the default parameters defined in the WEKA. In the second stage of our experiments, we have selected two of the best performing algorithms from each category and then optimized the parameters of each chosen algorithm by using the WEKA’s CVParameterSelection meta-classifier. The columns of Table 2 show in order the algorithmic category, the selected algorithms, the performances of the algorithms before-and-after the parameter tuning and the parameters that have been optimized. We have provided a single pCCI value for NaïveBayes since the method has no parameters to be tuned. The parameters of the BayesNet, Bagging, RandomCommittee and LMT methods are the decision variables that guide the learning process. With the default parameters, the algorithms in the “Functions” category, i.e. MultilayerPerceptron and RBFNetwork, have achieved the highest success in classifying the patterns. After the parameter optimization, the overall best result has been obtained with Bagging that uses an optimized RBFNetwork as a classifier. This is a logical consequence: Bagging builds an ensemble of RBFNetworks and hence performs slightly better than a single RBFNetwork.

A Comparative Study of Artificial Intelligence

423

Table 2. Performance of the classifiers before and after parameter tuning Category

Algorithm

Bayes

BayesNet NaïveBayes MultilayerPerceptron RBFNetwork IBk Kstar Bagging RandomCommittee Jrip PART LMT RandomForest

Functions Lazy Meta Rules Trees

pCCI before tuning 90.13 90.38 95.42 95.00 89.00 88.21 91.96 93.33 89.04 89.58 94.04 95.00

pCCI after tuning 92.21 – 95.75 96.58 93.67 91.33 96.75 96.26 90.21 91.00 94.67 95.17

Parameters tuned

Search Algorithm = TAN – L = 0.1 and M = 0.1 B = 5 and W = 0.01 K=7 B = 50 Classifier = RBFNetwork Classifier = MultilayerPerceptron F = 7 and O = 4 M=7 splitOnResiduals = True I = 200

To see how DNNs compare to the WEKA’s classifiers, we have implemented a state-of-the-art DNN as the last stage of our experiments. We have used the model given in Sect. 2.2 and implemented the 1D-CNN on Python 3.6, using Keras library and Tensor Flow. Since no information about the batch size or the optimizer has been given in the proposed model, we have tuned these parameters in our implementation. The performance of the fully optimized 1D-CNN is presented in Table 3. Table 3. Performance of the 1D-CNN after parameter tuning Category Algorithm pCCI after tuning Parameters tuned DNN 1D-CNN 94.85 Batch size = 100, Optimizer = Adam Note that the result presented here is not the same as one given in [10]. One of the reasons of this change is the randomization process in generating the data. Another may stem from the difference between our work and the referenced study in terms of the selected values of batch size and optimizer type. The comparison of the results of the 1D-CNN and WEKA classifiers has shown that optimized algorithms in the “Functions”, “Meta” and “Trees” categories of WEKA have performed better than the 1D-CNN

In order to provide more insight about the classification results, we present the confusion matrix for the best performing classifier, i.e. Bagging, in Table 4. The rows of the confusion matrix show the number of instances for which the true pattern is the row and the predicted one is the column. It can be seen from the confusion matrix that a great number of patterns have been predicted correctly. As might be expected, patterns that show similarity in terms of the trajectory they follow have been confused with each other. The instances of upward-trend and downward-trend have been predicted as upward-shift and downward-shift respectively. A similar relation, although less

424

U. Avci et al.

frequently observed, can be seen in the exact opposite situation. Normal pattern can produce a waveform shape when the noise is added to the sample mean. In such cases, the instances of normal pattern have been predicted as cycle patterns. Table 4. Confusion matrix of the fine-tuned Bagging model Classified as! NOR UT DT US DS CYC NOR 377 0 0 0 0 1 UT 0 385 0 27 0 0 DT 0 0 395 0 23 0 US 0 6 0 387 0 0 DS 0 0 19 0 381 0 CYC 2 0 0 0 0 397

4 Conclusion Control chart pattern recognition is an important and widely studied field in quality control literature. Due to availability of new intelligent techniques such as Support Vector Machines and Deep Neural Network, CCPR implementations of those techniques have been studied recently. This study is another attempt to contributing the literature on that stream of work. We specifically consider 45 WEKA classifiers and a 1D-CNN structure to determine their relative performances. As depicted on the above confusion matrix of the best performing classifier, which is Bagging, the recognition of patterns is outstanding. As the future work, the results presented in this work may be implemented in control chart design with various metrics.

References 1. Montgomery, D.C.: Introduction to Statistical Quality Control. Wiley, London (2007) 2. Nelson, L.S.: The Shewhart control chart: test for special causes. J. Qual. Technol. 16(4), 237–239 (1984) 3. Hachicha, W., Ghorbel, A.: A survey of control-chart pattern-recognition literature (1991– 2010) based on a new conceptual classification scheme. Comput. Ind. Eng. 63(1), 204–222 (2012) 4. Roberts, S.W.: Control chart tests based on geometric moving averages. Technometrics 42 (1), 97–101 (2000) 5. Ducan, A.J.: Quality Control and Industrial Statistics, 5th edn. Richard D. Irwin, Homewood (1986) 6. Nelson, L.S.: Interpreting Shewhart X control charts. J. Qual. Technol. 17(2), 114–116 (1985) 7. Cheng, C.S.: A neural network approach for the analysis of control chart patterns. Int. J. Prod. Res. 35(3), 667–697 (1997) 8. Davis, R.B., Woodall, W.H.: Performance of the control chart trend rule under linear shift. J. Qual. Technol. 20(4), 260–262 (1988)

A Comparative Study of Artificial Intelligence

425

9. Yang, W.A., Zhou, W., Liao, W., Guo, Y.: Identification and quantification of concurrent control chart patterns using extreme point symmetric mode decomposition and extreme learning machines. Neurocomputing 147(1), 260–270 (2015) 10. Zan, T., Liu, Z., Wang, H., Wang, M., Gao, X.: Control chart pattern recognition using the convolutional neural network. J. Intell. Manuf. 31(3), 703–716 (2019). https://doi.org/10. 1007/s10845-019-01473-0 11. Bouckaert, R.R., et al.: WEKA Manual for Version 3-7-8. University of Waikato, Hamilton (2018)

A Capacity Allocation Model for Air Cargo Industry: A Case Study Dilhan İlgün(&) and S. Emre Alptekin Galatasaray University, Ciragan Cad., No. 36, 34349 Istanbul, Turkey [email protected]

Abstract. The transportation sector is one of the basic elements of the country’s economy. Today, the growth and enrichment of the economy, competitive conditions due to the need to send products faster, such as security increases the importance of the transport sector. Due to meeting these needs, the importance of air cargo transportation is increasing day by day. Air cargo transportation can be reserved for allotment agreements or opened directly for free sale. Defining the proportion of the capacity for allotment agreements and free sale are among the most fundamental issues in the air cargo industry. That remains an open problem; since capacity allocation is an essential factor affecting the profitability of air cargo companies. In this paper, we used Conditional Value at Risk (CVaR) and Artificial Neural Network (ANN) models to solve the capacity allocation problem. We used real datasets for different destinations. The results of the capacity allocation model provide a basis for pricing policies. The paper concludes by giving open research issues related to the capacity allocation problem and the cargo industry. Keywords: Capacity allocation
Artificial neural network
CVaR
Air cargo industry

1 Introduction One clear result of globalization and developing technologies are the increase of air cargo transportation daily. Companies need to move products faster and safer in the increasingly competitive environment. The global economy depends on the ability of high-quality products at competitive prices to consumers worldwide. Air cargo carries less than 1% of global trade volume but 35% by value [1]. Therefore, the air cargo industry directly contributes to the global economy. Air cargo transportation preference for safer, traffic-free, more reliable, high-value products and fast can be emphasized as the strengths of air cargo transportation. However, contrary to these strengths, the capacity of air cargo companies is limited. At this point, they face a critical problem in controlling limited cargo space. Companies can sell their capacity to customers at different prices. However, they must sell the capacity before the flight, as it cannot be sold after. Hence, the revenue strategies of air cargo transportation are considered as directly related to efficient capacity management [2]. Cargo capacity can be sold using two main mechanisms: Contract sales (allotment sales), Opened as free sales. Guaranteed capacity contract: In this type of contract, the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 426–437, 2022. https://doi.org/10.1007/978-3-031-09176-6_49

A Capacity Allocation Model for Air Cargo Industry: A Case Study

427

customer and the air cargo company sign an agreement for guaranteed capacity on a particular flight. Thus, air cargo carriers can sell some of their available capacity to carriers with agreements. A common type of contract sale is the Block Space Agreement. Block Space Agreements can come in two different forms: soft and hard. In a soft block space contract, customers can cancel their allocation on a particular flight without penalty, if they give the carrier an advance notice. In a hard block space contract, the customer pays the total cost, even if the customer offers no shipments on a particular flight. Correct management of aircraft capacity by deciding on how much capacity will be allocated for contract sales and how much capacity should be opened as free sales is the main research problem in our paper. In the related literature, few studies are addressing this problem, therefore we believe that the work makes an essential contribution to the literature. We proposed two separate models to determine the total capacity assigned to the allotment. The following sections of the paper are organized as follows. Section 2 is the literature review, whereas Sect. 3 describes the model formulations for Conditional Value at Risk (CVaR) and Artificial Neural Network (ANN), respectively, and discusses model assumptions. Section 4 summarizes the case study, describes the data we use, and discusses each model’s results. Finally, Sect. 5 presents the methodology’s generalizability and future research ideas.

2 Literature Review Until the mid-1980s, the studies on air cargo transportation focused on determining the air cargo transportation system, the operational process, and the general developments in the sector. Later then, quantitative decision-making methods were used for air cargo operations. The number of studies on the air cargo side has increased in recent years [3]. One of these studies is revenue management. Revenue management is the process of understanding, predicting, and influencing consumer behavior to maximize revenue or profit. Revenue management was first used in the airline industry and then widely used in other sectors. All airlines use revenue management systems to increase their revenues. In contrast, revenue management studies are much less common in air cargo. This is because air cargo and air passenger transportation have very different characteristics from each other’s [4]. One of the main differences is capacity. While there is a one-dimensional capacity formed by the number of seats in passenger transportation, air cargo transportation has a multidimensional capacity in weight, position, and volume. Air cargo companies’ financial success depends on their efficient capacity usage. Free capacity cannot use post-flight, and the lack of return of lost capacity shows how critical it is to plan capacity correctly. In addition to capacity fluctuation and multi-dimensional capacity limits, lack of demand certainty also complicates capacity management. There is a risk of cancellation of the request. Hard block allotment agreements can be used to withstand some cancellations. Overbookings are used to avoid empty capacity due to demand cancellation. On the other hand, overbookings increase the risk of offloading and returning to the company as a complaint and damaging customer relations.

428

D. İlgün and S. E. Alptekin

Pre-allocation, contract, or spot sales are channels through which airlines can sell their cargo capacity. According to their performance in the previous year, air cargo companies allocated the capacity on selected routes to forwarders according to their performance. Considering other possible tenders, cancellations, and overbooking from forwarders, air cargo companies choose to accept or reject policies to maximize their expected revenues. Air cargo companies often face complex problems. Some routes have insufficient capacity to fulfill orders, while others have idle capacity [5]. Capacity utilization is considered critical in diverse areas. How to maximize capacity utilization is a problem that needs to be explored. Wilken et al. [6] discussed the problem of choosing a suitable peak hour and provided information on the annual capacity utilization of airports worldwide in the form of traffic ranking curves. They derived the functional relationships between peak hour and annual air transport movements’ volumes for each type of airport capacity. Furthermore, capacity allocation becomes a significant problem if an air cargo firm sells the capacity to multiple forwarders and orders exceed the fixed capacity [7]. Despite numerous articles on nonlinear resource allocation problems for the other sectors, few articles describe mathematical models of the air cargo allocation problem. There is a mathematical model to fill this gap in the literature in our study. This model will also be a novel source for future air cargo capacity allocation studies. Our second model is an artificial neural network model. After creating these two separate models, we will compare the results of the models. Moussawi-Haidar [8] has solved the single-leg air cargo revenue management problem. He determined how much weight and volume capacity to sell on allotment agreements. For medium-sized industries, he solved the problem using dynamic programming. He created a discrete-time capacity control model for allotment requests to decide which allocation request will be accepted. Amaruchkul and Lorchirachoonkul [2] proposed a model for allocating cargo capacity to forwarders earlier in the booking horizon. They considered a single air cargo carrier. They aimed to select the allotments that maximize the total contribution. They derived the probability distribution of actual usage using a discrete Markov chain to solve the problem with a dynamic programming method. Additionally, they proposed two heuristics for large-scale allocation problems. This study’s main objective is to contribute to the air cargo capacity allocation problem literature. We aim to make the air cargo company more profitable and effective using the capacity with the proposed capacity allocation models. As the first step of our methodology, we used the CVAR model, which is based on [9]. We added risk factor for soft allotment contracts to make this model more coherent with real-life. We solve the cargo and passenger aircraft model of four different destinations via GAMS. Machine learning offers a promising way to solve many problems for freight transport by leveraging the power of data [10]. Neural network applications are a highly interdisciplinary field, whose examples range from signal processing to medicine [11]. As the second step, we used Artificial Neural Network with MATLAB to predict demand for four destinations.

A Capacity Allocation Model for Air Cargo Industry: A Case Study

429

3 Model Formulation Since air cargo companies face the risk of misallocating and under-utilizing their capacity, risk management has become the key to make healthy capacity planning. In order to deal with the risk of not allocating the air cargo capacity efficienctly, various methods could be used. We used the CVaR model, one of the commonly known risk management model, for effective risk management in our work. Mathematical models are generally used, when the system is not so complicated. As the complexity of the system increases, the need to alternative models appear. Hence, due to the complexity of the air cargo capacity allocation problem, we consider the ANN model as our second approach, besides the CVaR model. ANN is a nonlinear model, which is easy to use and understand compared to other statistical methods. Furthermore, ANN is a non-parametric model, thus eliminating the error in parameter estimation [24]. Another important reason we use ANN is that it works well for large datasets and is suitable for complex operations as in air cargo industry [26]. 3.1

Conditional Value at Risk (CVaR)

Risk is defined as the probability of a decrease in the economic benefit resulting in monetary loss, expense, or loss associated with a transaction in financial theory [12]. Inefficient capacity allocation is one of the fundamental risk factors in air cargo industry, since it may cause economic losses. Firms must have a risk management system to identify the risks they face and measure and control them. Regulatory and supervisory authorities use risk measurement to ensure stability in the market and reduce systemic risk [12]. Risk measurements help a decision-maker, when choosing between options. Risk measurements allow risk calculations, help determine the amount of capital that an institution needs, and are used for reporting purposes in disclosing corporate risks [13]. In the decision-making process, different risk measurement techniques can be used. The Conditional Value at Risk model developed by Philippe Artzner et al. in 1999. It is based on the criticisms of the VaR method, which was developed to express the maximum expected loss in monetary terms in a certain time period and at a certain probability level. Conditional Value at Risk, basically identifies as an expected loss, when the potential loss exceeds the value at risk [12]. CVaR [x] ¼ Ef x l x [ VaR[x]g

ð1Þ

CVaR is always higher than VaR and can be defined as the average of losses that exceed the VaR (Eq. 1). According to the risk diversification principle, combining various assets in a portfolio is expected to reduce or keep absolute risk at the same level. This principle is given by Eq. 2. RiskðA þ BÞ  RiskðAÞ þ RiskðBÞ

ð2Þ

The main result of the studies of Rockafellar and Uryasev [14] is the proof that CVaR can be expressed by Eq. 3:

D. İlgün and S. E. Alptekin

430

 CVaRa ½ X  ¼ minh
R


 1 E ð x  hÞ þ hþ 1a

ð3Þ

We can summarize the CVaR of a portfolio as the average amount that the portfolio will lose in the worst case by X%. We used the CVaR method to add this risk to our study. Although the parameters of allotment agreements are generally certain (hard block space agreements), uncertain parameters arise from free sales. The customer pays the hard block space agreement fee, even if the purchased capacity is not used. In the soft block space agreement, the customer does not pay the fee, if not using the agreed capacity. Nevertheless, the unit fee for this agreement is higher. Hence, we added risk for soft block space agreements to make the model more suitable for real-life scenarios and reduce the unused capacity. Model entries are as follows: k: Destination j: Monthly cumulative figures for the selected destination jk TAs : Allotment tariff via soft block space agreement for destination k jk TAh : Allotment tariff via hard block space agreement for destination k jk XAs : Allotment amount via soft block space agreement for destination k jk XAh : Allotment amount via hard block space agreement for destination k SURjkAs : Allotment show up rate via soft block space agreement for destination k TFjk : Free weight tariff for destination k and sample j XFjk : Free weight amount for destination k and sample j SURjkF : Free weight show-up rate for destination k and sample j DjkF : Free weight demand for destination k and sample j C jk : Flight capacity for destination k and sample j ujk : Auxiliary variable to linearize the model k: Risk profile a: Confidence level MinX jk X jk X jk  As Ah F

N X M X

jk jk TAh XAh

j¼1 k¼1

 hX X  i N M jk jk jk jk jk þ ð 1  k Þ hþ þ k T X  T D SUR F F F As As j¼1 k¼1

1 XN XM jk u j¼1 k¼1 ð1  aÞ



ð4Þ s.t ujk  0;

j ¼ 1; . . .; N

ujk þ h þ TFjk DjkF SURjkF  0; jk jk þ XAs SURjkAs þ DjkF SURjkF  C; XAh

k ¼ 1; . . .; k

j ¼ 1; . . .; N

k ¼ 1; . . .; k

j ¼ 1; . . .; N

k ¼ 1; . . .; k

ð5Þ ð6Þ ð7Þ

A Capacity Allocation Model for Air Cargo Industry: A Case Study

0  XFjk  DjkF ;

j ¼ 1; . . .; N

jk jk þ XAs SURjkAs XAjk ¼ XAh

k ¼ 1; . . .; k

j ¼ 1; . . .; N

XFjk ¼ XFjk SURjkF

j ¼ 1; . . .; N

k ¼ 1; . . .; k

k ¼ 1; . . .; k

0k1 SURjkAs ;

SURjkF  1

XFjk ; DjkF ; SURjkF ; TFjk  0 XAjk ; SURjkAs ; DjkA  0

431

ð8Þ ð9Þ ð10Þ ð11Þ

j ¼ 1; . . .; N

k ¼ 1; . . .; k

ð12Þ

j ¼ 1; . . .; N

k ¼ 1; . . .; k

ð13Þ

j ¼ 1; . . .; N

k ¼ 1; . . .; k

ð14Þ

There are two basic terms in the objective function (Eq. 4). The first term represents revenue from hard block space agreements, and the second one is cumulative revenue that gets from the free sales and soft block space revenue., Constraint (7) states that the total free sales and allotment sales could not exceed the capacity for related flights. Constraint (8) states that free sales for selected destinations cannot exceed the demand. Finally, constraint (9) and constraint (10) state the total allotment weight and free sale weight respectively. We solved the model separately for different fleet types (passenger and freighter) and different k (risk profile) parameter values. If the value of k is equal to 1, this situation represents a risk-neutral approach, whereas k  0.5 means a high level of risk aversion. 3.2

Artificial Neural Network (ANN)

Artificial intelligence (AI) first appeared at a conference in the United States in 1956 in the efforts of scientists. They aimed to make computer programs wise [15]. One of the AI approaches is ANN. ANN aims to transfer the thinking and working ability of the human brain to the computer and are inspired by the biological nervous system. ANNs are algorithms that perform their learning by using inputs to generate new information, make generalizations, and classify [16]. Neural network research expands its scope to account for the fuzzy nature of real-world, reasoning, complex and largely unknown processing performed [17]. Artificial neural networks are commonly used in control, modeling, and prediction applications. Nonlinear Autoregressive Exogenous is an ANN model proposed by Lin et al. in 1996 [18]. It is for use in problems, where the model output depends on past data. NARX is a dynamic ANN model with many layers of feedback and forward calculation [25]. The “X” in the model name indicates external values and refers to other external variables included in the model. The NARX model is based on the linear ARX model used in time series. NARX networks are feedback ANN models that are successful in nonlinear system modeling and time series

432

D. İlgün and S. E. Alptekin

prediction applications. It has been determined from the performance determinant that NARX is faster and performs more effectively than traditional feedback network structures [19]. In addition, NARX networks usually converge faster and exhibit more effective learning [20, 21]. Since the proposed model has a seasonal market demand in different destinations, we incorporated the NARX model in our study. Unlike other feedback networks, NARX networks only receive feedback from neurons in the output layer, not hidden layers (Eq. 14 [22]): yðtÞ ¼ f ðyðt  1Þ; yðt  2Þ; . . .; yðt  nyÞ; xðt  1Þ; xðt  2Þ; . . . ; xðt  nxÞÞ ð15Þ In Eq. 15, y (t-1), y (t-2),…, y (t-ny) denotes the network outputs, while x (t-1), x (t-2),…, x (t-nx) represent the network inputs. nx and ny are the number of previous inputs and outputs to be applied to the model for feedback, respectively. Y (t) connected to the output signal is calculated by the previous output signal and the return of the previous input signal [23].

4 Empirical Study The product offered for sale in air cargo is capacity and its capacity becomes unavailable after the scheduled flight date and time. Therefore, efficient capacity management for air cargo transportation will positively increase income and profitability. The capacity can be sold as periodic allocation agreements or open for spot/contract sale in air cargo transportation. Air cargo companies can focus on allotment sales to minimize the risk of their aircraft being under-utilized. We present a capacity model that divides the total capacity by allocation agreements and free sales using historical data. Few studies have focused on capacity allocation in the air cargo industry. Wada et al. [9] used the CVaR model for the allocation of air cargo capacities. The training data for this model are based on real data provided by a major cargo airline company for four different destinations from different regions between January 2018 and October 2019. In total, we collected 130 monthly results during this period. The database includes information of each flight with specific details of flight date, shipment destination, flight type (passenger or freighter), allotment and free sale demand, unit prices for allotment and free sales, the load factor of flight, and capacity. After the model implementation, we used the two-month dataset to evaluate the model’s results. Twenty-four monthly results constitute approximately 16% of the total dataset. We compared the model results with the actual results. CVaR In this section, we will show the optimization formulation of the problem. Model inputs and outputs are given in Fig. 1.

A Capacity Allocation Model for Air Cargo Industry: A Case Study

433

Fig. 1. Input & output parameters – CVaR model

Artificial Neural Network Due to the seasonal nature of air cargo demand, allotment requests and shipments may be lower or higher in some months. By adding seasonality to the model, we aimed to produce much more accurate predictions. The sales structure can be different for each destination. Allotment sales fill most of the capacity for some destinations, while it may be the opposite for others. We added the destination as input to the model to give different results according to the destination points. Fleet type is another parameter affecting the proposed model, so we added the fleet type to the model. As a result of the model, we will determine the percentage of the available capacity allocated to allotment sales and what percentage will be offered as free sales. The model produces the ratio of allotment sales in total capacity as output. Model inputs and outputs are as in Fig. 2.

Fig. 2. Input & output parameters - ANN model

We added the inputs and outputs to the model via the MATLAB application. For this, initially, we applied the normalization process. We used D_Min_Max normalization. We used the two-year dataset as training input for different destinations in the model. We used a separate two-month dataset as test data. We trained the model using different hyperparameters, such as layer and neuron numbers. We calculated R2 ratios by comparing the data obtained from the experiments with the actual data. We have chosen the model with the highest R2 ratio as the base model. The selected model consists of ten neurons and seven hidden layers. We use Levenberg-Marquard and MSE for training parameters, tansig for the transfer function, and learngd for adaptation learning function.

434

4.1

D. İlgün and S. E. Alptekin

Results

We compared CVaR model results with actual data. We predicted the first two months of 2020 with the forecasting model established with CVaR for comparison. We used the actual results for the selected period to measure the consistency of the forecast data. The graph with the forecast values and actual allotment ratio values created with the CVaR model and the scatter plot of the actual and predicted allotment ratios is given in Fig. 3.

Fig. 3. Scatter plot of the actual and predicted allotment ratios - predicted allotment ratio and actual values using CVaR model

We calculated R2 ratios between model results and actual results to examine the results from different networks. When we examined the calculated R2 values, we obtained the highest R2 value as 0.9335 in the seven hidden layers NARX model. We estimated allotment rates for the first two months of 2020 with the forecasting model with the seven hidden layers NARX network. We considered the actual results for the specified period to measure the forecasting data’s consistency with the actual data. Figure 4 shows the graph with the predicted values and the actual values created with the NARX network and the scatter plot of the actual and predicted allotment ratios.

Fig. 4. Scatter plot of the actual and predicted allotment ratios - predicted allotment ratio and actual values using NARX model

We compared CVaR and ANN model results with each other. When we examine the coefficient of determining R2 values that show the relationship between the model results and the actual results, the R2 value created by the ANN model is 0.9335, while the CVaR model is 0.3189. Estimates of the ANN model formed a ratio close to 1, and

A Capacity Allocation Model for Air Cargo Industry: A Case Study

435

the allotment ratio could be explained by 93.35% with the model. Predicted data and graphs of actual data produced by CVAR and ANN models are shown in Fig. 5.

Fig. 5. Predicted allotment ratios and actual values.

5 Conclusion The air cargo transportation business is an essential source of income for airlines. Despite this importance, there are far fewer articles in air cargo transportation than on the passenger side. The main reason why there is less paper in air cargo is that the processes in air cargo are more complex and challenging to manage and model. One of these complexities is the correct planning of capacity in air cargo. Despite its complexity, capacity management in air cargo transportation is one of the most critical issues for air cargo companies to earn more profit. Our study aimed to determine the correct capacity ratio for contract sales and free sales. For this purpose, we created two different prediction models by examining the historical data. Then, we applied our models to an air cargo company and compared the model results with the actual results. Finally, we have summarized these models’ differences, advantages, and weaknesses. We first used the CVAR model in the study. The CVAR model we used was based on the 2017 study by Wada et al. on capacity allocation in the air cargo industry. We adapted this model to the company’s problem. We added risk for soft block space agreement to the model to make the model more compatible with real life. We ran the model on GAMS for cargo and passenger aircraft of four different destinations. Then we compared the model results with the actual estimation results. When we compared the CVAR model result with the actual allocation ratios, we calculated the R2 value as 0.3189. The second model we used was the Artificial Neural Networks model. Again, we used MATLAB to solve the model. We used an approximately two-year dataset as training and a two-month dataset as test data in the model. In MATLAB, we created models for various networks and evaluated the results using the dataset we created for testing. We determined that the best result is the NARX model with seven hidden layers in our analyses. When we compare the results we obtained from this model with the allocation ratios realized, we found the value of R2 as 0.9335.

436

D. İlgün and S. E. Alptekin

When we evaluated the statistical results, we observed that the NARX network results were better than the CVAR model. The CVAR model and the ANN model are very different models. In contrast, the CVAR model aims to maximize revenue, and the ANN model aims to maximize capacity utilization considering the allotment capacity ratios in the past. As a result, when the model results are examined in terms of load factor and revenue, the CVAR model provides a 3% higher return than the ANN model. However, higher use of capacity is targeted in line with company objectives. For this reason, we recommend using the ANN model, which gives more compatible results with real life for the company. 5.1

Further Research

For future studies, a separate forecast model can be used for allotment demand estimation for the capacity model. Based on this forecast model, future capacity planning can be done. Additional capacity for overbooking can be added to the used models. If the ANN model is used, the region can be added to the model as an additional input. Since we selected samples from different regions, we did not need to add region input to the model. We suggested two different models in our study. However, a different model in which the two models are used as a hybrid model can be used in future studies. For the hybrid model, the show-up rates included as input in the CVaR model can be estimated using the past period data with the ANN model. Then the capacity allocation problem can be solved with the CVaR model. After selecting the capacity allocation model, a dynamic pricing model can be applied for the free sale capacity. Acknowledgements. This work has been supported by the Turkey’s Council of Higher Education (CoHE 100/2000 Doctoral Scholarship Program) and this research has been financially supported by Galatasaray University Research Fund, with the project number FBA-2022-1091.

References 1. IATA. https://www.iata.org/en/programs/cargo/sustainability/benefits. Accessed 07 Apr 2021 2. Amaruchkul, K., Lorchirachoonkul, V.: Air-cargo capacity allocation for multiple freight forwarders. Transp. Res. Part E Logist. Transp. Rev. 47, 30–40 (2011) 3. Feng, B., Li, Y., Shen, H.: Tying mechanism for airlines’ air freight capacity allocation. Eur. J. Oper. Res. 56, 322–330 (2015) 4. Dongling, H.: A study on air cargo revenue management. Ph.D. thesis. National University of Singapore, Department of industrial & systems engineering, Singapore (2010) 5. Feng, B., Li, Y., Shen, Z.: Air freight operations: Literature review and comparison with practices. Transp. Res. Part C Emerg. Technol. 56, 263–280 (2015) 6. Wilken, D., Berster, B., Gelhausen, M.C.: New empirical evidence on airport capacity utilisation: Relationships between hourly and annual air traffic volumes. Res. Transp. Bus. Manag. 1, 118–127 (2011) 7. Meng, Q., Zhao, H., Wang, Y.: Revenue management for container liner shipping services: Critical review and future research directions. Transp. Res. Part E 128, 280–292 (2019)

A Capacity Allocation Model for Air Cargo Industry: A Case Study

437

8. Moussawi-Haidar, L.: Optimal solution for a cargo revenue management problem with allotment and spot arrivals. Transp. Res. Part E Logist. Transp. Rev. 72, 173–191 (2014) 9. Wada, M., Delgado, F., Pagnoncelli, B.K.: A risk averse approach to the capacity allocation problem in the airline cargo industry. J. Oper. Res. Soc. 68, 643–651 (2017) 10. Barua, L., Zou, B., Zhou, Y.: Machine learning for international freight transportation management: a comprehensive review. Res. Transp. Bus. Manag. 34, 100453 (2020) 11. Fausett, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, India (1993) 12. https://www.spk.gov.tr/SiteApps/Yayin/YayinGoster/1014. Accessed 07 Apr 2021 13. Kızılok, M.: Çok Değişkenli Bağımlı Risklerin Modellenmesi ve Optimal Aktueryal Kararlar. PHd Thesis, Ankara University, Ankara Turkey (2010) 14. Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–42 (2000) 15. Uysal, A.E.: Yapay Zekanın Temelleri ve Bir Yapay Sinir Ağı Uygulaması. Master’s thesis, Marmara University. Institute of social sciences. Istanbul Turkey (2009) 16. Zang, G.P., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998) 17. Yegnanarayana B.: Artificial Neural Networks. Prentice-Hall India, New Delhi, India (1999) 18. Lin, T., Horne, B.G., Tino, P., Giles, C.L.: Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans. Neural Netw. 7(6), 1329–1338 (1996) 19. Aşkın, D., İskender, İ, Mamızadeh, A.: Farklı Yapay Sinir Ağları Yöntemlerini kullanarak kuru tip transformatör sargısının termal analizi. Gazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 26(4), 905–913 (2011) 20. Diaconescu, E.: The use of NARX neural networks to predict chaotic time series. Wseas Trans. Comput. Res. 3(3), 182–191 (2008) 21. Xie, H., Tang, H., Yu-He, L.: Time series prediction based on NARX neural networks: an advanced approach. In: 2009 International Conference on Machine Learning and Cybernetics, no. 3, pp. 1275–1279 (2009) 22. Yavuz, E.: Yapay Sinir Ağı Kullanarak Kontrol Alan Ağları için Çevrim içi Mesaj Zamanlaması Optimizasyonu. PHd thesis, Süleyman Demiral University, Institute of social sciences, Isparta Turkey (2018) 23. https://www.mathworks.com/help/deeplearning/ug/design-time-series-series-narx-feedbackneural-networks.html;jsessionid=c6bb77ba506d0a05436cfe0bc9b 24. Yılmaz, B.: Akarçay Havzasında Çözünmüş Oksijen Değerlerinin Yapay Sinir Ağları ile Belirlenmesi. Ministry of Forestry and Water Management, Turkey (2015) 25. Karaatlı, M., Demirci, E., Baykaldı, A.: Forecasting commercial credit interest rates with ANN NARX and VAR models. J. Bus. Res. Turk 12(3), 2327–2343 (2020) 26. Hellermann, R.: Capacity Options for Revenue Management: Theory and Application in the Air Cargo Industry. Springer, New York (2006). https://doi.org/10.1007/3-540-34420-9

Analysis of Covid-19 News Using Text Mining Techniques Emine Çağatay1(&), Bahar Y. Sünnetci1, Selin Orbay2, and Tolga Kaya1 1

Department of Management Engineering, Istanbul Technical University, 34367 Istanbul, Turkey [email protected] 2 Hisar School, 34077 Istanbul, Turkey

Abstract. COVID-19, which has taken the whole world under its influence, has been a remarkable period to investigate the emotions and behaviors of people in extraordinary situations. The findings addressed during this defining moment to the fluctuation of the number of cases and certain turning points. It is a matter of debate how much these important moments are affected by the attitudes of the authorities directing the public. The aim of this study is to determine in which periods and what expressions the news in the newspapers is used by using text mining techniques such as word clouds, clustering and sentiment analysis. In order to do this, COVID-19 news published in the last two years from three respected newspapers were used. Results reveal that, there are significant changes in the main themes of the COVID-19 related news with the release of the vaccine. Moreover, when all periods are inspected, it has been observed that different topics like herd immunity, vaccination, variants and human rights have come to the forefront in various print media sources. Keywords: Clustering
Coronavirus
COVID-19
Sentiment analysis
Text mining
Vaccination

1 Introduction The COVID-19 period, which started at the end of 2019 and still maintains its effect, has transformed society in many ways. Moreover, the pandemic period caused many emotional states to be experienced at once. Turning points can be determined by scrutinizing the media tools that witness and lead this transformation. The influence of the newspapers, which wanted to broadcast the events clearly and quickly to the people acting with the psychology of the pandemic, inevitably changed their expressions and views. These printed media sources which can be considered as a reflection of their readers, directed news according to the mood changes. The purpose of this study is to identify COVID-19 related articles from the beginning of the pandemic and analyze what people feel at certain points. In addition, it aims to shed light on the pandemic period by examining the effects of the news made by respected newspapers around the world on people. This study was conducted with a total of 360 articles from the New York Times, Washington Post, and The Guardian. Word cloud, sentiment analysis, and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 438–445, 2022. https://doi.org/10.1007/978-3-031-09176-6_50

Analysis of Covid-19 News Using Text Mining Techniques

439

other text mining techniques were applied to be able to understand the differences in the development of the vaccine and the other periods. The following sections of this paper are as follows; in the 2nd part, the literature review is included, in the 3rd part the methodology used is given, in the 4th part the details of the data set are summarized, in the 5th and 6th parts the findings and the conclusion of the research are given.

2 Literature Review The disease caused by SARS-CoV-2, which emerged in Wuhan, China at the beginning of December 2019, caused the COVID-19 epidemic. On the 30th day of January 2020, the World Health Organization declared a state of emergency [1]. All these news reflect their own opinions and feelings about the epidemic. Therefore, it’s thought that all news in this direction affects people's emotions. [2]. Sentiment analysis studies have taken their place in the literature in order to determine the effect of such a large-scale crisis on the feelings of people. According to Barkur et al., they used Twitter posts containing specific words/hashtags, while making these analyzes. Two hashtags (#IndiaLockdown and #IndiafightsCorona) were specifically considered in order to analyze the feelings of Indians about the quarantine with 24,000 tweets. In the analysis, word clouds were created showing the sentiments of the tweets [3]. Another study by Kaur and Sharma, collected COVID-19 related tweets using the Twitter API and analyzed positive, negative, or neutral emotions with the help of machine learning approaches [4]. In text mining, 4 methods are popular. The first one is the term-based method which doesn’t always give accurate results due to synonyms and polysemy words. Second, the phrase-based method analyzes with more meaningful results that contain more than one word instead of words. The third one, concept-based method, concepts can be analyzed over text or documents and often uses Natural Language Processing techniques. The last one is the pattern taxonomy method is works best among other methods [5]. R programming language was developed, especially for text analysis. It has a large package library where advanced text modeling techniques can be applied. Data preparation, string operations, tokenization, normalization, lowercasing, stemming, and more are the stages of text mining in R language [6]. According to Our World in Data, the first vaccination in the world was made on 1 December 2020. On August 23, 2021, FDA approval was received for the PfizerBioNTech vaccine [7].

3 Methodology Word Cloud. They used to understand the frequency of words in the text. The fact that it is popular and sometimes used unnecessarily causes it to lose its importance. Therefore, it should be taken into consideration not to mislead even if it is used correctly [8]. Tokenization. This method is used for splitting text content into tokens. These tokens need to be meaningful text elements. Although it is complex in some languages which

440

E. Çağatay et al.

have no word limit, the language of the texts to be used in this study is English. However, these texts are newspaper articles and must be in a format that can be read by machines [9]. N-gram Tokenization. N-gram tokenization is a language-independent technique that addresses problems posed by morphological processes, reducing IR performance [10]. Hierarchical Cluster Analysis. It is aimed to create clusters at different levels hierarchically. These clusters are expected to be meaningful in themselves. This method can be divided into two, linkage and the detecting cluster centers method [11]. Dendrograms solutions were initially obtained with agglomerative algorithms, these algorithms work locally, not on the entire dataset. Whereas, partitional clustering algorithms operate on the entire dataset [12]. Sentiment Analysis. Idea mining or sentiment analysis is the mining of people's opinions and feelings about any object, phenomenon, and its qualities. The main purpose of sentiment analysis is to detect positive or negative emotions in the given text [13]. K-Means Clustering. Cluster analysis is widely used in text mining studies to carry a2 þ b2 ¼ c2 out analyzes such as pattern recognition and classification. To run this algorithm, it is necessary to determine the number of k cluster centers first. Then the algorithm measures the distance of each point to the centers shown below and assigns it to one of them. fx1 ; x2 . . .xn g represents cluster numbers for n observations. fm1 ; m2 . . .mk g represents initial cluster centers which shown in Eq. 1. It is important to iterate this process to reach final results [14]. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xd
2 d xi ; mj ¼ xi1  mj1 j¼1




ð1Þ

4 Data In this study, newspaper articles from The New York Times, Washington Post, and The Guardian were scanned and published in 2020 and 2021. A total of 360 newspaper articles were collected from each newspaper in a 24 months and 5 news per month. Articles published before and after the date of vaccine discovery are shown as follows (Tables 1, 2 and 3). Table 1. The number of articles before and after the vaccine launch date Before vaccine After vaccine 165 195

Analysis of Covid-19 News Using Text Mining Techniques

441

Table 2. Dataset description Variable News_ID File_Name Year Month Day Newspaper Title Text

Type Numeric Character Numeric Numeric Numeric Character Character Character

Definition Numbers between 1 and 144 for each of the news Unique code system given to each file Year of publication Month of publication Day of publication Newspaper code, e.g. GU for Guardian Newspaper headline Text of article

Table 3. News quantity First 6 months Transaction phase Last 6 months 90 180 90

Newspaper articles contain some characters like stopwords, numbers, punctuation, brackets, and spaces that would mislead the model. Therefore, data has been prepared by using the corpus function.

5 Findings 5.1

Time Periods

When the first six months, the transition phase, and last six months are analyzed for all the data, the result shows that; while social distance, controlling the disease and its center was mentioned in the first stages, human rights, clinical trials, and immunity issues started to be mentioned in the last six months of 2020 and the first six months of 2021, which we call the transition phase. In the last six months, variants and vaccine have started to be mentioned frequently by completely differentiating other periods (Figs. 1, 2 and 3).

Fig. 1. First six months

Fig. 2. Transaction phase

Fig. 3. Last six months

442

5.2

E. Çağatay et al.

Vaccination

While investigating the pre and post-vaccination period, 2 December 2020 was taken as a turning point, the date on which the vaccination was started. The topics that were frequently repeated before this vaccination period; were positive cases, tests, and controlling the disease. In the post-vaccination period, the terms fully vaccinated and the delta variant remained on the agenda which are related to the vaccine doses. Phrases such as “get vaccinated” leading to vaccination were shared by the newspapers (Figs. 4, 5).

Fig. 4. Before vaccination

5.3

Fig. 5. After vaccination

K-Means Clustering

Among the clustering techniques examined, it was decided that the K-means algorithm and number of cluster centers as three could work better with this data. Three has been determined as the number of cluster centers. The texts in the first cluster focus on vaccination and disease control. The second cluster includes topics related to herd immunity, immune response, and social distance. The last cluster contains news about positive tests, vaccination, and the impact of the vaccine on pregnant women in general (Figs. 6, 7 and 8).

Fig. 6. Cluster 1

Fig. 7. Cluster 2

Fig. 8. Cluster 3

Analysis of Covid-19 News Using Text Mining Techniques

5.4

443

Sentiment Analysis

Sentiment analysis techniques were primarily applied to the pre-vaccine and postvaccine periods, but no significant output could be obtained. Later, these techniques were applied to periods. The first 6 months and the transition phase of the epidemic reflect almost the same feelings. Negative emotions predominate during these periods. The feelings of trust and fear remained at a close level. In the last 6 months of the epidemic, positive emotions predominate over negative emotions. The difference between fear and trust, which was close to each other in the first two periods, also widened. Confidence has prevailed over fear (Figs. 9, 10, 11, 12, 13 and 14).

Fig. 9. First 6 months count

Fig. 11. Transition phase count

Fig. 13. Last 6 months count

Fig. 10. First 6 months percentage

Fig. 12. Transition phase percentage

Fig. 14. Last 6 months percentage

444

E. Çağatay et al.

6 Conclusion The texts of the COVID-19 news from three different printed publications were examined. It is a fact that newspapers reflect their readers, there were variations in the time intervals covering the pandemic in the analyzed texts. First of all, the first months of the pandemic, the last months, and the transitional stage in the middle were examined with word clouds. By performing K-means clustering analysis on 360 news in the data set, the news of the pandemic period was divided into three main clusters. Sentiment analysis techniques were used for this text mining study. As a result of the examinations, it was seen that the news in the first six months of the epidemic and the transition phase generally reflected negative emotions. Contrary to these two periods, positive emotions came to the fore in the last six months of the epidemic. It has been determined that the vaccine and variants affect the issues that are on the agenda. In the future, the analyses conducted in this study could be extended so as to include COVID-19 related news published in the respected newspapers of different countries like Japan, France, Germany etc. Furthermore, a classification model can be developed based on the clusters suggested in this study.

References 1. Harapan, H., et al.: Coronavirus disease 2019 (COVID-19): a literature review. J. Infect. Public Health 13(5), 667–673 (2020) 2. Alamoodi, A., et al.: Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: a systematic review. Expert Syst. Appl. 167(114155) (2021) 3. Barkur, G., Vibha, G.B.K.: Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: evidence from India. Asian J. Psychiatry 51, 102089 (2020) 4. Kaur, C., Sharma, A.: Twitter sentiment analysis on Coronavirus using Textblob. EasyChair, pp. 2516–2314 (2020) 5. VijayGaikwad, S., Chaugule, A., Patil, P.: Text mining methods and techniques. Int. J. Comput. Appl. 85(17), 42–45 (2014) 6. Welbers, K., Van Atteveldt, W., Benoit, K.: Text analysis in R. Commun. Methods Meas. 11 (4), 245–265 (2017) 7. Ritchie, H.: Coronavirus (COVID-19) Vaccinations–Statistics and Research. Our World in Data. https://ourworldindata.org/covid-vaccinations?country=OWID_WRL. Accessed 5 Mar 2020 8. Kwartler, T.: Text mining in practice with R 74. John Wiley & Sons, Hoboken (2017) 9. Vijayarani, S., Janani, R.: Text mining: open source tokenization tools-an analysis. Adv. Comput. Intell. An Int. J. (ACII) 3(1), 37–47 (2016) 10. McNamee, P.: N-gram tokenization for Indian language text retrieval. In: Working Notes of the Forum for Information Retrieval Evaluation, pp. 12–14 (2008) 11. Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview, II. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7(6) (2017) 12. Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 515–524 (2002)

Analysis of Covid-19 News Using Text Mining Techniques

445

13. Pawar, A.B., Jawale, M.A., Kyatanavar, D.N.: Fundamentals of sentiment analysis: concepts and methodology. In: Sentiment Analysis and Ontology Engineering, pp. 25–48 (2016) 14. Wang, J., Su, X.: An improved K-Means clustering algorithm. In: 2011 IEEE 3rd International Conference on Communication Software and Networks, pp. 44–46. IEEE (2011)

Development of Digital Twin for Centrifugal Rotating Equipment Assets Nodirbek Yusupbekov1, Farukh Adilov2, and Arsen Ivanyan2(&) 1

Tashkent State Technical University, Tashkent, Uzbekistan LLC “XIMAVTOMATIKA”, Tashkent, Republic of Uzbekistan {Farukh.Adilov,Arsen.Ivanyan}@himavtomatika.uz 2

Abstract. Centrifugal Rotating equipment is very critical part of industrial enterprise and processes in oil and gas, chemical and petrochemical branches. They are primarily used to boost the operating pressure and transfer liquid and gas respectively in a safe and reliable manner. Safe, efficient, and reliable machines are required to maintain dependable manufacturing processes that can create saleable, on-spec product on time, and at the desired production rate. Centrifugal compressor performance has a significant impact on overall plant performance in terms of energy usage, efficiency, and throughput. Centrifugal pumps are by far the most used of the pump types, among all the installed pumps in a typical petroleum plant almost 80–90% are centrifugal pumps. And one of the most challenging aspects of a rotating machinery professional or operator’s job is deciding whether an operating machine should be shut down due to a perceived problem or be allowed to keep operating. The objective of this paper is to describe the research of authors in the creation of virtual digital twin for centrifugal rotating equipment assets category and ensure the right decision-making on the operation mode of rotating machine not by human but by intelligent IT-technology. This paper is one of original scientific papers from authors describing principal results on research and digital twin developments for exact different types of industrial assets. Keywords: Centrifugal rotating equipment
Centrifugal compressor
Centrifugal pumps
Performance
Digital twin
Efficiency
Reliability Operation mode




1 Introduction Centrifugal rotating equipment is one of the most distributed assets’ categories in industrial applications. Centrifugal compressors are used to transport the gases and to increase pressure of gases in process plants, power plants, and other industries. Compressor performance has a significant impact on overall plant performance in terms of energy usage, efficiency, and throughput. Centrifugal pumps are most used of the pump types because of their design simplicity, high efficiency, wide range of capacity, head, smooth flow rate, and ease of

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 446–455, 2022. https://doi.org/10.1007/978-3-031-09176-6_51

Development of Digital Twin for Centrifugal Rotating Equipment

447

operation and maintenance. Among all the installed pumps in a typical refinery or petrochemical complex, almost 80–90% are centrifugal pumps. Centrifugal compressors and pumps are the categories of centrifugal equipment considered as subjects of our research for digital twin implementation and will be presented in this paper. The objective and originality of the paper is to describe innovative technology of industrial digitization related to the increase of efficiency of real categories of process equipment. This paper is one from the set of papers in our focus which as per our scientific-research plan will create fully digitized portfolio of solutions for whole set of process equipment assets of chemical and petrochemical industries. Digital twin of centrifugal equipment assets is designed and developed to monitor, maintain, and track the reliability of assets in a plant. It can be used by reliability engineers to monitor reliability of centrifugal equipment and identify any failure or fault. The monitoring and quick identification of failures helps in taking decisions and planning for maintenance [1]. By taking advantage of advanced “digital twin” technology enabling the most advanced monitoring, analytical, and predictive capabilities, process engineers can implement around-the-clock monitoring of plant data, and ongoing operational health checks and recommendations to close performance gaps [2]. Digital twin provides a single platform for performance monitoring and vibration monitoring for different departments like operations, maintenance, and reliability. For example, cavitation in a centrifugal pump is caused due to high flow and low-pressure head. When the operations department increases the production by increasing the flow rate beyond the best efficiency point (BEP), then the action induces cavitation and leads to a subsequent rise in vibration. If the rise in the vibration is very large, then the operator can view the indication and change the operating conditions of the pump. The reliability department views the severity of the cavitations, and the secondary damage caused to the equipment. The maintenance department schedules maintenance actions and orders spare equipment to plan the shutdown of the maintenance activity [3–5]. Digital Twin plays a vital role in synergizing various functional divisions of an industry such as maintenance department, operations department, and reliability department. The application provides various operating conditions of an equipment to the operator. It also consists of a calculation engine that helps the operator to calculate efficiency, plant load factor, and failure modes depending on the operating conditions. In the real plant digital twin of centrifugal equipment can use the wireless condition-based monitoring to monitor the reliability of rotating assets (pumps, compressors) using wireless devices. The approach of using the wireless devices effectively manages the maintenance cost of the equipment. The organization of this papers starting from this Introduction section which provides overview of centrifugal equipment assets and objectives of their digitization, continuing with Methods and Results of our research including key parameters of developed mathematical models as well as human-machine interface of software application used in asset digitization solution, then passing to Conclusion section with main outcomes of research and future investigations plan, ending with References and Literature review section referring to sources used during writing of this paper.

448

N. Yusupbekov et al.

2 Methods and Results As first step of our research we started to investigate from simpler models of centrifugal pumps. Centrifugal pumps are widely used because of their design simplicity, high efficiency, wide range of capacity, head, smooth flow rate, and ease of operation and maintenance. Due to the variation in flow versus pressure & light to moderate viscous fluid handling ability centrifugal pumps are more flexible and commonly used in process & pipeline applications. Centrifugal pumps can provide a wide range of flow rate over a certain pressure range. Hence, pressure generated by centrifugal pump depends on the flow rate of the pump. The operating and maintenance cost of centrifugal pumps are lower compared to Positive Displacement pumps. Around 20% of the world's electrical energy is used by pumps. If a pump fails, it can cause an entire plant to shut down and the result in losses. The following measured parameters are monitored by digital twin of centrifugal pump as it has following impact on performance and health of the centrifugal pump if not operated in operating range (please refer to Fig. 1 below with illustration of general view of our digital twin model based on specialized Asset Sentinel application). – Flow rate: The centrifugal pump curve has high and low flow limits, which can cause significant mechanical damage to the pump if not avoided. At the low flow, flow recirculation can damage a pump, while at the high flow, excessive NPSHR, horsepower and choke flow can result in mechanical damage to impellers, casing, shaft, bearings, and seals. API 610 states that a Centrifugal pump should be operated within preferred operating region of 70–120% of Best Efficiency Point (BEP). – Suction Pressure: When a pump is under low pressure or vacuum conditions, suction cavitation occurs. The pump is being “starved” or is not receiving enough flow. When this happens, bubbles or cavities will form at the eye of the impeller. As the bubbles carry over to the discharge side of the pump, the fluid conditions change, compressing the bubble into liquid and causing it to implode against the face of the impeller. – Suction and Discharge temperature: The significant increase in discharge temperature over the suction temperature indicates degradation in pump efficiency. It may occur due to improper operating conditions or health issue of the pump. – Discharge Pressure: A lower discharge pressure lower than the desired value indicates either high flow or the inability of pump to fulfill the head requirement. – Suction and Discharge temperature: The temperature of the fluid at the pump inlet is usually of most concern as vapor pressure can have a significant effect on pump performance. The significant increase in discharge temperature over the suction temperature indicates degradation in pump efficiency. It may occur due to improper operating conditions or health issue of the pump. – Tank level in lube oil system: Loss in lube oil tank level can lead to loss of lubrication and pump trip scenario. The reservoir may be pressurized or vented.

Development of Digital Twin for Centrifugal Rotating Equipment

449

– Supply pressure and temperature in lube oil system: Temperatures and pressures are measured at all important locations in the system, including temperatures from oil sumps, return lines from bearings, gears and other mechanical components. Temperatures and pressures are often recorded on the suction and discharge sides of each compression stage to offer the operator a sense of the health of the system. The readings can be taken locally or transmitted to a monitoring station. The flow of oil to each bearing is regulated individually by orifices, particularly important for lubrication points requiring different pressures. – Return temperature in lube oil system: Heat generated by friction in the bearings is transferred to the cooling medium in the oil coolers. Air-cooled oil coolers may be employed as an alternative to water-cooled oil coolers. – Differential pressure across Filters in lube oil system: Filters clean the lube oil before it reaches the lubrication points, and a differential pressure gauge monitors the degree of fouling (flow restriction) of the filters. – Tank Level in lube oil system: The oil in the overhead tank compensates for pressure fluctuations and serves as a rundown supply if pressure is lost. If the level in the tank falls excessively, a level switch shuts down the pump. A moderate oil temperature is maintained by a constant flow of oil through the overhead tank. – Seal Pressure in lube oil system: The seal oil supply system must be capable of supplying higher seal oil pressure than the highest possible pump process pressure inboard of the oil seal - which may include settle out, or pump discharge pressure. The seal pressure at supply and at all important locations of the seal system.

Fig. 1. Overall view of Digital Twin for centrifugal pump asset.

The following are Key Performance indicators (KPIs) to be monitored to optimize the Energy consumption in Digital Twin model:

450

N. Yusupbekov et al.

– Power Deviation: This KPI represents the deviation of Operating power with the expected at current operating conditions. % Power Deviation ¼ ðOperatingShaftPower  Design PowerÞ=Design Power  100

ð1Þ

– Efficiency Deviation: This KPI represents the deviation of Operating efficiency with the expected efficiency at current operating conditions. Efficiency Deviation ¼ ðOperating efficiency  Expected efficiencyÞ

ð2Þ

– Degradation Loss: Degradation in a pump can be defined as the pump operating outside of acceptable operating limits. Degradation Loss ¼ ðOperating Hydraulic Power=Operating EfficiencyÞ  Expected Power:

ð3Þ

% Degradation Loss ¼ ðDegradation Loss=Electric PowerÞ  100;

ð4Þ

where, Expected Power is the design power which is available from the pump performance curve. – BEP Loss: BEP is the best efficiency point from the pump performance curve. It is a constant value for a pump. The number of losses incurred by operating pump away from BEP value are captured as BEP losses. BEP Loss ¼ ðExpected Efficiency at BEP  Expected EfficiencyÞ  Motor Power  Motor Efficiency

ð5Þ

%BEP Loss ¼ ðBEP Loss=Electric PowerÞ  100

ð6Þ

– Recycle Loss: Recycle loss in a pump can be defined as the loss occurred due to recycle of the operating fluid. Recycle loss ¼ ðHydraulic Power  Operating PowerÞ  Operating Efficiency ð7Þ Recycle loss% ¼ ðRecycle loss=Electric PowerÞ  100

ð8Þ

The following indicators help to know the overall performance of the centrifugal pump: 1. Operating Head The pressure of the liquid can be stated in terms of meters of head of the liquid column. As in case of volumetric flow rate, the head generated by the pump for a single point of operation is the same for any liquid. Depending on the density of the liquid what changes is the reading on the pressure gauge. The difference between the discharge head and the suction head is termed as Operating Head.

Development of Digital Twin for Centrifugal Rotating Equipment

451

Operating Head HðmÞ ¼ ðPd  PsÞ  10=q

ð9Þ

Where, Pd = Discharge pressure (kg/cm2). Ps = Suction pressure (kg/cm2). q = Specific gravity of the liquid. 2. Expected Head Expected head is the actual design head which is derived from the pump performance curve at current volumetric flow rate. 3. Head Deviation %Head Deviation ¼ ððOperating Head  Expected HeadÞ=Expected HeadÞ  100 ð10Þ 4. Hydraulic Power Power is consumed by a pump to move and increase the pressure of a fluid. The power requirement of the pump depends on several factors including the pump and motor efficiency, the differential pressure and the fluid density, viscosity, and flow rate. The hydraulic power, which is also known as operating or absorbed power, represents the energy imparted on the fluid being pumped to increase its velocity and pressure. Hydraulic Power PH ðKWÞ ¼ ðQ  q  g  HÞ=ð3:6  106Þ

ð11Þ

Where, Q = Volumetric flow in m3/h. q = Liquid density in Kg/m3 at pumping temperature. g = Gravitational acceleration in m/s2. H = Differential Head in m (meters of liquid column). 5. Operating Shaft Power Operating Shaft Power is the power delivered by the driver at the shaft of the pump. Operating Shaft Power PSðKWÞ ¼ PE  g M

ð12Þ

Where, PE – Electrical Power (kW). ηM = Motor Efficiency (%). 6. Expected Power Expected power is the design power which is derived from the pump performance curve at operating volumetric flow rate. %Power Deviation ¼ ððOperating Power  Expected PowerÞ=Expected PowerÞ  100

ð13Þ 7. Electric Power The power delivered to the electric motor connected to pump is called Electrical Power. Increase in electric power consumption at same load indicates degradation in pump efficiency. Electric Power is input to the model for pump efficiency

452

N. Yusupbekov et al.

calculation. If direct power indication is not available, then it can be calculated by using current and voltage to motor. pffiffiffi Electric Power PEðKWÞ ¼ ð 3*V  I  PFÞ=1000

ð14Þ

Where, V = Voltage measured in volts. I = Current measured in ampere. PF = Power Factor. 8. Operating NPSHA The Net Positive Suction Head available (NPSHa) is the pressure at the pump suction, above the vapor pressure of the liquid, expressed as head of liquid. It is the characteristic of Pump Inlet piping, fluid temperature and vapor pressure. NPSHa (available) must be greater than NPSHr (required) for the pump system to operate without cavitating and hence trouble-free operation of the pump. NPSHa ¼ Hss  Hfs  Hvp ¼ P þ Z  Hfs  Hvp

ð15Þ

where Hss = Static Suction Head, expressed in metre of Liquid Column (mLC). = P + Z, if the pump is going to be installed below the free surface of liquid. = P−Z, if the pump is going to be installed above the free surface of liquid. P = Absolute pressure over free surface of liquid at source. Z = Vertical distance between free surface of liquid at source and centerline for horizontal pump = Vertical distance between free surface of liquid at source and suction eye impeller for vertical pump. Hfs = Friction loss in suction line, expressed in metre of Liquid Column (mLC). Hvp = vapour pressure of liquid at suction Temperature, expressed in metre of Liquid Column (mLC). 9. Expected NPSHA Expected NPSH is the required NPSH which is derived from the pump performance curve at operating volumetric flow rate. 10. NPSH Deviation %NPSH Deviation ¼ ððOperating NPSHa  Expected NPSHÞ=Expected NPSHÞ  10

ð16Þ

11. Operating Efficiency Pump efficiency is defined as the ratio of water horsepower output from the pump to the shaft horsepower input for the pump. Operating Efficiency ¼ ðOperating Power=Operating Shaft PowerÞ  100

ð17Þ

12. Expected Efficiency Expected efficiency is the design efficiency which is derived from the pump performance curve at operating volumetric flow rate.

Development of Digital Twin for Centrifugal Rotating Equipment

453

13. BEP Efficiency The Best Efficiency Point (BEP) is defined as the flow at which the pump operates at the highest or optimum efficiency for a given impeller diameter. When we operate a pump at flows greater than or less than the flow designated by the BEP, we call this “operating pumps away from the Best Efficiency Point”. 14. BEP Flow The Best Efficiency Point (BEP) Flow is defined as the flow at which the pump operates at the highest or optimum efficiency for a given impeller diameter. When we operate a pump at flows greater than or less than the flow designated by the BEP, we call this “operating pumps away from the BEP”. As per the affinity law, model calculates BEP flow at different speed. The performance of a centrifugal pump is characterized by two sets of performance curves. The first set consists of curves of Power versus Flow rate. The second set consists of curves of Head versus Flow rate. Digital Twin facilitates indication of operating points on performance curves. It helps to monitor centrifugal pump performance against design curves and analyzing upset scenarios. Please refer to Fig. 2 illustrating performance curve of centrifugal pump.

Fig. 2. Performance curve of centrifugal pump

3 Conclusion The main objective of developed digital twin is to increase reliability of asset keeping monitored its’ operability and avoid any failure or fault in its operation.

454

N. Yusupbekov et al.

The fault management can inform operators of any violations of the nominal pump operating range and deviations from the expected characteristic and make this data available for further processing through the historian. The fault symptoms or models are used to: • Warn against potential damage to pumps under unfavorable operating conditions. • Provide early warning of damages to the pump. Contribution of this paper provides intelligent mechanism which allows to digitize fully real industrial solution [6–9] and avoid failures and faults with following messages, alarms, and notifications to warn operating personnel of unfavorable operating conditions: • Low pump efficiency: It is determined by means of the deviation of the operating pump efficiency from the design efficiency. • Cavitation: It is determined by means of the calculated NPSH value, early warning when a minimum NPSH reserve is undershot: The minimum NPSH is the design NPSH, or user configured value. • High gas content: It is determined by means of the reduction in delivery height or head. • Blockage: It is determined based on a limit value for an electrical power being undershot. • Dry running: It is determined based on a (second, lower) limit value for an electrical power being undershot. • Incorrect direction of rotation (the motor was connected incorrectly and rotates in the wrong direction): It is determined when the delivery height or head falls significantly (>40%) but with only a slight deviation ( 0 only for n = 1, or π101 = π110 = 0. Systemic Restriction: Guaranteed Utilization: ensures that the fraction of the time the system is functional is at least a pre-specified value, 0 < k ≤ 1, in the long run. Specifically, 1 − π201 ≥ k. For industrial printers or manufacturing equipments that are serviced by the original equipment manufacturers, this rate is vital [9], since in many customer contracts this rate is used as a metric for liabilities of the provider. Next, we proceed with our numerical analysis.

3

Numerical Analysis

For the numerical analysis L1 = 1, L2 = 3 and cp = 100. (Note that the model construction we have in Sect. 2 implicitly indicates that L1 = 1.) The other parameters we used for the numerical analysis are as follows: cc /cp = {1, 2, 3 . . . , 15}, and, α = {0.01, 0.05, 0.1}. Hence we have 45 test instances. We report (i) the long run expected maintenance cost rate of the optimal policy, i.e., the objective function value (Fig. 3(a)); (ii) the number of yellow signals

Optimal Intervention Planning for Partially Observable Systems

503

before intervention under the optimal policy (Fig. 3(b)); (iii) the optimality gap in Naive Policy #1, (Fig. 3(c)); (iv) the optimality gap in Naive Policy #2, (Fig. 3(d)).

Fig. 3. The effects of cp on (a) the long run expected maintenance cost rate of the optimal policy (b) the number of yellow signals before intervention under the optimal policy (c) the optimality gap in Naive Policy #1 (d) the optimality gap in Naive Policy #2 (Color figure online)

Our observations in the numerical study results are all intuitive. We observe that the long run expected maintenance cost rate increases in both cc /cp and α converging to the cost rate of Naive Policy 2, αcp /1 + α, see Fig. 3(a). Likewise, the number of yellow signals received before intervention decreases in cc , converging to 1, see Fig. 3(b). The optimality gap in Naive Policy #1 increases, whereas the optimality gap in Naive Policy #2 decreases both in cc /cp and in α (see Figs. 3(c) and 3(d)) since as the cost ratio increases the implementation of the Naive Policy #1 becomes more costly compared to the Naive Policy #2. Hence, implementation of preventive maintenance interventions is preferable in early yellow states. In terms of α: the probability of receiving a red signal increases in α, therefore in the optimal policy preventive maintenance interventions are performed at early stages, resembling the Naive Policy #2, where the reverse is true for the Naive Policy #1.

504

4

O. Karaba˘ g et al.

Conclusion

We consider a single component system prone to failures with partially observable system status and model the problem as Markov Decision Process (MDP). We describe the nature of the problem and provide the MDP representation. Through a linear programming formulation we aim to obtain optimal maintenance policy by minimizing the total expected maintenance cost rate. We also provide some sub-optimal but easy-to-implement policies. In a numerical study we present our findings. We observe that the long run expected maintenance cost rate increases in both cc /cp and α converging to the cost rate of Naive Policy 2. Likewise, the number of yellow signals received before intervention decreases in cc , converging to 1. This work is an initial attempt to generalize the introduced approach to the multiple component systems, where the modeling complexity will be higher, which we plan to study as the future work.

References 1. Alaswad, S., Xiang, Y.: A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliab. Eng. Syst. Saf. 157, 54–63 (2017) 2. Keizer, M., Olde, C.A., Flapper, S.D.P.: Condition-based maintenance policies for systems with multiple dependent components. Eur. J. Oper. Res. 261(2), 405–420 (2017) 3. Neves, M.L., Santiago, L.P., Maia, C.A.: A condition-based maintenance policy and input parameters estimation for deteriorating systems under periodic inspection. Comput. Ind. Eng. 6(3), 503–511 (2011) 4. Giorgio, M., Guida, M., Pulcini, G.: An age-and state-dependent Markov model for degradation processes. IIE Trans. 43(9), 621–632 (2011) 5. Lin, X.S., Basten, R.J.I., Kranenburg, A.A., Van Houtum, G.J.: Condition-based spare parts supply. Reliab. Eng. Syst. Saf. 168, 240–248 (2017) 6. Ohnishi, M., Kawai, H., Mine, H.: An optimal inspection and replacement policy under incomplete state information. Eur. J. Oper. Res. 27(1), 117–128 (1986) 7. Ohnishi, M., Morioka, T., Ibaraki, T.: Optimal minimal-repair and replacement problem of discrete-time Markovian deterioration system under incomplete state information. Comput. Ind. Eng. 27(1–4), 409–412 (1994) 8. Arts, J., Basten, R.: Design of multi-component periodic maintenance programs with single-component models. IISE Trans. 50(7), 606–615 (2018) 9. Karaba˘ g, O., Eruguz, A.S., Basten, R.J.I.: Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system. Reliab. Eng. Syst. Saf. 200, 106955 (2020) 10. AlDurgam, M.M., Duffuaa, S.O.: Optimal joint maintenance and operation policies to maximise overall systems effectiveness. Int. J. Prod. Res. 51(5), 1319–1330 (2013) 11. Jiang, R., Kim, M.J., Makis, V.: Availability maximization under partial observations. OR Spectr. 213(3), 691–710 (2013)

Combined Approach to Evaluation of Microcredit Borrowers Solvency Elchin Aliyev(&)

, Elmar Aliev

, and Adila Ali

Institute of Control Systems of ANAS, B. Vahabzadeh street 9, AZ1141 Baku, Azerbaijan [email protected]

Abstract. The assessment of the client’s solvency is an integral part of the work of any commercial bank or microfinance organization to determine the possibility of issuing a microloan to a particular borrower. A preliminary analysis of the solvency and credit history of a potential microcredit borrower allows to assess in advance the risks of non-repayment on time or the probabilities of timely repayment of a bank loan. Even though numerous scientific studies (including scoring analysis methods) are devoted to solving such problems, this article discusses an unconventional approach to the multi-criteria assessment of the solvency of potential microcredit borrowers, which is based on a fuzzy analysis of their solvency indicators. In particular, the proposed fuzzy inference system in combination with existing statistical methods for data analysis can serve as an additional effective option for an information system for supporting credit decision making. This approach was tested on the example of credit histories of ten arbitrary microcredit borrowers and was compared with the corresponding solvency assessments obtained using scoring analysis, Pareto and Bord methods. The practice of bank lending has shown that the combined approach to assessing the solvency of potential borrowers of microcredits is more balanced, that is, it allows to identify a group of people more reliably with high credit discipline and those who create an area of increased credit risk. Keywords: Solvency indicator Fuzzy inference system


Microcredit
Pareto rule
Bord method


1 Introduction Scoring systems and statistical evaluation methods used by commercial banks to assess risks in the field of microcredit do not and cannot reflect cause-effect relations between the objective and subjective characteristics of a potential borrower, on the one hand, and its level of solvency on a certain date, on the other. So, for example, in works [1, 2], some fuzzy methods for assessing the solvency of natural parsons based on the use of a fuzzy inference system (FIS) are considered. In addition, in [3, 4], assessing the solvency of potential borrowers of microcredits is carried out based on the use of a fuzzy method of weighted maximin convolution of qualitative criteria, which provides for fuzzification of applicants’ personal data, i.e., representing these criteria by appropriate fuzzy sets. Based on the introduced fuzzy formalisms, the presented article © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 505–513, 2022. https://doi.org/10.1007/978-3-031-09176-6_58

506

E. Aliyev et al.

considers a multi-criteria assessment of potential borrowers of microcredits by the use of the FIS.

2 Problem Definition Currently, various scoring systems are actively used by banks to assess the level of solvency of their customers. In a matter of minutes, scoring system can assess the credit history of any potential borrower on a specific date. In the case of granting microcredits, data directly from the applications of potential borrowers, and, if necessary, from other available sources (for example, from social networks) are used as initial information. According to [5], each potential microcredit borrower solvency is evaluated based on the following scoring system of indicators: • x1 – age, which estimated in the interval [0.1, 0.3]: the older the applicant, the more reliable he is; • x2 – sex, which estimated as follows: 0 points for male, and 0.4 points for female, since the woman is considered as a more responsible borrower and less prone to risks and adventures; • x3 – settledness, which estimated in the interval [0.042, 0.42]: the evaluate directly depends on the time of permanent residence at the provided address; • x4 – work risks, which estimated as follows: 0 points – for applicants working in hazardous productions, 0.16 points – for applicants working under moderate risk to life, 0.55 points – for applicants working in safe productions; • x5 – work in large companies, which estimated as follows: 0.21 points are added to the overall grade of applicant, if he (she) works in a large enterprise; • x6 – seniority, which estimated in the interval [0.059, 0.59]: the longer a applicant works, the more he (she) is reliable; • x7 – assets, which estimated as follows: 0.45 points are separately added, for example, for the presence of insurance, a deposit account and property. As is easy to see, to approve microcredit, the potential borrower must get a final grade of at least 1.25 points, while the maximum score is 3.82. Suppose that a commercial bank considers applications of 10 natural parsons aj (j = 1  10) for microcredits. Verifying the personal data of the applicants, the bank manager generates preliminary assessment data according to the criteria xi (i = 1  7). As a result, the scoring system calculates points, which are summarized in Table 1. The problem is to use the FIS to identify the best applicant for a microcredit among applicants. Considering the preliminary data of the above-mentioned scoring analysis, it is necessary to formulate cause-effect relations between the quality criteria of the applicant’s credit history and its level of solvency on a specific date in the notation of fuzzy sets. In other words, it is necessary to adapt the FIS so that in the process of processing loan applications for microcredit, it could quickly provide aggregation of fuzzy conclusions relative to the solvency of applicants.

Combined Approach to Evaluation of Microcredit Borrowers Solvency

507

Table 1. Preliminary data generated by the scoring system Applicant Evaluation x2 x1 a1 0.161 0.0 a2 0.222 0.4 a3 0.117 0.4 a4 0.185 0.0 a5 0.298 0.4 a6 0.213 0.0 a7 0.109 0.0 a8 0.205 0.4 a9 0.171 0.4 a10 0.255 0.0

criteria x3 0.044 0.132 0.231 0.374 0.268 0.412 0.172 0.389 0.303 0.253

x4 0.00 0.55 0.16 0.55 0.16 0.00 0.55 0.16 0.00 0.16

x5 0.00 0.21 0.00 0.00 0.00 0.21 0.00 0.21 0.00 0.21

x6 0.061 0.326 0.350 0.248 0.069 0.473 0.424 0.164 0.504 0.261

x7 0.45 1.35 0.90 0.45 0.45 1.35 0.45 0.90 1.35 0.90

3 Multi-criteria Assessment of the Solvency Using the FIS In the process of microcrediting, the management of a commercial bank subscribes to certain rules regarding the assessment of the satisfactory solvency of a potential borrower. These rules are formulated in the form of the following judgments: e1: “If the borrower’s age is preferred and borrower has lived at the specified address for a long time, borrower’s work experience is preferable, and borrowers also owns significant assets, then the borrower is satisfactory from the point of view of solvency”; e2: “If, in addition to the above, the potential borrower works in an enterprise that is safe from the point of view of risk to life, then, in this case, borrower is more than satisfactory from the point of view of solvency”; e3: “If, in addition to the requirements given in e2, the borrower is a woman working in a fairly large company, then she is perfect (as fully complying with all credit requirements)”; e4: “If the borrower’s age is preferred and borrower has lived at the specified address for a long time, borrower’s work experience is preferable, and borrower works in an enterprise that is safe from the point of view of risk to life, and the borrower is a woman working in a fairly large company, then the applicant is very satisfactory from the point of view of solvency”; e5: “If the age of the borrower is not suitable and borrower lives at the specified address for a short time, however, borrower works in a large company and without much risk to its life, already has a solid work experience, but does not have certain assets, then borrower is still satisfactory from the point of view of solvency”; e6: “If the borrower lives at the specified address for a short time, does not have a solid permanent work experience and does not have any assets, then borrower is unsatisfactory from the point of view of solvency”.

508

E. Aliyev et al.

As a result of the analysis of these information fragments the full set of input and output characteristics of the future model is established in the form of the corresponding terms of the linguistic variables xi (i = 1  7) and the linguistic variable y = ”Satisfaction of the borrower’s solvency”, which are summarized in the Table 2. Table 2. Input and output characteristics of the fuzzy model Criterion x1 x2 x3 x4 x5 x6 x7 y

Name Age Sex Settled way of life Work risks Day-to-day work Work experience Availability of assets Borrower satisfaction

Terms F1 = PREFERRED, ¬F1 = UNPREFERRED F2 = DESIRED F3 = LONG-CONTINUED, ¬F3 = SHORT-LIVED = =

ACCEPTABLE

F5

F6

=

LONG,

¬F6 =

SHORT

F7

=

SIGNIFICANT,

¬F7 =

UN

=

UNSATISFACTORY, S

F4

[0, 0.55] {0, 0.21}

PREFERABLE

THAN SATISFACTORY, VS P

=

Universe [0.1, 0.3] {0, 0.4} [0.042, 0.42]

[0.059, 0.59] [0, 1.35]

INSIGNIFICANT

= SATISFACTORY, MS = = VERY SATISFACTORY,

MORE

{0, 0.1, …, 1}

PERFECT

The evaluation concepts xi (i = 1  7) are considered as qualitative criteria, and the numerical estimations of the solvency of alternative microcredit borrowers are the degrees of compliance with these criteria. The set of alternatives (applicants) is denoted as A = {a1, a2, …, a10}, and the set of criteria is denoted as F = {F1, F1, …, Fm}, where each criterion is the fuzzy subset of the universe A in the form of Fi = {µFi(a1)/ a1, µFi(a2)/a2, …, µFi(a10)/a10}. As membership functions restoring this kind of fuzzy sets, following Gaussian functions are chosen [6]: µ(u) = exp[-(u – ui)2/ri2], u 2 [0, ui], where ui is the maximum score provided for in the i-th scoring point; r2i ¼ Pn 2 k¼1 ðu  ui Þ =n is the standard deviation. For the fuzzification of the qualitative evaluated criterion F1 = PREFERRED (age), the Gaussian function with the density r12 = 0.0138 was selected empirically. Thus, the evaluative concepts, as criteria for assessing the solvency of borrowers, can be described in the form of the following corresponding fuzzy sets: •

(age): F1 = {0.2475/a1, 0.6442/a2, 0.0889/a3, 0.3845/a4, 0.9997/a5, 0.5787/a6, 0.0716/a7, 0.5209/a8, 0.3004/a9, 0.8639/a10}; • DESIRED (sex): F2 = {0.0081/a1, 1/a2. 1/a3, 0.0081/a4, 1/a5, 0.0081/a6, 0.0081/a7, 1/ a8, 1/a9, 0.0081/a10}; • LONG-CONTINUED (settled way of life): F3 = {0.0564/a1, 0.1851/a2, 0.4837/a3, 0.9579/a4, 0.6251/a5, 0.9987/a6, 0.2863/a7, 0.9806/a8, 0.7570/a9, 0.5672/a10}; PREFERRED

Combined Approach to Evaluation of Microcredit Borrowers Solvency

•

509

(work risks): F4 = {0.0528/a1, 1/a2, 0.2279/a3, 1/a4, 0.2279/a5, 0.0528/ a6, 1/a7, 0.2279/a8, 0.0528/a9, 0.2279/a10}; • PREFERABLE (day-to-day work): F5 = {0.0555/a1, 1/a2, 0.0555/a3, 0.0555/a4, 0.0555/ a5, 1/a6, 0.0555/a7, 1/a8, 0.0555/a9, 1/a10}; • LONG (work experience): F6 = {0.0565/a1, 0.4888/a2, 0.5534/a3, 0.3008/a4, 0.0616/ a5, 0.8688/a6, 0.7535/a7, 0.1551/a8, 0.9269/a9, 0.3290/a10}; • SIGNIFICANT (availability of assets): F7 = {0.2742/a1, 1/a2, 0.7236/a3, 0.2742/a4, 0.2742/a5, 1/a6, 0.2742/a7, 0.7236/a8, 1/a9, 0.7236/a10}. ACCEPTABLE

To describe the output characteristics of the model the appropriate universe is chosen as U = {0, 0.1, 0.2, …, 1}. Then, according to [7], 8u 2 U the terms of the y from the right-hand sides of the rules e1  e6 can be described in the form of the following fuzzy sets with the corresponding membership functions: S = SATISFACTORY: lS(u) = u; MS = MORE THAN SATISFACTORY: lMS(u) = u(1/2); VS = VERY SATISFACTORY: lVS(u) = u2; P = PERFECT: lP(u) = 1, if u = 1 and lP(u) = 0, if u < 1; US = UNSATISFACTORY: lUS(u) = 1–u. Thus, considering the introduced designations, the rules e1  e6 can be represented as follows: e1: (x1 = F1) & (x3 = F3) & (x6 = F6) & (x7 = F7) ) (y = S); e2: (x1 = F1) & (x3 = F3) & (x4 = F4) & (x6 = F6) & (x7 = F7) ) (y = MS); e3: (x1 = F1) & (x2 = F2) … & (x6 = F6) & (x7 = F7) ) (y = P); e4: (x1 = F1) & (x2 = F2) & (x3 = F3) & (x4 = F4) & (x5 = F5) & (x6 = F6) ) (y = VS); e5: (x1 = :F1) & (x3 = :F3) & (x4 = F4) & (x5 = F5) & (x6 = F6) & (x7 = :F7) ) (y = S); e6: (x3 = :F3) & (x6 = :F6) & (x7 = :F7) ) (y = US). Applying the intersection rule for fuzzy sets from the left sides and Lukasiewicz’s implication µ(a, u) = min{1, 1–µ(a) + µ(u)}, the fuzzy relations are formed in the form of corresponding matrices. Intersection of these matrices provides a general functional solution in the form of the following matrix:

510

E. Aliyev et al.

According to [6, 7], a fuzzy conclusion regarding the solvency of the j-th potential borrower is reflected in the form of the fuzzy subset Ek of the universe U with the corresponding values of the membership function from the j-th row of the matrix R. For numerical estimates of the borrower solvency the defuzzification is applied. For example, for the fuzzy conclusion relative to the estimate of the 1st applicant solvency: E1 = {0.9436/0, 0.9919/0.1, 0.9919/0.2, 0.9742/0.3, 0.8742/0.4, 0.7742/0.5, 0.6742/0.6, 0.5742/0.7, 0.4742/0.8, 03742/0.9, 0.2742/1}, establishingP the level-sets E1a and calculating their corresponding cardinal numbers as MðEa Þ ¼ nj¼1 rj =n, we have: • • • • • • • • • •

for 0 < a < 0.2742: Da = 0.2742, E1a = {0, 0.1, 0.2, …, 0.9, 1}, M(E1a) = 0.50; for 0.2742 < a < 0.3742: Da = 0.1, E1a = {0, 0.1, …, 0.8, 0.9}, M(E1a) = 0.45; for 0.3742 < a < 0.4742: Da = 0.1, E1a = {0, 0.1, …, 0.7, 0.8}, M(E1a) = 0.40; for 0.4742 < a < 0.5742: Da = 0.1, E1a = {0, 0.1, …, 0.6, 0.7}, M(E1a) = 0.35; for 0.5742 < a < 0.6742: Da = 0.1, E1a = {0, 0.1, …, 0.5, 0.6}, M(E1a) = 0.30; for 0.6742 < a < 0.7742: Da = 0.1, E1a = {0, 0.1, 0.2, 0.3, 0.4, 0.5}, M (E1a) = 0.25; for 0.7742 < a < 0.8742: Da = 0.1, E1a = {0, 0.1, 0.2, 0.3, 0.4}, M(E1a) = 0.20; for 0.8742 < a < 0.9436: Da = 0.0694, E1a = {0, 0.1, 0.2, 0.3}, M(E1a) = 0.15; for 0.9436 < a < 0.9742: Da = 0.0306, E1a = {0.1, 0.2, 0.3}, M(E1a) = 0.20; for 0.9742 < a < 0.9919: Da = 0.0177, E1a = {0.1, 0.2}, M(E1a) = 0.15. Then, according to [8], the numerical estimate is obtained in the following form: Z

amax

FðE1 Þ ¼ ð1=amax Þ 0

Z

0:9919

MðE1a Þda ¼ ð1=0:9919Þ

MðE1a Þda ¼ ½0:2742
0:50 þ 0:1
0:45

0

þ 0:1
0:40 þ . . . þ 0:0694
0:15 þ 0:0306
0:20 þ 0:0177
0:15=0:9919 ¼ 0:3542:

Numerical estimates of solvency for other potential borrowers are established in the same way: F(E2) = 0.5926, F(E3) = 0.4826, F(E4) = 0.5234, F(E5) = 0.4645, F (E6) = 0.5967, F(E7) = 0.4823, F(E8) = 0.5692, F(E9) = 0.5485, F(E10) = 0.5090.

4 Multi-criteria Assessment of the Solvency Using the Methods of Pareto and Bord To form an advances portfolio under itself capabilities, a commercial bank selects the most creditworthy borrowers using the Pareto rule [8]. According to this rule, at the 1st stage all applicants are ranked by solvency indicators xi (i = 1  7). For the borrowers under consideration (see Table 1), this ranking is summarized in Table 3.

Combined Approach to Evaluation of Microcredit Borrowers Solvency

511

Table 3. Ranking of alternative microcredit borrowers by solvency indicators Order Solvency indicators of comparative estimation borrowers x1 x2 x3 x4 x5 1 a5 a5 a6 a4 a2 2 a10 a2 a8 a7 a8 3 a2 a8 a4 a2 a10 4 a6 a9 a9 a8 a6 5 a8 a3 a5 a5 a4 6 a4 a10 a10 a10 a7 7 a9 a6 a3 a3 a5 8 a1 a4 a7 a6 a3 9 a3 a1 a2 a9 a9 10 a7 a7 a1 a1 a1

of x6 a9 a6 a7 a3 a2 a10 a4 a8 a5 a1

x7 a9 a6 a2 a3 a10 a8 a7 a4 a5 a1

Further, a comparative analysis of borrowers is carried out by establishing pairwise preferences according to the principle: for “applicant ak”, the sign “-” is set in the cell, where row xi and column aj intersect, because the value of the indicator xi for the applicant ak is less than for the applicant aj, and at the intersection with the column ar there is a sign “+”, because the value of the indicator xi for the applicant ak is greater than for the applicant ar. If the indicators of applicants are equal, then the sign “0” is fixed. According to the Pareto rule, if columns do not contain the sign “-”, then corresponding applicants are preferred. For example (see Table 4), for applicants a2 and a8, column a1 contains only signs “+”, which means that applicants a2 and a8 are preferable to applicant a1. Similar pairwise comparisons have shown that other applicants are also preferred over a1. Therefore, excluding the alternative a1, the Pareto rule performs pairwise comparisons of the remaining applicants, which is easily simulated on a computer due to the triviality of the algorithm. The Pareto rule provides more credit decisions than it is necessary. Therefore, to complete the estimation process of the potential borrower solvency the Bord method is used. According to this method the alternative applicants are ranked for each indicator on a ten-point system in order of descending with the appropriation of the corresponding ranks. The total rank is determined for each credit decision (see Table 5). As a result, the applicant with the highest summarized rank is the best.

512

E. Aliyev et al. Table 4. Table of pairwise preferences of alternative applicants a2 x1 x2 x3 x4 x5 x6 x7 a8 x1 x2 x3 x4 x5 x6

a1 + + + + + + + a1 + + + + + +

a3 + 0 − + + − + a2 − 0 + − 0 −

a4 + + − 0 + + + a3 + 0 + 0 + −

a5 − 0 − + + + + a4 + + + − + −

a6 + + − + 0 − 0 a5 − 0 + 0 + +

a7 + + − 0 + − + a6 − + − + 0 −

a8 + 0 − + 0 + + a7 + + + − + −

a9 + 0 − + + − 0 a9 + 0 + + + −

a10 − + − + 0 + + a10 − + + 0 0 −

Table 5. Total ranks of compared potential applicants Applicant Criteria for comparative assessment of applicants x1 x2 x3 x4 x5 x6 x7 a1 3 2 1 1 1 1 1 a2 8 9 2 8 10 6 8 a3 2 6 4 4 3 7 7 a4 5 3 8 10 6 4 3 a5 10 10 6 6 4 2 2 a6 7 4 10 3 7 9 9 a7 1 1 3 9 5 8 4 a8 6 8 9 7 9 3 5 4 7 7 2 2 10 10 a9 a10 9 5 5 5 8 5 6

Points total Order

10 51 33 39 40 49 31 47 42 43

10 1 8 7 6 2 9 3 5 4

5 Conclusion The solution of the main problem was carried out using the FIS, which can be useful for the formation of the credit portfolio under limited financial resources in the commercial bank. On the example of 10 alternative potential borrowers of microcredits aj (j = 1  10), the results obtained using the FIS, as well as scoring analysis, Pareto and Bord methods are summarized in Table 6. As can be seen, the rankings of alternative applicants obtained using arithmetic averaging and scoring analysis completely overlap and differ insignificantly from the ordinal estimates obtained using the Pareto and Bord methods. Some difference is observed when comparing the statistical results with the ranking of applicants obtained using the FIS, especially in the case of estimating the

Combined Approach to Evaluation of Microcredit Borrowers Solvency

513

best applicant. This is not startingly, since the ordinal estimates obtained by the methods of scoring analysis, Pareto and Bord do not consider the specific weights of the criteria for estimation the solvency of potential borrowers, while the structure of the FIS implicitly considers the priority of some indicators of solvency over others. Table 6. Results of assessments of the current solvency of microcredit borrowers Borrower Average

ScoringPareto rule Bord method analyze Estimate Rank Estimate Rank Rank Estimate Rank 0.1023 10 0.72 10 10 10 10 0.4557 1 3.19 1 1 51 1 0.3083 5 2.16 5 7 33 8 0.2581 7 1.81 7 6 39 7 0.2350 9 1.65 9 9 40 6 0.3797 3 2.66 3 3 49 2 0.2436 8 1.71 8 8 31 9 0.3469 4 2.43 4 2 47 3 0.3897 2 2.73 2 4 42 5 0.2913 6 2.04 6 5 43 4

FIS

a1 a2 a3 a4 a5 a6 a7 a8 a9 a10

Estimate Rank 0.3542 10 0.5926 2 0.4826 7 0.5234 5 0.4645 9 0.5967 1 0.4823 8 0.5692 3 0.5485 4 0.5090 6

References 1. Rzayev, R.R., Aliyev, A.A.: Estimation of credit borrowers solvency using fuzzy logic. J. Autom. Inf. Sci. 1, 114–127 (2017) 2. Rzayev, R.R., Aliyev A.A.: Credit rating of a physical person based on fuzzy analyses of his/her solvency. Syst. Means Inform. 27(3), 202–218 (2017). (in Russian). https://doi.org/10. 14357/08696527170316 3. Gaziyev, Z.Z.: Assessment of microcredit borrowers by the fuzzy maximin convolution method. Math. Mach. Syst. 2, 89–98 (2020). (in Russian) 4. Aliyev, E., Gaziyev, Z.: Weighted assessment of the microcredit borrower solvency using a fuzzy analysis of personal data. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds.) 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing – ICAFS-2020. ICAFS 2020. Advances in Intelligent Systems and Computing, vol. 1306, pp. 531–539. Springer, Cham (2021). https://doi.org/10. 1007/978-3-030-64058-3_66 5. Molchanov, K.: Increasing the probability of issuing a loan–instructions from the League (in Russian). https://www.liga.net/creditonline/uvelichivaem-veroyatnost-vydachi-kredita-instruk ciya-ot-liga-kreditonlajn 6. Rzayev, R.R.: Analytical Support for Decision-Making in Organizational Systems. Palmerium Academic Publishing, Saarbruchen (2016).(in Russian) 7. Andreichenkov, A.V., Andreichenkova, O.N.: Analysis, Synthesis, Planning Decisions in the Economy. Finance and Statistics, Moscow (2000).(in Russian) 8. Application of Pareto and Bord methods for selection of investment projects (in Russian). https://afdanalyse.ru/publ/investicionnyj_analiz/teorija/primenenie_metodov_pareto_i_bord a_pri_vybore_investicionnykh_proektov/27-1-0-330

Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender System Mohamed Bennawy(&) and Passent el-Kafrawy School of Information Technology and Computer Science, Center of Informatics Science, Nile University, Giza 12588, Egypt {m.zohair,pelkafrawy}@nu.edu.eg

Abstract. The Association for Computing Machinery ACM recommendation systems challenge (ACM RecSys) [1] released an e-tourism dataset for the first time in 2019. Challenge shared hotel booking sessions from trivago website asking to rank the hotels list for the users. Better ranking should achieve higher click out rate. In this context, Trivago dataset is very important for e-tourism recommendation systems domain research and industry as well. In this paper, description for dataset characteristics and proposal for a session-based recommender system in addition to a comparison of several baseline algorithms trained on the data. The developed model is personalized session-based recommender taking into consideration user search preferences. Technically, paper compare between six different models vary from learning to rank, nearest neighbor and popularity approaches and compared results with two benchmark accuracy. Taking into consideration the ability to deploy model into production environments and the accuracy evaluation based on mean reciprocal rate as per challenge guidelines. Our winning experiment is using one learning to rank model achieving 0.64 mean reciprocal rate compared to 37 model achieving 0.68 by ACM challenge winning team [2]. Keywords: Session-Based recommender systems Kafka
Spark
Machine learning


Event stream processing


1 Introduction Twenty years ago, travelers were relying on travel agencies to arrange trips, book flight tickets and make hotel reservations. Nowadays, travelers search for any desired destinations themselves and book their trip based on their interest and preferences. Therefore, the online hotel booking portals become more competitive and looking for a quick, efficient and robust recommender system. Understand traveler interest and recommending hotels is a difficult task and the tourism domain is very complex as well. In order to plan trip a lot of factors is being considered including type of visit, preferred timing, destination interest, personal preferences, attractions, transportation and budget. Also, travelers have fewer history than other industries like music or movies recommendation. Most of the travelers book once or twice a year. Finally, travelers may search for destination anonymously which © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 514–523, 2022. https://doi.org/10.1007/978-3-031-09176-6_59

Recommendations on Streaming Data

515

means maximizing gain from any implicit event is needed. Consequently, information about the traveler and his own taste and current taste is harder to detect. Recommenders need to analyze the session events for first-time traveler to pick recommendation list of hotels that most convenient to travelers. Trivago dataset reflects different interactions of travelers starting from searching for accommodations, destination or landmarks and the recommendations list represents to the traveler. Traveler can interact with the hotels shown in the list by clicking on the content of the hotel to forward his page to the booking website (click out). The recommendation system should utilize all the information of the session, user and displayed list of hotels and develop a model to predict the top accommodation list that most likely will be clicked by the traveler.

2 E-Tourism Datasets During the past few years, many e-tourism datasets were released to support researchers in the recommendation system solutions and best practices. However, most of the datasets reflect information about the content itself or ratings which can be scaped from the website. A limited number of datasets focus on the user interactions and show different interaction patterns for the users. In the next section, list of etourism datasets with the main characteristics for each. – Booking [3] The data is from the booking giant portal Booking.com. Dataset reflect customer reviews and ratings for around 1.5 K luxury hotels across Europe. In addition to ratings, more data included about traveler information, reviews text, hotel location and flag to distinguish between positive and negative reviews. – Datafiniti’s [4] Dataset released by Datafiniti’s Business Database 5 years ago. It contains 35 K reviews for around 1 K hotels with additional metadata related to hotel location, name, score and reviewer information. – Goibibo [5] Dataset for leading e-tourism portal in India goibibo.com. Similar to the previous datasets, GB19 reflect data related to reviews, hotel metadata and reviewer information for around 33 K hotel and the number of rows 4 K. – MyTrip [6] Dataset for another hotel booking portal in India MakeMyTrip.com. the dataset contains 615 K hotel. Also, dataset have the same information as GB19 [4] metadata for hotels and reviewers in addition to the rating. – TripAdvisor [7] TripAdvisor.com hotel ratings for one month in 2009. Dataset contains the overall rating and addition information about the rating aspects (value, room, location and reception and check in/out process and rating from 1 start to 5 starts.

3 Trivago Dataset Description This section describes Trivago dataset characteristics and data structure and some major analytics and insights.

516

3.1

M. Bennawy and P. el-Kafrawy

Overview

Dataset consist of user item interactions for Trivago website users between first of November, 2018 and eight of November, 2018. The data contains different user interactions that includes, making a click that forward to booking website called a click out action, an interaction with image, or select a filter. Each interaction is logged with timestamp which reflect the exact timing of the action with items. Each group of actions or events compose a user session. A session is a series of events or actions within specific time window. The session has no time gab between the interaction more than 60 min. If the user stops using the portal and return back after minutes it will be considered to be within the same session. This allows us to have some data related to session itself like the country, list of items interacted with during the session and the search and filters used. In Fig. 1, Illustration for train and test split in Trivago dataset. Each row in the train split represents a session with an overall 832 K session in total representing 6.5 days of sessions. In each session a series of interactions represented by unmarked boxes like click on an image, info, deal, refine search parameters or searching for a new accommodation or destination. Also, marked boxed with (x) represent the click out actions which means that user showed an interest and clicked on a specific hotel to proceed in booking in hotel website outside Trivago website. As mentioned in click out actions click out impression item which is the list of items shown for the user before the click out action. Our goal is to enhance the ranking of this list to bring the click out items at the first position in the list. This will enhance our main evaluation metric reciprocal rank which will be discussed in the Sect. 4.1. Finally, a validation and test splits which have 78 K and 291 K number sessions with the same format and the validation split cover 0.5 day of sessions and test 1 day.

Fig. 1. User item interactions in different session with illustration for the train and test split. Given a set of impression or recommendation the target is to get a re-ranking list of recommendation to enhance the recommendation list rank [8].

In Table 1. A list of all action types and illustration of the reference value associated with each action with a small description about the action.

Recommendations on Streaming Data

517

Table 1. Actions types with description Action Type Click out item Interaction item rating Interaction item info Interaction item image Interaction item deals Change of sort order Filter selection Search for item Search for City / POI

Identifier Item ID Item ID Item ID Item ID Item ID Order name Filter Item ID City/POI

Description Forward to booking site via clicking on item Any item Interaction rating or review Interaction with item information Interaction with an image View more deals button Sort order Change Filter selection, e.g., four stars Search for the name of an accommodation Search for City/Point of interest (POI)

In Table 2 general statistic about dataset training and test dataset. Table 2. Description of different actions types. File size (MB) Row count Unique sessions Unique users Click outs Min. date Max. date Avg. sessions per user Avg. actions per user

3.2

Train Dataset 2,100 15,932,992 910,683 730,803 1,586,586 1st of Nov 2018 7th of Nov 2018 1.24 21.8

Test Dataset 535 3,782,335 291,381 250,852 528,779 8th of Nov 2018 9th of Nov 2018 1.16 15.07

Files Description

Trivago dataset contains three files: trian.csv, test.csv, item_metadata.csv. The first two files train.csv and test.csv contains user sessions each session have multiple transactions for user item actions. The sessions included in the train file happened before November 7, 2018 and any session after is located in the test file. Files contains twelve column user id, session id, timestamp, step, action type, reference, platform, city, device, current filters, impressions and prices. The step column reflects the sequence of the action within the session also depending on the action the value of the reference table changes for example in case action is search for destination the reference will hold the country. The last file item_metadata.csv contains information about the hotels. File contains two columns item id and properties which a pipe separated list of item properties. Item properties capture 157 distinct properties e.g., hotel start, free WIFI, swimming pool, balcony, laundry service …etc. there is an imbalance in in the number of properties per

518

M. Bennawy and P. el-Kafrawy

item as not all items have the same list of properties around 20% of the items have one single property. 3.3

Descriptive Analysis

In this section, our data exploration and analysis findings on Trivago dataset. Exploration is so important to better understand the dataset, help in generating implicit and explicit features and guide modelling experiments and architecture options. The data are a collection of user item interactions across 55 different countries. Each session is a user interaction across limited time window called session till click out action. Figure 2 show the top 10 countries in terms of total number interactions in the training dataset and on the right-side graph search related action types which hold the value of the searched item.

2,634,304

Brazil

1,627,520

USA Germany United Kingdom Mexico India

1,001,105 918,900 833,785 679,747

Tokyo Disneyland

2,093

Kyoto Staon

1,303

Las Vegas Strip

1,238

Tokyo Staon

1,233

Shinjuku Staon

1,114

Melbourne CBD

1,099 1,082

Australia

595,003

Times Square

Turkey

564,271

Sydney CBD

974

Japan

547,480

Osaka Staon

960

Italy

527,046

Hakata Staon

927

Fig. 2. In the left side top 10 countries in terms of total number interactions in the training dataset. In the right-side top POI search destination.

The search related action types are very useful as it reflects the real explicit preference for the traveler in this session. Despite the importance of search related actions, a huge sparsity issue exists only 3.4% of all interactions have searches for destinations or POIs. Therefore, recommender cannot deal with the sparsity issue and rely on the search related action types. In this case, recommender need more implicit feature to be able to capture the traveler preference. Traveler usage and actions patterns are the main source to create extra implicit features. The frequency of the interactions performed during session is different. In Table 3 illustration for the interaction types and the number of interaction occurrences with % from the total. The top interaction type used by travelers is interaction with the accommodation image with around 74% of all

Recommendations on Streaming Data

519

interactions. the second top used action type is the click out with item with around 10% of the total interactions. Table 3. Frequency of interaction types. Action type Interaction item image Click out item Filter selection Search for destination Change of sort order Interaction item info Interaction item rating Interaction item deals Search for item Search for POI

Number of interactions Percentage 11,860,750 74.44 1,586,586 9.96 695,917 4.37 403,066 2.53 400,584 2.51 285,402 1.79 217,246 1.36 193,794 1.22 152,203 0.99 137,444 0.86

For better understanding, a correlation between the click out action and previous action types is implemented. This approach helps to identify the most common action type than occurs before the click out. The top common action types before the click out action were another click out followed by interaction with image. And in both actions the click out item is similar to the same item of click out or image interaction. This leads that the previous interactions can strongly guide the model to the click out item. Some cases are hard to recommend for example if the click out is the first action in the session without any previous interactions (20% of the click outs). Another observation is that the number of interactions unevenly distributed as most sessions have only one interaction and few sessions have a very complex pattern with more than 100 interactions. Another sparsity exists in the user information and previous preferences as 84% of the users have only one session. This led that model should capitalize on the current session information and implicit features to avoid this sparsity in addition to any explicit features and the user historical preferences if available. Using the three feature types implicit session features, explicit session features and user historical features would help the model to merge between traveler previous preferences and current session interest. In the next section, the evaluation metrics, different benchmark models, and different modelling experiments to infer the accommodations of the click outs.

4 Experiments Recommendation engine have different implementation techniques and in order to choose the perfect technique, first answering what is the expected output from my recommender. In our problem, reorder the list of hotels showed in the impression column based on the propensity for each hotel click out action so it is a ranking task.

520

M. Bennawy and P. el-Kafrawy

In this section, different evaluation metrics aside with a set of baseline algorithms to be compared as a benchmark with other experiments. Session Based Recommenders have different approaches vary from the basic and straightforward methods to the sophisticated ones [12–19]. Classification, nearest neighbors, popularity and matrix factorization are examples for the simple approaches. Factorization Machine, Recurrent Neural Network GRU4Rec [9, 10] and Graph Neural Network SR-GNN [11] are examples for the complex approaches. Recommender accuracy isn’t guaranteed when choosing a complex model so it is recommended to start a simple algorithm before adding any complexity. Adding Complexity will lead to complex deployment and integration processes which isn’t recommended in the realworld use cases. For the mentioned reasons, our experiments focus on the straightforward algorithms. 4.1

Evaluation Metrics

Trivago dataset challenge used the mean reciprocal rank (MRR) as the evaluation metric. Given a recommended list, MRR calculate the reciprocal of the rank at which as shown in Eq. 1. MRR ¼

1 XN 1 : i¼1 rank N i

ð1Þ

Another performance metric is introduced, which is the precision@K where K equal to three. Given all click out predictions N and the top three items in recommended list, precision@3 measure if the clicked items in the top 3 items as shown in Eq. 2. Avg:Precision@3 ¼

1 XN ½Ic 2 top3  : i¼1 N 3

Table 4 show model output using the introduced evaluation metrics.

Table 4. MRR and precision@3 evaluation metrics Model LightGBM Logistic regression nn-Interactions nn-Item Position Popularity-users Popularity-absolute Random

MRR 0.645 0.640 0.632 0.500 0.500 0.290 0.288 0.177

Precision@3 0.229 0.227 0.223 0.181 0.181 0.103 0.102 0.051

ð2Þ

Recommendations on Streaming Data

4.2

521

Benchmarks

• Basic benchmark; Position model is the first benchmark algorithm which reflects the given order of the impression list. The impression list is the seen list by the user and results considered as basic benchmark model. This means ignoring any sessionbased data and relying on the position of the hotel in the original impression list. As a second baseline, the Random list is considered which reflect a complete random selection for the hotels. This is considered as a lower bar benchmark and as shown in Table 4, it has the leas MRR and Precision@3. • Popularity-Based Model; the importance of the hotel in the list is identified by the frequency users interacted with the hotel in the past. In this approach we don’t capture the features and interactions during the session meanwhile emphasizing on the previous history and click outs for the user. In Table 4 two models using the popularity-based model (Popularity-users, Popularity-absolute). Popularity absolute is ranked based on the absolutes number of clicks the item received in the training phase. In case the hotel has no click outs in the training dataset, then follow the original position. Popularity-absolute is better than random however it is less than position which is our benchmark. Popularity users rank based on the distinct number of users who clicked on the items. The performance is better than the popularity absolute as it removes the repeated clicks on the same items. • Nearest Neighbor Algorithms; nearest neighbor algorithm calculates predictions by calculate the similarity measure between the item and the clicked item and rank based on the closeness between items. The last interacted item in the session is considered the target item afterwards measure similarity between this item and all items in the impression list. The first algorithm nn-item measure the similarity based on item metadata. In the metadata 157 features identifying the properties of the hotel. So, we constructed a vector of length 157 for the last interacted item and the all hotels in the list and rank the nearest hotels first. Table 4 shows that nn-items is better than position benchmark which make sense because the session reflect taste and preferences and weight the latest interaction the most. Second approach is nninteraction which is similarity between items-based n the numbers of sessions cooccurrences of these items. A binary vector representing the item with all sessions and the session will be 1 in case item has at least one interaction in the session. Then calculate the cosine similarity between the last clicked item and all items in the recommended list. Finally, rank based on the closeness of the cosine similarity. As shown in Table 4 nn-interactions is better than all previous experiments. As we consider the similarity in all user behaviors in that item. • Classification; Solve the problem by classification algorithm, where a multi-class for all hotels and the probability of clicking out the hotel. Initially transforming the impression list into rows and add extra column reflecting if item is clicked or not as or target variable. Engineered features are generated to be included in logistic regression algorithm features. Features include item price, number of previous interactions with the item, item position in the list and last item flag. Logistic regression has shown better accuracy as shown in Table 4 which means that features are very relevant.

522

M. Bennawy and P. el-Kafrawy

• Learning to Rank; the main difference in the learning to rank is looking at combinations of items that introduce the minimum loss functions. In the previous example we ranked the hotels based on click out probability for each hotel. In this approach a optimization function consider rank of each pair of items. Using LightGBM implementation for LambdaRank method [20]. The input features for the model are similar to logistics regression model with an additional input that specify the search query. LightGBM shows the highest accuracy over all experiments Table [5]. High accuracy is justified by two factors 1) choosing the best features to represent the session 2) using a powerful algorithm.

5 Conclusion Paper presented an e-tourism dataset in the domain of session-based recommendation engines. Paper analyzed dataset with a descriptive analysis highlighting main characteristics, trends in data and file data structure. Imbalanced data and data sparsity were the main challenges after exploring the dataset also paper took into consideration the production deployment as sometimes deployments won’t be feasible in production streaming data. Data challenges and deployment constraints make it hard to achieve high performance on the evaluation metrics. Paper shared feature selection process and applied a comparison between different recommendation approaches achieving high accuracy with a simple approach and easy to deploy on production. Future work is to try a more sophisticated algorithms including deep learning networks.

References 1. ACM Recommender Systems. https://recsys.acm.org/. Accessed 22 Feb 2022 2. Jankiewicz, P., Kyrashchuk, L., Sienkowski, P., Wójcik, M.: Boosting algorithms for a session-based, context-aware recommender system in an online travel domain. In: Proceedings of the Workshop on ACM Recommender Systems Challenge (RecSys Challenge 2019). Association for Computing Machinery, New York, NY, USA, Article 1, pp. 1–5 (2019). https://doi.org/10.1145/3359555.3359557 3. Booking.com. 2019. 515K hotel reviews data in Europe. https://www.kaggle.com/jiashenliu/ 515k-hotel-reviews-data-in-europe. Accessed 22 Feb 2022 4. Datafiniti’s Business Database (2019). https://data.world/datafiniti/hotel-reviews. Accessed 22 Feb 2022 5. Goibibo.com. Indian hotels. https://www.kaggle.com/PromptCloudHQ/hotels-on-goibibo. Accessed 23 Feb 2022 6. MakeMyTrip.com. Indian hotels. https://www.kaggle.com/PromptCloudHQ/hotels-onmakemytrip. Accessed 23 Feb 2022 7. TripAdvisor.com. European hotels. https://www.kaggle.com/andrewmvd/trip-advisor-hotelreviews. Accessed 27 Feb 2022

Recommendations on Streaming Data

523

8. Volkovs, M., Wong, A., Cheng, Z., Pérez, F., Stanevich, I., Lu, Y.: Robust contextual models for in-session personalization. In: Proceedings of the Workshop on ACM Recommender Systems Challenge (RecSys Challenge 2019). Association for Computing Machinery, New York, NY, USA, Article 2, pp. 1–5 (2019). https://doi.org/10.1145/ 3359555.3359558 9. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In ICLR, pp. 1–10 (2016) 10. Hidasi, B., et al.: Parallel recurrent neural network architectures for feature-rich sessionbased recommendations. In: RecSys, pp. 241–248. ACM (2016) 11. Wu, S., et al.: Session-based recommendation with graph neural networks. In: AAAI, pp. 346–353 (2019) 12. Wang, Z., Gao, Y., Chen, H., Yan, P.: Session-based item recommendation with pairwise features. In: Proceedings of the ACM Recommender Systems Challenge 2019. ACM (2019). https://doi.org/10.1145/3359555.3359559 13. Rabhi, S., Sun, W., Perez, J., Kristensen, M.R., Liu, J., Oldridge, E.: A pipelined hybrid recommender system for ranking the items on the display. In: Proceedings of the ACM Recommender Systems Challenge 2019. ACM (2019). https://doi.org/10.1145/3359555. 3359565 14. Perlich, C., Provost, F., Simonoff, J.: Tree induction vs. logistic regression: a learning-curve analysis. J. Mach. Learn. Res. 4(December 2003), 211–255 (2003). https://doi.org/10.1162/ 153244304322972694 15. Rabhi, S., Sun, W., Perez, J., Kristensen, M.R., Liu, J., Oldridge, E.: Accelerating recommender system training 15x with RAPIDS. In: Proceedings of the ACM Recommender Systems Challenge 2019. ACM (2019). https://doi.org/10.1145/3359555.3359564 16. Schedl, M., Zamani, H., Chen, C.-W., Deldjoo, Y., Elahi, M.: Current challenges and visions in music recommender systems research. Int. J. Multimed. Inf. Retr. 7(2), 95–116 (2018). https://doi.org/10.1007/s13735-018-0154-2 17. Verstrepen, K., Goethals, B.: Unifying nearest neighbors collaborative filtering. In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys 2014), pp. 177–184. ACM, New York, NY (2014). https://doi.org/10.1145/2645710.2645731 18. Volkovs, M., Wong, A., Cheng, Z., Pérez, F., Stanevich, I., Lu, Y.: Robust contextual models for in-session personalization. In: Proceedings of the ACM Recommender Systems Challenge 2019. ACM (2019). https://doi.org/10.1145/3359555.3359558 19. Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010). ACM, New York, NY, pp. 783– 792 (2010). https://doi.org/10.1145/1835804.1835903 20. Ke, G., et al.: Light- GBM: a highly efficient gradient boosting decision tree. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30. Curran Associates, Inc., pp. 3146–3154 (2017)

Determining Annual and Monthly Sales Targets for Stores and Product Categories in FMCG Retail Buse Mert(&) , Defne İdil Eskiocak , İskender Ülgen Oğul Mustafa Kaan Aslan , and Erem Karalar

,

Migros Ticaret A.Ş, 34758 Ataşehir, İstanbul, Turkey [email protected]

Abstract. The retail industry, especially fast-moving consumer goods (FMCG) retail, has many variables affecting sales. Therefore, it is of great importance for companies to accurately plan their annual sales target. The aim of this study is to determine the annual sales target of Migros T.A.Ş, one of the largest FMCG retailers in Turkey. These targets are then communicated to the marketing teams and stores monthly. This process was previously done manually by the Sales, Marketing, and Finance departments. With this project, both annual and monthly sales forecasts are created for each store and product category using machine learning methods. Within the scope of this study, we create various features, and compare the results of various state-of-the-art algorithms such as Random Forest, LightGBM, and Prophet. By using machine learning we aim to both automate manual processes and determine a baseline sales estimate without bias. Afterward, these base model outputs are optimized by including business information of the relevant departments. Keywords: Retail


Machine learning
Forecasting

1 Introduction In fast-moving consumer goods (FMCG) retailing, sales forecasting is essential for operational and strategical planning [1]. For this reason, it is critical to know the monthly and annual sales targets by store and product categories in advance for sales and marketing teams to plan their processes. In this way, the sales department communicates the monthly sales targets of the stores and the marketing department of the product categories to the relevant people in the field teams. The right targets enable the company to make more profit and work efficiently. Traditionally, experts in sales and marketing departments calculate the sales targets by analyzing the company's sales in previous years and the current inflation. Manually making these estimations creates a significant workload. Additionally, there may be overlooked patterns due to the human factor. These estimations may be adequate for the short-term; however, they degenerate over long periods. This study aims to lighten the workload of relevant departments and create acceptable long-term forecasts. Also, as we aim to reduce the necessary workload with systematic production of predictions © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 524–530, 2022. https://doi.org/10.1007/978-3-031-09176-6_60

Determining Annual and Monthly Sales Targets for Stores

525

based on Machine Learning methods, we strive to update our model and estimations continually in 15-day periods. This approach ensures the usage of recent data in model training to make it more successful. In this paper, we aim to determine the best-performing algorithm for a long-term sales forecasting problem. Thus, we conduct various experiments using different algorithms. At first, we employ three machine learning algorithms which are Random Forest, LightGBM, and XGBoost. After inspecting the error metrics from these algorithms, we see that the accuracy of predictions decreases as the forecast horizon becomes longer. We then try another algorithm called Prophet which seems to perform better for the long-term predictions. The organization of the paper is as follows: Sect. 2 includes our search in the relevant literature. Section 3 introduces our methodology for approaching the problem. Section 4 demonstrates our experiments by using various algorithms. Section 5 presents the discussion of the study and concludes the paper with suggestions for future research.

2 Literature Review [2] tries various machine learning algorithms on 16.892 records to predict product sales in a retail store. These records have 16 features such as product name, sales, date, and product quantity. In this case, especially Decision Tree and Random Forest algorithms reveal a big difference in accuracy, F1 score, and other metrics compared to other algorithms. Among the mentioned algorithms, Random Forest performs better than Decision Tree in all metrics except recall. Working with a similar type of data, [3] trains 14.204 data points with features containing information about product, store, and sales revenue using various algorithms such as Linear Regression, Gradient Boosting, and AdaBoost. According to the results, Gradient Boosting algorithm has the lowest error rates (RMSE and R2). [4] uses a total of 1.017.209 rows collected from Rossmann stores in Germany. This study differs from others by forecasting total sales independent of the product demand aspect. Various algorithms are used such as Linear Regression, Random Forest, and XGBoost, which is a derivative of Gradient Boosting. Among others, XGBoost delivers the best results. [5] uses Walmart data from 3 states in the USA. The algorithms that stand out with their performance are Ridge, Linear Regression, and XGBoost. The algorithm with the lowest error metrics is XGBoost. In the light of our research, we conduct experiments with Random Forest, LightGBM, and XGBoost, which are the algorithms frequently used in similar studies. Random Forest is a method that constructs multiple decision trees and ensembles their results with different approaches according to the initial objective (classification or regression) [6]. Both LightGBM and XGBoost are algorithms based on Gradient Boosting Machines, and they perform well in problems from diverse sectors. They differ from each other mainly by their approach to tree growth. In LightGBM, tree

526

B. Mert et al.

growth is leaf-wise, while in XGBoost, it is depth-wise [7, 8]. Additionally, we tried Facebook's Prophet algorithm which will be further detailed in the following sections.

3 Methodology Migros has more than 2000 stores and 30 product categories. Sales and Marketing teams are responsible for determining both the company's monthly and total annual sales targets for each store and product category. The method these teams use to estimate sales targets is based on their intuition and experience, also takes a lot of time and effort. With this study, we make predictions for the future with Machine Learning methods using past data. This way, we aim to provide an approach that is both free from personal biases and can capture patterns that cannot be seen with the human eye. In addition, this estimation method saves time and effort and can be updated to keep up with changing patterns. We use sales, promotion, and holiday data to create features. We aggregate shopping data to find daily sales values for store and product categories. We also include features related to the company's three most effective promotions to reflect the impact of the relevant promotion on the sales of a product category. The retail sector has a wide variety of product categories, such as stationery, meat, fish, fruit and vegetables, and dry food. Each product category is affected differently on various holidays; for instance, alcohol and turkey sales increase significantly during New Year's Eve. Likewise, there is a considerable sales increase due to the promotions in cosmetics and personal care categories for March 8, International Women's Day. The sales of the relevant product categories are affected not only on the day of these special occasions but for a certain period before. Hence, we produce various historical features to capture the impacts fully. In addition, the days of the week can also affect sales; for example, there is an increase in sales on weekends. We create various features to reflect this effect on the model. We train our models with at least five years of data so that the models can accurately reflect all the seasonal effects on the forecasts. The data includes approximately 120 million rows on a store, product category, and daily basis. We train models on daily data to make them learn about the daily effects, and then they are aggregated on a monthly-store-product category basis at the request of the sales and marketing departments. We choose the estimation period either as the end of the current year or the following year at the request of the sales and marketing departments. If we create predictions before September, it is necessary to produce forecasts until the end of the current year. If we make predictions including and after September, we generate predictions until the end of the following year (see Fig. 1). A very long forecast horizon causes various problems; for example, since the long-term promotion data is unknown, this information cannot be included in the model and causes a decrease in success.

Determining Annual and Monthly Sales Targets for Stores

527

Fig. 1. An example of training moment and forecasting horizon

4 Experiments After the data preparation, we experiment with Random Forest, XGBoost, and LightGBM algorithms in light of our literature research. We choose the months between March and December of 2021 as the forecasting period, and we create our predictions from all the models in February 2021. As a modeling approach, for the reasons explained above, we decide to generate forecasts on a store-product category-day basis and then aggregate them on a storeproduct category-month basis in line with the needs of the sales and marketing departments. Table 1. Monthly error metrics (Mean Absolute Percentage Error) for Random Forest, LightGBM and XGBoost Year-Month 202103 202104 202105 202106 202107 202108 202109 202110 202111 202112

Random Forest LightGBM 7% 8% 3% 5% 9% 8% 10% 9% 30% 16% 40% 31% 42% 30% 45% 39% 46% 42% 54% 49%

XGBoost 8% 7% 10% 8% 17% 26% 30% 36% 41% 41%

528

B. Mert et al.

Fig. 2. Actual (blue) and predicted sales (RF: red, LightGBM: green, XGBoost: purple)

When we examine the outputs of the experimented algorithms (see Table 1 & Fig. 2), the error rates are high because of the length of the estimation period and because we do not know the future values of various features (i.e., promotions). Although all three models produce high error rates, we can see that the Random Forest algorithm is the quickest to degenerate (see Fig. 2). The other two algorithms mostly outperform Random Forest (see Table 1). These algorithms are not time series methods; thus, they don’t create lagged values automatically. Additionally, we cannot manually calculate the lagged values of recent history because future sales values are unknown at the time of forecast. Therefore, we cannot incorporate lags of recent history in the models. As the previous models’ results are not up to par with the needs of the sales and marketing departments, we choose to test the Prophet algorithm. Prophet is an algorithm developed by the Facebook data science team and made available as open-source. This algorithm is time series based and is very successful in capturing seasonality effects [9]. Additionally, it can work with missing data, and outliers do not have a considerable effect on the model. The model uses only the sales data and the lagged values it creates. Table 2. Monthly error metrics (Mean Absolute Percentage Error) for Prophet Year-Month 202103 202104 202105 202106 202107 202108 202109 202110 202111 202112

Prophet 2% 2% 9% 0% 2% 0% 4% 6% 9% 21%

Determining Annual and Monthly Sales Targets for Stores

529

Fig. 3. Actual (blue) and predicted (red) sales using Prophet algorithm

When we analyze the mean absolute percentage error values on a year-month basis, we see that the errors remain below 5% in almost all months (see Table 2). When we look at the month of May, the error rate is 9%. This is due to extraordinary circumstances. Most importantly, mandatory curfews were declared to decrease the spread of Covid-19 in May 2021. Secondly, the government placed sales restrictions on some products in the markets. Since it is unlikely to predict these error-causing factors in advance, these estimations made in February do not contain any information about the relevant situations. Similarly, the error values start to increase as of October (see Fig. 3). The main reason for this surge is the inflation increase. The margin of error rises to 21% in December with the boost in sales because of New Year's. Since the model is currently in production and is retrained and scored every 15 days, it learns the inflation increase and produces forecasts accordingly.

5 Conclusion As a result of this study, we see that the Prophet algorithm produces the best performing results in the long term. While Random Forest, LightGBM, and XGBoost algorithms are successful for the short term, we observe that the error values increase over long periods because of the inability to incorporate promotions or the lagged sales values. We ensure that all departments use united sales forecasts with the help of this study. We also contribute to eliminating biases that may arise from the human factor. While Prophet performs mainly well in all stores, some newly opened stores which do not have an extensive history of data have slightly worse error rates. To overcome this difficult situation, we plan to create more historical data for new stores using backcasting methods in our future studies.

530

B. Mert et al.

References 1. Basson, L.M., Kilbourn, P.J., Walters, J.: Forecast accuracy in demand planning: a fastmoving consumer goods case study. J. Transp. Supply Chain Manag. 13(1), 1–9 (2019) 2. Ofoegbu, K.: A comparison of four machine learning algorithms to predict product sales in a retail store. Doctoral dissertation, Dublin Business School (2021) 3. Krishna, A., Akhilesh, V., Aich, A., Hegde, C.: Sales-forecasting of retail stores using machine learning techniques. In: 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), pp. 160–166. IEEE (2018) 4. Ramya, B.S.S., Vedavathi, K.: An advanced sales forecasting using machine learning algorithm. Int. J. Innov. Sci. Res. Technol. 5(5), 342–345 (2020) 5. Shilong, Z.: Machine learning model for sales forecasting by using XGBoost. IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 480–483. IEEE. (2021, January) 6. Ho, T.K.: Random decision forests. In: 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995) 7. Ke, G., et al: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017) 8. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794. (2016) 9. Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37–45 (2018)

The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations Guided by Social Influence Sevra Çiçekli1(&) 1

and Tuncay Gürbüz2

İstanbul Kültür University, Bakırköy, 34158 İstanbul, Turkey [email protected] 2 Galatasaray University, Ortaköy, 34349 İstanbul, Turkey

Abstract. Nowadays, there is an enormous increase in alternatives almost in every area with the development of technology. Thus, it has become harder for a single decision maker (DM) to completely evaluate each alternative for a problem on hand. In group decision making (GDM) problems, each DM has a different effect on the final decision because of their background. Also, it has been observed that DM’s judgments are affected by those of other DMs in the group. This is defined as social influence (SI). Social Influence Network (SIN) is useful especially when there is incomplete preference information. In this paper, a proposed model has been used to demonstrate the effect of seed value choice in completion process of missing fuzzy information given by DMs. Keywords: Fuzzy preference relation decision making


Social influence network
Group

1 Introduction In GDM the selection of the best alternative is done by a group of decision maker (DM). However, most of the time DMs should not have equal relative importance because they may have different expertise levels. Assigning appropriate weights to DMs is highly important since it directly affects the final decision. During the decision process, DMs are usually free to talk and exchange opinions about the alternatives. A social influence (SI) can easily emerge from this interaction, and it may directly affect the final selection. GDM models which considers SI has been first suggested by [1]. Opinions of people are affected by the people they trust especially when there is an uncertainty. Thus, the DMs’ opinions about the alternatives and the trust level to the other DMs are gathered. The information gathered from the DMs may be incomplete because the DM may not have any idea about the alternative, or the other DM. The objective of this paper is to demonstrate the effect of seed value choice in the completion process of missing information based on [2]. [3] suggested to complete the missing data by using the information given by the trusted DMs and showed that when the influence of DMs is considered, the opinions of DMs converge to a final aggregated

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 531–539, 2022. https://doi.org/10.1007/978-3-031-09176-6_61

532

S. Çiçekli and T. Gürbüz

opinion. However, there is no study in the literature that compares different seed value choices. The rest of the paper is organized as follows: Sect. 2 presents the studies in the literature, Sect. 3 presents the preliminaries related to GDM. In Sect. 4, the suggested approach is presented. In Sect. 5, an application has been conducted to illustrate the proposed methodology. Finally, the conclusion and the future research area are given in the Sect. 6.

2 Literature Review Previously, influence modelling and estimation of the influence level has been studied the by [4] and [5]. The social influence may directly affect the final selection, GDM models which considers social influence has been first suggested [6]. In the literature, there are studies to improve the result of the GDM by considering the interpersonal trust. In the proposed methods by [7] and the given statements by DM are aggregated to calculate a global trust level.

3 Preliminaries Each DM has a different contribution on the final selection of alternatives on a finite set X ¼ fx1 ; :::; xn g. There are m individual
 FPRs P1 , …, Pm after obtaining the preferences from the DMs where Pk ¼ pkij k 2 {1, …, m} and i, j 2 {1, …, n}. A FPR P can be defined on a X  X fuzzy set where the membership function is lP : X  X ! [0, 1] as follows [8]: 8 1 if xi is definitely preferred to xj > > > > x 2 ð0:5; 1Þ if xi is slightly preferred to xj <
 if xi and xj are evenly preferred ð1Þ lp xi ; xj ¼ 0:5 > > y 2 ð0; 0:5Þ if x is slightly preferred to x > j i > : 0 if xj is slightly preferred to xi Missing values in a FPR can be obtained by using additive transitivity. Even when the information provided by DM is partially consistent, by using
a set  of operators given, pij can be estimated with alternative k where pij = lP xi ; xj . An Ordered Weighted Average (OWA) operator of dimension m is a mapping f : Rm ! R and it is associated with a weight vector W ¼ ðw1 , …, wm Þ where wk 2 [0, 1] and Pm w ¼ 1. Assume that the preference values (p1 ; :::pm Þ should be aggregated. Then k¼1 k the OWA operator is defined as follows: OWA ðp1 ; :::; pm Þ ¼

Xm k¼1

w k p rð k Þ

ð2Þ

The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations

533

where r is a permutation function prðkÞ  prðk þ 1Þ for all k 2 {1, …, m−1} and r:{1, …, m} ! {1, …, m}. Therefore, the combined preference pij is obtained with OWA (p1ij ; :::pmij Þ for i, j 2 {1, …, n}. A nondecreasing proportional fuzzy quantifier Q can be defined as follows: for y1  y2 , lQ ðy1 Þ  lQ ðy2 Þ: 8

>
 <  if 0:25d  u x
rði þ 1Þ  u x
rðiÞ \0:75:d  si ¼ > > [ if 0:75:d  u
xrði þ 1Þ  u
xrðiÞ \1:5:d :  if 1:5:d  u xrði þ 1Þ  u xrðiÞ

ð15Þ

 1 Xn1
uðxrði þ 1Þ  xrðiÞ i¼1 n1

ð16Þ

where

d¼

5 Experiments and Evaluation In this section an application has been conducted to select the best alternative among X = {x1 ; x2 ; x3 ; x4 ; x5 ; x6 ; x7 ; x8 ; x9 ; x10 g with six DMs where E = {e1 ; e2 ; e3 ; e4 ; e5 ; e6 g. The fuzzy ranking preferences obtained from DMs for both the alternatives and the trust on the other DMs are given in the table below (Table 1). Table 1. Collected incomplete fuzzy rankings of alternatives and experts DMs e1 e2 e3 e4 e5 e6

Fuzzy rankings of alternatives x5  x7 x8  x1 x3 [ x4  x2 x10 x6 [ x2  x1  x3  x9 x5 x3 x5 [ x10  x1 [ x2 [ x6 x7 x8 x6 [ x2  x1 [ x9 x5 [ x8 x3 [ x5  x1 [ x10 [ x6 [ x2 x10 x4 [ x5  x6 [ x2

Fuzzy ranking of experts e2  e1 [ e4  e5 e3 [ e2 e4  e5 [ e6 e3  e6  e2 [ e5 e4 [ e3 [ e2 e1 [ e5 e6 e3  e5  e6 [ e1 e2 e6  e2  e5 [ e4

The FPRs are obtained by using (9) and (10). The matrix given in (17) and (18) represent the FPRs obtained by the fuzzy rankings of trust on the other DMs and the alternatives respectively for the first DM.

The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations

  0:5   0:21    e P1 ¼   0:36   0:29     0:5   0:23   0:5   0:41   0:73 P1 ¼     0:55   0:55      

0:77 0:5 0:77 0:68 1  0:82 0:82  

0:5 0:23 0:5 0:41 0:73  0:82 0:82  

0:79 0:5  0:07 0  0:59 0:32 0:59 0:5 0:82  0:64 0:64  

     

           

0:64 0:71 0:93 1   0:5 0:57 0:43 0:5  

0:27 0 0:27 0:18 0:5  0:32 0:32  

         

0:45 0:18 0:45 0:36 0:68  0:5 0:5  

537

ð17Þ

0:45 0:18 0:45 0:36 0:68  0:5 0:5  

         

                   

ð18Þ

After building the FPRs for the DM evaluations, the calculated weights of the DMs can be seen in the following fuzzy adjacency matrix: 2

0:33 6 0 6 6 0 W ¼6 6 0:16 6 4 0:13 0

0:33 0:22 0:21 0:16 0:08 0:26

0 0:3 0:39 0:21 0:3 0

0:19 0:21 0 0:27 0 0:11

0:15 0:18 0:14 0:1 0:29 0:22

3 0 0:1 7 7 0:25 7 7 0:1 7 7 0:2 5 0:41

ð19Þ

As it is described previously, after obtaining the incomplete FPRs, seed values are used for estimation of missing information. In [2], the seed values are obtained from the opinions of the trusted DMs. However, in this study, the seed values are considered as 0.5. The missing information are calculated and the following completed FPR is obtained for the first DM.   0:5   0:23   0:5   0:41   0:73 P1 ¼   0:49  0:55   0:55   0:49   0:49

0:77 0:5 0:77 0:68 1 0:73 0:82 0:82 0:73 0:73

0:5 0:23 0:5 0:41 0:73 0:52 0:82 0:82 0:52 0:52

0:59 0:32 0:59 0:5 0:82 0:57 0:64 0:64 0:57 0:57

0:27 0 0:27 0:18 0:5 0:3 0:32 0:32 0:3 0:3

0:51 0:27 0:48 0:43 0:7 0:5 0:57 0:57 0:5 0:5

0:45 0:18 0:45 0:36 0:68 0:43 0:5 0:5 0:43 0:43

0:45 0:18 0:45 0:36 0:68 0:43 0:5 0:5 0:43 0:43

0:51 0:27 0:48 0:43 0:7 0:5 0:57 0:57 0:5 0:5

 0:51  0:27  0:48  0:43  0:7  0:5  0:57  0:57  0:5  0:5 

ð20Þ

538

S. Çiçekli and T. Gürbüz

Once the individual FPRs are completed, the aggregation step is performed to obtain a collective FPR P using the given weights. The process converged after nine iterations as it is shown below:    0:5 0:57 0:41 0:54 0:47 0:53 0:57 0:57 0:62 0:45     0:43 0:5 0:37 0:41 0:31 0:43 0:54 0:5 0:57 0:27     0:59 0:63 0:5 0:55 0:52 0:58 0:57 0:65 0:59 0:51     0:49 0:59 0:5 0:5 0:51 0:51 0:55 0:6 0:58 0:45    0:53 0:69 0:48 0:49 0:5 0:61 0:54 0:62 0:57 0:46   ð21Þ P ¼    0:47 0:57 0:42 0:49 0:39 0:5 0:59 0:54 0:62 0:34   0:43 0:46 0:54 0:47 0:46 0:41 0:5 0:52 0:57 0:4     0:43 0:5 0:56 0:42 0:38 0:46 0:48 0:5 0:51 0:44     0:38 0:43 0:35 0:45 0:43 0:38 0:43 0:49 0:5 0:39     0:55 0:73 0:62 0:55 0:54 0:66 0:59 0:56 0:63 0:5  The dominance degrees of the alternatives are calculated using (5) are as follows: uðxi Þ ¼ ½:52, .43, .57, .53, .55, .49, .47, .47, .42, .6]. Since the highest dominance degree is obtained from the alternative 10, the best alternative is selected as 10. The following fuzzy rankings of the alternatives are generated using (15) and (16) as follows: x10 [ x3 [ x5 [ x4  x1 [ x6 [ x7 [ x8  x2  x9

6 Conclusion Even though the social influence plays an important role in GDM, it is ignored in most of the studies in literature. The objective of this study is to display the effect of seed value choice on the completion process of missing information. Thus, fuzzy rankings are used to obtain the preferences of DMs for alternatives and the other DMs. SIN is built with given preferences and missing information is estimated with the help of SIN. A collective FPR is built after estimating missing information considering the interactions among the DMs. Finally, the best alternative is selected with the dominance degree based on the collective FPR. Also, an application has been conducted. It has been observed that, because of the seed values used, the final ranking of the alternatives has changed compared to [2]. For further research, the fuzzy ranking scale could be more detailed in order to express the preferences more accurately, and different nondecreasing proportional fuzzy quantifier could be used. Also, a novel approach can be studied to complete the missing information and reach the consensus using the SIN.

References 1. Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. J. Web Semant. 5(2), 58–71 (2007)

The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations

539

2. Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., Loia, V.: Fuzzy group decision making with incomplete information guided by social influence. IEEE Trans. Fuzzy Syst. 26 (3), 1704–1718 (2017) 3. Liang, Q., Liao, X., Liu, J.: A social ties-based approach for group decision-making problems with incomplete additive preference relations. Knowl.-Based Syst. 119, 68–86 (2017) 4. Zhu, B., Zeshui, X., Jiuping, X.: Deriving a ranking from hesitant fuzzy preference relations under group decision making. IEEE Trans. Cybern. 44(8), 1328–1337 (2014) 5. Friedkin, N.E., Eugene C.J.: Social influence networks and opinion change-advances in group processes, pp. 1–29(1999) 6. Wu, J., Chiclana, F., Herrera-Viedma, E.: Trust based consensus model for social network in an incomplete linguistic information context. Appl. Soft Comput. 35, 827–839 (2015) 7. Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. J. Web Semant. Softw. Eng. Semat. Web 5(2), 58–71 (2007) 8. Wang, Y.M., Fan, Z.P.: Fuzzy preference relations: aggregation and weight determination. Comput. Ind. Eng. 53(1), 163–172 (2007) 9. Yager, R.R.: Families of OWA operators. Fuzzy Sets Syst. 59(2), 125–148 (1993) 10. Chiclana, F., Herrera, F., Herrera-Viedma, E.: Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations. Fuzzy Sets Syst. 97 (1), 33–48 (1998) 11. Alonso, S., Herrera-Viedma, E., Chiclana, F., Herrera, F.: Individual and social strategies to deal with ignorance situations in multi-person decision making. Int. J. Inf. Technol. Decis. Mak. 8(02), 313–333 (2009) 12. Yager, R.R., Filev, D.P.: Induced ordered weighted averaging operators. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 29(2), 141–150 (1999) 13. DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974) 14. Friedkin, N.E., Johnsen, E.C.: Social influence networks and opinion change-advances in group processes (1999)

Evaluation of Cryptocurrencies Dynamically Based on Users’ Preferences Using AHP Abdul Razak Zakieh1(&)

, Semih Utku1

, and Fady Amroush2

1

Dokuz Eylul University, Izmir, Turkey [email protected], [email protected] 2 Niuversity, Berlin, Germany [email protected]

Abstract. The fast pace of creating new cryptocurrencies makes it hard or even impossible to know which one of them best suits an investor’s needs. Increasingly, investors are starting to need a decision support system with which they can determine which cryptocurrencies are suitable for investment and which ones are not. In the formation of a decision support system, it is necessary to create suggestions according to personal preferences and tendencies. In this study, a decision support system was developed. The system allows investors to understand what they need and offers them cryptocurrencies that suit their preferences. On-chain parameters instead of off-chain ones were used for efficiency. In the developed system, a set of on-chain features is asked of investors, and individual weights are calculated for the selected features using the Analytic Hierarchy Process (AHP) algorithm. Using the calculated weights and the investor’s preferences, the system gives each cryptocurrency a mark of 100 and sorts the cryptocurrencies based on the mark where the system will provide different recommendations for each investor. We defined and determined the most important on-chain features. In addition, based on the answers of a focus group of cryptocurrency experts and investors, we concluded that the most important on-chain features to be considered for investment are High Volume, High Total Staked and High Percentage of Total Supply Circulating. Keywords: Blockchain support system
AHP


Cryptocurrencies
On-chain analysis
Decision

1 Introduction Blockchain is a peer-to-peer technology that saves data into an immutable digital record, called a ledger. Blockchain-based applications are distributed and decentralized where a consensus protocol is used to establish the trust between the peers to eliminate the need for having a third party. The name blockchain originates from the chain of blocks in the system. Each block has data and hash of its previous block; thus the blocks are connected like a chain. Researchers used blockchain technology in many fields. P. Tasatanattakool and C. Techapanupreeda [1] talked about the financial applications of Blockchains, like Bitcoin and Ripple, and the non-financial applications like Hyperledger, Election Voting, and Smart Contracts. T. Alladi et. al [2] made a review for the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 540–547, 2022. https://doi.org/10.1007/978-3-031-09176-6_62

Evaluation of Cryptocurrencies Dynamically Based on Users’ Preferences

541

blockchain applications in industry and industrial IoT. Cornelius C. Agbo et al. [3] did a review on blockchain studies in the healthcare field. Ju MyungSong et al. [4] discussed the uses of blockchain in the traceability of supply chains. Though researchers try to use blockchain technology in various applications, the most common use is financial applications. The technologies beyond blockchains, cryptography, and distributed computing are old but the emergence of blockchain started with Bitcoin [5]. Bitcoin is a cryptocurrency that uses blockchain technology to make transactions between two parties without having a third party, a bank for example, as an intermediate [5]. As there is no centralized authority that controls the price of a cryptocurrency, its success depends only on the market. Several market-related parameters have roles in affecting the price of a crypto-currency. There are two types of parameters: on-chain and offchain. On-chain parameters are the ones we can acquire from the blockchain itself like circulating supply, total supply, and market cap. On the other hand, off-chain parameters are the ones that affect the crypto currency, but we cannot obtain them from the chain itself. Tweets, social influencers, and the cryptocurrency development team are examples of off-chain parameters. There are more than 10,000 cryptocurrencies in the market [6] and deciding which one of them is a good investment opportunity is a hard task. For that reason, we decided to make a system that helps investors rate cryptocurrencies according to their preferences. We do not aim to predict price and our main purpose is to make a decision support system that helps investors choose the cryptocurrencies they believe in from the significantly increasing number of cryptocurrencies available. In the following sections, we talk about the related works, materials and methods we used, how we collected and preprocessed the data, and the model we developed to rate the cryptocurrencies. In the last section, we talk about the results we obtained.

2 Related Work Understanding cryptocurrencies and predicting their price has been the main interest for researchers. In [7], researchers studied the anonymity of cryptocurrencies by analyzing PIVX which is a cryptocurrency based on Bitcoin. Alexey Mikhaylov [8] tried to predict sustainable growth by analyzing the cryptocurrency’s open innovative market. Predicting the price of a cryptocurrency drew the attention of researchers as well. In [9], an attempt to predict the price of Bitcoin was made by using a Bayesian optimized recurrent neural network (RNN) and a Long Short-Term Memory (LSTM) network. Other researchers studied the relationship between social media and the price of a cryptocurrency. C. Lamon et al. [10] studied the effect of news headlines and tweets on the price of Bitcoin, Ethereum, and Litecoin by assigning labels to news headlines and tweets then making predictions based on the generated labels. J. Lansky [11] analyzed the prices of 1278 cryptocurrencies between 2013 and 2016 from the length of existence, biggest price drops, and highest price increase point-of-view. A. Hayes [12] used a regression model to determine the factors that give a cryptocurrency its value. The result was that coins’ mining difficulty; rate of unit production; and the cryptologic algorithm have the main role in determining the value. In other words, we can say that the increased rate in circulating supply is the main reason for detecting the cryptocurrency price according to

542

A. R. Zakieh et al.

the results. In [13], researchers tried to detect the factors that affect the price of a Bitcoin and found that the number of transactions is one of the most important ones. In addition, they concluded that the social indicators play a role in short-term price prediction. Building recommendation systems is important to help users make better decisions. In [14], the researcher used the prices of cryptocurrencies to build a cryptocurrency portfolio recommendation system where association rule mining algorithm was used. Analytic Hierarchy Process (AHP) [15] is a multi-criteria decision-making algorithm that was used in decision support systems widely. In [16], AHP and a discrete-event simulation tool were used to recommend a blockchain protocol that can be used for the storing of electronic health records. AHP has been also used to assist users in product selection [17]. To the best of our knowledge, most of the previous work used off-chain parameters to predict prices although off-chain parameters provide a short-term price prediction [13]. References [12] and [13] pointed to the importance of two on-chain parameters: circulating supply and volume. That motivated us to focus on the on-chain parameters and try to rate cryptocurrencies according to the preferences of users instead of trying to predict prices.

3 Material and Methods 3.1

Select Preferred Features

The first step in the system asks the investor to select the most important on-chain features according to their opinion. Blockchain networks have many important parameters, and narrowing them down to the most important ones was a hard task. Due to the lack of resources regarding on-chain parameters, we constructed a focus group of experts consisting of 15 individuals with over 10-year of experience in IT and blockchain; and most of them are also master’s degrees holders. This focus group, which participated in several meetings, answered questionnaires, and responded to open-ended questions, helped us with understanding and selecting the most important on-chain features. The 10 most important on-chain parameters selected according to our focus group of experts are: Circulating Supply, Market Cap, Volume over 24 h, Percent of Total Supply Circulating, Total Staked, Staking Reward, Whales Percentage, Total Value Locked, Number of Market Pairs and Date Added. Circulating Supply is the number of coins that are circulating in the market and available to the public. Market Cap is the total value of the market, which equals the price multiplied by the circulating supply. Volume over 24 h is the value of the transactions in the last 24 h. Percent of Total Supply Circulating is the coins available to the public out of the total existing coins for a specific cryptocurrency. We can calculate the percentage of total supply circulating using (1). Total Staked is the total value staked as a percentage of circulating supply whereas Staking Reward is the reward stokers receive as a percentage of their staked asset. Whales’ percentage/concentration is the percentage of circulating supply held by whales where a whale is an address holding at least 1% of the circulating supply. Total Value Locked is the value of the cryptocurrencies locked to take on loans. The Number

Evaluation of Cryptocurrencies Dynamically Based on Users’ Preferences

543

of Market Pairs is the number of pairs that can be exchanged with cryptocurrencies. Finally, Date Added is the date in which the cryptocurrency was added to the market. We gathered data for about 10,000 cryptocurrencies. However, Total Staked, Staking Reward, Whales Percentage, and Total Value Locked are not available for all those cryptocurrencies. Percent of total supply circulating ¼ circulating supply= total supply

3.2

ð1Þ

Select High/Low Preferences

The second step in the decision support system asks the investors whether they prefer a high or low value for each feature they selected in step 1. For each one of the on-chain features, the investor might prefer its low or high value. For instance, an investor might prefer a high staking reward (to earn a high reward themselves) while another investor might prefer a low staking reward (thinking that the price of such cryptocurrency will drop when collecting the high rewards). 3.3

Answers to Comparative Questions

Three investors might have the same selected features and high/low preferences, assuming feature1 and feature2. Nevertheless, the first investor might prefer feature1 to feature2 and the second investor might prefer feature2 to feature1. The third investor might consider feature1 and feature2 as having the same importance. Therefore, each one of those investors has to have different recommended cryptocurrencies in which to invest. To understand what the investors prefer exactly and offer them the most suitable cryptocurrencies, they must first answer a set of comparative questions. The questions are designed to make the investors compare pairs of selected features. Investors can select whether they consider two features the same or whether they deem one of them to be better than the other on a scale from 2 to 9 times. The number of generated questions can be calculated from (2) where n is the number of selected features. Number of questions ¼

3.4

ð n  ð n  1Þ Þ 2

ð2Þ

Rating and Sorting Cryptocurrencies

The decision support system uses the selected features, high/low preferences, the answers to comparative questions, and the value of each selected feature to rate each cryptocurrency. The steps of the rating process are data collecting, preprocessing, rating each cryptocurrency and then sorting the results. Data Collecting. We want to use on-chain parameters in our study rather than offchain. That is because on-chain parameters are directly extracted from the chain of the cryptocurrency, which makes them more important than the off-chain ones. Nevertheless, manual extraction of on-chain parameters from the chain itself is both difficult,

544

A. R. Zakieh et al.

time-consuming, and resource-consuming. The main reasons for this demanding process are the huge chain length and the thousands of chains we have nowadays. For example, Bitcoin’s chain size is 383.445 GB [18]. Consequently, we gathered data from different resources that either analyzed the chain directly or used other resources that provided those data. We used four different resources to gather the required parameters. Preprocessing. We skipped any project that had at least one of the following criteria: (1) having a circulating supply bigger than the total supply, (2) having 100% whales, (3) having zero circulating supply, and (4) having a total value stacked more than 100%. In addition, we omitted any cryptocurrency that had a missing value in a feature in which the user is interested in. Rating Each Cryptocurrency. The core of the decision support system is the rating model. The rating model is used because we want the investor to have the cryptocurrencies sorted according to their compliance with the parameters the investor selected [21–23]. Equation (3) is the equation we developed to calculate the rating for a specific cryptocurrency.

rating ¼

Xn

L :W k k¼1 k

ð3Þ

n is the number of selected features and W k is the weight of selected feature number k. Lk depends on whether the user prefers high or low value for k th selected feature and it is defined in (4). Lk ¼

8
3600 >3600

Pure greedy Objective Time (ms) 0.0236 0.02 0.1115 0.0216 0.2415 0.0340 0.2827 0.0426 0.33 0.0523

GRASP with a ¼ 0:8 Objective Time (ms) 0.02274 0.032 0.1262 0.0369 0.243 0.0468 0.3012 0.0623 0.3512 0.08

Summary of Results

From both tables demonstrated in the previous section, we can see that the operating times of pure greedy and GRASP algorithm are rather shorter than B&B technique. Considering first objective function, solutions obtained using GRASP approximates the best solution obtained with the help of B&B algorithm. Moreover, GRASP outperforms pure greedy algorithm where a is set to 1.0. Thanks to the adaptive strategy procedure embedded in GRASP algorithm, GRASP can approximate more than pure greedy algorithm to the optimal solutions. It must be noted that B&B cannot provide optimal solutions to those instances with 140 and 200 in 3600 s. Considering second objective function, B&B cannot provide good solutions in a reasonable amount of computational time. Consequently, it can be said that both pure greedy and GRASP outperform exact solution method with respect to total conflict probability. However, solutions obtained using pure greedy heuristic are better compared to GRASP algorithm. It is due to the fact that design of GRASP algorithm is more favorable to the first objective function. Although pure greedy algorithm outperforms GRASP with respect to second objective function, the results of GRASP algorithm is close to the results of pure greedy algorithm.

6 Conclusion and Future Directions This paper dealt with the assignment of flights to the bridge-equipped gates at an airport. The objectives are to maximize the total flight-to-gate assignment utility while minimizing total conflict probability. Since these objective functions are conflicting, it is not hard to provide an optimal solution with respect to one objective function without deteriorating to other one. Due to the complexity of the problem, GRASP algorithm is proposed to solve it in a reasonable amount of computational time. In order to test the performance of the proposed algorithm, five test instances with different size are generated. From constructing test results, we can conclude that GRASP has its particular features to be able to capture good solutions in a short computational time, especially for AGAP with big sizes. As a future direction, GRASP algorithm can be modified by changing the gate selection procedure in construction phase. For example, in the proposed GRASP algorithm, as soon as flights are sorted in descending order of desirability index, they

A GRASP Algorithm for Multi-objective Airport Gate Assignment Problem

557

are assigned to the gate maximizing flight-to-gate assignment utility. However, this gate selection procedure can be revised and a new procedure favorable to both objective functions can be proposed. In the adaptive phase of GRASP algorithm, in addition to 1–1 exchange move, such moves as apron exchange move, interval exchange move or greedy move can also be implemented.

References 1. Karsu, Ö., Azizoğlu, M., Alanlı, K.: Exact and heuristic solution approaches for the airport gate assignment problem. Omega 103, 102422 (2021) 2. Marinelli, M., Palmisano, G., Dell’Orco, M., Ottomanelli, M.: Optimizing airport gate assignments through a hybrid metaheuristic approach. In: Żak, J., Hadas, Y., Rossi, R. (eds.) EWGT/EURO -2016. AISC, vol. 572, pp. 389–404. Springer, Cham (2018). https://doi.org/ 10.1007/978-3-319-57105-8_19 3. Bouras, A., Ghaleb, M.A., Suryahatmaja, U.S., Salem, A.M.: The airport gate assignment problem: a survey. Sci. World J. 2014, 1–27 (2014) 4. Daş, G.S.: New multi objective models for the gate assignment problem. Comput. Ind. Eng. 109, 347–356 (2017) 5. Narciso, M.E., Piera, M.A.: Robust gate assignment procedures from an airport management perspective. Omega 50, 82–95 (2015) 6. Cheng, C.-H., Ho, S.C., Kwan, C.-L.: The use of meta-heuristics for airport gate assignment. Expert Syst. Appl. 39(16), 12430–12437 (2012) 7. Brazile, R.P., Swigger, K.M.: GATES: an airline gate assignment and tracking expert system. IEEE Expert 3(2), 33–39 (1988) 8. Ding, H., Lim, A., Rodrigues, B., Zhu, Y.: New heuristics for over-constrained flight to gate assignments. J. Oper. Res. Soc. 55(7), 760–768 (2004) 9. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6(2), 109–133 (1995) 10. Pi, Y., Song, X., Sun, J.: Mathematical models, GRASP algorithms and fitness landscape analysis for aircraft gate assignment problem. In: 2014 Tenth International Conference on Computational Intelligence and Security, pp. 64–68 (2014) 11. Drexl, A., Nikulin, Y.: Multicriteria airport gate assignment and Pareto simulated annealing. IIE Trans. 40(4), 385–397 (2008)

Preprocessing Approach Using BADF Filter in MRI Images for Brain Tumor Detection S. U. Aswathy1,2(&) and Ajith Abraham1 1

Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research, Excellence, P.O. Box 2259, Auburn, Washington 98071-2259, USA [email protected], [email protected] 2 Department of Artificial Intelligence and Data Science, Jyothi Engineering College, Thrissur 679531, Kerala, India

Abstract. The pre-processing approach is the first stage in the diagnostic procedure. This is particularly significant in noisy and fuzzy photos. It is one of the prerequisite procedures for achieving great efficiency in subsequent image processing steps. The initial step toward an automated CAD (Computer Aided Detection) system for a range of medical applications is image pre-processing. This phase in the medical profession is critical in generating promising outcomes that aid doctors in lowering death rates. There are a variety of methods for increasing brain MRI that are both accurate and automated. A basic technique for automated pre-processing is provided in this work. When compared to other filters, this approach employs an Adaptive Diffusion Filter in conjunction with a Boosted Anisotropic Diffusion Filter, which outperforms the current anisotropic diffusion filter. For a total of 20 photos, the necessary labor is put to the test. According to the author, BADF assists the radiologist in doing precise brain examinations, hence minimizing risk factors. Keywords: Preprocessing Bilateral filtering


Adaptive histogram equalization
BADF


1 Introduction In recent years, the medical industry has benefited from the development of information technology and e-health care systems, which enable clinical specialists to give better health care to patients. Because of aberrant cell proliferation within the brain, brain tumors have a negative impact on individuals. It has the potential to impair brain function and perhaps put one's life in danger. Benign tumors and malignant tumors are the two forms of brain tumors that have been recognized. SPECT, MRI, ultrasound, CT, PECT, PET, and X-ray are all noninvasive medical imaging modalities commonly used for brain tumor detection [1]. Present research work used MRI images for identifying brain tumor. The goal of this study is to give importance to preprocessing technique which plays a vital role in identifying tumor brain tumors. Early detection of a brain tumor is critical for effective therapy. A radiological examination is essential once a brain tumor has been clinically discovered to assess its © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 558–567, 2022. https://doi.org/10.1007/978-3-031-09176-6_64

Preprocessing Approach Using BADF Filter in MRI Images

559

location, size, and influence on the surrounding regions. Based on this information, the best treatment, whether surgery, radiation, or chemotherapy, is determined. It goes without saying that detecting a tumor in its early stages greatly enhances a tumorinfected patient's chances of survival [3]. As a result, the radiology department has prioritized research into brain tumor utilizing imaging techniques. In this work, image processing is used to identify brain tumors. Various strategies are used to improve the image from the database collection during the pre-processing stage. Pre-Processing stage is carried out by applying various filtering processes to the image. It enhances image quality and eliminates noise. Because brain images are more sensitive than other medical images, they should have the least amount of noise and the highest possible quality. The complete block diagram of brain tumor identification employing Preprocessing approaches is shown in Fig. 1.

Fig. 1. Overall architecture of brain tumor detection

2 State of Art Models Several researchers in the last decades have proposed various pre-processing and optimization techniques. Image enhancement involves manipulation of strength and contrast, noise reduction, elimination of the background, sharpening of the edges, filtering etc. Zhou and Bai [4] suggested Fuzzy connectivity based on frequency nonuniformity correction. Jaya et al. [5] proposed a weighted media filter-based system. High frequency components are suppressed by using weighted median filters to denoise. Anand and Sahambi [6] created a wavelet-based bilateral filtering technique to reduce noise in magnetic resonance Image. Undecimated Wavelet Transform (UWT) is the noise coefficient used to eliminate noise. George and Karnan [7] conducted object removal and transformed the tracking algorithm into a pre-processing phase. Hamamci et al. [8] proposed using a Median Filter to reduce salt and pepper noise as well as Poisson noise in input photos. To minimize background noise while keeping the image's border points, Ramalakshmi and Chandran [9] suggested an improved anisotropic filter version. Saad et al. [10] for the pre-processing and image enhancement, the global thresholding are used to obtain binary image. Sonavane et al. (2017) [21] examined two databases, one a clinical database for brain MRI and the

560

S. U. Aswathy and A. Abraham

other a Standard Digital Database for Screening Mammography (DDSM). The suggested approach has a 68.85% accuracy on the DDSM mammography database and a 79.35% accuracy on the clinical brain MRI database. For brain MR image tumor segmentation, Maurya et al. (2021) suggested an image processing approach based on morphology. Skull stripping is a method of eliminating scalp tissue, the skull, and the dura from filtered photographs. Finally, the attributes of the skull stripped picture are examined to determine whether or not a tumor exists. If a tumor is found, a morphology-based approach is used to segment the tumor on brain imaging. 2.1

Key Highlights

• Developing a novel procedure for enhancing meningioma MRI images, this reduces the mortality rate. • Two distinct current filtering techniques are compared to the proposed Adaptive Boosted Anisotropic Diffusion filter (BADF). Organization of paper: As we already come across the overview of Preprocessing in brain tumor in Sect. 1 and Literature review in Sect. 2, remaining part of paper is; Sect. 3 with the methodology, Sect. 4 with performance analysis and finally concludes with Sect. 5.

3 Methodology MRI images of brain tumor will be enhanced by this proposed system. Data acquisition is done by Cancer Image Archive. Proposed methodology in preprocessing is shown as block diagram in Fig. 2. Image Preprocessing and Enhancement: Preprocessing is done before identifying the tumor in the picture in order to improve the optical inspection's reliability. The relevant information is extracted once the MRI scans of the brain have been collected and pre-processed. Preprocessing can be done in a number of different ways. Image enhancement or improved inputs will be offered to other automated image processing processes with the goal of improving image readability. The MRI image is turned into a normal image with no noise, film artefacts, or labels after the enhancement methods. Gaussian and Poisson noises will commonly corrupt MRI Image [11]. The idea that white Gaussian noise is additive underpins most de-noising algorithms. Bilateral Filtering: Bilateral filtering preserves the edges of the Image. In image processing applications such as image de-noising and picture enhancement, bilateral filtering is becoming more popular [12]. In standard low pass filtering, each point's pixel is supposed to be equal to the pixel of the neighboring points:

hðy0 Þ ¼ kd1 ðy0 Þjj

Z

1 1

f ðd0 Þcðd0 ; y0 Þdd

ð1Þ

Preprocessing Approach Using BADF Filter in MRI Images

561

where (cðd; y0 Þ) determines the arithmetic contact among the region center x and a close by point d. kd ðy0 Þ ¼ jj

Z

1

1

cðd0 ; y0 Þdd

ð2Þ

Regardless, the pixels at the margins and the closing positions are practically equal. As a result, the distinctions have become hazy. This filter combines grey rates based on numerical closeness and multispectral similarity: 0

hðy Þ ¼

kr1 ðy0 Þjj

Z

1

1

f ðd0 Þsðf ðd0 Þ; f ðy0 Þdd0

ð3Þ

where, sðf ðd0 Þ; f ðy0 Þ computes the photographic resemblance among the region center x and a close by point d. For this case, the kernel evaluates the resemblance between pixels for photometry. The continuous standardization in this case is, kr1 ðy0 Þ ¼ jj

Z

1

1

sðf ðd0 Þ; f ðy0 Þdd0

ð4Þ

The bilateral filtering described as given below: hðy0 Þ ¼ k 1 ðy0 Þjj

1 Z

f ðd0 Þcðd0 ; y0 Þsðf ðd0 Þ; f ðy0 Þdd0

ð5Þ

1

R1 where, k ðy0 Þ ¼ 1 cðd0 ; y0 Þf ðf ðd0 Þ; f ðy0 Þdd0 Bilateral filtration is described as a region that combines area and distance filtering. It substitutes a comparable and close-toaverage pixel value for the original. The clinical database input is shown in Fig. 2(a), Fig. 2 illustrates bilateral filtering for all inputs (b). Curvelet Transform: Wavelet transform serves as the foundation for curvelet transform, which is a multi-scale transformation. The curvelet transform has three key components: dimension, position, and orientation. It has excellent orienting properties [13]. For noise reduction, the curvelet transform is the most effective approach. It may be used in graphical applications since it accurately shows curved objects [14]. Edge detection and picture de-noising are the two most essential applications. Figure 2 illustrates the picture of bilateral filtering for all of those inputs (c). A curvelet coefficient t (i, j, k) is given by,   t ð x; y; zÞ : f ; ux;y;z

ð6Þ

where, X = 0, 1… –gives the Scale limit; y = 0, 1… gives the orientation limit; z = (c1, c2), c1, c2 2 Z – gives the translation limit. Adaptive Histogram Equalization: This is a different way of equalizing histograms than the traditional one. By changing the intensity picture values, it enhances visual contrast [16–18]. For all of the inputs, Fig. 2 depicts the image of bilateral filtering (d).

562

S. U. Aswathy and A. Abraham

According to the physical definition of the histogram, every bar on the levelled histogram is the same height. ps ðsÞds ¼ pr ðr Þdr

ð7Þ

Fig. 2. Overall flow of proposed work

Assumes = T(r) is a monotonically increasing function in the interval, as well as its inverse function. r ¼ T 1 ðsÞ is a monotonic function also. According to (7), we can deduce "

#

1 ps ðsÞ ¼ pr ðr Þ ds= dr

¼ pr ð r Þ r¼T 1 ðsÞ

1 ¼1 pr ð r Þ

ð8Þ

The mapping link for the traditional histogram equalization approach is as follows: fi ¼ ðm  1ÞT ðr Þ ¼ ðm  1Þ

Xi k¼0

qk Q

ð9Þ

The traditional histogram equalization algorithm's mapping connection is as follows: eðiÞ ¼ pi logpi

ð10Þ

The entropy of the whole image is E¼

Xn1 i¼0

eð i Þ ¼ 

Xn1 i¼0

pi logpi

ð11Þ

BADF: Anisotropic diffusion, a non-linear and space-variant alteration, is applied to the original image. Its fundamental goal is to smooth an image without blurring it while maintaining crucial features. Figure 3(b) [19] shows the bilateral filtering picture. Following the production of the diffused picture, the proposed BADF improves on the current anisotropic diffusion filter by employing a partial differential equation (PDE). When the number of iterations is set to 20, satisfactory results have been obtained

Preprocessing Approach Using BADF Filter in MRI Images

563

based on extensive testing. The image's size is then calculated so that the four closestneighbor discrepancies may be computed. rN Ii1;j1 ¼ Ii11;j1  Ii1;j1

ð12Þ

rS Ii1;j1 ¼ Ii1 þ 1;j1  Ii1;j1

ð13Þ

rE Ii1;j1 ¼ Ii1;j þ 1  Ii;j1

ð14Þ

rW Ii1;j1 ¼ Ii1;j1  Ii1;j1

ð15Þ

where I (i1, j1) = f (i1, j1) in the first iteration and f (i1, j1) represents the input image Identified changes in the east, west, north, and south are represented by the variables _N, _S, _E, and _W.

Fig. 3. (a) shows the adaptive histogram equalized image (b) Boosted Adaptive Diffusion filter.

where K is a scalar that determines the degree of smoothness in that K should fulfil (K > 1), with higher values of K producing smoother output. In the conventional anisotropic diffusion filter, the value of K is fixed to 7. The parameter K is determined automatically in this study using Eq. based on local statistics (15).    meanf   i;j    k ¼2 ð16Þ  0:75  r fi;j          Ii;j ¼ Ii;j þ 0:25 gN  rN Ii;j þ gS  rS Ii;j þ gE  rE Ii;j þ gW  rW Ii;j ð17Þ where Ii;j is a smoothed image.

564

S. U. Aswathy and A. Abraham

4 Performance Measures For brain MRI imaging, the following performance metrics are examined. PSNR, MSE, SNR, and SSIM are important metrics to consider while creating values. For an experimental assessment of the intended research, 20 astrocytoma-related brain MR Image were chosen, along with 15 normal images, for a total of 35 images. After resizing each image, tests are run. In our research, we looked at BF, CT, AHE, and BADF (Tables 1, 2, 3 and 4). Table 1. Gives Peak signal to noise ratio for abnormal and normal image

Table 2. Gives Mean square error for ratio for abnormal and normal image.

Table 3. Gives Signal to noise ratio for abnormal and normal image image.

Preprocessing Approach Using BADF Filter in MRI Images

565

Table 4. Structural similarity index measure for abnormal and normal image are tabulated as

5 Performance Evaluating Plots The range of SSIM values is −1 to 1, with −1 being the lowest and 1 being the greatest photographs (Fig. 4).

Fig. 4. Performance evaluating plots for tumor images for various comparative measures are considered. (a) plots of PSNR (b) plots of SSIM values (c) plots the MSE values (d) plots the SNR values of various filters is given

566

S. U. Aswathy and A. Abraham

A plot for computational time is been taken in which our model BADF outperforms better with 3.21s when compared to other like ADF (4.1s), bilateral filter (7.2s) and curvelet transform (7.5s) describes the time taken for each filtering technique. The effectiveness of BADF as Preprocessing technique for detecting brain tumor as it gives quality and as well as noise free image for further stage when compared to other preprocessing models. Based on the aforementioned performance analysis, BADF appears to produce superior results than the other filters, which can be supported using the PSNR values obtained and the other factors considered.

6 Conclusion Pre-processing astrocytoma photographs is the focus of the investigation. This research endeavor reduces unnecessary noise and improves image quality by employing several filtering algorithms. Several image filtering techniques, such as BF, CT, and AHE, are used to evaluate the performance of BADF in this study. The goal of this proposed work is to choose the best filtering algorithms that reduce noise. The preprocessing approach used helps us to save computation time, in this work the analyses of four types of filters is done and came to conclusion that optimal BADF is better, which is confirmed using the previously mentioned quantitative indicators. BADF surpasses all other filters in this scenario.

References 1. Aswathy, S.U., Devadhas, S.S.K.: An improved tumor segmentation algorithm from T2 and FLAIR multimodality MRI brain images by support vector machine and genetic algorithm. Cogent Eng. 5(1), 1470915 (2019) 2. Sharif, M., Javaria, A., Muhammad, W.N., Muhammad, A.A., Nazeer, M., Shafqat, A.S.: A unified patch based method for brain tumor detection using features fusion. Cogn. Syst. Res. 59, 273–286 (2020) 3. Toğaçar, M., Burhan, E., Zafer, C.: BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med. Hypotheses 134, 109531 (2020) 4. Sukumaran, A., Abraham, A.: Automated detection and classification of meningioma tumor from MR images using sea lion optimization and deep learning models. Axioms 11(1), 15 (2022) 5. Dhas, G.G.D., Kumar, S.S.: A survey on detection of brain tumor from MRI brain images. In: International conference on control, Instrumentation, Communication and Computational Technologies, pp. 871–877 (2014) 6. Anand, C.S., Jyotinder, S.S.: Wavelet domain non-linear filtering for MRI denoising. Magn. Reson. Imaging 28(6), 842–861 (2010) 7. George, E.B., Karnan, M.: MRI Brain Image enhancement using filtering techniques. Int. J. Comput. Sci. Eng. Technol. (IJCSET) 3, 2229–3345 (2012) 8. Hamamci, A., Nadir, K., Kutlay, K., Kayihan, E., Gozde, U.: Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging 31(3), 790–804 (2012)

Preprocessing Approach Using BADF Filter in MRI Images

567

9. Ramalakshmi, C., Chandran, A.J.: Automatic brain tumor detection in MR images using neural network based classification. Biometrics Bioinf. 5(6), 221–225 (2013) 10. Saad, N.M., Salahuddin, L., Abu-Bakar, S.A.R., Muda, S., Mokji, M.M.: Brain lesion v segmentation of diusion-weighted MRI using thresholding technique. In: 5th Kuala LumpurInternational Conference on Biomedical Engineering, pp. 604–610 (2011) 11. Asokan, A., Anith, J.: Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images. ISA Trans. 100, 308–321 (2020) 12. Rachmad, A.I., Nur, C., Riries, R.: Image enhancement sputum containing mycobacterium tuberculosis using a spatial domain filter. In: IOP Conference Series: Materials Science and Engineering, vol. 546, p. 052061 (2019) 13. Karthik, R., Menaka, R., Chellamuthu, C.: A comprehensive framework for classification of brain tumour images using SVM and curvelet transform. Int. J. Biomed. Eng. Technol. 17, 168–177 (2015) 14. Nayak, D.R., Ratnakar, D., Banshidhar, M., Vijendra, P.: Automated pathological brain detection system: a fast discrete curvelet transform and probabilistic neural network based approach. Expert Syst. Appl. 88, 152–164 (2017) 15. Bhadauria, H.S., Dewal, M.L.: Medical image denoising using adaptive fusion of curvelet transform and total variation. Comput. Electr. Eng. 39, 1451–1460 (2013) 16. Aswathy, S.U., Glan Devadhas, G., Kumar, S.S.: Brain tumor detection and segmentation using a wrapper based genetic algorithm for optimized feature set. Clust. Comput. 22(6), 13369–13380 (2018). https://doi.org/10.1007/s10586-018-1914-8 17. Aswathy, S.U., Devadhas, G.G., Kumar, S.S.: A tumour segmentation approach from FLAIR MRI brain images using SVM and genetic algorithm. Int. J. Biomed. Eng. Technol. 33, 386–397 (2020) 18. Magudeeswaran, V., Fenshia, J.S.: Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images. Int. J. Imaging Syst. Technol. 27, 98–103 (2017) 19. Tan, T.L., Kok-Swee, S., Chih, P.T., Chong, A.K.: Contrast enhancement of computed tomography images by adaptive histogram equalization-application for improved ischemic stroke detection. Int. J. Imaging Syst. Technol. 22, 153–160 (2012) 20. Kumar, R.R., Abhinav, K., Subodh, S.: Anisotropic diffusion based unsharp masking and crispening for denoising and enhancement of MRI images. In: International Conference on Emerging Frontiers in Electrical and Electronic Technologies, pp. 1–6 (2020) 21. Maurya, R., Wadhwani, S.: Morphology based brain tumor identification and segmentation in MR images. In: IEEE Bombay Section Signature Conference, pp. 1–6 (2021)

Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge Selen Burçak Akkaya(&)

and Mahmut Ali Gökçe

Yaşar University, Selçuk Yaşar Kampüsü, Üniversite Caddesi Ağaçlı Yol No: 37-39, 35100 Bornova/İzmir, Turkey {selen.akkaya,ali.gokce}@yasar.edu.tr

Abstract. Use of Automated Guided Vehicles (AGVs) for material handling purposes has become increasingly popular. They introduce flexibility to the system by increasing the speed, responsiveness, and freight capacity as well as enabling increased productivity, safety, efficient resource utilization and reducing costs. These advantages can be realized by intelligent assignment of AGVs to jobs, and routing of AGVs to meet production plans. We look at the problem of scheduling and routing of a heterogenous fleet of AGVs, consisting of different types based on purpose of use, freight, and battery charge capacity used for handling transfer jobs in a production environment. The objective is to optimize the schedules and routes of AGVs by minimizing the penalty cost for the late delivery of a parts and energy consumption of the vehicles. To this end, a novel mixed integer linear programming model for a heterogenous fleet of AGVs along with charging and energy consumption is proposed where partial recharging is allowed. Proposed model is validated and verified with a test case using IBM OPL CPLEX and results are provided. Keywords: AGV


Scheduling-routing
Partial recharge
Heterogenous fleet

1 Introduction Technological advances over past decade have enabled many smart(er) systems to be used in production environment. Digitalization of manufacturing systems have become inevitable as a result of Industry 4.0. With Industry 4.0, the aim is to achieve a smart factory environment, where advanced manufacturing technologies are used, and human involvement is as little as possible in decision making processes through cyber-physical systems (CPS) [1]. Many technological developments such as Internet of Things (IoT), cloud computing, additive manufacturing (3D printing) and RFID (Radio Frequency Identification) are currently being implemented to manufacturing systems. One such important implementation is the usage of Automated Guided Vehicles (AGV). AGVs are used in production, material sorting, warehousing, yard operations, and service systems. They can be integrated with other equipment and are versatile. Main benefits

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 568–576, 2022. https://doi.org/10.1007/978-3-031-09176-6_65

Intelligent Scheduling and Routing of a Heterogenous Fleet

569

can be summarized as, reduced costs, increased productivity, decreased energy consumption and enhanced safety due to less human intervention in the system [2]. Systems where a fleet of AGVs are operating to perform transporting jobs are referred as Automated Guided Vehicle System (AGV System) [3]. These systems generally consist of the AGV fleet and the network where AGVs perform the assigned transportation jobs. The AGV system is constantly monitored and controlled. For the system there are several important decisions to be made; like AGV-job assignment, routing of the fleet, charging or swapping the batteries, and block and collision avoidance. Contribution of this study is the novel model for intelligent dispatching & routing of (Load and Battery) for Capacitated AGVs with the consideration of energy efficiency. A novel MILP model with the objective of minimizing costs incurred due to delay of delivery for the jobs and energy utilization by AGV system is proposed. As output, the method yields the assignment of transportation jobs to AGVs, the routes for AGVs and energy consumption and charging plan of each AGV. The rest of this study is structured as follows. In Sect. 2, the problem is described and assumptions are explained. The analysis of the existing related literature is presented in Sect. 3. In Sect. 4, the proposed solution method is presented. The proposed model’s verification and computational results are in Sect. 5. Finally, last section summarizes findings, and gives future plans and directions for further research.

2 Problem Definition We assume a production system composed of jobs with predetermined routes and due dates, a heterogenous fleet of AGVs to carry the material and a network for AGVs’ navigation, including depots, junction points, workstations and charging stations. A job shop production is assumed, where each job has a due date and route to follow according to a production plan and schedule. It is assumed that each job order visits each workstation on its route at most once, (no recirculation). Job orders could be of parts, tools, equipment, and work in process inventory. For the AGVs, these job orders are transformed into transfer jobs, representing movement of goods, which need to be picked up from a source (origin) and to be delivered to a destination based on the route. On the workstations, job orders will be processed and will not be ready for handling until the processing is completed. Therefore, there is an associated ready and due time for each transfer job, representing the earliest and latest time that can be picked-up or delivered according to the plan. Ready and due times are calculated based on the final due date, travelling time between workstations and processing time on the workstations such that final due date is met. In addition, each transfer job has known load amount, service time for loading/unloading. The precedence relationship between transfer jobs based on the route is satisfied through ready times and due times. Transportation of jobs are performed by a heterogeneous fleet of AGVs (maxi, midi and mini) with different load and battery capacities. AGVs are assumed to be powered by rechargeable batteries. When necessary, AGVs can travel to charging stations to have their batteries recharged. AGVs consume energy while traversing and transporting jobs and servicing (loading and unloading operations) with different rates.

570

S. B. Akkaya and M. A. Gökçe

Decisions that need to be made are two-fold. Transfer jobs must be assigned to AGVs. Also sequencing of jobs and routing for AGVs must be made. The routing decisions include partial recharging when needed. A job can only be assigned to a single AGV. If the AGV has enough battery charge and load capacity, more than one job can be assigned to it. A job can be picked up from its source location by an AGV as soon as it becomes available if it is assigned to it. Jobs can be delivered to destination locations after being picked up. Deliveries can occur before or after due time. However, if delivered after, this job is considered late and is penalized.

3 Literature Review Literature review is based on journal articles, proceedings, book chapters and case studies for years 1995–2021 from databases Web of Science, ScienceDirect and Google Scholar using the keywords; “AGV routing”, “AGV scheduling and routing”, “AGV”. Decisions for AGVs in production systems are primarily Scheduling and Vehicle Routing Problem related. Scheduling and vehicle routing problems are in their more generic form proven to be NP hard. Consequently, scheduling and routing of these systems, which is more complex is also NP hard [4]. Optimization models may be insufficient for solving large instances. For this reason, many researchers focus on heuristics. [4] provides a detailed literature review on AGV scheduling and routing at manufacturing, distribution, transshipment, and transportation systems. Different methodologies to optimize AGV systems are reviewed and categorized mainly as exact and heuristic, simulation, meta-heuristic methods, and artificial intelligent based approaches. Production and AGV scheduling, and routing decisions can be made either simultaneously or separately. However, generally these problems are studied separately since integrated scheduling and routing creates a challenging NP optimization problem [5]. One of the earlier studies by [6], address the problem of simultaneous scheduling of machines and material handling system where a number of identical AGVs are being used in a Flexible Manufacturing System. Objective is to minimize the makespan. Authors also propose an iterative procedure, where at each iteration a new schedule is generated through a heuristic and completion times are used as time windows for the problem. [7] studies the problem of the simultaneous production scheduling and routing for vehicles with conflict-free path selection. Due to the complex nature of the problem, authors propose a bi-level decomposition algorithm with the overall objective of minimizing the total tardiness of set of tasks as solution methodology. Moreover, they provide a mixed integer linear formulation in decomposed two levels: master problem of task assignment and scheduling; routing subproblem. [8] addresses the dispatching and conflict-free routing problem of capacitated AGV system with the use of homogeneous AGVs. They propose an optimization model and local/random search methods. Specific types of AGVs use rechargeable batteries as power source. To avoid unwanted situations related to availability, battery capacities and power consumptions of AGVs should be considered in the decision-making process. [6] emphasize on the

Intelligent Scheduling and Routing of a Heterogenous Fleet

571

lack of consideration of power consumption in commonly used task scheduling methods of AGVs. To fill this gap, they provide a model with the consideration of AGV’s battery consumption and an algorithm that can meet the real-time application in the factory. To the best knowledge of the authors, proposed model is the first to make scheduling and routing decisions for a heterogeneous fleet of AGVs, with energy efficiency consideration and partial recharge. The proposed model allows a more flexible, and ecofriendly approach. Furthermore, the model indirectly ensures the given production plan to be followed, by penalizing late deliveries and preventing deviations, when possible.

4 Solution Approach A novel MILP model is proposed in this section to solve routing and scheduling problem of AGVs using the structure and assumptions provided in previous section. 4.1

Indices, Sets and Parameters

Set N is for nodes and is indexed by i, j and h. N is defined as N = D [ CS [ SP [ IP where D = {0}, CS, SP = PP [ DP, and IP shows the sets of AGV depot, charging stations, service points (where PP and DP are pick-up and delivery points) and junction points. Additionally, N0 represents the set locations (nodes) except the depot and is defined as N0 = N\D. Parameter CRi, i 2 N, is 1 for CS, zero otherwise. Charge rate (time/unit) is fixed and equals to chargeRate. Travel network is shown as A = {(i, j) | i 2 N, j 2 N, i 6¼ j}. Distances and travelling time are represented by dij and tij, (i,) 2 A. Heterogenous fleet consists of three types of AGVs, V = Vmax [ Vmid [ Vmin indexed by v. Vmax, Vmid and Vmin shows max, medium and min capacity AGVs, respectively. Load capacity is Cv v 2 V, and takes values Cmax, Cmid and Cmin based on type. Battery capacity is Bv v 2 V, and takes values Bmax, Bmid and Bmin based on type. Initial charge of each AGV is InChargev, v 2 V. AGVs consume a fixed battery charge rate (units/distance) crl while travelling and cro (units) during servicing. Set of jobs is J = {(OrjJ, DesJ) | OrjJ 2 PP, DesJ 2 DP} indexed by w, where OrjJ and DesJ represent pick-up and delivery nodes of each job. Each job has load amount, ready, due and service times, represented by lw, rw, duew and sw, w 2 J respectively. Unit costs for pentalty cost of late delivery (cost per time), spent unit energy (cost per unit) and charging (cost per unit) are shown as clate, cenergy and ccharge. 4.2

Decision Variables

Binary decision variables xijv and ywv are defined for routing and dispatching. xijv is 1 if AGV v travels to node j immediately after visiting node i, where (i,j) 2 A| i 2 N, j 2 N, i 6¼ j,v 2 V. ywv is 1 if AGV v travels to node j immediately after visiting node i, where (i,j) 2 A| i 2 N, j 2 N, i 6¼ j,v 2 V. For scheduling of AGVs, avij represents the

572

S. B. Akkaya and M. A. Gökçe

arrival time of AGV v to node j from node i, v 2 V, (i,j) 2 A| i 2 N, j 2 N, i 6¼ j and depvij represents departure time of AGV v to from j to node i, v 2 V, (i,j) 2 A| i 2 N, j 2 N, i 6¼ j. Variables ptw, dtw and latenessw are pick-up time, delivery time and lateness duration of job w 2 J, respectively. Load amount, energy consumption and battery charge of AGV v when it arrives to node j travelling from node i, v 2 V, (i, j) 2 A| i 2 N, j 2 N, i 6¼ j are represented by variables loadvij, evij and bvij respectively. Finally, cvi is used for units charged for AGV v 2 V on station i 2 N. 4.3

The Model

Min

X

c latenessw w2J late

X

X

þ

v2V

c e ði;jÞ2A energy vij

þ

X

X v2V

c c i2N vi charge

ð1Þ

s.t X

x0jv  ywv 8 v 2 V; w 2 J

ð0;jÞ2A

X ði;jÞ2A

xijv ¼

X

X

xjhv 8 v 2 V; j 2 N

xi0v  ywv 8 v 2 V; w 2 J

ði;0Þ2A

X v2V

X

ðj;hÞ2A

ywv ¼ 18w 2 J

ð2Þ ð3Þ ð4Þ ð5Þ

ði;jÞ2A

xijv  ywv 8 w 2 J; v 2 V; i 2 PP &i ¼ OrjJ

ð6Þ

ði;jÞ2A

xijv  ywv 8 w 2 J; v 2 V; j 2 DP &j ¼ DesJ

ð7Þ

X

  0 þ t0j  av0j þ M 1  x0jv 8ð0; jÞ 2 A; v 2 V avhi þ CRi cvi chargeRate þ ti0  avi0 þ Mð2  xhiv  xi0v Þ8ðh; iÞ; ði; 0Þ 2 Aj i 2 N0; v 2 V

ð8Þ ð9Þ

  avhi þ CRi cvi chargeRate þ tij  avij þ M 2  xhiv  xijv 8ðh; iÞ; ði; jÞ 2 Aj i 2 ð10Þ N0nSP; j 2 N0; v 2 V   avhi þ CRi cvi chargeRate þ tij þ sw ywv  avij þ M 2  xhiv  xijv 8ðh; iÞ; ði; jÞ 2 J ð11Þ Aj i 2 SP &i ¼ OrjJorDesJ; v 2 V; w 2   ð12Þ 0  depv0j þ M 1  x0jv 8ð0; jÞ 2 A; v 2 V   avhi þ CRi cvi chargeRate  depvij þ M 2  xhiv  xijv 8ðh; iÞ; ði; jÞ 2 Aj i 2 N0 nSP; v 2 V

ð13Þ

Intelligent Scheduling and Routing of a Heterogenous Fleet

573

  avhi þ CRi cvi chargeRate þ sw ywv  depvij þ M 2  xhiv  xijv 8ðh; iÞ; ði; jÞ 2 ð14Þ Aj i 2 SP; &i ¼ OrjJorDesJ; v 2 V; w 2 J   ptw  depvij þ M 2  ywv  xijv 8ði; jÞ 2 Aj i 2 PP &i ¼ OrjJ; v 2 V; w 2 J ð15Þ   dtw  depvij þ M 2  ywv  xijv 8ði; jÞ 2 Aj i 2 DP &i ¼ DesJ; v 2 V; w 2 J

ð16Þ

ptw  rw 8w 2 J

ð17Þ

ptw  dtw 8; w 2 J

ð18Þ

latenessw  dtw  duew 8 w 2 J   depvij  avij  tij þ M 1  xijv 8ði; jÞ 2 A; v 2 V

ð19Þ ð20Þ

  avij þ sw  dtw þ M 2  ywv  xijv 8ði; jÞ 2 Aj j 2 DP&j ¼ DesJ; v 2 V; w 2 J

ð21Þ

  avij þ sw  ptw þ M 2  ywv  xijv 8ði; jÞ 2 Aj j 2 PP&j ¼ OrjJ; v 2 V; w 2 J

ð22Þ

loadv0j ¼ 08ð0; jÞ 2 A; v 2 V

ð23Þ

loadvi0 ¼ 08ði; 0Þ 2 A; v 2 V

ð24Þ

loadvij  Cv 8ði; jÞ 2 A; v 2 V

ð25Þ

  loadvij  loadvjh þ M 2  xijv  xjhv 8ði; jÞ 2 Aj j 2 N0nSP; v 2 V

ð26Þ

  loadvjh  loadvij þ M 2  xijv  xjhv 8ði; jÞ 2 Aj j 2 N0nSP; v 2 V

ð27Þ

  loadvij þ lw ywv  loadvjh þ M 3  xijv  xjhv  ywv ði; jÞ 2 Aj j 2 PP &j ¼ Orj; v 2 V; w 2 J

ð28Þ

  loadvij  lw ywv  loadvjh þ M 3  xijv  xjhv  ywv ði; jÞ 2 Aj j 2 DP &j ¼ ð29Þ DesJ; v 2 V; w 2 J   dij crl  evij þ M 1  xijv 8ði; jÞ 2 A; v 2 V ð30Þ   dij crl þ cro  evij þ M 2  xhiv  xijv 8ðh; iÞ; ði; jÞ 2 Aj i 2 SP &i ¼ OrjJor DesJ; v 2 V; w 2 J

bv0j ¼ InChargev x0jv  ev0j 8ð0; jÞ 2 A; v 2 V   bvjh  bvij þ CRj cvj  evjh þ M 2  xjhv  xijv 8ðj; hÞ; ði; jÞ 2 Aj j 2 N0; v 2 V

ð31Þ ð32Þ ð33Þ

574

S. B. Akkaya and M. A. Gökçe

  bvj0  bvij  evj0 þ M 2  xj0v  xijv 8ðj; 0Þ; ði; jÞ 2 Aj j 2 N0; v 2 V

ð34Þ

bvij  Bv xijv 8ði; jÞ 2 A; v 2 V

ð35Þ

xijv 2 f0; 1gði; jÞ 2 Aj i 2 N; j 2 N; i 6¼ j ; v 2 V

ð36Þ

ywv 2 f0; 1g w 2 J; v 2 V

ð37Þ

cvi  0 i 2 N; v 2 V

ð38Þ

avij  0; depvij  0; loadvij  0; evij  0; bvij  0ði; jÞ 2 Aj i 2 N; j 2 N; i 6¼ j ; v 2 V ptw  0; dtw  0; latenessw  0 w 2 J

ð39Þ ð40Þ

The objective function (1) minimizes costs incurred due to lateness of delivery for the transportation jobs and energy usage by AGV system. (2)–(7) shows main routing constraints. Constraints (5)–(22) are concerned with scheduling and dispatching in addition to routing decisions. (5) explains that a job can be assigned to a single AGV. Constraints (6) and (7) ensure AGVs to travel to a job’s source and destination nodes during their trip if that job is assigned to them. Time related constraints are shown in constraints (8)–(22). (8)–(14) shows arrival and departure time calculations of AGVs to locations. Pick-up and delivery time calculations of jobs are provided in (15) and (16). Constraints (17) and (18) explain that the pick-up time of a job cannot be lower than its ready time and greater than its delivery time respectively. (17) shows the delivery time calculation and (18) provides the lateness calculation of a job. Constraints (23)–(29) represent load constraints. Load calculations during the trip of AGVs are provided through constraints (26)–(29). Energy consumption calculations of AGVs are provided with (30) and (31). Constraints (32)–(35) are concerned with battery charge of AGVs respectively. Finally, constraints (39)–(40) explain the domains of decision variables.

5 Computational Results and Conclusions The model is tested and validated with a test problem. Test problem consists of total 20 locations (nodes), where a single AGV depot, four service points and six charging stations. Two job orders with routes 1-6-12 and 3-6-12 and final due date of 40 and 50 respectively is present. Based on these job orders, there are four transportation jobs to be assigned. Time windows of transportation jobs are generated according to the final due date and routes. A total of three vehicles, two of each type (load and charge capacities 20, 15 and 10 units respectively) are used for the handling operations. Travelling, service and unit charging times, unit energy consumption while travelling and loads are assumed as one unit. The solution is presented in Table 1.

Intelligent Scheduling and Routing of a Heterogenous Fleet

575

Table 1. Verification and solution representation of small test problem AGV Route 1

0-2-1-5-6-10-12-1311-8-3-0

2

0-3-8-7-6-2-0

Assigned jobs 1 2 4 3

Pick-up times 2 3 6 6

Delivery time 6 6 9 9

Total consumed energy 17

Total charging 0

10

0

Solution time is 00:03:54 min. Total cost is found as 27. No charging is required in this instance and jobs are delivered on time by two AGVs. If the initial charge is decreased, AGVs are forced to recharge. As a result, successful solution of this test problem and the result verifies and validates our model.

6 Conclusions and Future Remarks AGVs are increasingly used in more industries in versatile roles. In this study, we present a novel mathematical model for intelligent routing and scheduling of AGVs, with partial recharging. Implementation of the study is given in a job-shop environment, in which routing of parts and a production plan is pre-determined. This data can be processed to come up with a transfer job list for the AGVs with ready and due times. The proposed model is verified and validated at a test problem. Currently, preparation and execution of a much larger experimental design, generated from [9] is underway. The authors hope to get more insight from the results of this larger experimental design and develop a heuristic method for solving really large practical instances, especially for warehousing operations. Although the implementation of the model is presented for a job-shop environment, the model can easily be implemented in other usage areas AGVs. Finally, the processing times and therefore ready and due times of jobs are taken as deterministic. But there is an inherent uncertainty in the real world. A good solution would be to come up with a robust solution, which will be good under many possible scenarios, instead of being the best solution for a single deterministic scenario.

References 1. Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017) 2. Bechtsis, D., Tsolakis, N., Vlachos, D., Iakovou, E.: Sustainable supply chain management in the digitalisation era: the impact of Automated Guided Vehicles. J. Cleaner Prod. 142, 3970– 3984 (2017). https://doi.org/10.1016/j.jclepro.2016.10.057 3. Vis, I.F.A.: Survey of research in the design and control of automated guided vehicle systems. Eur. J. Oper. Res. 170, 677–709 (2006)

576

S. B. Akkaya and M. A. Gökçe

4. Fazlollahtabar, H., Saidi-Mehrabad, M.: Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study. J. Intell. Rob. Syst. 77(3–4), 525–545 (2013). https://doi.org/10.1007/s10846-013-0003-8 5. Wang, F., Zhang, Y., Su, Z.: A novel scheduling method for automated guided vehicles in workshop environments. Int. J. Adv. Rob. Syst. 16(3), 1–13 (2019) 6. Bilge, U., Ulusoy, G.: Time window approach to simultaneous scheduling of machines and material handling system in an FMS. Oper. Res. 43(6), 1058–1070 (1995). https://doi.org/10. 1287/opre.43.6.1058 7. Nishi, T., Hiranaka, Y., Grossmann, I.E.: A bilevel decomposition algorithm for simultaneous production scheduling & conflict-free routing for AGVs. Comput. Oper. Res. 38(5), 876–888 (2011) 8. Miyamoto, T., Inoue, K.: Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems. Comput. Ind. Eng. 91, 1–9 (2016) 9. Solomon, M.M.: algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35, 254–265 (1987)

Linguistics

A MCDM Method for Measuring Digital Capability Maturity Based on Linguistic Variables and Fuzzy Integral Chen-Tung Chen1(&), Alper Ova2, and Wei-Zhan Hung3 1

2

Department of Information Management, National United University, Miaoli City, Taiwan [email protected] Faculty of Business Administration, İstanbul Bilgi University, İstanbul, Turkey 3 Department of Digital Business Management, Kao Yuan University, Kaohsiung City, Taiwan

Abstract. Digital transformations are risky and difficult missions so that the managers should be supported with suitable assessment tools. In fact, enterprises must understand the digital capabilities before they implement the digital transformation strategies. Maturity model (MM) is an effective and useful tool to evaluate the initial status of digital capabilities of the company appropriately. But, the maturity of digital capabilities must evaluate the maturity level by multiple experts with respect to multiple influenced factors in the evaluation process. Due to experts always are difficult to use the crisp values to indicate their evaluation values, it is reasonable way to express their opinions by using the linguistic variables. Besides, there are some degree of interaction and dependence among influenced factors, this paper proposed a systematic method to measure digital capability maturity based on linguistic variables and fuzzy integral. Finally, an example used to explain the computational steps to verify the effectiveness of the digital capability maturity model. Keywords: Maturity model


Linguistic variables
Fuzzy integral

1 Introduction In recent years, the businesses model has been influenced by the rapid development of new information technologies such as big-data tools, artificial intelligence method and industry 4.0 [1]. It became an important management issue for managers to apply digital technologies to construct new business operational model and to increase the competitiveness in the market. In fact, digital transformation (DT) is a critical issue for enterprises to construct new operational way in business functions such as customer relationships, internal processes and value creation [2]. Because the constraints of limited resources, lack the expertise and knowledge of companies, it is not easy to implement digital transformation projects successfully [3]. In reality, enterprises must understand the digital capabilities before they implement the strategies of digital transformation. Maturity model (MM) is useful to evaluate the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 579–587, 2022. https://doi.org/10.1007/978-3-031-09176-6_66

580

C.-T. Chen et al.

initial status of digital capabilities of the company appropriately. The digital capability maturity model can assist companies in planning and evaluating digital transformation programs effectively [3]. In a maturity model, different levels of maturity must be defined. Maturity models typically provide an assessment of the current maturity stage and a series of recommendations for improvement [4]. Digital transformation is not only about introducing new technologies, investing in tools or upgrading existing systems but also is needing to understand the current status of digital capabilities to successfully build a new business model to improve competitive advantage [3, 5, 6]. In general, evaluators usually use crisp values to make judgments and assume that the impact factors of maturity model are independent. However, the opinions of experts are always fuzziness and uncertainties, the use of linguistic variable can express their subjective assessments completely. Enterprise digital transformation is a dynamic process and influence factors may affect each other. It is not suitable to use simple weighting aggregation (SWA) method to calculate the maturity degree of digital capabilities. Therefore, the aim of this paper is to present a systematic method by combining linguistic variables with fuzzy integral to measure the digital capability maturity of digital transformation. This paper divided into six sections. The related literatures review presented in Sect. 2. The definitions and notations of the linguistic variables and fuzzy integral introduced in Sect. 3. A systematic method of digital capability maturity presented in Sect. 4. An example showed to explain the computational steps in Sect. 5. At the end of this paper, conclusions and future research directions are discussed in Sect. 6.

2 Literatures Review Enterprises are focusing on the applications of information technologies across organizations, it changes the ways about creating values and the way in touch with customers [1, 2]. Although managers are increasingly willing to invest in the digital transformation of their businesses, they are always lack knowledge about the current status of digitalization and strategic guidance to achieve the purpose of digital transformation [3]. Therefore, Schumacher et al. [6] proposed a maturity model which includes 8 major dimensions and 65 key success indicators. The eight major dimensions are divided into information technology, products, customers and partners, the process of value creation, data and information flow, operational standards, employees, development strategy and leadership. They divided the status of maturity into five levels. To address the challenges by digital and network technologies, Pulkkinen, et al. [7] divided the maturity status into five levels of maturity and consider a total of 69 criteria in six key performance areas. The six key performance areas (KPA) are development strategies, business model, process, performance indices, information interfaces and flow. In a word, we can find that the problems in the evaluation process of the enterprise digital capability maturity are: (i) The architecture of the digital capability maturity model must include multiple dimensions and indicators. (ii) The evaluation process of digital capability maturity involves the ambiguity of experts’ subjective cognitions.

A MCDM Method for Measuring Digital Capability Maturity

581

(iii) In the framework of digital capability maturity, it must be considered that the relationships among all dimensions. Due to the interdependence of influencing factors, it is adequate to use fuzzy integral to calculate the degree of the relationships between factors. In fact, the fuzzy integral has applied to solve numerous MCDMs problems [8–10]. Therefore, this paper combined linguistic variables with fuzzy integral to construct a fuzzy group multicriteria decision-making method (GMCDM) to measure the digital capability maturity of digital transformation.

3 Definitions and Notations ~ ¼ ða; b; cÞ. Definition 3.1. A positive triangular fuzzy number (PTFN) represented as A ~ can be defined as follows (shown in Fig. 1) [11, The membership function (uA~ ð xÞÞ of A 12]: 8 xa < ba ; a  x  b xc uA~ ð xÞ ¼ bc ;bxc : 0; otherwise

ð1Þ

where a > 0 and a  b  c.

~ Fig. 1. Membership function of A.

~ ¼ ðal ; am ; au Þ and the defuzzification value (A ~ Definition 3.2. Suppose that A ~ A can be computed as [13]: ~ def ¼ al þ am þ au A 3

def

) of

ð2Þ

Definition 3.3. Linguistic variables. In real life, decision makers often face many complicate situations that are difficult to clearly express their opinions by using quantitative ways. It is suitable to apply

582

C.-T. Chen et al.

linguistic variables to show the subjective evaluations of all experts in evaluation process [14, 15, 16]. For example, expert can use five linguistic variables to describe the importance degree of criteria such as very low(s1 ), low(s2 ), normal(s3 ), high(s4 ), and very high(s5 ). Each linguistic variable can represent as a triangular fuzzy number. For instance, the “high(s4 )” can represent as s4 ¼ ð0:5; 0:75; 1:0Þ. Definition 3.4. Let X = {x1 ; x2 ; . . .; xn }, g be a fuzzy measure function, P(x) is a power set. For 8A, B 2 P(x), A \ B = ∅, then g has the following properties [8, 17]: gð£Þ ¼ 0; gðXÞ ¼ 1

ð3Þ

gk ðA [ BÞ ¼ gk ðAÞ þ gk ðBÞ þ kgk ðAÞgk ðBÞ

ð4Þ

where −1 < k < ∞. Assume that X = {x1 ; x2 ; . . .; xn } and gi ¼ gk ðfxi gÞ. The fuzzy measure function gk ðfx1 ; x2 ; . . .; xn gÞ can be illustrated as follows [8, 17]. gk ðfx1 ; x2 ; . . .; xn gÞ n n1 n P Q P ¼ gi þ k gi1
gi1 þ . . . þ kn1
g1
g2
. . .
gn ¼1 ¼i þ 1 i¼1 i i 1 2 1 n  Q 1 ¼k ð1 þ kgi Þ  1

ð5Þ

i¼1

If h is a measurable function which R defined in the space X. Let hðx1 Þ  hðx2 Þ. . .  hðxn Þ. The fuzzy integral value ( h
dg) based on functions g(∙) and h(∙) can be defined as follows [8, 17]. R

h
dg ¼ hðxn Þ
gðHn Þ þ ½hðxn1 Þ  hðxn Þ
gðHn1 Þ þ . . . þ ½hðx1 Þ  hðx2 Þ
gðH1 Þ ¼ hðxn Þ
½gðHn Þ  gðHn1 Þ þ hðxn1 Þ
½gðHn1 Þ  gðHn2 Þ þ . . . þ hðx1 Þ
gðH1 Þ ð6Þ Where H1 ¼ fx1 g; H2 ¼ fx1 ; x2 g; . . .; Hn ¼ fx1 ; x2 ; . . .; xn g ¼ x.

4 Proposed Model In general, the digital capabilities maturity evaluation can be regarded as a group multiple criteria decision-making (GMCDM) problem. The problem includes multiple experts Dk ðk ¼ 1; 2; . . .; KÞ, multiple criteria C i ði ¼ 1; 2; . . .; nÞ, multiple indicators with respect to each criterion C ij ði ¼ 1; 2; . . .; n; j ¼ 1; 2; . . .; ti Þ, the weight of each criterion wj ðj ¼ 1; 2; . . .; nÞ, and the weight of each indicator with respect to each criterion wij ði ¼ 1; 2; . . .; n; j ¼ 1; 2; . . .; ti Þ. The computational procedures of the evaluation model of digital capabilities maturity can be explained as the following steps.

A MCDM Method for Measuring Digital Capability Maturity

583

(1). Collect the initial data from experts ~ ki can Experts used the linguistic variables to express their evaluations. The w ~ kij is the fuzzy weight represent the fuzzy weight of criterion ci by expert Dk . The w of j-th indicator with respect to criterion Ci by expert Dk . The ~xkij is the fuzzy rating of j-th indicator with respect to criterion C i by expert Dk . (2). Aggregate the initial data of all experts as follows ~xij ¼

K 1X ~xk K k¼1 ij

ð7Þ

~i ¼ w

K 1X ~k w K k¼1 i

ð8Þ

~ ij ¼ w

K 1X ~k w K k¼1 ij

ð9Þ

~ i is the aggregation of fuzzy weight of where ~xij is the aggregation fuzzy rating, w ~ ij is the aggregation of fuzzy weight of j-th indicator with respect criterion C i , and w to criterion Ci . (3). Transfer the aggregation ratings and weights into crisp values. Suppose that ~xij ¼     ~ ij into crisp values xij and ~ ij ¼ wij1 ; wij2 ; wij3 , transfer ~xij and w xij1 ; xij2 ; xij3 and w wij as follows. xij ¼ wij ¼

xij1 þ xij2 þ xij3 3

ð10Þ

wij1 þ wij2 þ wij3 3

ð11Þ

(4). Compute the fuzzy measure of each criterion as follows. 1 þ ki ¼

Yt j j¼1

ð1 þ ki  wij Þ

ð12Þ

According to the Eq. (6), we can compute the fuzzy integral value of criterion C i as Mi . (5). Compute the final fuzzy measure as follows. 1þk ¼

Yn i¼1

ð1 þ k  wi Þ

ð13Þ

According to the Eq. (6), we can compute the final fuzzy integral value as M. (6). Transfer the final fuzzy integral value into linguistic variable to describe the maturity status of the digital capabilities.

584

C.-T. Chen et al.

5 Example Suppose that top manager wants to understand the maturity status of digital capability before implementing the digital transformation strategy. Top manager assigned three experts to make the maturity evaluations in accordance with three criteria such as information technology (C1), management capability (C2), and process capability (C3). There are three indicators should be evaluated under each criterion. The steps of maturity evaluations model can illustrate as follows. Step 1. The weights and ratings of three experts by using the linguistic variables (refer to Table 1) as shown in Tables 2, 3 and 4. Step 2. Aggregated the linguistic variables of three experts as Tables 2, 3 and 4 and transferred linguistic variables into crisp values as Table 5 and Table 6. Step 3. Computed the fuzzy measure and the fuzzy integral values of three criteria as Table 7. Step 4. According to data of the Table 7, the degree of digital capability maturity can be computed as 8.594. Transferred the degree value into performance rating (refer to Table 1) as (uG ð8:594Þ ¼ 0:56) and (uVG ð8:594Þ ¼ 0:44). Check to the Table 1, the maturity status of digital capability is between “Good” and “Very Good”. Step 5. Based on the Table 7, the maturity strength order of three criteria is C2 > C1 > C3. The worst indicator is C11 and the next indicators are C 21 ; C 23 ; C33 : Table 1. Fuzzy performance ratings and weights s1 s2 s3 s4 s5

Weights Very Low (VL) Low (L) Medium (M) High (H) Very High (VH)

Fuzzy numbers (0, 0, 0.25) (0, 0.25, 0.5) (0.25, 0.5, 0.75) (0.5, 0.75, 1) (0.75, 1, 1)

Performance ratings Very Poor (VP) Poor (P) Medium (M) Good (G) Very Good (VG)

Fuzzy numbers (0, 0, 2.5) (0, 2.5, 5) (2.5, 5, 7.5) (5, 7.5, 10) (7.5, 10, 10)

Table 2. Linguistic weights of three criteria by three experts C1 C2 C3

D1 M VH H

D2 H H H

D3 H VH VH

A MCDM Method for Measuring Digital Capability Maturity Table 3. Linguistic weights of all indicators by three experts C11 C12 C13 C21 C22 C23 C31 C32 C33

D1 M VH H H VH H M VH VH

D2 H H H VH H H M H VH

D3 H H VH H H H M VH VH

Table 4. Linguistic ratings of all indicators by three experts C11 C12 C13 C21 C22 C23 C31 C32 C33

D1 M VG G G VG M M G G

D2 M G VG M VG G G VG G

D3 G G G G VG G VG G M

Table 5. Aggregated fuzzy weights of criteria and indicators by three experts Weights Crisp C1 (0.42, 0.67,0.92) 0.67 C11 C12 C13 C2 (0.67,0.92,1.00) 0.86 C21 C22 C23 C3 (0.58,0.83,1.00) 0.81 C31 C32 C33

Fuzzy weights (0.42, 0.67,0.92) (0.58,0.83,1.00) (0.58,0.83,1.00) (0.58,0.83,1.00) (0.58,0.83,1.00) (0.5,0.75,1.00) (0.25,0.5,0.75) (0.67,0.92,1.00) (0.75,1.00,1.00)

Crisp 0.67 0.81 0.81 0.81 0.81 0.75 0.50 0.86 0.92

585

586

C.-T. Chen et al. Table 6. Aggregated fuzzy ratings of indicators by three experts Criteria Indicators Fuzzy ratings C11 (3.33, 5.83,8.33) C1 C12 (5.83, 8.33,10) C13 (5.83, 8.33,10) C2 C21 (4.17, 6.67,9.17) (7.5,10,10) C22 C23 (4.17, 6.67,9.17) C3 C31 (5, 7.5,9.17) C32 (5.83, 8.33,10) C33 (4.17, 6.67,9.17)

Crisp 5.83 8.06 8.06 6.67 9.17 6.67 7.22 8.06 6.67

Table 7. Fuzzy measure and fuzzy integral values Criteria k Weights Fuzzy integral Indicators −0.99 0.67 8.00 C11 C1 C12 C13 C2 0.86 8.695 C21 C22 C23 C3 0.81 7.906 C31 C32 C33

k Weights Ratings −0.986 0.67 5.83 0.81 8.06 0.81 8.06 −0.99 0.81 6.67 0.81 9.17 0.75 6.67 −0.994 0.50 7.22 0.86 8.06 0.92 6.67

6 Conclusions Recently, how to implement digital transformation successfully is an important management issue for enterprises to construct new business models to increase the competitive advantages in the market. In fact, digital transformations are risky and difficult undertakings so that the managers should be supported with appropriate assessment tools. Therefore, companies must understand the digital capabilities before they want to implement digital transformation strategy. Due to the evaluations of experts always are not easy to express as crisp values, the linguistic variables suitable used to describe their opinions in this paper. In additional, there are interactions and dependence among influenced factors for measuring digital capability maturity. The major contribution of this study is to propose a systematic assessment model of digital capability maturity based on linguistic variables and fuzzy integral. According to the proposed method, managers not only can understand the status of the digital capability but also can indicate the strength and weakness directions to improve the digital capability. In the future, we can combine other MCDM methods with fuzzy integral to construct the maturity model of digital capability.

A MCDM Method for Measuring Digital Capability Maturity

587

References 1. Rothberg, H., Erickson, S.: Big data systems: knowledge transfer or intelligence insights. J. Knowl. Manag. 21(1), 92–112 (2017) 2. Zaoui, F., Souissi, N.: Roadmap for digital transformation: a literature review. Procedia Comput. Sci. 175, 621–628 (2020) 3. Stich, V., Zeller, V., Hicking, J., Kraut, A.: Measures for a successful digital transformation of SMEs. Procedia CIRP 93, 286–291 (2020) 4. Colli, M., Sala, R., Pirola, F., Pinto, R., Cavalieri, S., Wæhrens, B.V.: Implementing a dynamic FMECA in the digital transformation era. IFAC-PapersOnLine 52(13), 755–760 (2019) 5. Albukhitan, S.: Developing digital transformation strategy for manufacturing. Procedia Comput. Sci. 170, 664–671 (2020) 6. Schumacher, A., Nemeth, T., Sihn, W.: Roadmapping to towards industrial digitalization based on an industry 4.0 maturity model for manufacturing enterprises. Procedia CIRP 79, 409–414 (2019) 7. Pulkkinen, A., Anttila, J.P., Leino, S.P.: Assessing the maturity and benefits of digital extended enterprise. Procedia Manuf. 38, 1417–1426 (2019) 8. Olawumi, T.O., Chan, D.W.M.: Application of generalized Choquet fuzzy integral method in the sustainability rating of green buildings based on the BSAM scheme. Sustain. Cities Soc. 61, 102147 (2020) 9. Wu, Y., Zhang, T., Yi, L.: Regional energy internet project investment decision making framework through interval type-2 fuzzy number based Choquet integral fuzzy synthetic model. Appl. Soft Comput. 111, 107718 (2021) 10. Hadjadji, B., Saumard, M., Aron, M.: Multi-oriented run length based static and dynamic features fused with Choquet fuzzy integral for human fall detection in videos. J. Vis. Commun. Image Represent. 82, 103375 (2022) 11. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Application. Prentice-Hall Inc, New Jersey (1995) 12. Celik, M., Ahmet Kandakoglu, I., Er, D.: Structuring fuzzy integrated multi-stages evaluation model on academic personnel recruitment in MET institutions. Expert Syst. Appl. 36(3), 6918–6927 (2009) 13. Cheng, C.H., Lin, Y.: Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. Eur. J. Oper. Res. 142(1), 174–186 (2002) 14. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I. Inf. Sci. 8, 199–251 (1975) 15. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning II. Inf. Sci. 8, 301–357 (1975) 16. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning III. Inf. Sci. 9, 43–80 (1975) 17. Abdullah, L., Zulkifli, N.: A new DEMATEL method based on interval type-2 fuzzy sets for developing causal relationship of knowledge management criteria. Neural Comput. Appl. 31 (8), 4095–4111 (2018). https://doi.org/10.1007/s00521-017-3304-1

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms Yinsheng Zhang(&) Institute of Scientific and Technical Information of China, Beijing 100038, China [email protected]

Abstract. The paper solves how to reform Aristotelian syllogisms (ASs) to make it compatible with classic logic, and further formally deduct them in logic programming languages. It asserts that there exist two challenging problems in Aristotelian categorical propositions (ACPs) among ASs. One is inconsistently to regard the particular quantifier as the existential quantifier meanwhile as the partial, another one is lacking a quantifier binding the second term. To overcome the two problems, new forms of categorical propositions (called expanded categorical propositions, ECPs) are introduced without semantic confusion in interpretations of the particular quantifier, and with the remedied second quantifier. Naturally, made up of ECPs, the forms of quantifier-expanded syllogisms (QESs) are constructed. To deduct QESs, a formal system, also a Turing machine, is designed to decide and symbolically generate valid conclusions. Thus, a semantics-consistent and form-intact system of QESs, with deductive rules based on mathematically computing models has been established. Keywords: Syllogisms
Inconsistency
Turing machines for deduction
Particular quantifier
Partial quantifier
Existential quantifier
Formal system

1 Introduction Syllogisms, also considered as Aristotelian syllogisms (ASs) if no special specification, are basic forms of inference, which are often treated as perfect, even as of common sense to be widely accepted, inherited and spread, as the following remark typically says: The step from Aristotelian physics to Galilean physics is nowadays a prime textbook example of a ‘paradigm shift’ in science—a shift not only between theories but form one world-image to an incompatible world-image. Could something similar, albeit on a smaller scale, be said of step in the last 19th century from Aristotelian to modern logic? A quick look at some of writings of Frege and Russell, or of textbook accounts of the birth of modern logic, would seem to indicate this. In the present note I wish to point out, however, that such an impression is mistaken. …… [for that] an Aristotelian perspective on quantifiers is far from being incompatible with modern logic; in fact, it is still capable of yielding new insights.…… [In conclusion,] Aristotle’s treatment of quantifier fits well into a modern of generalized quantifiers [1]. However, some challenging problems with syllogisms have been addressing since the modern times, which give insights different with, even opposite to, the above © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 588–605, 2022. https://doi.org/10.1007/978-3-031-09176-6_67

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

589

remark. One challenging problem is that Aristotelian categorical propositions (ACPs), which make up ASs, accept different semantics in quantitatively modeling ACPs — Euler and Gergonne put their respective diagrams upon identical ACPs as in Illustration 1. An essential distinction between Euler and Gergonne is the contradiction-containing interpretations on the particular quantifier: Euler takes the particular quantifier in proposition I and O as “existential but not all”, or “partial”; while Gergonne takes it as “existential and possibly all”–the former is restrict to the universe of the set X, conveying incompleteness, but the latter is unrestrict to the universe, conveying indeterminacy of whether it is incomplete or not. In other words, there exists a particularquantification confusion, or a semantic ambiguity, between the incompleteness and the indeterminacy in ACPs.

Illustration 1. Euler diagrams and Gergonne diagrams of ACPs (●, ⊠, , , and || denote samples of the relationship between X and Y, or schemes of intersection between X and Y, which are attached by the present paper, not in the citation) [2, pp. 198–199 (Book I/I 24a,17–27), 3, p.129, p. 455, 4, p. 350, 5, 6].

Another challenging problem is that an ACP is quantifier-monadic, namely, the term y lacks an expression of quantification in the relation of quantifier-bound term x. The shortage ditto hinders ACPs from entering classic logic based on first-order logic (FOL) that requires intact binding variables-terms with quantifiers, when expressing knowledge in FOL-based formal language. The two defaults, i.e. the problems of confusion and insufficiency of quantification of ACPs, cause ASs losing their reliable versions. Further, if FOL is the accepted logic universally compatible with mathematics, then ACPs, and ASs should not be compatible in exact mathematical sense. That is to say, without a clear interpretation to sovle the defaults, ASs would contain an inconsistency of the particualr quantifier (simultaneosly exclusing and including the universe of X), and remain an incompleteness in representing quantified Y.

590

Y. Zhang

Since emergence of the diffrent interpretations between Euler and Gergone, the efforts to overcome the ambiguity of the particualr quantifier seem to prefer Gergone’s scheme, i.e. interpreting the particular as the exstential. This interpretation faces a difficulty to explain if Euler was wrong, as Stenning and Lambalgen quest: “How could a brilliant mathematician like Euler make such a fundamental mistake?” [7, pp. 302–303]. As far as the shortage of second quantifier is concerned, quantifier-dyadic propositions, namely the categorical with double quantifiers for the both terms, were introduced in the logical history, as mentioned by Kneale [4, p. 253] that Hamilton first systematically proposed the quantifier-dyadic forms to improve ACPs. However, he did not introduce the existential quantifier, and still used the ambiguous concept “the particular”, which in effect does participation in, not avoiding, the confusion of the two kinds of semantics of the particular quantifier. Especially, he did not formally distinguish the partial and the existential by setting two independent symbols with respective semantics. So, his scheme has not been applied up to now in fact. Hence, ASs fall in a predicament with the two defaults: the inconsistency of the particular categorical propositions I and O with an antinomy of unrestriction and restriction to the universal set, and the non-intact, monadic forms of ACPs different from FOL. The troubles inquire about what the primary or authentic forms of syllogism are, as Harrison says: Still, it’s not clear that this [the ASs interpreted by first-order logic] is really a faithful exegesis of how Aristotle and/or the medieval logicians really thought about syllogistic reasoning [8, pp. 317–320]. Besides, suppose the defaults of ACPs and ASs were confirmed, what reforms should be made, so that the syllogisms be semantics-consistent, quantifier-binder intact, and closed to the primary syllogisms (only removing the improper semantics and syntax, as closing as possible to ASs in forms), is a task that the present study aims to complete. For improving the forms of ACPs with the semantics, [9] and [10] proposes the expanded categorical propositions (ECPs), which are the form of dyadic categorical propositions in the structure, “Q1X are/are not Q2Y”, where Q1, Q2 2 {8, 〒, 9},〒 reads “the partial”, or “exists but not all”, as given in Table 1 (updated a bit), where “be” is omitted and “not” is denoted by “¬”. ECPs characterized by the following points: (i) ECPs cover both Euler and Gergonne’s interpretations on the particular quantifier. As ECPs denominate their respective concepts with independent and different symbols (9, 〒) to replace one symbol “Ŧ” (“particular”), they harmonize Euler and Gergonne’s schemes, removing the inconsistency. Consequently, no matter which interpretation from Euler or Gergonne belongs to the original from Aristotle, or more reasonable for the reform, ECPs guarantee cover, or mostly closed to, the version containing the two possible ones.

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

591

Table 1. Expanded categorical propositions (ECPs). Forms

Combinations of Sample of Relation of sets of X to Y 9x9y {●, ⊠, , } 8x9y {●, } 〒x9y {⊠, } 9x8y {●, } 9x〒y {⊠, } 8x8y {●} 8x〒y {} 〒x8y {} 〒x〒y {⊠} 9y¬[8x] {║, , ⊠} 〒y¬[8x] {⊠, } 9x¬[8y] {║, , ⊠} 8x¬[8y] {║} 〒x¬[8y] {⊠, }

Expressions in English Existential elements of X are existential elements of Y All elements of X are existential Y Partial elements of X are existential elements of Y Existential elements of X are all elements of Y Existential elements of X are partial elements of Y All elements of X are all elements of Y All elements of X are partial elements of Y Partial elements of X are all elements of Y Partial elements of X are partial elements of Y Existential elements of Y are not elements of X Partial elements of Y are not elements of X Existential elements of X are not elements of Y All elements of X are not elements of Y Partial elements of X are not elements of Y

(ii) ECPs are quantifier-dyadic, considering Hamilton’s formal proposal to arrange every term to be quantifier-bound, remedying the formal shortage of ACP quantification. (iii) ECPs are mathematically defined. Their quantifiers are grounded on set operations, and ECPs are on combinations of relations of sets. So, they are exact and disambiguous, which make ECPs and further QESs both numerically and logically computable, especially for executable programs. (iv) ECPs essentially are abstract structures of natural languages (at least English). They adhere to grammars, which include the laws that words are in an order for some definite semantics. For example, the copula of negation “be not” should not be followed by an undistributed quantifiers 〒 or 9. So, ECPs are symmetry and completely permuted of the quantifiers and terms, excepting that〒 and 9 are forbidden. This point makes their logic are coherent with the traditional and philosophical logic, including Aristotelian logic when it is clarified, corrected and remedied upon its ambiguities, confusion and limitations. Thus, some questions are naturally posed that it how to construct quantifierexpanded syllogisms (QESs) using ECPs, and to deduct QESs. The present study tries to answer them in the following contexts. In Sect. 2, the questions will be represented in formal ways for further analysis and answering. In Sect. 3, a formal system (meanwhile as a Turing machine to run by Chomsky 0-type grammar) is built to deduct QESs. Section 4 is to certify this formal system. Finally, Sect. 5 is for summarization.

2 Modeling the Deduction Goal for Expanded Syllogisms ASs are categorized into 4 figures as in Table 2, where copula refers to “be”/“be not”. The term “figure” (or “Figure”) for a syllogism is understood as a permutation of terms in a deduction.

592

Y. Zhang Table 2. The structures of Aristotelian syllogisms (AS). Figure 1 Major Q1Y copula Z Minor Q2X copula Y Conclusion Q3X copula Z

Figure 2 Q1Z copula Y Q2X copula Y Q3X copula Z

Figure 3 Q1Y copula Z Q2Y copula X Q3X copula Z

Figure 4 Q1Z copula Y Q2Y copula X Q3X copula Z

(1) is a flatten deductive structure of ASs in Table 2. RðQ1  fX; Y gÞ ^ RðQ2  fY; Z gÞ  RðQ3  fX; Z gÞ

ð1Þ

In (1), ⊙ is a connective, modifying R to be a copula like “be”/“be not”, i.e. ⊙ 2 {+, ¬} (+ can be omitted); R is a relation “identity” or “coincidence”; Q1, Q2, Q32 QA = {8, Ŧ}; and • is the functor to associate its left variable with only one of the two sets S1 and S2 in its right bracket. That is, • is to fold Qi in a list {S1, S2}, namely, to associate only the first element in its followed list, giving {Qi S1, S2}. Thus (1) can be represented as a relating expression (R, ^,!) such that \QA ; ; V [ ‘ T

ð2Þ

where, V, 6-permutations of the two non-repeated ones among (x, y, z), and T, the truth. That is, to solve true ones in (1) is also expressed as the problem to infer truth given the variables of QA, ⊙ and V. Being compared with (1) and (2), the deductive structure of QESs are established as (3), and Q1, Q2,Q3,Q4,Q5,Q6 2 QE = {8, 〒, 9} RE ðQ1 X; Q2 Y Þ ^ RE ðQ3 Y; Q4 Z Þ  RE ðQ5 X; Q6 Z Þ

ð3Þ

where, RE keeps the same semantics in (1) with the exception that RE is commutative, namely, RE(p, q) = RE(q, p). (3) is also a relating expression (RE, ^, !) by (4). \QE ; ; V [ ‘ T

ð4Þ

(4) is to find valid solutions in a space of (5), (6) and (7). Q4E ! Q2E

ð5Þ

2 ! 

ð6Þ

Vp ! Vc

ð7Þ

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

593

where, V ¼ Vp [ Vc ; Vp ¼ fðy; z; x; yÞ; ðz; y; x; yÞ; ðz; p; y; xÞ; ðz; y; y; xÞg; Vc ¼ fðz; xÞ; ðx; zÞg: The possible schemes of (5), (6) and (7), or forms of QESs, will be available with such the variable combinations, reaching 46656:

The 46656 QESs instantiated by (5), (6) and (7) are construed as shown in Table 3. Table 3. The structure of quantifier-expanded syllogisms (QESs)

Major Minor Conclusion figure 1 Or Conclusion figure 2

Premise figure 1 Q1 y ⊙1 Q2 z Q3 x ⊙2 Q4 y Q5 x⊙3 Q6 z Q 6 z ⊙3 Q 5 x

Premise figure 2 Q2 z ⊙1 Q1 y Q 3 x ⊙2 Q 4 y Q5x ⊙3 Q6 z Q6 z ⊙3 Q5 x

Premise figure 3 Q1 y ⊙1 Q2 z Q4y ⊙2 Q3 x Q5 x⊙3 Q6 z Q6 z⊙3 Q5 x

Premise figure 4 Q2 z ⊙1 Q1 y Q4y ⊙2 Q3 x Q5 x⊙3 Q6 z Q6 z⊙3 Q5 x

(4) is considered to be difficult to be solved according to existing studies such as [8], for their too many possible permutations and interactions of variables within premises or conclusions, and between premises and conclusions. It requires the principles for finding true or false relations between sets Q5X and Q6Z corresponding to a combination among the functors = {●, ⊠, , , ║}, which depend on the transitional or not property of the double commutative relations (Q3X, Q4Y) and (Q1Y, Q2Z) with the combinations of functors on . In other words, (4) can be defined as a problem to find true quantitative and qualitative relations (X, Z), matching the same and logic-coherent relation family (X, Y) and (Y, Z), in a space . Upon this problem, in Sect. 3, solutions using a formal system are tried to be given. The formal system sets up the rules of generating variables of conclusions by the ones of certain premises, offering a solution to the quest how to find true conclusions facing huge combinations based on [7, p. 302]. In principle, a law of reaching true conclusions relies on sufficient and necessary possibility for transitional schemes between Q5X and Q6Z, i.e., realizes a match of variables of premises for the ones of the definite conclusion by logic operations. To do so, the laws of AS are considered as references under the more detailed partitions of conditions after disambiguity of the particulars of ACPs and instantiation of quantification on Y; moreover, empirical approach is adopted to abstract the laws in a possible grammar-specified space for the derived logical results.

594

Y. Zhang

3 A Formal System Deciding and Generating Valid Quantifier-Expanded Syllogisms 3.1

Introduction to the Formal System

A formal system (“System extended”) theoretically instantiates (4), and as a set of algorithms executes (4) although it is only a prototype. It is a generator of language of 0-type Chomsky grammar (Turing machine), which is well known as characterized by a, b 2 (VN [ VT)*, and 9a 2 VN for a generation a ! b. Where, VN is non-terminal symbols; VT, terminal; * refers to any permutations of the inner elements [11, 12]. Definition 1.

is construed by a 4-turple:

In Definition 1, “I” is the initial symbol to generate two premises at beginning, and further automatically produce corresponding mappings by rules. It is also supposed that two well-formed premises could serve as an input to be checked for a valid or not conclusion by rules. To generate a valid and definite conclusion given two premises, the system would utilize the rules from RE to match the conditions (variables’ permutation) of the given premises, to produce a result. That is, given a QES, the conclusion would be checked if it matched the premises-generating result (i.e., the conclusion produced by the nonterminal symbol “C”); if not matched, output ∅. That is to say, RE is the rules, which serve as a grammar and stipulations of validation, to generate well- formed and logically true conclusions. Define RE ¼ R1 [ R2 [ R þ [ R It is supposed there is such a priority for applications of the rule set that R1 > R2 > {R+ [ R−}. In the below context of this section, we give RE step by step. 3.2

General Rules

R1 is the rules for grammatically forming well-formed QES, even its logic value is false, underlying the other rules.

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

595

J1, J2 are major and minor (first and second premises). “C” indicates conclusion. R1/1 to R1/5 turn propositional logic into predicate logic. R1/6 is the quantifier generation rule. R1/7 is a copula generation rule. R1/8 says that universally exchanging the name “x” and “z” in a QES is allowed. R2 is the rules coming from the conventional rules of AS (no matter for which one of 4 figures). R2/1: For any QES, exchanging two premises will remain the same conclusion still:

R2/2: For any ECP, the two terms with their respective quantifier are convertible if no resulting in a partial or ex-istential quantifier following the negative copula.

R2/3: The middle term must be universally taken at least once in the premises. where, | is exclusive “Or”, i.e., XOR; “middle term” refers to the repeated term in the two premises (i.e., “y” playing a transitional role; while “non-middle term” refers to “x”, “z”).

596

Y. Zhang

R2/4: For an ECP, a negative copula should be universally taken for its followed term.

R2/5: From two negative premises there is no conclusion.

R2/6: For a QES, if a premise with a negative copula, the copula of its conclusion should be negative too.

3.3

Rules for the Positive QESs

+

R is the rules for the two positive premises (i.e., the two premises are all with positive copulas), which are based on R2/1to R2/3.

The instinctive senses are explained below. R+/1: If the two quantifiers in a premise are all universal, then the quantifier of the non-middle term in another premise should preserve in the conclusion, and the quantifier of the middle term in this premise should be designated to another term in the conclusion. R+/2: If the two quantifiers of middle term and non-middle term in a premise equal to the two quantifiers of non-middle term and middle term, then the quantifiers of the two non-middle terms should be kept in the conclusion. R+/3: If the quantifiers of the two middle terms are universal or existential (they may be same or different), and the two quantifiers of the two non-middle terms are partial or existential (they may be same or different), then the quantifiers of the two terms in the conclusion are all existential. (When double existential quantifiers come to the two middle terms, it would be forbidden by R2/3. Other rules might be same, not sufficient to generate a valid one with its single one.) R+/4: If the two quantifiers of a premise are all existential, then the quantifiers of the two terms in the conclusion are all existential. R+/5: If the quantifier of the middle term in a premise is universal, and another quantifier in this premise is partial or existential; meanwhile, in another premise, the

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

597

quantifier of the middle term is partial, another term’s quantifier is partial or existential, then, the quantifier of the non-middle term in the latter premise should be existential in the conclusion, another term in the conclusion should be quantified by the partial. R+/6: If the quantifier of the middle term in a premise and the quantifier of the nonmiddle term in another premise are all universal, and the quantifier of the non-middle term in the former premise and the quantifier of the middle term in the latter premise are the partial and the existential (partial and the existential are convertible), then, the quantifier of non-middle term in the premise, where the middle term is with the universal quantifier, should be partial in the conclusion, and another term should be quantified by the universal. 3.4

Rules for the Negative QESs

R– is the rules for one premise with a negative copula, which are instantiated with R– /1 to R–/11, where, by “negative term” a “negative proposition” (no matter for a premise or conclusion), and a “negative QES” we respectively mean the term following (in the right-hand of) the negation symbol ¬, a proposition with a negation copula, and a QES with a negative proposition.

R–/1: If a middle term is negative, in the premise the non-middle term owns a partial quantifier; and in the positive premise, all the two terms hold universal quantifiers, then, the non-middle term in the negative premise should maintain its quantifier as the positive in the conclusion. R–/2: If a middle term is negative, in the premise the non-middle term owns a partial quantifier; and in the positive premise, the non-middle term holds a universal quantifier, the middle term has the partial or existential quantifier, then, the non-middle term in the negative premise should obtain an existential quantifier as the positive in the conclusion. R–/3: If a middle term is negative, in the premise the non-middle term owns a universal quantifier; and in the positive premise, the non-middle term owns a universal quantifier, then, the universal quantifier of the non-middle term in the negative premise should preserve in the conclusion as positive.

598

Y. Zhang

R–/4: If a middle term is negative, in the premise the non-middle term owns an existential quantifier; and in the positive premise, the non-middle term owns a universal quantifier, then, the quantifier of the non-middle term in the negative premise should gain the existential quantifier as positive in the conclusion. R–/5: If a middle term is negative, in the premise the non-middle term owns a universal quantifier; and in the positive premise, the non-middle term owns a partial or existential quantifier, then, the quantifier of the non-middle term in the positive premise should gain the existential quantifier as positive in the conclusion. R–/6: If the two middle terms are all positive, and in the negative premise the middle term owns a partial quantifier, then, the quantifier of the non-middle term in the positive premise should gain the partial quantifier as positive in the conclusion. R–/7: If the two middle terms are all positive, and in the positive premise the nonmiddle term owns a universal quantifier, then, the quantifier of the non-middle term in the positive premise should gain the universal quantifier as positive in the conclusion. R–/8: If the two middle terms are all positive, and in the positive premise the middle term owns a universal quantifier, and quantifier of the non-middle term in the negative premise is existential, then, the quantifier of the non-middle term in the positive premise should gain the existential quantifier as positive in the conclusion. R–/9: If the two middle terms are all positive, and in the positive premise the nonmiddle term owns a partial and existential quantifier, and in the negative premise the middle term owns universal quantifier, then, the quantifier of the non-middle term in the positive premise should gain the existential quantifier as positive in the conclusion. R–/10: If a middle term is negative, and in the two premises the non-middle terms have no universal quantifier, then, this QES will lose its validity. R–/11: In a negative QES, if the two middle terms are all positive, and in the positive premise the middle term owns a partial and existential quantifier, then, it results in invalidity. 3.5

Examples of Deductions

Example 1. Outputting a grammatically well-formed but logically invalid QES (only applying R1). Running (R1/1) to (R1/4), getting (1) to (5) (here (1) to (4) are omitted for the same as the rules).

Considering instantiating all variables of terms, quantifiers and copulas according to R1, the next steps will check its logic valuations by R2, R+, and R–.

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

599

Example 2. Outputting a grammatically well-formed and logically valid QES (applying any rules in REif needed). Suppose example 1 has been given. By R2/4, R2/5, it would be dropped for negating the partial or existential quantifier, and for the two negative premises.

Suppose it has had an input (8z¬8y)^(8x + 〒y) at random, which would have not violate R2/4, R2/5, then there would be a new QES premises as a selected or stochastic input for a true result, for example,

Finally, by R1, R2, R+, and R–, it can filter out all the invalid QESs, remaining all QESs with logical truths. Note that is not an application system, but only a theoretical model to output the corresponding generations given a definite input. So, some details, say, search strategies for a special premise or conclusion, are not designed. The significance of is of effective calculability—it can turn double sets into definite relations, by which constitute some combinations (among ) to exactly represent propositions; and given certain this kind of propositions (ECPs), it can make symbol operations to achieve matched results holding a coherent logic. Next, two important questions we have now are how many and which ones valid QESs deducts, and how confirm the deducted results. The questions are solved by Theorem 1 and Theorem 2 in Sect. 4.

4 Exhaustion and Soundness of the Formal System Definition 2. The exhaustion of is defined as the property that all the true QESs are picked out from any well formed QESs; and the soundness, that a picked QES stands its ground by some logical inferences. Theorem 1.

is exhausted.

Proof. For positive QESs (no negative premise, and therefore no negative conclusion), the Premise Figure 1 is framed in Table 4; the intersections of the table are Q5, Q6 for x, z, i.e., the quantifiers for the terms of Conclusion Figure 1. In Table 4, 45 valid QESs with positive Premise Figure 1 and Conclusion Figure 1 are picked out and listed in Attachment List 1. By R2/2, exchanging terms with their quantifiers in an ECP, we get valid QESs with Premise Figure 1 and Conclusion Figure 2. To exchange the name the two non-middle terms “x” and “z” according to R1/8, and to exchange the positions of the two premises according to according to (R2/1), we also have positive Premise Figure 2, 3, 4 (with Conclusion Figure 1, 2, and applicable

600

Y. Zhang

rules among R1, R2, and R+ are to be used). Thus positive QESs all deduct, of which the total number reaches 45*4*2 = 360. Table 4. Quantifier permutations of the Conclusion Figure 1 for the positive Premise Figure 1 ((Q1y + Q2z)^(Q3 x + Q4y)! (Q5x + Q6z). Where, the numbers in circles like ①… are the serial number of R+; the numbers followed “P” in the brackets are serial numbers of ECPs in attached List 1; ∅ indicates violating R2/3: “at least one middle term distributed”. However, R1 and R2 are supposed to be applied preferentially and universally, but not all marked in the table. So are Tables 5, 6.) Q3 Q4 〒〒 Q1 Q2 〒〒 ∅

〒8

〒9

8〒

〒9⑤ (P28) 〒8② (P30) 〒9⑤ (P32) 99③ (P2)

∅

∅

89

9〒

∅

∅

98

〒9⑤ (P40) ∅ ∅ 〒8⑥ (P42) ∅ ∅ 〒9⑤ (P44) 8〒⑥ 9〒⑤ 99③ (P6) (P7) (P8)

99 ∅

〒8

∅

〒9

∅

8〒

9〒⑤ (P1)

88

〒〒① 〒8① (P10) (P11)

〒9① 8〒① 88①②(P14) 89① (P12) (P13) (P15)

9〒① 98① (P16) (P17)

89 9〒

9〒⑤ (P19) ∅

99③ (P21) ∅

98

∅

99

∅

9〒⑤ 99③ (P25) (P26) ∅ 99③ (P41) ∅ 98② ∅ (P43) ∅ 99③④ ∅ (P45)

99③ (P20) 99③ (P29) 〒8⑥ (P31) 99③④ (P33)

∅

88

〒〒① (P34) ∅ 〒8① (P36) ∅ 〒9① (P38) 8〒② 8〒① (P4) (P5)

∅ 99③ (P3)

∅ ∅

8〒⑥ 89① (P22) (P23) ∅ 9〒① (P35) ∅ 98① (P37) ∅ 99①④ (P39)

89② (P24) ∅ ∅ ∅

∅ ∅ 99③ ④ (P9) 99① ④ (P18) 99③④ (P27) ∅

For negative QESs (one negative premise, therefore one negative conclusion), the negative Premise Figure 1 are framed in Table 5, 6 with different negative terms in the premises. In Table 5, the premises (Q1y + Q2z)^(Q3x¬Q4y) are deducted, 15 + 3 = 18 valid conclusions are deducted. Amongst which, N5, N8, and N11 have double Conclusion Figures. For example, 〒8 and 〒8 corresponds to 98 (N4) ②, which represents (〒 y + 8z)^(〒x¬8y)!(9 x¬8z) by R–/2, and this QES is numbered as N4. Among the deducted 15 valid QESs, the conclusions of N5, N8, and N11 are symmetry, i.e., with two Conclusion Figures Q5 x¬Q6z and Q6z¬Q5x. Thus 18 valid negative QESs with Premise Figure 1 and with negative middle term are achieved. Table 6 is for (Q1y¬Q2z)^(Q3x + Q4y) with one negative premise, yet the two middle terms are all positive. Similarly, 15 + 3 = 18 conclusions (N16 to N30) are deducted. Among which, the conclusions of N20, N23, and N26 have double Conclusion Figures.

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

601

Table 5. Quantifier permutations for the negative Premise Figure 1 (a middle term is negative). (Table 5 is about the structure of quantifiers in (Q1y + Q2z)^(Q3 x¬Q4y)!Q5x¬Q6z or . The (Q1y + Q2z)^(Q3 x-Q4y)!Q6z¬Q5x ——the latter ones are marked by a square like numbers in circles like ①… are serial numbers of R–; the numbers followed “N” in the brackets are serial numbers of ECPs in attached List 2; the empty intersection indicates violating R2/4 that denies the partial or existential; ∅ means falling in R–/10).

Q1Q2

Q3¬Q4 〒〒 〒8 〒9 8〒 88 89 9〒 98 99 〒〒 ∅ 98(N1)⑤ ∅ 〒8 98(N4)② 88(N5)③ 98(N6)④ 〒9 ∅ 98(N13)⑤ ∅ 8〒 ∅ 98(N2) ⑤ ∅ 88 〒8(N7)① 88(N8) ③ 98(N9)④ 89 ∅ 98(N14)⑤ ∅ 9〒 ∅ 98(N3) ⑤ ∅ 98 98(N10)② 88(N11)③ 98(N12)④ 99 ∅ 98(N15)⑤ ∅

Table 6. The quantifier permutation for the negative Premise Figure 1 (Table 6 is about the structure of quantifiers in (Q1y¬Q2z)^(Q3 x + Q4y)!Q5x¬Q6z, where the two middle terms are all positive, ∅ is due to R–/11.) Q3 Q4 Q1¬Q2

〒〒

〒8

〒9

8〒

88

89

9〒

98

99

∅

〒8⑥(N16)

∅

∅

〒8⑥(N17)

∅

∅

〒8⑥(N18)

∅

98⑨(N19)

98⑨(N22)

98⑨(N25)

88⑦(N20)

88⑦(N23)

88⑦(N26)

98⑨(N21)

98⑨(N24)

98⑨(N27)

∅

98⑧(N28)

∅

∅

98⑧(N29)

∅

∅

98⑧(N30)

∅

〒 〒 〒8 〒9 8〒 88 89 9〒 98 99

Based on Table 5 and Table 6, i.e, the QESs with negative Premise Figure 1, we use R1/8 to exchange the name the two non-middle terms “x” and “z”, use R2/1 to exchange the positions of the two premises, and use R2/2 to permute negative terms, we have 4 negative Premise Figures, respectively either positive or negative middle term y. For examples, the negative Premise Figure 1, 2 with respective sub-figures of positive and negative y go following: The negative Premise Figure 1.1: {〒, 8, 9} y + {〒, 8, 9}z {〒, 8, 9} x¬y The negative Premise Figure 1.2: {〒, 8, 9} y¬z

602

Y. Zhang

{〒, 8, 9} x + {〒, 8, 9} y The negative Premise Figure 2.1: {〒, 8, 9} z + {〒, 8, 9} y {〒, 8, 9} x¬y The negative Premise Figure 2.2: {〒, 8, 9} z¬y {〒, 8, 9} x + {〒, 8, 9} y Similarly, we can have 3.1, 3.2, 4.1 and 4.2 (omitted here). Using R2/2 to acquire Conclusion Figure 1 or 2. Totally, the number of 4 valid negative Premise Figures with 2 Conclusion Figures is (15 + 3)*2*4 = 144. So the total valid QESs (with positive and negative Premise Figures) number 360 + 144 = 504, which cover all the valid QESs and drop all the invalid QESs, namely, every achieved and dropped conclusions are resulted from RE. Hence theorem 1 is proved. is sound.

Theorem 2.

Proof. In Table 4, 5, and 6 are all confirmed true, which are derived from confirmed rules of ASs under their conditions after disambiguity, and from empirical checks. So, is sound (reliable). Example 3. An empirical check of the valid QES: P22 (in Attached List 1): (8y9z)^( 8x〒y)!(8x〒y).(see Illustration 2). By R+/6, it immediately confirms the conclusion, which can be shown by a complete combination of Gergonne diagrams as following (with reference to Table 1).

Z

X X

Illustration 2. The Gergonne diagrams for P22.

Example 4. A verification of the valid QES N30 (in Attached List 2): (9y¬8z)^ (9x8y)!(9x¬8z) by Gergonne diagrams in Illustration 3, which shows all the possible schemes of (with reference to Table 1 and Illustration 3).

X Y

Z

XY

Z

Z

Z

X Y

Illustration 3. The Gergonne diagrams for N30.

Z

XY

Z

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

603

5 Conclusion In the respective view of Euler, Gergonne or Hamilton, ASs might be effective, although possibly, with some space to be improved. However, when we summarize their different even opposite views and make them in unified semantics of integrated forms of categorical propositions, we encounter crises—(5-i) the inconsistent interpretations on quantification, and (5-ii) incomplete formalization of quantification. The two problems essentially all contain the ambiguity of indeterminacy and incompleteness in describing quantification, as it is not discriminating between the undetermined state (“9”, Gergonne’s semantics, meaning whether complete (“8”) or not (“〒”) is not undetermined yet) and the incompleteness (“〒”, mereology of Euler’s semantics) under the particular interpretations (for the problem (5-i)), as well as incompletely quantifying all the terms (Hamilton intended to overcome, for the problem (5-ii)). So, harmonizing the disagreements of the interpretations on the particular quantifier by disambiguity and improvements on default forms on the second term are desirable. The present study has given the solutions tackling the troubles. After casting QESs, naturally, deductions with their corresponding principles on QESs are required. By introducing the rules in ASs’ deduction system, with considering the special conditions interpreted by ECPs, and by checking the truths by empirical results of logic deductions for various forms, QESs are definitely deducted in the light of the algorithms such as of Turing machines (0-type Chomsky grammar), although the schemes for the algorithms appear complicated. Acknowledgement. Some results in the paper are cited from the author’s doctoral thesis and post-doctoral academic report; the author is full of gratitude to the respective tuition of Prof. Dachun Liu and Prof. Shushan Cai.

Appendix Attached List 1. Valid QESs in positive Premise Figure 1 and Conclusion Figure 1 (In the left, the middle term y is universal; while in the right, it respectively partial, universal and existential. For comparison, 8 repeated ones, marked by # in the right column, are not removed.) № P1 P2 P3 P4 P5 P6 P7 P8

Premises without negative middle term 8y〒z/〒x〒y/9x〒z 8y〒z/〒x8y/9x9z 8y〒z/〒x9y/9x9z 8y〒z/8x〒y/8x〒z 8y〒z/8x8y/8x〒 8y〒z/8x9y/8x〒z 8y〒z/9x〒y/9x〒z 8y〒z/9x8y/9x9z

Rules app R+/5 R+/3 R+/3 R+/2 R+/1 R+/ 6 R+/5 R+/3

№ P28 P2# P29 P30 P11# P31 P32 P20#

Premises without negative middle term 〒y〒z/〒x8y/〒x9z 8y〒z/〒x8y/9x9z 9y〒z/〒x8y/9x9z 〒y8z/〒x8y/〒x8z 8y8z/〒x8y/〒x8z 9y8z /〒x8y/〒x8z 〒y9z/〒x8y/〒x9z 8y9z/〒x8y/9x9z

Rules app R+/5 R+/3 R+/3 R+/2 R+/1 R+/6 R+/5 R+/3 (continued)

604

Y. Zhang Attached List 1. (continued)

№ P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27

Premises without negative middle term 8y〒z/9x9y/9x9z 8y8z/〒x〒y/〒x〒z 8y8z/〒x8y/〒x8z 8y8z/〒x9y/〒x9z 8y8z/8x〒y/8x〒z 8y8z/8x8y/8x8z 8y8z/8x9y/8x9z 8y8z/9x〒y/9x〒z 8y8z/9x8y/9x8z 8y8z/9x9y/9x9z 8y9z/〒x〒y/9x〒z 8y9z/〒x8y/9x9z 8y9z/〒x9y/9x9z 8y9z/8x〒y/8x〒z 8y9z/8x8y/8x9z 8y9z/8x9y/8x9z 8y9z/9x〒y/9x〒z 8y9z/9x8y/9x9z 8y9z/9x9y/9x9z

Rules app R+/3,/4 R+/1 R+/1 R+/1 R+/1 R+/1,/2 R+/1 R+/1 R+/1 R+/1,/4 R+/5 R+/3 R+/3 R+/6 R+/1 R+/2 R+/5 R+/3 R+/3,/4

№ P33 P34 P5# P35 P36 P14# P37 P38 P23# P39 P40 P8# P41 P42 P17# P43 P44 P26# P45

Premises without negative middle term 9y9z/〒x8y/9x9z 〒y〒z/8x8y/〒x〒z 8y〒z/8x8y/8x〒z 9y〒z/8x8y/9x〒z 〒y8z/8x8y/〒x8z 8y8z/8x8y/8x8z 9y8z/8x8y/9x8z 〒y9z/8x8y/〒x9z 8y9z/8x8y/8x9z 9y9z/8x8y/9x9z 〒y〒z/9x8y/〒x9z 8y〒z/9x8y/9x9z 9y〒z/9x8y/9x9z 〒y8z/9x8y/〒x8z 8y8z/9x8y/9x8z 9y8z/9x8y/9x8z 〒y9z/9x8y/〒x9z 8y9z/9x8y/9x9z 9y9z/9x8y/9x9z

Rules app R+/3,/4 R+/1 R+/1 R+/1 R+/1 R+/1,/2 R+/1 R+/1 R+/1 R+/1,/4 R+/5 R+/3 R+/3 R+/6 R+/1 R+/2 R+/5 R+/3 R+/3,/4

Attached List 2. Valid QESs with negative Premise Figure 1 (N1 to N15 are of negative middle terms; N16 to N30, of positive middle terms The QESs with * have two Conclusion Figures.) № N1 N2 N3 N4 N5* N6 N7 N8* N9 N10 N11* N12 N13 N14 N15

Premises with a negative middle term 〒y〒z/8x¬8y/9z¬8x 8y〒z/8x¬8y/9z¬8x 9y〒z/8x¬8y/9z¬8x 〒y8z/〒x¬8y/9x¬8z 〒y8z/8x¬8y/8x¬8z 〒y8z/9x¬8y/9x¬8z 8y8z/〒x¬8y/〒x¬8z 8y8z/8x¬8y/8x¬8z 8y8z/9x¬8y/9x¬8z 9y8z/〒x¬8y/9x¬8z 9y8z/8x¬8y/8x¬8z 9y8z/9x¬8y/9x¬8z 〒y9z/8x¬8y/9z¬8x 8y9z/8x¬8y/9z¬8x 9y9z/8x¬8y/9z¬8x

Rules app R–/5 R–/5 R–/5 R–/2 R–/3 R–/4 R–/1 R–/3 R–/4 R–/2 R–/3 R–/4 R–/5 R–/5 R–/5

№ N16 N17 N18 N19 N20* N21 N22 N23* N24 N25 N26* N27 N28 N29 N30

Premises with a negative middle term 〒y¬8z/〒x8y/〒x¬8z 〒y¬8z/8x8y/〒x¬8z 〒y¬8z/9x8y/〒x¬8z 8y¬8z/〒x〒y/9x¬8z 8y¬8z/8x〒y/8x¬8z 8y¬8z/9x〒y/9x¬8z 8y¬8z/〒x8y/9x¬8z 8y¬8z/8x8y/8x¬8z 8y¬8z/9x8y/9x¬8z 8y¬8z/〒x9y/9x¬8z 8y¬8z/8x9y/8x¬8z 8y¬8z/9x9y/9x¬8z 9y¬8z/〒x8y/9x¬8z 9y¬8z/8x8y/9x¬8z 9y¬8z/9x8y/9x¬8z

Rules app R–/6 R–/6 R–/6 R–/9 R–/7 R–/9 R–/7 R–/7 R–/9 R–/9 R–/7 R–/9 R–/8 R–/8 R–/8

Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

605

References 1. Westerstah, D.: Aristotelian syllogisms and generalized quantifiers. Studia Logica XLVII I (4), 577–585 (1989) 2. Cooke, H.P., Tredennick, H.: Aristotle: Prior Analytics, Categories, On Interpretation, Prior Analysis. Loeb Classical Library, Later printing Edition. Harvard University Press. Cambridge (1938) 3. Preece, W.E., Goetz, P.W.: “Syllogistic”. The new Encyclopaedia Britannica, vol. 32, 15th edn. Encyclopaedia Britannica Inc., London (2009) 4. Kneale, W., Kneale, M.: The Development of Logic, 1st edn. Oxford at the Clarendon Press, Oxford (1962) 5. Euler, L.: Lettres à une Princesse d′Allemagne, l′ Academie Imperiale des Science. St. Petersberg (1768) 6. Gergonne, J.D.: Essai de dialectique rationelle. Annales des mathématiques qures et appliqués 7, 189–228 (1816–1817) 7. Stenning, K., Van Lambalgen, M.: Human Reasoning and Cognitive Science, 1st edn. The MIT Press, Cambridge MA (2008) 8. Harrison, J.: Handbook of Practical Logic and Automated Reasoning/Syllogism, 1st edn. Cambridge University Press, Cambridge (2009) 9. Zhang, Y.: Improvements of categorical propositions on consistency and computability. J. Multiple-Valued Logic Soft Comput. 33(4–5), 397–413 (2019) 10. Zhang, Y.: Expanded Syllogisms and Automated Reasoning, 1st edn. Science and Technology Documentary Publishing House, Beijing (2009) 11. Turing, A.: On computable numbers, with an application to the entscheidungs problem. In: Proceedings of the London Mathematical Society, vol. 42,pp. 230–231, 263–265 (1936– 1937) 12. Chomsky, N.: Syntactic Structures, 2nd edn. De Gruyter, Berlin (1975)

Social Sentiment Analysis for Prediction of Cryptocurrency Prices Using Neuro-Fuzzy Techniques Şule Öztürk Birim(&)

and Filiz Erataş Sönmez

Manisa Celal Bayar University, Salihli 45300, Turkey [email protected]

Abstract. This study aims to provide an intelligent system that uses neurofuzzy techniques to predict daily prices of selected cryptocurrencies using a combination of twitter sentiment and google trend data. Although previously used to predict bitcoin prices, Neuro-fuzzy systems are used with this study for the first time with sentiment analysis to predict price trends of digital currencies. An adaptive neuro-fuzzy interface-based network was used to predict the prices of three selected cryptocurrencies, Ethereum, Ripple and Litecoin. The difference from the current study is that Twitter sentiment and Google trends have not been used as a predictor in a neuro-fuzzy network before. ANFIS has the advantage of combining the properties of fuzzy systems and neural networks. This advantage is manifested in producing lower error and higher accuracy in predictions. According to the findings, different results were obtained for different cryptocurrencies in the model in which the ANFIS estimation method was used. For ETH and LTC, the best forecast performance is obtained when twitter sentiment and google trends are used together. The Twitter sentiment model took second place by only a small margin. For XRP, only twitter sentiment shows the best forecast performance. Keywords: Neuro-fuzzy network


ANFIS
Cryptocurrencies

1 Introduction The importance of the financial system has gradually increased, especially with the dominance of neo-liberal economic policies worldwide after 1980. With the rapid development of the financial system and the addition of technological innovations to this development, the importance of financial gains has enlarged in the world economy. Today, there are many different financial instruments traded in financial markets. Bitcoin, the first decentralized cryptocurrency created by Satoshi Nakamoto in 2008, is a new market instrument for economic units that act with the motive of increasing their financial returns. The current market volume of Bitcoin, the first cryptocurrency launched in 2009, is nearly 808 billion dollars. In the last decades, one of the most prominent developments in economics is the rapid growth of the cryptocurrency market. Demand for digital currencies continues to increase as the number of them increases. These days more than 2000 types of cryptocurrencies are heavily transacted © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 606–616, 2022. https://doi.org/10.1007/978-3-031-09176-6_68

Social Sentiment Analysis for Prediction of Cryptocurrency Prices

607

on the market. Digital currencies are impressive since their values are not related to classical financial assets and they do not follow governmental regulations [1, 2]. For homo-economicus, who try to maximize their benefit, the main goal is to increase their financial gains. In this context, estimating the price of any financial instrument and minimizing the difference between the actual price and the estimated price have great importance. Price estimation of any financial instrument is the process of evaluating the future value of that instrument so that economic agents have a preliminary knowledge of potential gains or losses if they invest in that instrument. Right after the millennium, it is possible to say that social media has had a great impact on human life, with the inclusion of Facebook in 2004, Twitter in 2006, and Instagram in 2010. According to the data in Statista, in the last quarter of 2021, the total daily active twitter user in the world is 217 million, and approximately 550 million tweets are sent daily [3]. Investors’ decision-making methods vary in financial markets. It is known that economic units can make irrational decisions as well as rationally, and therefore financial markets have an unbalanced and non-stationary structure. Keynes (1936) emphasized that expectations in the economy cannot be modeled theoretically and that economic activities may differ depending on the changes that occur due to animal spirits [4]. Keynes defined animal spirits as unconscious mental activities. Akerlof and Shiller (2010) primarily referred to the concept used by Keynes (1936), emphasizing the idea that according to Keynes, animal spirits are the cause of economic fluctuations [5]. According to Akerlof and Shiller (2010), the invisible hand expressed by Adam Smith is the basis of Classical economics, and the animal spirits expressed by Keynes is the basis of the Keynesian theory that explains the instability of capitalism. In classical economic theory, individuals act rationally, and the market is always in equilibrium. Contrary to classical economic theory, the economy sometimes becomes unstable, and crises occur because economic agents act irrationally for non-economic reasons. To learn how the economy works, human behavior (especially animal spirits) needs to be incorporated into economic theory. The motivation of this study is to try to acquire new insights on market behavior. The motivation of this study is to obtain new insights into market behavior. For this purpose, Twitter and google trends, which are one of the most important elements of social media, were chosen as the research subject. Although cryptocurrencies have some positive features such as hedging and diversification for investors, it is still considered risky because the factors affecting its price are more difficult to predict than other financial instruments [6]. In this study, the effect of social media on the price behavior of selected cryptocurrencies is investigated with sentiment analysis. The main hypothesis, as mentioned in the efficient market hypothesis claimed by Fama (1969), is the possibility that economic units that choose cryptocurrencies as an investment tool can also affect the market through social media, in the presence of a market where prices can react simultaneously to all information in the market. Economic units can affect the market subjectively through tweets in which they express their opinions or beliefs [7]. Although digital currencies are attractive their prices show large fluctuations [8]. These fluctuations make the prediction of prices a difficult task. Investors need ways to help them determine which digital asset they should invest in. In recent times, people experience an internet-dependent life which puts online opinions in an important place

608

Ş. Ö. Birim and F. E. Sönmez

in our decision-making processes. Especially social media platforms like Twitter provides a vast number of opinions or comments about any topic someone can think of. Twitter is a source of live updates about digital currencies providing emotions or sentiment expressions of investors. Behavioral economics indicates that emotions and sentiments can have a significant influence on people’s behaviors and decision-making processes [9]. Sentiment analysis is used to identify the emotion in a text by finding the polarity. Polarity means whether the text represents a positive, negative or neutral perspective [10]. Twitter includes the opinions, emotions, or sentiments of digital currency users. In addition to Twitter comments, google search data provides valuable information about people’s concerns, curiosity, and tendencies about the topics. Google summarizes its search data in google trends and provides a valuable data source about the popular topics that individuals are interested in. In this study sentiments in the tweets along with google trends data are analyzed to predict prices of cryptocurrencies. Due to the highly fluctuating nature of digital currency prices, conventional statistical and economic models used to predict traditional financial assets are found to be insufficient for digital currency price prediction [11]. An effective model which can be used as a decision support tool for digital currency investment is a necessity both for academicians and practitioners. When the literature is examined, it is seen that sentiment analysis is generally used for twitter, and its effect on bitcoin price is generally investigated. Gurrib and Kamalov [10], Pano and Kashef [12], Ibrahim [13], Balfagih and Keselj [14], Mohapatra et al. [15], Gurdgiev and O’Loughlin [16], Santos and de Paula [17], Park and Lee [18], Karalevicius [19], Preisler et al. [20], Shen et al. [21], Abraham et al. [22], Stengqvist and Lönnö [23], Collianni et al. [24] and Mai et al. (2015) [25] can be cited as an example of studies using this method. Hasan et. al [26], McCoy and Rahimi [27], Kraaijeveld and De Smedt [28], Valencia et al. [29], Steinert and Herff [30], Jain et al. [31] and Lamon et al. [32] can be given as an example for studies that deal with more than one cryptocurrency. In addition, there are studies examining the effects of both twitter sentiment and Google trends on cryptocurrencies such as Woolk [33] and Mnif et al. [34]. This research applies a neuro-fuzzy system to predict digital currencies’ prices using twitter sentiment scores and google trends data. Prediction performance of the sentiment and trends will be compared to see which variable has more predictive power. Following section explains methodology used in this paper, then the results are given to explore prediction performances of the proposed models.

2 Methodology In this study, to forecast prices of three selected cryptocurrencies that are Ethereum (ETH), Litecoin (LTH) and Ripple (XRP) sentiments reflected in Tweets and search volume demonstrated in Google Trends are analyzed with ANFIS. Research framework for the proposed methodology can be seen in Fig. 1. The following section describes the steps of the research framework in more detail.

Social Sentiment Analysis for Prediction of Cryptocurrency Prices

609

Fig. 1. Research framework

2.1

Collection of Tweets

Twitter data is scraped with Twitter API using Python 3.8. Tweets are downloaded based on their relevant hashtags that are #ethereum, #eth, #ripple, #xrp, #litecoin, #ltc. Data is collected from December 1st 2021, to March 8th 2022. Data is restricted to only English tweets since the sentiment analysis tool is proper for analyzing English language. The total tweets collected for three cryptocurrencies contains over 4 million tweets. The exact numbers before and after removing the duplicates can be seen in Table 1. Table 1. Number of tweets for the three cryptocurrencies

Ethereum Ripple Litecoin

2.2

Total number of Tweets Collected 3697725 645435 143594

Number of Tweets after removing duplicates 1054837 278494 106999

Preprocessing

Before sentiment analysis tweets should be preprocessed to remove noise and unnecessary elements in the text. Therefore, collected tweets need intensive preprocessing. Several preprocessing techniques applied after removing the duplicates from the collected tweets. To implement preprocessing techniques, Regular expression (re) and Natural Language Toolkit (NLTK) libraries in Python are used. First punctuations, links, emoticons and user mentions (@username) are removed from the tweets. If the Tweet is posted as a Retweet “RT” symbol at the beginning is removed. Tweets with less than three words are removed from the dataset since they may not be proper for sentiment analysis. Hashtag symbols are also removed but the hashtags are kept since they may include meaningful words. Next, contractions in the words are replaced with their expanded versions such as “they’re” and “you aren’t” were replaced with “they are” and “you are not”. Numerical symbols are also removed since they are irrelevant for sentiment analysis. Slang abbreviations such as are replaced with their expanded versions using the slang list obtained from https://github.com/rishabhverma17/sms_ slang_translator. For instance, “asap” and “afk” are replaced with “as soon as possible”

610

Ş. Ö. Birim and F. E. Sönmez

and “away from keyboard”. Stopwords which do not own meaning individually such as ‘the’, ‘to’, ‘who’ etc. are removed from the data based on the NLTK’s English stop word list. All words converted to lowercase to eliminate any differences based on format. Lemmatization is applied to every word to convert it to base format. For instance, “calling” or “calls” are both converted to “call”. Tokenization is applied to separately write each word in a list to make the data proper for further sentiment analysis. An example from the dataset before and after preprocessing can be seen in Table 2. Table 2. An example of a tweet before and after preprocessing Before Preprocessing 'Ethereum is Coin of the Day on #LunarCrush!\n\nGa laxy Score™ 69.5/100\nAltRank™ 8/3337\nPrice $4, 716.69 +5.63% \n\nView real-time #ethereum metri cs at https://t.co/TBMgIgOYZj $eth #LunarShare'

2.3

After Preprocessing 'ethereum coin day lunarcrush gala xy score altrank price view real tim e ethereum metrics eth lunarshare'

Sentiment Analysis

Sentiment analysis is conducted to find the degree of positivity or negativity of a written text. Polarity score is given to text to reveal either the text has a positive, negative or a neutral sentiment [35, 36]. In this study, an open-source tool, Valence aware dictionary and sentiment reasoner (VADER) analysis is used to compute polarity score for each cryptocurrency related tweet. VADER is chosen in this study because it is specifically designed for social media text analysis and it is a tool validated by humans [37]. VADER gives four standardized polarity scores for tweets changing between 0 and 1. Four scores represent positive, negative, neutral, and compound sentiment in the tweets. Compound sentiment represents the overall polarity score for the text. In this study all the four polarity scores are used as predictors of cryptocurrency prices not to lose any sentiment meaning in the dataset. For every tweet in the dataset four polarity scores were calculated. There are more tweets for 98 days in the dataset. Not to lose any sentiment expressed in the tweets and to make the dataset proper with price analysis, the tweets sentiment data converted to an hourly-based version. To do this, average polarity scores of tweets in each hour were computed and recorded. 2.4

Collection of Google Trends Data

To see how much interest people show to a specific topic, number of searches in the engines about the topic is a useful indicator. to see the volume of searches, Google provides a tool named Google trends. Google trends constructs a dataset including search volumes for the specified search terms during the identified time interval. Google trends data has been used before to predict cryptocurrency prices [38, 39]. This study wants to see the predictive power of Google trends along with the predictive power of sentiments reflected in the tweets.

Social Sentiment Analysis for Prediction of Cryptocurrency Prices

611

To be proper with the tweets sentiment dataset, hourly Google Trends data between December 1st 2021 and March 8th 2022 is downloaded using Pytrends library in Python. Trend dataset includes hourly search volumes for terms ethereum, eth, litecoin, ltc, ripple and xrp for the selected coins. 2.5

Collection of Cryptocurrency Price Data and Merging

Price data for Ethereum, Ripple and Litecoin are downloaded from Yahoo Finance with a Python-based API. Hourly data were obtained between December 1st 2021 and March 8th 2022. Dataset includes a total of 2130 datapoints. Whole price data contains opening price, the highest price, the lowest price, the closing price and the volume of transactions in each hour. The predicted variable in this study is the Close price of the cryptocurrency. The remaining price data is used as the predictor of Close price. After the price data is downloaded previously prepared sentiment and trends data is merged with the price data. therefore, the whole dataset for each cryptocurrency has been prepared. A section from the ETH dataset can be seen in Table 3. Table 3. A section from the ETH dataset Hour

Trends Ethereum 2021-12-01 07 78 2021-12-01 08 66 2021-12-01 09 57

2.6

Eth 34 24 33

Sentiment Scores Neg Neu Pos 0.01 0.93 0.05 0.01 0.92 0.06 0.02 0.92 0.06

Compound 0.09 0.10 0.12

Close price 4765.73 4703.63 4744.90

Prediction with Adaptive Neuro-Fuzzy Inference System (ANFIS) Method

Neuro-fuzzy systems combines the functionality of fuzzy models and neural networks. Fuzzy networks have ability to aid decision making by modeling input-output relationships with linguistic variables and provides successful results in prediction problems [40]. Fuzzy system (ANFIS) used in this study applies neural network (NN) learning algorithms in the model. By utilizing NN algorithms, ANFIS can produce data-driven rule system in adjusting parameters in an adaptive network structure, this in return produces accurate forecasts [41]. ANFIS utilizes 5 layers to yield final output. Layer 1 is the fuzzification layer in which membership degrees of inputs are calculated; layer 2 is the rule layer in which strength of each rule is computed; layer 3 normalizes firing strength of each rule; layer 4 calculates output of each rule while layer 5 produces final output of the network [40, 42]. For details of the calculations in each layer studies of Jang, 1993 can be examined.

612

Ş. Ö. Birim and F. E. Sönmez

2.6.1 Determining Input Variables, Training and Test Sets This study aims to see the effect of tweets sentiment and google trends on the three digital currencies. With this aim, we want to see the prediction performance of sentiments and trends both comparatively and individually. Therefore, for each cryptocurrency we have three sets of inputs that are sentiments only, trends only and sentiments and trends together. Output for each set was close price of the cryptocurrency. For every model the dataset is divided into train and test sets. The first 80% of the data is used for training while the remaining %20 was used as the test set. The model learned from the training set first. Then the performance of the learned model is observed on the test set for every input model. 2.6.2 Determining ANFIS Parameters ANFIS has several hyperparameters that have an important effect on the performance. These hyperparameters are type of membership function, number of membership degrees and optimizer. Before determining the final architecture for every run, different values of the hyperparameters are run and the combination which yields the minimum loss function value which is calculated with Mean Squared Error (MSE) was chosen as the best parameter combination and used for comparison of the input models. For the majority of the models, Adagrad optimizer produced the least loss value for the training set. For the remaining, Adam optimizer produced the best performance. Gaussian membership function was used for all the models since it brought applicable best performance in the training sets. All the parameters chosen for each input model can be seen in Table 4. 2.7

Performance Evaluation

To compare the performance of the input models two popularly used performance indicators that are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used. These metrics are calculated using the predictions on the test set. Performance indicators are used to evaluate the differences between the actual and predicted values. As the indicator gets the lower values, the performance of the model increases.

3 Results The proposed input-output models were run with a fuzzy neural network based on the ANFIS implementation of [43]. There are three main input models for each cryptocurrency. Twitter sentiment, google trends were used both separately and together to predict each cryptocurrency prices. There was a total of nine models for the three cryptocurrencies. All the models, hyperparameters used in the models and their performance scores based on the test set are demonstrated in Table 4.

Social Sentiment Analysis for Prediction of Cryptocurrency Prices

613

Table 4. Proposed models and performance scores Currency Model ETH

XRP

LTC

Membership function

Sentiment Gaussian Trend Gaussian Sentiment + Trend Gaussian Sentiment Gaussian Trend Gaussian Sentiment + Trend Gaussian Sentiment Gaussian Trend Gaussian Sentiment + Trend Gaussian

Number of memberships

Optimizer MAPE RMSE

5 6 5 6 6 7 6 6 5

Adagrad Adagrad Adagrad Adagrad Adagrad Adam Adagrad Adagrad Adagrad

0.0531 186.1720 0.1494 450.5367 0.0505 185.4197 0.0471 0.0442 0.0547 0.0514 0.0535 0.0502 0.0530 7.0428 0.0997 13.0223 0.0517 6.9962

For ETH and LTC when sentiment and trend used together prediction performance was the best with the lowest MAPE and RMSE values. For ETH and LTC sentiment only model revealed the second-best performance with a slight difference from the best model. For XRP, sentiment only model produced the best prediction performance. For XRP sentiment and trend model was found as the second-best model. For all the three cryptocurrencies trend only model revealed the worst prediction scores among the three input models. This finding points out the importance of sentiment in digital currency price prediction. Google trends data can provide sufficient prediction performance when only used with twitter sentiment. Prediction power of the best models come from the twitter sentiment information in the data.

4 Discussion and Conclusion In this study, we presented several models that utilizes twitter sentiment and Google trends data to predict prices of Ethereum, Ripple and Litecoin. First, we manually downloaded tweets related to the selected digital currencies. Then we preprocessed the tweets to make them proper for sentiment analysis. Then with VADER we computed polarity scores of each tweet. Hourly dataset was constructed by merging sentiments, Google trends and close price data of the ETH, XRP and LTC. The main purpose was to examine the effect of expressed twitter sentiments on the price changes of cryptocurrencies with using ANFIS which is a successful implementation of neuro-fuzzy network. For all the models sentiments were found to have larger prediction power on the prices since sentiments were included as predictors in the best models for the three cryptocurrencies. Sentiment was the most successful predictor for XRP. For ETH and LTC, sentiment with trend models produced best results. Google Trends data can be a successful predictor when used with Twitter sentiments. Our findings support that analysis of sentimental expressions in social media can provide beneficial results when predicting future developments in economics. Hybrid approach of ANFIS with fuzzy systems and neural networks may lead lower errors and higher accuracy in predictions. Future studies may compare the performance of various ANFIS models with other machine learning algorithms.

614

Ş. Ö. Birim and F. E. Sönmez

References 1. Livieris I.E., Pintelas E., Stavroyiannis S., Pintelas P: Ensemble Deep learning models for forecasting cryptocurrency time-series. Algorithms. 13, 1–21 (2020). https://doi.org/10. 3390/A13050121 2. Sun, X., Liu, M., Sima, Z.: A novel cryptocurrency price trend forecasting model based on LightGBM, Financ. Res. Lett. 32 (2020). https://doi.org/10.1016/j.frl.2018.12.032 3. Statista (2022). https://www.statista.com/search/?q=twitter&qKat=newSearchFilter&sortMe thod=idrelevance&isRegionPref=1&statistics=1&accuracy=and&isoregion=0&isocountryS earch=&category=0&interval=0&archive=1 4. Keynes, J.M.: The General Theory of Employment, Interest and Money. Palgrave Macmillan, United Kingdom (1936) 5. Akerlof, G.A., Shiller, R.J.: Animal Motives, How Human Psychology Directs the Economy and Why it is Important for Global Capitalism. Scala Publishing, Istanbul (2010) 6. Catania, L., Grassi, S.: Modelling crypto-currencies financial time-series, p. 15 (2017) 7. Fama, E.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1969) 8. Selmi, R., Tiwari, A.K., Hammoudeh, S.: Efficiency or speculation? A dynamic analysis of the Bitcoin market. Econ. Bull. 38, 2037–2046 (2018) 9. Kraaijeveld, O., De Smedt, J.: The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. J. Int. Financ. Mark. Inst. Money 65, 101188 (2020). https://doi.org/10.1016/j.intfin.2020.101188 10. Gurrib, I., Kamalov, F.: Predicting bitcoin price movements using sentiment analysis: a machine learning approach, Studies in Economics and Finance. ahead-of-p (2021). https:// doi.org/10.1108/sef-07-2021-0293 11. Derbentsev, V., Babenko, V., Khrustalev, K., Obruch, H., Khrustalova, S.: Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices. Int. J. Eng. Trans. A Basics 34, 140–148 (2021). https://doi.org/10.5829/IJE.2021.34.01A.16 12. Pano, T., Kashef, R.: A complete VADER-based sentiment analysis of bitcoin (BTC) tweets during the era of COVID-19. Big Data Cognit. Comput. 4(4), 1–17 (2020) 13. Ibrahim, A.: Forecasting the early market movement in bitcoin using twitter’s sentiment analysis: an ensemble-based prediction model. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (2021). https://doi.org/10.1109/ IEMTRONICS52119.2021.9422647 14. Balfagih, A.M., Keselj, V.: Evaluating sentiment classifiers for bitcoin tweets in price prediction task. In: 2019 IEEE International Conference on Big Data, pp. 5499–5506 (2019) 15. Mohapatra S., Ahmed N., Alencar P: KryptoOracle: a real-time cryptocurrency price prediction platform using twitter sentiments, 2019 IEEE International Conference on Big Data, pp. 5544–5551 (2019) 16. Gurdgiev, C., O’Loughlin, D.: Herding and anchoring in cryptocurrency markets: investor reaction to fear and uncertainty. J. Behav. Exp. Financ. 25 (2020). https://doi.org/10.1016/j. jbef.2020.100271 17. Santos, W.R., De Paula, H.B.: Prediction of cryptocurrency values using sentiment analysis of news and tweets. Revista Brasileira de Computacao Aplicada 12(1) (2020). https://doi. org/10.5335/rbca.v12i1.8831 18. Park, H.W., Lee, Y: How are twitter activities related to top cryptocurrencies’ performance? Evidence from social media network and sentiment analysis. Drustvena Istrazivanja 28(3) 435–460 (2019).https://doi.org/10.5559/di.28.3.04

Social Sentiment Analysis for Prediction of Cryptocurrency Prices

615

19. Karalevicius, V.: Using sentiment analysis to predict interday Bitcoin price movements. J. Risk Financ. 19(1), 56–75 (2018) 20. Preisler, B., Mieskes, B.M., Becker, C.: Bitcoin value and setiment expressed in tweets (2019). http://ceur-ws.org/Vol-2458/paper6.pdf 21. Shen, D., Urquhart, A., Wang, P.: Does twitter predict bitcoin. Econ. Lett. 174(C), 118–122 (2019) 22. Abraham, J., Higdon, D., Nelson, J., Ibarra, J.: Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci. Rev. 1(3), 1–22 (2018) 23. Stenqvist, E., Lönnö, J.: Predicting bitcoin price fluctuation with twitter sentiment analysis, pp. 3–28 (2017). KTH Royal Institute of Technology School of Computer Science and Communication, Stockholm, Sweeden 24. Colianni, S., Rosales, S.M., Signorotti, M.: Algorithmic trading of cryptocurrency based on twitter sentiment analysis (2015). CS229 Project 25. Mai, F., Bai, Q., Shan, J., Wang, X., Chiang, R.H.: From bitcoin to big coin: the impacts of social media on bitcoin performance. SSRN Electron. J. 35(1), 1–46 (2018) 26. Hasan, S.H., Hasan, S.H., Ahmed, M.S., Hasan, S.H.: A novel cryptocurrency prediction method using optimum CNN. CMC-Comput. Meter. Contin. 71, 1051–1063 (2022). https:// doi.org/10.32604/cmc.2022.020823 27. McCoy, M., Rahimi, S.: Prediction of highly volatile cryptocurrency prices using social media. Int. J. Comput. Intell. Appl. 19(4), 1–28 (2020). https://doi.org/10.1142/ S146902682050025X 28. Kraaijeveld, O., De Smedt, J.: The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. J. Int. Financ. Mark. Inst. Money 65 (2020) 29. Valencia, F., Gomez-Espinosa, A., Valdes-Aguirre, B.: Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy 21(6) (2019) 30. Steinert, L., Herff, C.: Predicting altcoin returns using social media. Plos One 13(12) (2018). https://doi.org/10.1371/journal.pone.0208119 31. Jain, A., Tripathi, S., Dwivedi, H.D., Saxena, P.: Forecasting price of cryptocurrencies using tweets sentiment analysis. In: 2018 Eleventh International Conference on Contemporary Computing (IC3), pp: 268–274 (2018) 32. Lamon, C., Nielsen, E., Redondo, E.: Cryptocurrency price prediction using news and social media sentiment (2017). http://cs229.stanford.edu/proj2017/final-reports/5237280.pdf 33. Wolk, K.: Advanced social media sentiment analysis for short-term cryptocurrency price prediction. Expert Syst. 37(2) (2019). https://doi.org/10.1111/exsy.12493 34. Mnif, E., Lacombe, I., Jarboui A: Users’ perception toward Bitcoin Green with big data analytics. Soc. Bus. Rev. 16(4), 592–615 (2021) 35. Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., Mora, H.: A framework for big data analytics in commercial social networks: a case study on sentiment analysis and fake review detection for marketing decision-making. Ind. Mark. Manag. 90, 523–537 (2019). https://doi.org/10.1016/j.indmarman.2019.08.003 36. Shivaprasad, T.K., Shetty, J.: Sentiment analysis of product reviews: a review. In: Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2017, pp. 298–303 (2017). https://doi.org/10.1109/ICICCT. 2017.7975207 37. Valencia, F., Gómez-Espinosa, A., Valdés-Aguirre, B.: Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy 21 (2019). https:// doi.org/10.3390/e21060589 38. Wołk, K.: Advanced social media sentiment analysis for short-term cryptocurrency price prediction. Expert. Syst. 37, 1–16 (2020). https://doi.org/10.1111/exsy.12493

616

Ş. Ö. Birim and F. E. Sönmez

39. Abraham, J., Higdon, D., Nelson, J: Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci. Rev. 1 (2018). https://scholar.smu.edu/data sciencereview, http://digitalrepository.smu.edu., https://scholar.smu.edu/datasciencereview/ vol1/iss3/1 40. Karabiyik, B.K., Ergün, Z.C.: Forecasting bitcoin prices with the anfis model. J. Dicle Univ. Fac. Econ. Adm. Sci. 11, 295–315 (2021) 41. Atsalakis, G.S., Atsalaki, I.G., Pasiouras, F., Zopounidis, C.: Bitcoin price forecasting with neuro-fuzzy techniques. Eur. J. Oper. Res. 276, 770–780 (2019).https://doi.org/10.1016/j. ejor.2019.01.040 42. Yazdanbakhsh, O., Dick, S.: A deep neuro-fuzzy network for image classification, Arxiv Preprint:2001.01686. (2019). http://arxiv.org/abs/2001.01686 43. Lenhard, G.: Adaptive-network-based fuzzy inference system (ANFIS) based on Keras on top of Tensorflow 2.0 (2020)

Exploring the Critical Factors of Digital Transformation Based on Linguistic Variables and DEMATEL Chien-Wen Chen1(&) and Chen-Tung Chen2 1

Department of Business Administration, Fen Chia University, Taichung, Taiwan [email protected] 2 Department of Information Management, National United University, Miaoli, Taiwan [email protected]

Abstract. Due to the rapid development of information technologies and the rise of intelligence products recently, digital transformation became an important management issue for enterprises to increase the competitive abilities in the market. Actually, digital transformation is a complex work that will affect all departments within a company. Managers must simultaneously balance the quantitative and qualitative resources to achieve the successful digital transformation. However, many factors will influence the strategies and actions of digital transformation for companies. Therefore, it is an important issue for company to explore the critical factors in the process of digital transformation. Because experts always cannot describe their opinions clearly, it is suitable to express the evaluations of experts by using the linguistic variables in the process of digital transformation. Under this situation, the DEMATEL (decision-making trial and evaluation laboratory) technique is applied to indicate influential relationships and to compute the weights of all critical factors. Based on the linguistic opinions and DEMATEL, this paper proposed a systematic way to explore the critical factors of digital transformation of company. An example is used to illustrate the procedures of the proposed model at last in this paper. Keywords: Digital transformation


Linguistic variables
DEMATEL

1 Introduction Recently, with the rapid development of information technology and the artificial intelligence, many intelligent products and equipment have developed to the market. Under this situation, digital transformation became an important management problem to cope with fierce market competition [1]. Digital transformation can take full advantages of digital technologies to accelerate the transformation of business activities [2]. Therefore, the purpose of digital transformation is to use new information technologies to enhance core processes such as improving the customer relationships, increasing operational efficiency of internal processes, and creating new business models and opportunities [3]. Digital transformation of enterprise is an evolutionary © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 617–625, 2022. https://doi.org/10.1007/978-3-031-09176-6_69

618

C.-W. Chen and C.-T. Chen

process that encompasses many different phases to demonstrate an organization’s ability to achieve its goals [4]. Digital transformation can be described as a continuous process of digital development that applies digital technologies in existing business models to constantly update and make continuous transformation of the organization for improving the operational efficiency and customer values. However, managers are often unaware of the different options and elements before undertaking a digital transformation strategy [5]. The main difficulties to the success of digital transformation strategies are the uncertainties of the application of digital technologies and the ambiguity of the meanings of digital transformation. Therefore, the first point is to identify the critical influence factors before implementing digital transformation strategy [6]. Therefore, the main purpose of this paper is to present a systematic method to explore the key influenced factors before implementing digital transformation strategy under the uncertain environment. This paper organized into six sections. Section 2 presents literatures review. The definitions, notations and operations of the fuzzy number and linguistic variables discuss in Sect. 3. The computational procedures of the proposed model present in Sect. 4. An example implemented in accordance with the computational process of the proposed model in Sect. 5. Finally, the conclusion and future research direction presented in Sect. 6.

2 Literatures Review In general, the scope of the digital transformation process consists of three parts such as digital, digitization and digital transformation [7]. The digital transformation is the integration of digital technologies and new business models into various enterprise functions to provide values to customers [8]. Warner and Wager [9] defined the digital transformation as the use of information technologies and devices (e.g., mobile devices, artificial intelligence, cloud service, blockchain technology, and IoT) to enable changes the operational processes, enhance the customer experience, or create new business opportunities and business models. Digital transformation is to use digital technologies to develop and implement new business models, prompting companies to re-evaluate existing operational functions, structures, and organizational cultures [7, 10]. When faced with the challenges of the competitive environment, most companies still cannot control and understand the nature, impact, and specific practices of digital transformation. In addition to the implementation of digital technologies, cultural and organizational aspects do not always consider fully in the process of digital transformation [11]. Enterprises have limited resources, lack of the professional skills and knowledge in real world, it is hard to implement specific digital transformation project successfully [11]. In fact, there are many dimensions that companies need to consider when undertaking the digital transformation such as management strategy, leadership, operations, culture, people, products, and technologies [8]. Many influencing factors and the uncertainty of digital technologies development need to be considered when choosing a digital transformation strategy [12]. In additional, it needs to deal with the

Exploring the Critical Factors of Digital Transformation

619

problem that there are interrelationships among these influenced factors. Under this situation, the DEMATEL (Decision Making Trial and Evaluation Laboratory) method is an effective tool to deal with the interrelationships between them [13–15]. In order to express the uncertainty and ambiguity opinions of experts completely, it is more feasible to express opinions of experts by using linguistic variables [16, 17]. Therefore, the linguistic variables used in this paper for experts to express their opinions for the interrelationships among these influenced factors. Then, this paper proposed a systematic method based on linguistic variables and DEMATEL to analyze and evaluate key influencing factors before implementing digital transformation strategy.

3 Definitions and Notations Definition 3.1. The membership function (uA~ ð xÞ) of a positive triangular fuzzy number ~ ¼ ða; b; cÞ can be defined as follows (shown in Fig. 1) [18]: (PTFN) A 8 xa > < ba ; a  x  b xc uA~ ð xÞ ¼ bc ; bxc > : 0; otherwise

ð1Þ

where a > 0 and a  b  c.

~ ¼ ða; b; cÞ. Fig. 1. Membership function of A

~ i ¼ ðail ; aim ; aiu Þ be triangular fuzzy numbers (TFN), i = 1,2,…, Definition 3.2. Let A n. Aggregated n triangular fuzzy numbers as follows. ~ ¼ ð al ; am ; au Þ A where al ¼ minfail g, am ¼ i

Qn i¼1

aim

1=n

ð2Þ

, and au ¼ maxfaiu g: i

620

C.-W. Chen and C.-T. Chen

~ ¼ ðal ; am ; au Þ and transfer A ~ into a crisp value (A ~ def ) as Definition 3.3. Suppose that A follows [19]. ~ def ¼ al þ 4  am þ au A 6

ð3Þ

Definition 3.4. Linguistic variables. In real life, experts can use linguistic variables easily to express the subjective evaluations in decision-making process [20]. For example, experts can use five linguistic variables to describe their evaluation values such as very poor (s1 ), poor (s2 ), normal (s3 ), good (s4 ), and very good (s5 ). Each linguistic variable can be represented by a triangular fuzzy number. For example, the linguistic variable “good” can be represented as s4 ¼ ð0:5; 0:75; 1:0Þ.

4 Proposed Model In general, many influenced factors should be considered by multiple experts in exploring the key influenced factors for implementing digital transformation strategy. This problem can be illustrated as follows: (i) A set of criteria is called C ¼ fcj jj ¼ 1; 2; . . .; ng. (ii) A set of experts is called Dk ðk ¼ 1; 2; . . .; KÞ. (iii) A set of linguistic variables. The procedures of the proposed method can be described as follows. (1). Experts use linguistic variables to express the influencing degree between any pair of criteria. The linguistic influence matrix of expert k can be constructed as ~ k ¼ ½~ak  A ij nn

ð4Þ

where ~akij is the influence degree of criterion ci on criterion cj by expert k and ~akij ¼ ðakijl ; akijm ; akiju Þ. (2). Aggregate the linguistic influence matrix of each expert to construct the initial direct linguistic influence matrix as ~ ¼ ½~aij  A nn ~aij ¼ ðaijl ; aijm ; aiju Þ,

where

aiju ¼ maxfakiju g. k

aijl ¼ minfakijl g, k

ð5Þ aijm ¼

Q

K k¼1

akijm

1=K ,

Exploring the Critical Factors of Digital Transformation

621

(3). Compute the defuzzification values of initial direct linguistic influence matrix as A ¼ ½aij nn a þ 4a

ð6Þ

þa

ijm iju where aij ¼ ijl . 6 (4). Normalize the matrix A as follows.

X ¼ ½xij nn where xij ¼

aij Z

and Z ¼ maxðmax i

Pn j¼1

aij ; max

ð7Þ Pn

j

i¼1

aij Þ.

(5). Establish the total influence relation matrix as follows. T ¼ XðI  XÞ1

ð8Þ

where I is identity matrix and T ¼ ½tij nn . (6). For criterion C i , calculate Di and Ri as follows. Di ¼

n X

tij

ð9Þ

tji

ð10Þ

j¼1

Ri ¼

n X j¼1

The Di means the total impact that criterion C i influence other criteria. The Ri represents the total impact received of C i by other criteria. The value of (Di þ Ri ) denotes the impact strength of criterion Ci on all other criteria. The value of (Di  Ri ) represents the influencing criteria with positive degree and influenced criteria with negative degree. (7). Compute the weight of each criterion as follows. ðDi þ Ri Þ wi ¼ Pn i¼1 ðDi þ Ri Þ

ð11Þ

where wi is the importance degree of criterion Ci .

5 Example Suppose that the top manager of a high-tech company wants to identify the critical factors for implementing digital transformation strategy. The top manager assigned three experts (E1 , E2 , E3 ) to identify the relationships among five critical factors such as information technology (C1), support of top manager (C2), human resource (C3), organization of project management (C4), and operational process (C5). According to the proposed method, the computational procedures can describe as follows.

622

C.-W. Chen and C.-T. Chen

Step 1. Each expert uses the linguistic variables (refer to Table 1) to express the relationships among critical factors as Tables 2, 3 and 4. Step 2. Construct the initial direct linguistic influence matrix as Table 5. Table 1. Linguistic variables in fuzzy DEMATEL [21] Linguistic variables No Influence Very Low Influence Low Influence High Influence Very High Influence

Abb NOI VLI LI HGI VHI

PTFN (0,0,0.25) (0,0.25,0.5) (0.25,0.5,0.75) (0.5,0.75,1) (0.75,1,1)

Table 2. Linguistic influence matrix of expert E1 C1 C2 C3 C4 C5

C1 NOI VHI VLI LI LI

C2 HGI NOI VHI LI HGI

C3 HGI LI NOI LI VL

C4 LI HGI HGI NOI LI

C5 LI LI VHI HGI NOI

Table 3. Linguistic influence matrix of expert E2 C1 C2 C3 C4 C5

C1 NOI HGI VLI LI LI

C2 HGI NOI HGI VLI HGI

C3 VH LI NOI LI LI

C4 VL VHI HGI NOI VLI

C5 LI LI VHI VHI NOI

Table 4. Linguistic influence matrix of expert E3 C1 C2 C3 C4 C5

C1 NOI VHI LI VLI LI

C2 HGI NOI VHI LI VHI

C3 HGI LI NOI LI VLI

C4 LI HGI HGI NOI LI

C5 VLI LI VHI HGI NOI

Exploring the Critical Factors of Digital Transformation

623

Table 5. Aggregated influenced matrix of three experts C1 C2 C3 C4 C5

C1 (0,0,0.25) (0.5,0.91,1) (0,0.31,0.75) (0,0.4,0.75) (0.25,0.5,0.75)

C2 (0.5,0.75,1) (0,0,0.25) (0.5,0.83,1) (0,0.31,0.75) (0.5,0.75,1)

C3 (0.5,0.83,1) (0.25,0.5,0.75) (0,0,0.25) (0.25,0.5,0.75) (0,0.31,0.75)

C4 (0,0.4,0.75) (0.5,0.83,1) (0.5,0.75,1) (0,0,0.25) (0,0.4,0.75)

C5 (0,0.4,0.75) (0.25,0.5,0.75) (0.75,1,1) (0.5,0.83,1) (0,0,0.25)

Step 3. Compute the defuzzification values of initial direct linguistic influence matrix as Table 6 and normalize the matrix as Table 7. Step 4. Establish the total influence relation matrix as Table 8 and compute the weights of criteria as Table 9. Therefore, the importance order of all criteria is “support of top manager (C2) > human resource (C3) > operational process (C5) > information technology (C1) > organization of project management (C4)”. Table 6. Initial influence matrix C1 C2 C3 C4 C5

C1 0.01 0.30 0.12 0.13 0.17

C2 0.26 0.01 0.28 0.12 0.26

C3 0.28 0.17 0.01 0.17 0.12

C4 0.13 0.28 0.26 0.01 0.13

C5 0.13 0.17 0.33 0.28 0.01

Table 7. Normalized influence matrix C1 C2 C3 C4 C5

C1 0.01 0.30 0.12 0.13 0.17

C2 0.26 0.01 0.28 0.12 0.26

C3 0.28 0.17 0.01 0.17 0.12

C4 0.13 0.28 0.26 0.01 0.13

C5 0.13 0.17 0.33 0.28 0.01

Table 8. Total influence matrix C1 C2 C3 C4 C5

C1 0.765 1.051 0.974 0.752 0.796

C2 1.119 0.982 1.254 0.880 0.980

C3 0.975 0.960 0.851 0.773 0.746

C4 0.938 1.096 1.137 0.690 0.809

C5 1.014 1.109 1.282 0.985 0.760

624

C.-W. Chen and C.-T. Chen Table 9. The weights of all criteria C1 C2 C3 C4 C5

D 4.811 5.198 5.498 4.08 4.091

R DþR 4.338 9.149 5.215 10.413 4.305 9.803 4.67 8.75 5.15 9.241

DR 0.473 −0.017 1.193 −0.59 −1.059

Weights 0.193 0.220 0.207 0.185 0.195

Rank 4 1 2 5 3

6 Conclusions Due to digital transformation is a complex process that affects all departments within a company, managers must simultaneously consider the internal and external resources to achieve the successful transformation of company. Many factors will influence the strategies and actions of digital transformation for companies. Therefore, it is an important issue is to explore and identify the critical factors in the process of digital transformation. However, there are interrelationships among the influenced factors, and the evaluations of experts always are fuzziness and uncertainties, it is not easy to identify the key critical factors of digital transformation. Therefore, the main contribution of this paper is to propose a linguistic DEMATEL method to explore the critical factors of digital transformation. The proposed method not only can determine the key factors but also can compute the weights of all influence factors. Based on the results of proposed method, we can understand the importance order of key factors to implement a digital transformation strategy and to increase the successful possibility of digital transformation. In the future, other types of linguistic variables will be considered to express the evaluation values of the interrelationships among the influenced factors. The other important direction is to design an interactive information system based on the linguistic DEMATEL method to explore the critical factors of digital transformation.

References 1. Rothberg, H., Erickson, S.: Big data systems: knowledge transfer or intelligence insights. J. Knowl. Manag. 21(1), 92–112 (2017) 2. Demirkan, H., Spohrer, J.C., Welser, J.J.: Digital innovation and strategic transformation. IT Prof. 18, 14–18 (2016) 3. Zaoui, F., Souissi, N.: Roadmap for digital transformation: a literature review. Procedia Comput. Sci. 175, 621–628 (2020) 4. Colli, M., Sala, R., Pirola, F., Pinto, R., Cavalieri, S., Wæhrens, B.V.: Implementing a dynamic FMECA in the digital transformation era. IFAC-PapersOnLine 52(13), 755–760 (2019) 5. Hess, T., Matt, C., Benlian, A., Wiesbock, F.: Options for formulating a digital transformation strategy. MIS Q. 15/2(131), 123–139 (2016)

Exploring the Critical Factors of Digital Transformation

625

6. Limani, Y., Hajrizi, E., Stapleton, L., Retkoceri, M.: Digital transformation readiness in higher education institutions (HEI): the case of Kosovo. IFAC-PapersOnLine 52(25), 52–57 (2019) 7. Saarikko, T., Westergren, U.H., Blomquist, T.: The internet of things: are you ready for what’s coming? Bus. Horiz. 60, 667–676 (2020) 8. Albukhitan, S.: Developing digital transformation strategy for manufacturing. Procedia Comput. Sci. 170, 664–671 (2020) 9. Warner, K.S.R., Wäger, M.: Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long Range Plan. 52(3), 326–349 (2019) 10. Casey, E., Souvignet, R.T.: Digital transformation risk management in forensic science laboratories. Forensic Sci. Int. 316, 110486 (2020) 11. Stich, V., Zeller, V., Hicking, J., Kraut, A.: Measures for a successful digital transformation of SMEs. Procedia CIRP 93, 286–291 (2020) 12. Yeh, C.C.: Using a hybrid model to evaluate development strategies for digital content. Technol. Econ. Dev. Econ. 23(6), 795–809 (2017) 13. Giri, B.C., Molla, M.U., Biswas, P.: Pythagorean fuzzy DEMATEL method for supplier selection in sustainable supply chain management. Expert Syst. Appl. 193, 116396 (2022) 14. Liang, Y., Wang, H., Zhao, X.: Analysis of factors affecting economic operation of electric vehicle charhing station based on DEMATEL-ISM. Comput. Ind. Eng. 163, 107818 (2022) 15. Karasan, A., Ilbahar, E., Cebi, S., Kahraman, C.: Customer-oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodology. Appl. Soft Comput. 118, 108445 (2022) 16. Herrera, F., Martinez, L., Sanchez, P.J.: Managing non-homogeneous information in group decision making. Eur. J. Oper. Res. 166, 115–132 (2005) 17. Liu, X., Chen, H., Zhou, L.: Hesitant fuzzy linguistic term soft sets and their applications in decision making. Int. J. Fuzzy Syst. 20(7), 2322–2336 (2018) 18. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Application. Prentice-Hall Inc., New Jersey (1995) 19. Zhao, H., Guo, S.: External benefit evaluation of renewable energy power in Chain for sustainability. Sustainability 7(5), 4783–4805 (2015) 20. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I, II, III. Inf. Sci. 8, 43–80 (1975). pp.199–251, pp.301–357, 9 21. Chang, B., Chang, C.W., Wu, C.H.: Fuzzy DEMATEL method for developing supplier criteria. Expert Syst. Appl. 38, 1850–1858 (2011)

Sentiment Analysis of Elon Musk’s Twitter Data Using LSTM and ANFIS-SVM Buğra Erkartal(&)

and Atınç Yılmaz

Beykent University, Ayazağa İstanbul 34450, Turkey [email protected]

Abstract. Social media plays a huge role spreading words to millions and influencing their opinions. Twitter is one of the most essential platform that reach over 300 million active users and 500 million tweets per day, it plays a significant role spreading the word around the world,. These tweets covers a various subjects from personal conversations to globally important topics such as updates about Covid19 and macroeconomic subjects. Especially in financial matters, it is a very common situation that business owners, even politicians report the news on Twitter first. The Tesla’s and SpaceX’s CEO and owner Elon Musk’s tweets had a huge impact on coin market or even stock exchanges. Although many accused him of market manipulation his tweets impact cannot be underestimated. In 2020 and 2021 there are various tweets that strike the stock market instantly both in the positive and negative direction. This study aims to predict the direction of his tweets and perform a sentiment analysis using both Long-Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Interface Systems (ANFIS)-SVM(Support Vector Machines) models. The dataset is obtained by using Twitter API which spans a time horizon of 5 years. In order to compare the results under same conditions same preprocessing steps are performed for both models. According to the results, LSTM performs a superior performance with its 72.2% accuracy against ANFIS-SVM model with 74.1%. Keywords: ANFIS


LSTM
Elon musk

1 Introduction This paper investigates the inflance of Elon Musk’s tweets on economic indexes Bitcoin (BTC), Dogecoin & Tesla Stock. Twitter is a cutting-edge digital spot for discussions, having its number one usage in preserving all of us updated with what’s occurring worldwide. As a known trend of well-known people’s tweets on diverse subjects has been on a current rise due to the excessive following of the celebs on social media. Therefore, a trend tweet for an organization can take its valuation toward the higher end & vice versa. Due to the fast paced nature of social media, by a single tweet people can instantaneously reach their followers and spread the word. Elon Musk has an account over 71 million followers with about 16 thousand tweets on twitter. While the total number of tweets he sent at the end of 2021 was around 13000. The charge of Bitcoin rose from approximately $32,000 to over $38,000 in few hours, growing the asset’s marketplace capitalization by $111 billion. The relevance of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 626–635, 2022. https://doi.org/10.1007/978-3-031-09176-6_70

Sentiment Analysis of Elon Musk’s Twitter Data

627

Musk’s tweets for economic markets has already grow to be obvious in different contexts. His tweet “thinking about taking Tesla non-public at $420” [1] ended as a fraud by the U.S. Securities and Exchange Commission and penalized with forty million dollars. Musk’s endorsement of the encrypted messaging provider Signal caused buyers buying the unrelated Signal Advance stock, growing the latter’s marketplace valuation from $fifty five million to over $three billion. These activities truly display the effect that management in social networks will have on economic markets and the decision-making conduct of individual buyers. A similar situation has occurred when Musk tweeted on Twitter on Sunday 31th January 2021 that he was at the Clubhouse at 10 pm LA tonight. Clubhouse Media Group, codenamed CMGR, rose nearly 45% on Monday, according to Markets Insider data. However, it was noted that the company had nothing to do with the Clubhouse audio application. According to a study run by Pipsley among 30,400 Americans, it was found that 37% of the respondents made at least one personal investment based on Elon Musk’s tweets [2]. Regardless of whether or not the tweets address the actual company, inverters tend to follow the flow crated by Musk. On January 2021, right after the market is closed he commented on twitter with a single word “Gamestonk!!” that supports amateur investors continued a frenzied run-up of GameStop for the fourth session in a row, that rises the stock prices of the share 680% in small period of time. Regardless of this tweets affect him and/or his companies his influence over the society can’t be negligible. Throughout this paper the social impact of the one of the richest people in the world tried to be analyzed with various techniques. After a brief literature survey given in section two, both LSTM and ANFIS-SVM model characteristics are introduces at section tree. Finally the results of the experiments are represented and interpreted at section four and five.

2 Literature Review Parveen and Pandey used the IMDB records obtained by a twitter API to analyze the sentiments. The version that they proposed constructed via way of means of the use of Natural Language Tool Kit (NLTK) at the twitter dataset. BoW technique is used and naïve classifier technique is carried out for the classification phase [3]. Neethu and Rajasree they are attempting to categorize the twitter posts containing information about electronics. In order to classify the sentiments into two groups they propose a new feature extraction method which creates a new feature vector [4]. Sankhla and Ram constructed their version of a supervised learning method namely, SVM and Naïve Bayes Classifier and achieved comparative evaluation to discover user’s behavior. They used “Budget 2018” records set and their results shows that the SVM have a higher accuracy with 91% whilse Naïve Bayes have an accuracy of 83% [5]. Le and Nguyen recommended several of sentiment analysis techniques such as Information Gain, Bigram, Object orientated extraction strategies on Twitter dataset. In order to boost the accuracy of the model Naive Bayes and Support Vector Machine tecniques are implemented at the classification phase [6]. Katta and Hegde proposed a hybrid adaptive neuro-fuzzy interface with SVM approach in order to analyze the tweets of the politicians. They gather their data throughout a twitter API and their results shows a

628

B. Erkartal and A. Yılmaz

80.6% accuracy with ANFIS-SVM model [7]. Dey et al. amassed records sets, which include film opinions and hotel reviews to evaluate the performances of Naïve Bayes and KNN (K Nearest Neighbors) classifiers. They observed out that the Naïve Bayes classifier has a barely higher overall performance throughout the various film records set. Besides the performances of each classifier are about identical at the hotel overview records dataset [8]. Jain and Dandannavar used Multinomial Naïve Bayes classifier and Decision tree classifier to make a sentiment evaluation on dataset from Apple. Their models proposes a new text evaluation framework for twitter records by the use of Apache spark. Two machine learning algorithms are examined and that they come to the result that decision tree classifier outperforms Multinomial Naïve Bayes classifier [9]. Padmaja and Hegde proposed a method for twitter sentiment analysis using adaptive neurofuzzy inference system in combination with a genetic algorithm. Their data contains 479 tweets and according to their findings their methodology has 92.56 accuracy. Pak and Paroubek used a Twitter API that can label the given sentiments according to the emojis and emiticons into three categories namely positive, negative and natural. A Multinomial Naïve Bayes approach is chosen for the type of the capabilities generated via way of means of n-gram and Part of Speech-tags (POS-tags). According to the authors a few POS-tags can be sturdy signs of emotional text [10]. Bifet and Frank amassed Twitter massages to categorize them using multinomial Naive Bayes, stochastic gradient descent, and the Hoeffding tree. According to their findings Stochastic Gradient Descent (SGD) outperforms the other methods [11] Agarwal et al. used tree models in order to classify sentiments into three categories. The choosen three models namely unigram model, the feature-based model, and the tree-kernel model’s performances are compared. Alongside the feature model has 100D features unigram model has more than 10000D. According to their final findings kernel tree based framework exceeded the other models performances [12].

3 Models 3.1

LSTM Model

Recurrent neural networks which LSTMs belongs to is widely used artificial neural network technique. In time series models many neural network designs cannot cope with the data containing different time intervals. LSTM is a RNN which can learn dependencies over time. LSTMs are good for classifying sequence and time-series data. The primary distinction among the architectures of RNNs and LSTMs is that the hidden layer of LSTM is a gated unit. It includes 4 layers that have interaction with each other in a manner to provide the output of that node in conjunction with the state. These matters are then surpassed onto the subsequent hidden layer. Unlike RNNs that have has a single node with an activation function value with tanh, LSTMs contains of 3 logistic sigmoid gates and one tanh layer. Gates were added with a purpose to restriction the data this is surpassed thru the node. They decide which a part of the data can be wanted by the subsequent node and which element is to be discarded. The output is commonly with inside the variety of 0–1 where ‘0’ means ‘reject all’ and ‘1’ means ‘encompass all’.

Sentiment Analysis of Elon Musk’s Twitter Data

629

Recursive neural Network (RNN) is a class of neural networks that form a directed loop for connections between neurons and is widely used in sentiment analysis. The fact that each output depends on all previous calculations, that is, keeping it in memory means that the processed information until then is remembered, and sequential information brings it to the fore in processing. All RNNs are like a chain of repeating modules. The repeating module normally has a simple structure. Whereas, the duplicate module for LSTM has a more complex structure. The pre-processing and transformation of the data set to the new structure is made ready to be processed as the first information of the LSTM model. In the study, these processes are carried out with MATLAB. The overall scheme of the model summarized in the Fig. 1 given below.

Fig. 1. Illustration of LSTM model.

Preparing the Data for Recurrent Neural Network. Tweets that have been categorized as positive or negative. These Tweets have been pre-categorised primarily based totally on emoji’s with inside the Tweet. Mathworks which are the developers of MATLAB put up them free. The presented database carries 1.6 million pre-categorised Tweets. In order the represent the relationship mathematically word-embeddings are used. Stanford NLP word embedding is used in this study and GloVe (GloVe is an unsupervised learning method to represent the words into vectors) to convert the phrases to 100D vectors. Parameters of LSTM Model. Recurrent neural networks, of which LSTMs are one of the most powerful tool, that can to recognize patterns in sequences of data. LSTM is a recurrent neural network (RNN) type of network which can learn dependencies over time. Since the twitter massages can be categorized as a time series data method is used to cross-reference the results with other model. The following parameters are used in the network: • Train over 1000 epochs (complete passes through the dataset). • Softsign activation function is used. • LSTM AdaGrad is used for updating the weights of the nodes. With the parameters of the model given above LSTM Model was run by using the corpora obtained from the University of Michigan Sentiment Analysis competition on Kaggle and Twitter Sentiment Corpus by Niek Sanders which contains exactly 1,578,627 classified tweets and each row is marked as positive sentiment and negative sentiment. The 10% of the corpus is used for the testing. The sample corpora is given at the figure below (Fig. 2).

630

B. Erkartal and A. Yılmaz

Fig. 2. Sample corpora.

The confusion matrix of the LSTM model is given at the table below. In order to save time in training period only 200,000 sentiments are used for training. In order to increase the performance of the model one can use a larger corpus. 3.2

ANFIS-SVM Model

The second model that is going to be proposed is an ANFIS-SVM model. Throughout the process fuzzy rule based model is implemented and then the decided rules are optimized by the SVM (Fig. 3).

Fig. 3. Illustration of ANFIS-SVM model.

ANFIS. ANFIS is one of the neuro-fuzzy model, which has the blessings of each neural structures and fuzzy rule. In this exploration work, ANFIS classifier is proposed to productively ruin down the twitter statistics. Let consider, ANFIS display comprises of reasserts of enter high-quality and negative key phrases in tweet in particular x and y and one output f. In ANFIS characteristic, statistics elements are additionally known as flexible (adaptive) nodes. The lUi and lVi are the fuzzy membership functions where they can have values between zero and one calculated from the follows in Eq. (1). Ui ð xÞ ¼

1


xwi
2vi 1 þ
ui


ð1Þ

Sentiment Analysis of Elon Musk’s Twitter Data

631

Whereas, vi and wi stands for the non-linear parameters of the membership features on linguistic labels. The following multiplication layer incorporates of settled nodes and this sediment duplicates the enter assessments and registers the item which can be calculated using Eq. (2) wti ¼ lUi ð xÞ  l Vi ð yÞ;

i ¼ 1; 2

ð2Þ

where, w is the multiplication layer, U and V are fuzzy sets. There are two more layer that sums the inputs and the other one calculates the first order polynomial values. Those equations are defined in Eq. (3) and Eq. (4) with respectively. ^i¼ wt

wti ; i ¼ 1; 2 wt1 þ wt2

^ i ðpi x þ qi y þ ri Þ; i ¼ 1; 2 ^ i fi ¼ wt wt

ð3Þ ð4Þ

where, qi, ri are fractions of the first order polynomial function. At last, with inside the very last layer consist of only one node. This layer also known as the output layer, calculates the final value of the aid of using Eq. (5) P wti fi ^ i fi ¼ Pi f ¼ wt ; i ¼ 1; 2 i wti

ð5Þ

Classification principles forces the ANFIS model to generate a set of fuzzy rules that can be used for the next phase of the model called SVM. İn order choose the best guideline for the classification process with respect to the concept of the tweet SVM is one of the quickest way. Support Vector Machines. SVMs are one of the generally used gadget mastering method that may classify and forecast the present information. They have sturdy theoretical history and first-rate empirical successes, their power comes from their capacity to split information each linearly and nonlinearly. In order to run SVMs efficiently the output must be categorical (ideally binary). We are given the training records which can be vectors in a few area X and their labels in which y1…yn. SVMs as a binary type of method, the hyper planes are calculated with the intention to break up the training records into 1/2 of through a maximal margin. The training values can be categorized as positive or negative depending on the position of the hyperplane. The closest vector near the hyperplane is also called support vectors. This perfect scenario only occurs when the data labels are linearly separable. If the vector lie very near the hyperplane they may be referred to as guide vectors. The training records won’t be linearly separable and at that case non-linear hyperplane is probably used in this case. Under those circumstances the training data points should be projected into a new space by using the following transfer function (Eq. (6)). K ðx:yÞ ¼ ð/ð xÞ:/ð yÞÞ

ð6Þ

632

B. Erkartal and A. Yılmaz

where K(x, y) is a kernel function that returns the dot product of the training data points in a feature space. According to the dual theorem the solution of the original problem and its dual should be the same. Therefore the computational complexity of the equation can be avoided by taking the dual representation of the equation above. That yields Eq. (7). f ð xÞ ¼ sign

X

ai ; yi K ðXi ; X Þ þ b

ð7Þ

i2S

where, ai is represented as a Lagrangian values in that i indicates i = 1, 2,.., N. yi is a hyperplane values of SVM, (Xi, X) is a kernel function of higher dimensional feature space and b is constant. In this study non-linear kernel functions are used to classify the statements correctly.

4 Application 4.1

Access Twitter Data

Matlab Twitter API and Datafeed Toolbox is used to access the tweets of Elon Musk. Data spans the time horizon between November 2012 and March 2021 containing 10487 tweets. The sample data set is given at the figure below (Fig. 4).

Fig. 4. Sample dataset.

4.2

Feature Extraction and Preprocessing

In order prepare the data for both networks some words should be removed from the dataset such as special characters, emoticons, abbreviations, hashtags, timestamps, urls and punctuations. Since each word is considered as a token preprocessing step helps to get more insight from the text. There are words existed in the text that can indicate the

Sentiment Analysis of Elon Musk’s Twitter Data

633

category of the sentiment. For achieving more natural results frequency of the words are calculated by bag of words (BOW) approach that is also used to generate the features. After feature extraction text is ready to be converted into vectors that is called the embedding layer. After the preprocessing the most common words might be visualized from the figure below (Fig. 5).

Fig. 5. Illustration of the most commonly used words.

In scientific terms, appearances of the most commonly used words are: “Tesla” 1566 times, “Model” 1302 times, “SpaceX” 1201 times, “but” 822 times. 4.3

Result Analysis

Investor and data scientist are increasingly looking into whether Elon Musk’s social media activities in order to predict the next move of the CEO. This study aims to predict the time of the tweets about the Turkish economy and direction of the impact over stock exchange. The following performance measures are used to evaluate the performance of both models. Accuracy is the overall performance of the model which are classified correctly from the total test samples. Where, True Negative is the correct estimation of negative labelled data point (denoted as TN), True Positive is the correct estimation of positive labelled data (denoted as TP), False Positive and False Negative are the wrongly estimated values of positive and negative labelled data point with respectively (denoted as FP and FN). The results are given at the Table 1 below.

Table 1. Experimental results Model TP (%) TN (%) Accuracy (%) LSTM 78.6 56.6 71.2 ANFIS-SVM 80.1 58.5 74.1

634

B. Erkartal and A. Yılmaz

As it can be observed from the Table 1 above ANFIS-SVM model have slightly better accuracy with 74%. And true positive ratios are way better than false positive ones for both models. If the actual tweet belong to the positive class then the model can classify the statement correctly with higher accuracy. On the other hand if the actual class of the tweet belongs to a negative class the accuracy drops down.

5 Conclusion Social media currently anticipate as a vital part in both in politics and economy. Elon Musk is one of the leading figures in this era. His twitter account followed by millions of people and his tweets affects both stock markets and crypto currencies. In this paper, a fuzzy based SVM model and LSTM model is proposed for sentiment analysis among the data collected from Twitter. This framework used to classify the economic statements. Throughout this study tweets are collected by using an API provided by MATLAB spanning a time horizon of almost 4 years. The two proposed frameworks uses the same preprocessing steps in order to test the performances of the models under same conditions. On the classification step, while SVM is used to optimize the fuzzy rules in ANFIS-SVM model, LSTM model uses RNN based approach to classify the sentiments. The experimental outcomes established that the proposed ANFIS-SVM approach slightly outperforms the LSTM model by nearly 3%, where LSTM’s accuracy is 71.2% and LSTM’s accuracy 74.2%. Both models performances are way better when the true class of the sentiment is positive where TP performance are 78.6% for LSTM 80.1% for ANFIS-SVM. This may occur because negatively labelled statements may include sarcasm or irony which is very hard for classification. In this study a corpora consisting of 200,000 sentiments are used to train the LSTM network. If a larger corpora is used model’s performance might be improved.

References 1. URL: TheVerge homepage. https://www.theverge.com/2018/8/7/17661178/tesla-elon-muskprivate-420-share-considering. Accessed 18 Mar 2022 2. URL: bussinessinsider homepage. https://markets.businessinsider.com/news/stocks/37-in-arecent-survey-say-theyve-made-trades-based-on-an-elon-musk-tweet-2021-3-1030164612. Accessed 18 Mar 2022 3. Parveen, H., Pandey, S.: Sentiment analysis on Twitter data-set using Naive Bayes algorithm. In: 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT), pp. 416–419 (2016) 4. Neethu, M.S., Rajasree, R.: Sentiment analysis in twitter using machine learning techniques. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–5 (2013) 5. Sankhla, N., Ram, S.: Sentiment analysis of Twitter datasets using support vector machine and Naive Bayes classifiers (2018)

Sentiment Analysis of Elon Musk’s Twitter Data

635

6. Le, B., Nguyen, H.: Twitter sentiment analysis using machine learning techniques. In: Le Thi, H., Nguyen, N., Do, T. (eds.) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol. 358, pp. 279–289. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17996-4_25 7. Katta, P., Hegde, N.P.: A hybrid adaptive neuro-fuzzy interface and support vector machine based sentiment analysis on political twitter data. Int. J. Intell. Eng. Syst. 12(1), 165–173 (2019) 8. Dey, L., Chakraborty, S., Biswas, A., Bose, B., Tiwari, S.: Sentiment analysis of review datasets using Naive Bayes and K-NN classifier arXiv Prepr. arXiv1610.09982 (2016) 9. Jain, A.P., Dandannavar, P.: Application of machine learning techniques to sentiment analysis. In: 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 628–632 (2016) 10. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, 2010, vol. 10, pp. 1320–1326 (2010) 11. Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) Discovery Science. DS 2010. LNCS, vol. 6332, pp. 1– 15. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16184-1_1 12. Agarwal, A., Balasubramanian, S., Kotalwar, A., Zheng, J., Rambow, O.: Frame semantic tree kernels for social network extraction from text. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 211–219 (2014)

Surveys

Review of Descriptive Analytics Under Fuzziness Elmira Farrokhizadeh1 and Başar Öztayşi2(&) 1

Industrial Engineering Department, Urmia University of Technology, Urmia, Iran 2 Industrial Engineering Department, Istanbul Technical University, Istanbul, Turkey [email protected]

Abstract. Business Analytics is one of the business management methods that deals with descriptive models which create meaningful insights to support and reinforce the business performance. It is one of the most widely used topics in business and Industry. The first state of each analytics is collecting valid data. Descriptive Analytics is the first stage of data processing that outlines historical data to acquire helpful information and organize the data for advanced analysis. In analyzing and classifying data from a statistical perspective, fuzzy sets and logic have become valuable tools to either model and handle imprecise data or establish flexible techniques to deal with precise data. Despite the popularity of Business Analytics in literature and the importance of Descriptive analytics as a first step, many aspects are still unclear. So due to the importance of Descriptive Analytics for organizations and the vagueness nature of data, we try to review Descriptive Analytics under fuzziness in this article. Keywords: Business analytics


Descriptive analytics
Fuzzy sets

1 Introduction Nowadays, data is one of the crucial and supportive issues for any research area, such as business, science, engineering, education, sociology, etc. But data is cheap and not meaningful alone. It gains value with different analytical techniques, which are analyzed to find the information behind the data. Whether internal or external, these data rapidly grow in high volume and different format is named big data. So big data, one of the important management challenges, has three essential and beneficial aspects: volume, velocity, and variety [1]. Big data can be defined as a set of complex and large datasets beyond traditional data processing applications and other relational database management tools to process, manage, and record all data in a given time frame [2]. Business Analytics, big data analytics, data analytics, and data science are four special techniques to meet business requirements [3]. The attention to Business Analytics is increasing significantly among industry professionals and academic scholars by rapid growth in big data. Business Analytics uses the data science, operational research, machine learning, and information systems fields to provide a competitive advantage for organizations [4]. Varshney and Mojsilović claim that BA is similar to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 639–645, 2022. https://doi.org/10.1007/978-3-031-09176-6_71

640

E. Farrokhizadeh and B. Öztayşi

the broad umbrella that includes many problems: demand forecasting, resource capacity planning, workforce planning, salesforce modeling and optimization, revenue forecasting, customer/customer/product analytics, and enterprise recommender systems [5]. Based on the INFORMS, BA can smooth the understanding of business objectives by analyzing the trend of the reporting data, forecasting the future model and optimizing the process to gain the strategic solution and reinforce the business performance [6]. There are many different definitions in the literature for BA, but the common factor is that BA concert the data to the actionable insight by combining the methods to gain better and faster decisions. BA can be categorized as descriptive analytics, predictive analytics, and Prescriptive Analytics. The first step is referred to Descriptive Analytics, which is trying to recognize the trend of business by studying the past and present data. It tries to answer the questions such as “What happened in the past?”, “Why did it happen?” and “What is happening now?”. Descriptive analytics started with statistical methods, but other methods such as classification, clustering, and categorizing can be applied when the size of historical data increases. After analyzing data and finding the hidden patterns, predictive analytics is applied by statistical and machine learning approaches to find future trends. Prescriptive analytics is the third step of business analytics which makes sense when you want to prescribe the action and make a decision. Kaur & Phutela compared different algorithms of descriptive analytics and their application area and then categorized them into four approaches: Decision Rules, Association Rules, Cluster Analysis, and Summarization Rules [7]. Classifications of Descriptive Analytics methods are represented in Table 1. Table 1. Classifications of descriptive analytics methods Descriptive analytics

Association rules

AIS Algorithm SETM Algorithm Apriori and AprioridTID Algorithm Partition Algorithm Frequent Pattern (FP) Growth Algorithm CMAR (Classification Based on Multiple Association Rules) Algorithm EClat Algorithm

Decision rules

Decision Trees VFDR VFDT CREA (Classification Rules Extraction Algorithm) Id3 RBS Value (Rules Based on Significance) IDRA (Incremental Decision Rules Algorithm) Heuristic Algorithms

Cluster analysis

Partitioning algorithms Hierarchy algorithms Density-based Grid-based Model-based

Summarization rules

APRX-Collection RP Global TOP-K BUS (Bottom-Up Summarization)

Review of Descriptive Analytics Under Fuzziness

641

In this era, data is one of the crucial buzzwords that can change our insight into the world. Descriptive Analytics is the first stage of data processing that outlines historical data to acquire helpful information and organize the data for advanced analysis. In analyzing and classifying data from a statistical perspective, fuzzy sets and logic have become valuable tools to either model and handle imprecise data or establish flexible techniques to deal with precise data. Many extensions of fuzzy sets are developed, such as Intuitionistic, Pythogerean, Hesitant, spherical, etc. These can be utilized in different conditions of the investigated problems. Despite the popularity of Business Analytics in literature and the importance of Descriptive analytics as a first step, many aspects are still unclear. So due to the importance of Descriptive Analytics for organizations and the vagueness nature of data, we try to review Descriptive Analytics under fuzziness from Scopus databases in this article. After the introduction, we present the methodology of this study and the process of selecting the articles. Then Literature of Fuzzy Association Rules is investigated in Sect. 3 and finally, results are represented in conclusion section.

2 Methodology There are many misunderstandings and unclear aspects of Descriptive Analytics under fuzziness because of the novelty of this field. So to clarify these aspects, we carry out a systematic literature review that allows us to identify, evaluate, and synthesize the existing knowledge of this topic from researchers and practitioners in the field in a systematic and reproducible way. In this regard, we search the academic papers in the Scopus database, one of the most recognized databases. Some thresholds are applied to the search area as a set of well-established selection criteria. Firstly, the publications should contain “Fuzzy Association Rules”/“Fuzzy Decision Rules”/“Fuzzy Cluster Analysis” or “Fuzzy Summarization Rules” in the title. Secondly, we only considered publications in English. Thirdly, regarding the publication type, we included articles and reviews types. Based on this limitation totally, 776 articles are found. The number of articles based on each search area is reported in Fig. 1. As you can see in the Fig. 1

Fuzzy Summarization Rules Fuzzy Cluster Analysis Fuzzy Decision Rules Fuzzy Assiciation Rules 0

50

100

150

Total number of articles between 2018_2022

200

250

300

350

Total number of articles

Fig. 1. Number of articles based on each method of descriptive analytics

642

E. Farrokhizadeh and B. Öztayşi

most popular Descriptive Analytics approaches is Fuzzy Association Rules. So, due to the popularity of this approach in the literature, we concentrate on the Fuzzy Association rules articles.

3 Fuzzy Association Rules (FARs) Association Rules is one of the most popular data mining approaches that try to find the dependency between collected data with the “If_Then” statement [8]. Due to the popularity of Association rules, widely use of Fuzzy Association rules is not far from expected. Fuzzy Association rules provide new framework problems for mapping the crisp data to uncertain data. Instead of using 0_1 logic, we can use fuzzy sets to handle the imprecise nature. This mapping improves the semantic content of the rules and makes them more understandable and sensible to humans [9]. As you can see in Fig. 2 there are many different application areas in the literature. Computer Science, Engineering, and Mathematics are widely used areas.

Fig. 2. Application area of fuzzy association rules

Fuzzification of Association Rules can be done by applying each element of it. New extension of Fuzzy Association Rules can be suggested with the addition of fuzziness in items, transactions, and rules components. Marín et al. claimed that Fuzzy Association Rules must be implicit as Association Rules in a Fuzzy framework, so it involves the study of fuzzifications of the crisp methods [10]. Item set in FARs can be fuzzified by using only one element without precisely determining which one because of the lack of information about the real value [11, 12] or referring to all elements of the item set with corresponding membership degree [13]. FARs can be obtained by using Fuzzy transactions that use a fuzzy subset of items Where the degree of each item is interpreted as the degree of its membership in the transaction [14]. This method is widely used in literature to fuzzification of the Assosian rules [10]. Rules are another

Review of Descriptive Analytics Under Fuzziness

643

element of Association Rules that can be fuzzified. It can be done in three different ways. Using the rules with the corresponding membership degree, which can be attained by the mining process [15]. The other way is using rules where different degrees are related to the antecedent and the consequent of the form ð A; aÞ ! ðB; bÞ. It means that B is involved in every transaction with at least b degree where A is included with degree at least. And the thired way is using rules in which both antecedent and consequent are fuzzy subsets of items [10]. Measuring is the other important issue in the Fuzzification of Assosiation Rules for aasesing the rules. There are many different measuring techniques in the literature but generally none of them is proved as best one. So finding the proper measuring in transforming the crisp measure to fuzzy form. Most of the FARs approaches in the literature used sigma-count cardinality measure [14], which is not a suitable cardinality based on experts’ views. Accuracy FARs are assessed by other methods in literature such as adjusted difference, weight of evidence [16], significance factor, which is a generalization of support based on sigma-counts [17], fuzzy covariance and correlation measures [18], partial correlation [19], mutual information [20], Linguistic measures [21] and many other assessment measures are proposed for assessing the FARs approaches which are referred in [22–26]. These are some of the different framework of FARs that combinations of these frameworks can be the potentional new approaches for proposed. On the other hand, many Fuzzy Association Rules methods were proposed by different extensions of Fuzzy sets. In [27], Fuzzy Association Rule Mining with Type-2 Membership Functions was proposed. In another study, rough-Apriori based mining technique was proposed for mining linguistic association [28]. In [29] author used Intuitionistic fuzzy sets and Hamming distance to mine the association rule between condition and conclusion. A hybrid Fuzzy Association Rule method was proposed by integrating rough set theory and fuzzy set in [30]. Intuitionistic Association Rules mining and many hybrid methods that integrate with it were proposed in many articles [31, 32]. Other extensions of fuzzy sets were applied for FARs, such as Type2 [33], Interval type 2 [34], Interval Fuzzy sets [35] and etc.

4 Conclusion Despite the popularity of Business Analytics in literature and the importance of Descriptive analytics as a first step, many aspects are still unclear. So due to the importance of Association Rules for organizations and the vagueness nature of data, we reviewed the most popular approach of Descriptive Analytics, Fuzzy Association Rules. We searched articles in the Scopus database and filtered the results based on proximity to the subject under consideration. So based on the results, Fuzzy Association Rules articles can be classified as fuzzification of the Items, Transactions, Rules or Measuring methods and combinations of them. On the other hand, Fuzzy Association Rules articles can be extended by different fuzzy sets in literature, such as Interval fuzzy set, Type2, Interval type2 and Intuitionistic fuzzy sets. All of these approaches have advantages and disadvantages, but the most significant benefit of all of them is using Fuzzy sets instead of 0_1 logic, preventing data losses. But many new extensions

644

E. Farrokhizadeh and B. Öztayşi

of fuzzy sets are not considered in the literature, so integrating new extensions of fuzzy sets such as Hesitant Fuzzy Sets (HFSs) can be considered a future study.

References 1. Duan, L., Xiong, Y.: Big data analytics and business analytics. J. Manag. Anal. 2, 1–21 (2015) 2. Oliveira, C., Guimaraes, T., Portela, F., et al.: Benchmarking business analytics techniques in big data. Procedia Comput. Sci. 160, 690–695 (2019) 3. Power, D.J., Heavin, C., McDermott, J., et al.: Defining business analytics: an empirical approach. J. Bus. Anal. 1, 40–53 (2018) 4. Mortenson, M.J., Doherty, N.F., Robinson, S.: Operational research from Taylorism to Terabytes: a research agenda for the analytics age. Eur. J. Oper. Res. 241, 583–595 (2015) 5. Varshney, K., Mojsilović, A.: Business analytics based on financial time series. IEEE Signal Process. Mag. 28, 83–93 (2011) 6. Chiang, R.H.L., Goes, P., Stohr, E.A.: Business intelligence and analytics education, and program development: a unique opportunity for the information systems discipline. ACM Trans. Manag. Inf. Syst. 3 (2012). Epub ahead of print. https://doi.org/10.1145/2361256. 2361257 7. Kaur, H., Phutela, A.: Commentary upon descriptive. In: Proceedings of the 2018 2nd International Conference on Inventive Systems and Control, pp. 678–683 (2018) 8. Pérez-Alonso, A., Blanco, I.J., Serrano, J.M., González-González, L.M.: Incremental maintenance of discovered fuzzy association rules. Fuzzy Optim. Decis. Mak. 20(4), 429– 449 (2021). https://doi.org/10.1007/s10700-021-09350-3 9. Delgado, M., Ruiz, M.D., Sánchez, D., et al.: Fuzzy quantification: a state of the art. Fuzzy Sets Syst. 242, 1–30 (2014) 10. Marín, N., Ruiz, M.D., Sánchez, D.: Fuzzy frameworks for mining data associations: fuzzy association rules and beyond. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 6, 50–69 (2016) 11. Djouadi, Y., Redaoui, S., Amroun, K.: Mining association rules under imprecision and vagueness: towards a possibilistic approach. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1–6. IEEE (2006) 12. Shyu, M.L., Haruechaiyasak, C., Chen, S.C., Kamal, P.: Mining association rules with uncertain item relationships. In: 6th World Multi-Conference Systemics, Cybernetics and Informatics (SCI 2002), pp. 435–440 (2002) 13. Muyeba, M., Khan, M.S., Coenen, F.: A framework for mining fuzzy association rules from composite items. In: Chawla, S. (eds.): PAKDD 2008 Workshops. LNCS (LNAI), vol. 5433, pp. 62–74. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00399-8_6 14. Delgado, M., Marin, N., Sanchez, D., et al.: Fuzzy association rules: general model and applications. IEEE Trans. Fuzzy Syst. 11, 214–225 (2003) 15. Delgado, M., Ruiz, M.D., Sánchez, D., et al.: A formal model for mining fuzzy rules using the RL representation theory. Inf. Sci. (Ny) 181, 5194–5213 (2011) 16. Au, W.H., Chan, K.C.C.: Data mining system for discovering fuzzy association rules. In: FUZZ-IEEE’99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315), pp. 1217–1222, vol. 3. IEEE (1999) 17. Au, W.H., Chan, K.C.C.: Mining fuzzy association rules in a bank-account database. IEEE Trans. Fuzzy Syst. 11, 238–248 (2003)

Review of Descriptive Analytics Under Fuzziness

645

18. Gyenesei, A.: A fuzzy approach for mining quantitative association rules. Acta Cybern. 15, 305–320 (2001) 19. Chueh, H.E., Lin, N.P.: Fuzzy partial correlation rules mining. In: 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA), pp. 91–94. IEEE (2009) 20. Lotfi, S., Sadreddini, M.: Mining fuzzy association rules using mutual information. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol. 1 (2009). http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Mining +Fuzzy+Association+Rules+Using+Mutual+Information#0 21. Wang, C., Pang, C.: Finding fuzzy association rules using FWFP-growth with linguistic supports and confidences. Int. Sch. Sci. Res. Innov. 3, 300–308 (2009) 22. Dubois, D., Hüllermeier, E., Prade, H.: A systematic approach to the assessment of fuzzy association rules. Data Min. Knowl. Discov. 13, 167–192 (2006) 23. Glass, D.H.: Fuzzy confirmation measures. Fuzzy Sets Syst. 159, 475–490 (2008) 24. Martin, T., Shen, Y., Majidian, A.: Discovery of time-varying relations using fuzzy formal concept analysis and associations. Int. J. Intell. Syst. 25, 1217–1248 (2010) 25. Serrurier, M., Dubois, D., Prade, H., et al.: Learning fuzzy rules with their implication operators. Data Knowl. Eng. 60, 71–89 (2007) 26. Wang, X., Liu, X., Pedrycz, W., et al.: Mining axiomatic fuzzy set association rules for classification problems. Eur. J. Oper. Res. 218, 202–210 (2012) 27. Chen, C.H., Hong, T.P., Li, Y.: Fuzzy association rule mining with type-2 membership functions. In: Nguyen, N.T. (eds.): ACIIDS 2015, Part II, LNCS (LNAI), vol. 9012, pp. 128–134. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15705-4_13 28. Choo, Y.-H., Bakar, A.A., Hamdan, A.R.: A rough-apriori technique in mining linguistic association rules. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) Advanced Data Mining and Applications. ADMA 2008. LNCS, vol. 5139, pp. 548–555. Springer, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88192-6_55 29. Pei, Z.: Extracting association rules based on intuitionistic fuzzy special sets. In: 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), pp. 873–878. IEEE (2018) 30. Roy, A., Chatterjee, R.: Realizing new hybrid rough fuzzy association rule mining algorithm (RFA) over apriori algorithm. In: Jain, L., Patnaik, S., Ichalkaranje, N. (eds.) Intelligent Computing, Communication and Devices. AISC, vol. 308. pp. 157–167. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2012-1_17 31. Sreenivasula Reddy, T., Sathya, R., Nuka, M.: Intuitionistic fuzzy rough sets and fruit fly algorithm for association rule mining. Int. J. Syst. Assur. Eng. Manag. (2022). Epub ahead of print. https://doi.org/10.1007/s13198-021-01616-8 32. Geetha, M.A., Acharjya, D.P., Iyengar, N.C.S.N.: Privacy preservation in fuzzy association rules using rough set on intuitionistic fuzzy approximation spaces and DSR. Int. J. Auton. Adapt. Commun. Syst. 10(1), 67–87 (2017) 33. Chen, J., Li, P., Fang, W., et al.: Fuzzy association rules mining based on Type-2 fuzzy sets over data stream. Procedia Comput. Sci. 199, 456–462 (2022) 34. Madbouly, M.M., El Reheem, E.A., Guirguis, S.K.: Interval type-2 fuzzy logic using genetic algorithm to reduce redundant association rules. J. Theor. Appl. Inf. Technol. 99, 316–328 (2021) 35. Burda, M., Pavliska, V., Murinová, P.: Computation of support and confidence for intervalvalued fuzzy association rules. Int. J. Comput. Intell. Syst. 13, 1014 (2020)

Fuzzy Centrality Measures: A Survey Fatima-ezzahra Badaoui1 , Azedine Boulmakoul1(&) , Ahmed Lbath2, Rachid Oulad Haj Thami3 , Ghyzlane Cherradi1 Lamia Karim4 , and Adil El Bouziri1 1

,

Computer Science Department, FSTM, Hassan II University of Casablanca, Casablanca, Morocco [email protected] 2 LIG/MRIM, CNRS, University Grenoble Alpes, Grenoble, France 3 ADMIR Lab. ENSIAS, Mohamed V University, Rabat, Morocco 4 ENSAB, Hassan 1st University, Settat, Morocco

Abstract. : Most real-world problems can be pictured as a set of connections and interactions between various entities. Together, these entities create a complex phenomenon investigated in the form of complex networks. Each of the entities in the network plays a particular role in the definition of the structure and the analysis of the studied problem. Several measures of centrality have been proposed in the literature to estimate the contribution and quantify the relevance of network entities. The most influential nodes are defined either locally, via the measurement of their connections with their directly related neighbors, or globally, via the measurement of the importance of their neighbors or their relevance in terms of contribution to the fast propagation of information based on the shortest paths. Due to the incompleteness of real-world data, crisp representations do not adequately describe the problem. Therefore, fuzzy graphs have been proposed to give more realistic representations by taking into account the uncertainties present in data. This paper proposes a state of the art of fuzzy centrality measures with a focus on proposed studies on urban traffic networks. Keywords: Complex networks


Fuzzy graphs
Fuzzy centrality measures

1 Introduction Complex networks or graphs, as defined in mathematics, are an abstraction of realworld systems in the form of entities or nodes connected by edges. This abstraction has solved important issues over decades in different fields: biology, medicine, telecommunication and transportation, etc. One of the main features of complex network analysis is the investigation of the contribution of network entities to the study of the problem known as centrality. The concept of centrality measures was first proposed by Bavelas [1, 2] for connected networks with an application on communication works. This work was funded by the CNRST project in the priority areas of scientific research and technological development “Spatio-temporal data warehouse and strategic transport of dangerous goods». © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 646–654, 2022. https://doi.org/10.1007/978-3-031-09176-6_72

Fuzzy Centrality Measures: A Survey

647

Since then, this subject has sparked the interest of scholars, leading to the proposal of different centralities referring to the importance of nodes based on different concepts. Katz centrality proposed in [3], measures node’s relative degree. Eigenvector and its alternatives are introduced by Bonacich [4–6] defining the importance of a node based on the importance of its neighbors. Freeman in [7] proposed degree centrality, which measures the relevance of a node according to the number of its direct connections, as well as two geodesic-based centralities, closeness and betweenness centrality measures. Closeness centrality is defined as the sum of the inverse of geodesics determining how the node is close to the remaining nodes. Betweenness reveals the node’s influence in the transmission or the control of information in the network via geodesics. In the literature, so many additional measures are available that we have listed only the most commonly used ones. Each of these proposed measures of centrality plays a distinct role in the analysis and interpretation of node importance. The crisp representation of real-world system as complex network, in which, for example connections between nodes either exist or not, fails to capture the uncertainties present in the data. Which leads to the loss of information, and therefore, causing an overestimation or underestimation of nodes importance. To fill this gap, a Fuzzy representation is introduced. Zadeh in [8] have proposed fuzzy logic theory, where he introduced the notion of fuzzy set defined by a membership function depicting the degree of belongingness in the range of ½0; 1. Kauffmann [9] have introduced the concept of fuzzy graph where the relations between nodes and edges are represented by a fuzzy relations. Later Rosenfeld [10], extended the notions of graph theory to the fuzzy graphs, proposing fuzzy paths, subgraphs, bridges among others. Consequently, novel or extended centrality measures were proposed to deal with the uncertainty in the fuzzy graphs showing more accurate results in the ranking of nodes importance. I extended the notions of graph theory to the fuzzy graphs, proposing fuzzy paths, subgraphs, bridges among others. Consequently, novel or extended centrality measures were proposed and are described in this paper to deal with the uncertainty in the fuzzy graphs showing more accurate results in the ranking of nodes importance. In this study, we represent a survey on the fuzzy centrality measures present in the literature, distinguished according to the type of contribution. The organization of this paper is as follows, we first define the fundamental concepts in Sect. 2. The description of the proposed fuzzy centrality measures, which are extensions of the traditional centrality measures or improvements of the proposed fuzzy centrality measures, is represented in Sect. 3, with a discussion of some applications of the proposed studies in different domains, with a highlighting of the contribution of fuzzy centrality in the study of urban traffic network systems. The final section presents a conclusion.

2 Fundamental Concepts In this section we represent the fundamental basics of fuzzy logic and fuzzy graphs.

648

2.1

F. Badaoui et al.

Fuzzy Logic

Zadeh in 1965 [8] proposed fuzzy logic a new theoretical approach to deal with realworld this problem. Where he introduced fuzzy sets, a generalization of crisp sets characterized by a membership function representing the element’s degree of belongingness to the fuzzy set within the interval ½0; 1. Fuzzy Numbers Fuzzy numbers also called type-1 fuzzy numbers. They are a specific case of fuzzy sets defined from the universal set ℝ to [0,1]. Defined as, convex, normalized, and piecewise continuous fuzzy sets, and with maximum membership value equals 1. Type-2 Fuzzy Sets Fuzzy type-2 fuzzy sets are extension of ordinary fuzzy sets. They are defined by threedimensional membership function to capture more information. It is defined from the Cartesian product X  ½0; 1 to [0,1], expressed as follows:
 ~ ¼ f ðx; uÞ; l ~ ðx; uÞ jx 2 X; u 2 U  ½0; 1g; A A where u is the secondary variable. Fuzzy Aggregation Operators Fuzzy aggregation operators combine a set of input values into a single output value, defined on ½0; 1n ! ½0; 1. Various aggregation operators were proposed (see Fig. 1).

Fig. 1. Fuzzy aggregation operators [10]

Fuzzy Rule-Based Systems Fuzzy rule-based systems are represented as a set of fuzzy rules able to deal with the uncertainties and imprecisions and non-linearity of complex real-world problems (see Fig. 2). For more details please see [11].

Fig. 2. Fuzzy rule-based system

Fuzzy Centrality Measures: A Survey

2.2

649

Fuzzy Graphs

~ ¼ ðr; lÞ is defined by r : V ! ½0; 1 and l : V  V ! ½0; 1, two A fuzzy graph G membership functions for vertices and edges resp, with lði; jÞ  rðiÞ \ rð jÞ8i; j 2 V.

3 Fuzzy Centrality Measures Fuzzy logic has attracted the interest of complex network researchers, which has led to the extension of complex network theory concepts to the fuzzy framework. In 1973, Kauffmann in [9] introduced fuzzy graphs, later Rosenfeld [10] extended the characteristics of graph theory to the fuzzy framework. Nair and Sarasamma [12] have proposed fuzzy social networks as fuzzy graph with fuzzy relations among entities. Accordingly, several centrality measures were proposed to deal with the fuzziness for more realistic ranking of importance of nodes. The proposed fuzzy centrality are either extension of the conventional centrality to fuzzy sets or proposition of novel fuzzy centralities based on the combination of several centralities or an improvement of fuzzy centrality or the utilization of centrality in fuzzy rule-based systems. 3.1

Proposed Fuzzy Centrality Measures

In this section, we discuss the proposed fuzzy centrality measures by mentioning their contributions. Figure 3 represents a chart of the distinguished propositions of fuzzy centrality measures.

Fig. 3. Proposed fuzzy centrality measures

650

F. Badaoui et al.

Each of the proposed fuzzy centrality measures plays a crucial and different role in the detection of important nodes. Therefore, the selection of right centrality to apply is purpose dependent. Some authors [13, 14, 20–22, 24] have proposed the extension of the well-known centrality measures to the fuzzy, framework for more realistic and efficient results. While others have proposed novel fuzzy centrality measures [18, 23, 25]. According to the comparison studies conducted by the authors between the proposed approaches and the conventional centrality measures, the proposed methods have shown robustness and efficiency in the detection of most important nodes. And the other partition of the researcher [15–17, 19] have proposed an improvement of the extended fuzzy centrality measures. Where they have proposed a relaxation of the computation of distance to all other nodes in closeness centrality, and the proposition of fuzzy attenuation factors for a better adjustment of the effectiveness of longer paths. Based on the applications on both of real-world data and exemplary data, the proposed approaches were more able to detect the most important nodes than the extended fuzzy centrality measures. The fuzzification of the computation of node’s importance provides a logical and more realistic results due to the amount of ambiguity and imprecisions present in real-world data. This reflects the relevance of applying fuzzy centrality measures. 3.2

Fuzzy Rule-Base Centrality Measures

Another solution for the application of the fuzzyfication of centrality measures is the usage of fuzzy rule-based systems, where the centrality measures are converted into linguistics fuzzy sets. In [26] three fuzzy rule-based techniques for selecting cluster heads in WSNs were assessed utilizing the 3 main factors, namely residual energy, centrality, and node density. A novel method for the identification and re-factorization of bad smells is proposed in [27] which are poorly structured complex programming codes. The codes are converted into directed weighted. To compute the betweenness, load, in-degree, out-degree, and closeness centrality, used as inputs of the fuzzy controller, in which they will be fuzzified using triangular membership function to output the type of the bad smells. An analysis of scientific co-authorship networks is proposed in [28] for the investigation of the reliability of each researcher within the research group. Two centrality measures, namely, proximity and information degree were used as inputs for the fuzzy inference system, each represented as a linguistic variable with triangular membership function. And based on the rule base of if-then the reliability of each node is computed. In [29], a fuzzy analysis is proposed of two network measures, namely, centrality and prestige. The results of these analysis showed that in online social networks there exists a relation between several centrality measures and prestige. In [30], the authors proposed a realistic analysis of vulnerability assessment via the definition of a fuzzy inference system. The proposed solution is based on graph theory, where each edge is characterized by a reliability value and betweenness centrality value. These measures are forming the input of the fuzzy inference system and are integrated to assess a vulnerability value.

Fuzzy Centrality Measures: A Survey

3.3

651

Application of Fuzzy Centrality Measures

Fuzzy centrality measures were applied for solving the problem of detection of important nodes with the integration of the fuzziness of real-world. Authors in [31–33] have applied fuzzy centrality for a better understanding of users’ queries proposing fuzzy WordNet. And fuzzy keyword extraction based on centrality measures was proposed in [34, 35]. The authors [36] have proposed a novel ensemble feature selection method based on the combination of PageRank and fuzzy logic. Application of Fuzzy Centrality Measures for Urban Transportation Networks A significant number of studies were conducted using conventional centrality measures, were they ignored to capture the uncertainties to represent the real-world data more accurately. In this paper [19], the authors have proposed a novel approach of spectral graph drawing based on fuzzy closeness indicators representing human perception of time distance to reach a destination. The authors [37] proposed a fuzzy centrality is based on the lattice of fuzzy transitive relations for the analysis of person’s movement in outdoors to determine the fuzzy centrality of trajectories. In [38, 39], the authors have studied the traffic congestion via the estimation of travel time which is modelled as triangular fuzzy number and as Gaussian fuzzy number resp, to capture the uncertainties in the calculation. The road intersections with the highest fuzzy dynamic centrality were detected as the most congested junctions.

4 Conclusion In this paper, we have surveyed the fuzzy centrality measures that were introduced after Zadeh’s fuzzy logic, leading to a great change in the study and abstraction of real-world problems. Most of the proposed fuzzy centralities are extensions of conventional centralities to the fuzzy framework, while others focused on applying rule-based systems or fuzzy aggregation operators, all providing better results and efficiency. Despite the important results of the proposed methods, they fail to effectively describe realworld uncertainties. The intuitionistic approach captures not only the membership degree but also the degree of non-membership, making it closer to reality. Then there is the picture fuzzy set, which is an extension of the fuzzy sets and intuitionistic sets, which represents a set by three membership functions, negative, neutral and positive. The application of the intuitionistic and picture fuzzy sets to centrality measures may yield better results, research on these topics remains open for future work.

References 1. Bavelas, A.: A mathematical model for group structures. Hum. Organ. 7(3), 16–30 (1948) 2. Bavelas, A.: Communication patterns in task-oriented groups. J. Acoust. Soc. Am. 22(6), 725–730 (1950) 3. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

652

F. Badaoui et al.

4. Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Soc. 2(1), 113–120 (1972) 5. Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23(3), 191–201 (2001) 6. Bonacich, P.: Some unique properties of eigenvector centrality. Soc. Netw. 29(4), 555–564 (2007) 7. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215– 239 (1978) 8. Zadeh, L.A.: Fuzzy sets. In: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, pp. 394–432 (1996) 9. Kaufmann, A.: Introduction to the Theory of Fuzzy Sets, vol. 1. Academic Press Inc, Orlando, Florida (1973) 10. Rosenfeld, A.: Fuzzy graphs, fuzzy sets and their applications. In: Zadeh, L.A., Fu, K.S., Shimura, M. (eds.), pp. 77–95 (1975) 11. Magdalena, L.: Fuzzy Rule-Based Systems. In: Springer Handbook of Computational Intelligence, pp. 203–218. Springer, Berlin, Heidelberg (2015). https://doi.org/10.1007/9783-662-43505-2_13 12. Nair, P.S., Sarasamma, S.T..: Data mining through fuzzy social network analysis. In: NAFIPS 2007–2007 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 251–255. IEEE (2007) 13. Liao, L.P., Hu, R.J., Zhang, G.Y.: The centrality analysis of fuzzy social networks. Fuzzy Syst. Math. 27(2), 169–173 (2012) 14. Hu, R.J., Li, Q., Zhang, G.Y., Ma, W.C.: Centrality measures in directed fuzzy social networks. Fuzzy Inf. Eng. 7(1), 115–128 (2015) 15. Davidsen, S.A., Padmavathamma, M.: A fuzzy closeness centrality using and ness-direction to control degree of closeness. In: 2014 First International Conference on Networks Soft Computing (ICNSC2014), pp. 203–208. IEEE (2014) 16. Hu, R.J., Zhang, G.Y., Liao, L.P.: The closeness centrality analysis of fuzzy social network based on inversely attenuation factor. In: Cao, B.Y., Nasseri, H. (eds.) Fuzzy Information & Engineering and Operations Research & Management. Advances in Intelligent Systems and Computing, vol. 211, pp. 457–465. Springer, Berlin, Heidelberg (2014). https://doi.org/10. 1007/978-3-642-38667-1_46 17. Hu, R., Liao, L., Chen, C., Zhang, G.: Closeness centrality measures in fuzzy enterprise technology innovation cooperation networks. Fuzzy Inf. Eng. 11(4), 494–505 (2019) 18. Samanta, S., Pal, M.: A new approach to social networks based on fuzzy graphs. J. Mass Commun. Journal. 5, 078–099 (2014) 19. Nourian, P., Rezvani, S., Sariyildiz, I. S., Van der Hoeven, F.: Spectral modelling for spatial network analysis. In: Proceedings of the Symposium on Simulation for Architecture and Urban Design, SimAUD (2016) 20. Tavassoli, S., Zweig, K.A.: Fuzzy centrality evaluation in complex and multiplex networks. In: Gonçalves, B., Menezes, R., Sinatra, R., Zlatic, V. (eds.) Complex Networks VIII, pp. 31–43. CompleNet 2017. Springer Proceedings in Complexity. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54241-6_3 21. Wen, T., Jiang, W.: Identifying influential nodes based on fuzzy local dimension in complex networks. Chaos Solitons Fractals 119, 332–342 (2019)

Fuzzy Centrality Measures: A Survey

653

22. Liao, L.P., Zhang, G.Y.: The centrality analysis of the fuzzy technology innovation network. In: Cao, B.Y., Zhong, Y.B. (eds.) Fuzzy Sets and Operations Research. ICFIE 2017. Advances in Intelligent Systems and Computing, vol. 872, pp. 149–165. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02777-3_14 23. Samanta, S., Dubey, V.K., Sarkar, B.: Measure of influences in social networks. Appl. Soft Comput. 99, 106858 (2021) 24. Wang, Q., Gong, Z.-T.: Structural centrality in fuzzy social networks based on fuzzy hypergraph theory. Comput. Math. Organ. Theory 26(2), 236–254 (2020). https://doi.org/10. 1007/s10588-020-09312-x 25. Zhang, H., Zhong, S., Deng, Y., Cheong, K.H.: LFIC: Identifying influential nodes in complex networks by local fuzzy information centrality. In: IEEE Trans. Fuzzy Syst. https:// doi.org/10.1109/TFUZZ.2021.3112226 26. Singh, A.K., Purohit, N.: An optimised fuzzy clustering for wireless sensor networks. Int. J. Electron. 101(8), 1027–1041 (2014) 27. Saheb Nasagh, R., Shahidi, M., Ashtiani, M.: A fuzzy genetic automatic refactoring approach to improve software maintainability and flexibility. Soft. Comput. 25(6), 4295– 4325 (2020). https://doi.org/10.1007/s00500-020-05443-0 28. de Oliveira, S.C., Cobre, J., Pereira, D.F.: A measure of reliability for scientific coauthorship networks using fuzzy logic. Scientometrics 126(6), 4551–4563 (2021). https:// doi.org/10.1007/s11192-021-03915-0 29. Raj, E.D., Babu, L.D., Ariwa, E.: A fuzzy approach to centrality and prestige in online social networks. In: Proceedings of the International Conference on Informatics and Analytics, pp. 1–6 (2016) 30. Zarghami, S.A., Gunawan, I.: A fuzzy-based vulnerability assessment model for infrastructure networks incorporating reliability and centrality. Eng. Constr. Architect. Manag. 27(3), 725–744 (2020). https://doi.org/10.1108/ECAM-10-2018-0437 31. Huang, Y.P., Kao, L.J., Tsai, T., Liu, D.: Using fuzzy centrality and intensity concepts to construct an information retrieval model. In: SMC 2003 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), vol. 4, pp. 3257–3262. IEEE (2003) 32. Jain, A., Lobiyal, D.K.: Fuzzy Hindi WordNet and word sense disambiguation using fuzzy graph connectivity measures. ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP) 15(2), 1–31 (2015) 33. Vij, S., Jain, A., Tayal, D., Castillo, O.: Fuzzy logic for inculcating significance of semantic relations in word sense disambiguation using a WordNet graph. Int. J. Fuzzy Syst. 20(2), 444–459 (2018) 34. Jain, A., Mittal, K., Vaisla, K.S.: FLAKE: Fuzzy graph centrality-based automatic keyword extraction. Comput. J. 65(4), 926–939 (2020). https://doi.org/10.1093/comjnl/bxaa133 35. Jain, M., et al.: Automatic keyword extraction for localized tweets using fuzzy graph connectivity measures. Multimed. Tools Appl. (2022). https://doi.org/10.1007/s11042-021-11893-x 36. Joodaki, M., Dowlatshahi, M.B., Joodaki, N.Z.: An ensemble feature selection algorithm based on PageRank centrality and fuzzy logic. Knowl.-Based Syst. 233, 107538 (2021) 37. Karim, L., Boulmakoul, A., Cherradi, G., Lbath, A.: Fuzzy centrality analysis for smart city trajectories. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 933–940. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-51156-2_108

654

F. Badaoui et al.

38. Boulmakoul, A., Badaoui, F.-E., Karim, L., Lbath, A., Oulad Haj Thami, R.: Fuzzy spatiotemporal centrality for urban resilience. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 307, pp. 796–803. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85626-7_92 39. Badaoui, F.E., Boulmakoul, A., Thami, R.O.H.: Fuzzy dynamic centrality for urban traffic resilience. In: 2021 International Conference on Data Analytics for Business and Industry (ICDABI), pp. 12–16. IEEE, 25–26 October 2021. https://doi.org/10.1109/ICDABI53623. 2021.9655939

Review of Fuzzy Multi-criteria Decision Making Methods for Intelligent Supplier Selection Dilek Akburak(&) Department of Industrial Engineering, Istanbul Kültür University, Istanbul, Turkey [email protected]

Abstract. Supplier selection is a focal process that affects the quality and cost performance of the company. Therefore, it is one of the well-known decision making problems that researchers and practitioners are most interested in. In order to choose the most strategic supplier, determining and prioritizing the selection criteria and choosing the appropriate method(s) directly affect the supply chain performance. In this study, the publications focused on multicriteria decision making (MCDM) on the supplier selection problems in the uncertain environment are reviewed from 2017 to the present (Feb. 2022). Due to the effect of uncertainty and vagueness, most recent approaches developed for supplier selection, are constructed by integrating MCDM approach(es) with fuzzy set theory. The most widely applied and integrated fuzzy MCDM methods are stated as AHP, ANP, TOPSIS, and VIKOR. These studies are categorized into single or multiple/integrated MCDM approach(es) with different fuzzy sets in various application industries. This study contributes to the literature to examine the most frequently applied and recently developed fuzzy MCDM approaches for intelligent supplier selection by considering various assessment criteria under imprecise environments. Also, newly improved ideas can be proposed with the help of the analysis of studies about intelligent supplier selection up to the present. Keywords: Fuzzy multi-criteria decision making making
Supplier selection


Intelligent decision

1 Introduction Current global trends, rapidly enhancing and changing technology, developments in production processes, as well as knowledgeable and constantly demanding customers, force organizations to achieve the optimum level in production processes, operations, and supply chain processes and to provide value to the customer. In order to improve the performance of the supply chain and reduce the risk of purchasing, pursuing longterm partnerships with a few reliable suppliers is the focal point [1]. In today’s competitive market, for every organization, one of its vital business strategies is to adopt increasing the rate of profitability and competitive advantage while ensuring sustainability. Therefore, it should be emphasized that the decision of the best supplier is a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 655–663, 2022. https://doi.org/10.1007/978-3-031-09176-6_73

656

D. Akburak

complicated process that directly affects a firm’s cash flow, profitability and reveals its environmental perspective [2]. For this reason, various factors identified while selecting the best supplier must be spot-on. Firstly, Dickson [3] proposed the 23 criteria for supplier evaluation as a result of a survey conducted in 1966. According to the results, the most significant criteria are product quality, delivery on time, historical performance and warranty procedure proposed by supplier. If the suitable supplier can be selected, the amount of profit, customer satisfaction, and the power in the competitive business environment is increasing, while decreasing purchase cost, and product delivery time [4]. Supplier selection is a MCDM problem that often contains imprecise or uncertain information such as demand, quality, cost, capacity and decision makers’ judgments. It is suggested to use an appropriate fuzzy set theory (FST) that was initiated by Zadeh [5] to model the uncertainty and vagueness in the problem. Thus, using FST, uncertain preferences might be modeled mathematically [6]. Furthermore, in many MCDM problems, linguistic evaluations are more preferred than sharp numerical evaluations by the DMs. The FST is included in the decision making process, to deal with the uncertainty of the linguistic evaluations [7]. Therefore, in this study, various applications of the fuzzy MCDM method(s) are reviewed for a special problem. Ghorabaee et al. [1] presented a review paper that covers the period 2001–2016, on the applications of fuzzy multiple attribute decision making (MADM) approaches for supplier selection problems. Simić et al. [6] presented a 50-year review of articles integrating MCDM methods with FST for supplier selection. This paper presents a review study including the publications that use the integration of MCDM with fuzzy logic for intelligent supplier selection in the period 2017–present (Feb. 2022). In addition, studies using the four most widely used fuzzy MCDM methods (AHP, ANP, TOPSIS, VIKOR) were categorized according to the application of single or multiple/integrated methods and the type of industry applied. This paper is structured as follows. Section 2 presents the applications of fuzzy MCDM methods for supplier selection by categorizing them into single or multiple/integrated forms and applied industry and a brief review analysis is conducted. The paper is concluded with discussion and recommended directions in Sect. 3.

2 Fuzzy MCDM Approaches for Supplier Selection In this review paper, several articles and book chapters published in the period 2017– Feb 2022 have been examined from various web sources such as Google Scholar, Web of Science, and Science Direct. In this section, the most widely used fuzzy MCDM methods and other applied methods are examined separately. The four popular fuzzy MCDM approaches are also categorized into single or multiple/integrated (comparison of the multiple fuzzy MCDM methods/integrated methodologies) and the applied industries are presented in Table 1. Also, a brief review analysis is given at the end of this section.

Review of Fuzzy Multi-criteria Decision Making Methods

2.1

657

Fuzzy AHP

Analytical Hierarchy Process (AHP) is one of the frequently applied MCDM techniques in the literature. The purpose of this method developed by Saaty [8] is to be able to assign the weights of various criteria via pairwise comparison matrices and choose the best alternative from a set of competing options concerning these criteria. To overcome the dimension of uncertainty in human nature, the conventional AHP method is combined with FST [9]. AHP can be applied as a single or integrated method based on the scope of the decision problem. Ecer [10] proposed an extension to AHP via interval type-2 fuzzy sets to find the greenest supplier. Yu et al. [11] proposed a novel approach by combining Artificial Neural Network (ANN), fuzzy TOPSIS (FTOPSIS), and fuzzy AHP (FAHP) for the retail industry. 2.2

Fuzzy ANP

The Analytic Network Process (ANP) which enables the incorporation of feedbacks between criteria and subcriteria was proposed by Saaty [12]. Wan et al. [13] utilized two MCDM approaches which are fuzzy 2-tuple (TL)-ANP and interval 2-tuple (ITL) ELimination and Choice Translating REality (ELECTRE) II to decide the appropriate supplier in the automotive industry. Wang et al. [14] developed an hybrid approach including fuzzy ANP (FANP) and Data Envelopment Analysis (DEA) for the food industry. Phochanikorn and Tan [15] presented a new extension of the MCDM model by combining ANP, VIKOR and Decision MAking Trial and Evaluation Laboratory (DEMATEL) methods with intuitionistic fuzzy (IF) sets for the palm oil industry. Tirkolaee et al. [16] developed an integrated method using ANP, DEMATEL and TOPSIS methods under fuzzy environment. 2.3

Fuzzy TOPSIS

The Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach was originally proposed by Hwang and Yoon [17] to rank the preferences of DMs’. This is one of the distance-based MCDM approach. The highest relative closeness is considered as the best selection. Bera et al. [18] developed an integrated methodology using interval type-2 fuzzy (IT2F) TOPSIS and IT2F-Multi-Objective Optimization based on Ratio Analysis (MOORA) to effectively overcome both objective and subjective criteria. Yucesan et al. [19] developed an integrated methodology using Best-Worst Method (BWM) and IT2F-TOPSIS for sustainable supplier selection. Yu et al. [20] developed a novel group decision approach to supplier selection in cases of fuzziness and ambiguity via an extension of TOPSIS with an interval-valued Pythagorean fuzzy set (IVPFS). Petrović et al. [21] compared the results of three MCDM methods (TOPSIS, WASPAS, Additive Ratio Assessment (ARAS)) for a supplier selection problem under fuzziness. Çalık [22] applied integrated TOPSIS and Pythagorean fuzzy (PF) AHP methodology for a sustainable supplier selection problem. Qu et al. [23] constructed a framework for green supplier selection in an IT company by integrating methods of TOPSIS and ELECTRE I in the fuzzy environment.

658

D. Akburak

2.4

Fuzzy VIKOR

The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method which refers to multi-criterion optimization and compromise solution was first proposed by Opricovic [24] as a new MCDM approach. Each alternative is evaluated independently with a criterion based on raw data or opinions of DMs without the need for preference [25]. Bahadori et al. [25] integrated ANN and fuzzy VIKOR (FVIKOR) to choose the optimal supplier for a military hospital. Banaeian et al. [26] firstly compared three decision making methods (VIKOR, Grey Relational Analysis (GRA) and TOPSIS) by considering FST for an agri-food industry. Çalı and Balaman [27] presented a novel study by combining ELECTRE I and VIKOR under the intuitionistic fuzzy environment. Sharaf and Khalil [28] compared the outputs of spherical fuzzy (SF) VIKOR, SFTODIM and SF-TOPSIS methods for evaluating the performances and selecting the best supplier. Kumar and Barman [29] integrated the fuzzy VIKOR and fuzzy TOPSIS methods to decide the greenest supplier for a steel manufacturer. Zhang et al. [30] combined fuzzy VIKOR with DEMATEL for a supplier selection problem. 2.5

Other Fuzzy MCDM Methods

The other MCDM approaches applied to decide on the optimal supplier under fuzzy environment are as follows: Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) in the hesitant fuzzy environment [31]; IF-COmplex PRoportional ASsessment (COPRAS) [32]; Weighted Aggregated Sum Product Assessment (WASPAS) with hesitant fuzzy sets [33]; DEMATEL with Pythagorean fuzzy set [34]. Liu et al. [35] developed a novel integrated MCDM approach by using fuzzy BWM and fuzzy MULTIMOORA for a sustainable supplier selection process. Yazdani et al. [36] proposed a novel integrated MCDM model with CRiteria Importance Through Intercriteria Correlation (CRITIC) and combined compromised solution (CoCoSo) for neutrosophic sets. Puška et al. [37] performed a hybrid study with MultiAttributive Border Approximation area Comparison (MABAC) and PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) methods under interval fuzzy set logic. Masoomi et al. [38] combined three well-known MCDM techniques (BWMCOPRAS and WASPAS) in fuzzy environment. Kahraman et al. [7] proposed a novel CRITIC method by considering the spherical fuzzy sets to rank the supplier selection criteria. 2.6

Review Analysis

As of 2005, there are more than 300 academic studies using fuzzy MCDM methods to decide the best supplier for various industries. In the period 2017-present (Feb. 2022), approximately 150 papers are published based on reviews for Science Direct, Web of Science, and Google Scholar. The distribution of studies in the period 2005–2022 is shown in Fig. 1. As can be seen from the line graph, many studies were published in 2019 and an upward trend might be observed. Since this paper has been written in Feb. 2022, publications for this year are limited.

Review of Fuzzy Multi-criteria Decision Making Methods

659

Fig. 1. Yearly change in the number of articles published

Table 1 illustrates that the most popular industries for supplier selection applications are automotive and manufacturing. Electronics, agriculture and retail are the following industries. Table 1. Categorization of fuzzy MCDM approaches for supplier selection MCDM Reference Single Multiple/ Applied method(s) method Integrated

Industry

AHP

Sport-Retail Electronics Manufacture Mining Renewable energy Electronics Automative Food Agriculture Electronics Food Manufacture Automotive Manufacture Manufacture Retail Agriculture Technology Construction Agri-food Automotive Retail Manufacture Automotive Manufacture

[39] [40] [10] [41] [42] ANP [43] [13] [14] [15] [16] [44] [18] TOPSIS [2] [19] [20] [21] [22] [23] [45] [26] [27] VIKOR [28] [29] [46] [30]

* * * * * * * * * * * * * * * * * * * * * * * * *

IF AHP FAHP-FVIKOR IT2F AHP FAHP- FTOPSIS-FDEMATEL FAHP-WASPAS FANP TL ANP-ITL ELECTRE II FANP-DEA IF DEMATEL-FANP-VIKOR FANP-FDEMATEL-FTOPSIS FANP-WASPAS IT2F TOPSIS-IT2F MOORA AHP-TOPSIS, AHP- MABAC, AHP-WASPAS BWM - IT2F TOPSIS TOPSIS with IVPFS FTOPSIS, FWASPAS, FARAS IVPF AHP-FTOPSIS FTOPSIS-ELECTRE I Interval valued IF TOPSIS FTOPSIS- FGRA-FVIKOR Fuzzy ELECTRE I-VIKOR SF-VIKOR, SF-TODIM, SF-TOPSIS FTOPSIS-VIKOR Fuzzy BWM-VIKOR Rough DEMATEL-FVIKOR

660

D. Akburak

3 Discussion and Conclusion Many companies in different industries are looking for the most suitable supplier to increase economic efficiency. Since there are several evaluation criteria among the alternatives, supplier selection is a critical MCDM case. To cope with incomplete or uncertain information and judgments of experts, fuzzy sets are taken into account in the MCDM processes. In this study, publications using fuzzy MCDM methods for supplier selection problems in the period 2017-present (Feb. 2022) are reviewed. Researches show that in recent years, the concepts of sustainability or becoming green have gained a crucial role in the selection of suppliers. Therefore, in many studies, it has been determined that MCDM applications have been focused on green supplier selection. A key point for MCDM is the complexity of the problem. Since a more complex decision model is obtained as the number of criteria is increased, it has been stated that hybrid methods are widely preferred to decrease the complexity of the progress and improve the scope of the decision model in the supplier selection problems. According to the deeply reviewed literature, the most widely applied fuzzy MCDM methods are stated as AHP, ANP, TOPSIS, and VIKOR. In addition to these methods, PROMETHEE, DEMATEL, COPRAS, TODIM, CRITIC etc. are other fuzzy MCDM methods are applied individually or in hybrid form for supplier selection. Furthermore, most of the publications are done in the automotive and manufacturing industries, while retail, agriculture, electronic are other application industries. For future studies, MCDM methods such as Evaluation based on Distance from Average Solution (EDAS) and ARAS, which are not often used for supplier selection problems via extending with appropriate fuzzy sets. Also, more comparative studies for the supplier selection can be conducted to show the efficiency of different approaches.

References 1. Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., Antucheviciene, J.: Supplier evaluation and selection in fuzzy environments: a review of MADM approaches. Econ. Res. Ekonomska Istrazivanja 30(1), 1073–1118 (2017) 2. Gupta, S., Soni, U., Kumar, G.: Green supplier selection using multi-criterion decision making under fuzzy environment: a case study in automotive industry. Comput. Ind. Eng. 136(140), 663–680 (2019) 3. Dickson, G.W.: An analysis of vendor selection systems and decisions. J. Purchas. 2(1), 5– 17 (1966) 4. Frej, E.A., Roselli, L.R.P., Araújo De Almeida, J., De Almeida, A.T.A.: Multicriteria decision model for supplier selection in a food industry based on FITradeoff method. Math. Probl. Eng. 2017, 1–9 (2017) 5. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965) 6. Simić, D., Kovačević, I., Svirčević, V., Simić, S.: 50 years of fuzzy set theory and models for supplier assessment and selection: a literature review. J. Appl. Log. 24, 85–96 (2017) 7. Kahraman, C., Onar, S.C., Öztayşi, B.A.: Novel spherical fuzzy CRITIC method and its application to prioritization of supplier selection criteria. J. Intell. Fuzzy Syst. 42(1), 29–36 (2022)

Review of Fuzzy Multi-criteria Decision Making Methods

661

8. Saaty, T.L.: How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 40, 9–10 (1971) 9. Laarhoven, V., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy sets and systems. Fuzzy Sets Syst. 11, 1–3 (1983) 10. Ecer, F.: Multi criteria decision making for green supplier selection using interval type 2 fuzzy AHP : a case study of a home appliance manufacturer. Oper. Res. 22, 199–233 (2020) 11. Yu, C., Zou, Z., Shao, Y., Zhang, F.: An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN AHP and TOPSIS methods. Kybernetes 49(9), 2263–2284 (2019) 12. Saaty, T.L.: The Analytic Network Process. RWS Publications, Pittsburgh (1996) 13. Wan, S.P., Xu, G.L., Dong, J.Y.: Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment. Inf. Sci. 385, 19–38 (2017) 14. Wang, C.N., Nguyen, V.T., Duong, D.H., Do, H.T.: A hybrid fuzzy analytic network process (FANP) and data envelopment analysis (DEA) approach for supplier evaluation and selection in the rice supply chain. Symmetry 10(6), 221 (2018) 15. Phochanikorn, P., Tan, C.: A new extension to a multi-criteria decision-making model for sustainable supplier selection under an intuitionistic fuzzy environment. Sustainability 11 (19), 5413 (2019) 16. Tirkolaee, E.B., Mardani, A., Dashtian, Z., Soltani, M., Weber, G.W.: A novel hybrid method using fuzzy decision making and multi-objective programming for sustainablereliable supplier selection in two-echelon supply chain design. J. Clean. Prod. 250, 119517 (2020) 17. Hwang, C.L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple Attribute Decision Making, pp. 58–191. Springer, Heidelberg (1981). https://doi.org/10. 1007/978-3-642-48318-9_3 18. Bera, A.K., Jana, D.K., Banerjee, D., Nandy, T.: Supplier selection using extended IT2 fuzzy TOPSIS and IT2 fuzzy MOORA considering subjective and objective factors. Soft. Comput. 24(12), 8899–8915 (2019). https://doi.org/10.1007/s00500-019-04419-z 19. Yucesan, M., Mete, S., Serin, F., Celik, E., Gul, M.: An integrated best-worst and interval type-2 fuzzy TOPSIS methodology for green supplier selection. Mathematics 7(2), 182 (2019) 20. Yu, C., Shao, Y., Wang, K., Zhang, L.: A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Syst. Appl. 121, 1–17 (2019) 21. Petrović, G., Mihajlović, J., Ćojbašić, Ž, Madić, M., Marinković, D.: Comparison of three fuzzy MCDM methods for solving the supplier selection problem. Facta Universitatis Series Mech. Eng. 17(3), 455–469 (2019) 22. Çalık, A.: A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft. Comput. 25(3), 2253–2265 (2020). https:// doi.org/10.1007/s00500-020-05294-9 23. Qu, G., Zhang, Z., Qu, W., Xu, Z.: Green supplier selection based on green practices evaluated using fuzzy approaches of TOPSIS and ELECTRE with a case study in a Chinese internet company. Int. J. Environ. Res. Public Health 17(9), 3268 (2020) 24. Opricovic, S.: Multicriteria optimization of civil engineering systems. Faculty Civil Eng. Belgrade 2(1), 5–21 (1998) 25. Bahadori, M., Hosseini, S.M., Teymourzadeh, E., Ravangard, R., Raadabadi, M., Alimohammadzadeh, K.: A supplier selection model for hospitals using a combination of artificial neural network and fuzzy VIKOR. Int. J. Healthc. Manage. 13(4), 286–294 (2017)

662

D. Akburak

26. Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I.E., Omid, M.: Green supplier selection using fuzzy group decision making methods: a case study from the agri-food industry. Comput. Oper. Res. 89, 337–347 (2018) 27. Çalı, S., Balaman, ŞY.: A novel outranking based multi criteria group decision making methodology integrating ELECTRE and VIKOR under intuitionistic fuzzy environment. Expert Syst. Appl. 119, 36–50 (2019) 28. Sharaf, I.M., Khalil, E.A.H.A.: A spherical fuzzy TODIM approach for green occupational health and safety equipment supplier selection. Int. J. Manage. Sci. Eng. Manage. 16(1), 1– 13 (2020) 29. Kumar, S., Barman, A.G.: Fuzzy TOPSIS and fuzzy VIKOR in selecting green suppliers for sponge iron and steel manufacturing. Soft. Comput. 25(8), 6505–6525 (2021). https://doi. org/10.1007/s00500-021-05644-1 30. Zhang, J., Yang, D., Li, Q., Lev, B., Ma, Y.: Research on sustainable supplier selection based on the rough DEMATEL and FVIKOR methods. Sustainability 13(1), 88 (2021) 31. Wan, S., Zou, W.C., Zhong, L.G., Dong, J.Y.: Some new information measures for hesitant fuzzy PROMETHEE method and application to green supplier selection. Soft Comput. 24 (12), 9179–9203 (2020) 32. Kumari, R., Mishra, A.R.: Multi-criteria COPRAS method based on parametric measures for intuitionistic fuzzy sets: application of green supplier selection. Iran. J. Sci. Technol. Trans. Electr. Eng. 44(4), 1645–1662 (2020). https://doi.org/10.1007/s40998-020-00312-w 33. Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A.: A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures. J. Clean. Prod. 238, 117901 (2019) 34. Giri, B.C., Molla, M.U., Biswas, P.: Pythagorean fuzzy DEMATEL method for supplier selection in sustainable supply chain management. Expert Syst. Appl. 193, 116396 (2022) 35. Liu, A., et al.: A novel two-stage integrated model for supplier selection of green fresh product. Sustainability 10(7), 2371 (2018) 36. Yazdani, M., Torkayesh, A.E., Stević, Ž, Chatterjee, P., Ahari, S.A., Hernandez, V.D.: An interval valued neutrosophic decision-making structure for sustainable supplier selection. Expert Syst. Appl. 183, 115354 (2021) 37. Puška, A., Nedeljković, M., Hashemkhani Zolfani, S., Pamučar, D.: Application of interval fuzzy logic in selecting a sustainable supplier on the example of agricultural production. Symmetry 13(5), 774 (2021) 38. Masoomi, B., Sahebi, I.G., Fathi, M., Yıldırım, F., Ghorbani, S.: Strategic supplier selection for renewable energy supply chain under green capabilities (fuzzy BWM-WASPASCOPRAS approach). Energ. Strat. Rev. 40, 100815 (2022) 39. Büyüközkan, G., Göçer, F.: Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Appl. Soft Comput. 52, 1222–1238 (2017) 40. Awasthi, A., Govindan, K., Gold, S.: Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int. J. Prod. Econ. 195, 106–117 (2018) 41. Ortiz-Barrios, M., Cabarcas-Reyes, J., Ishizaka, A., Barbati, M., Jaramillo-Rueda, N., & de Jesús Carrascal-Zambrano, G.: A hybrid fuzzy multi-criteria decision making model for selecting a sustainable supplier of forklift filters: a case study from the mining industry. Ann. Oper. Res. 307(1), 443–481 (2021) 42. Nguyen, V.T., Hai, N.H., Lan, N.T.K.: Spherical Fuzzy multicriteria decision-making model for wind turbine supplier selection in a renewable energy project. Energies 15(3), 713 (2022) 43. Ghadimi, P., Toosi, F.G., Heavey, C.: A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain. Eur. J. Oper. Res. 269 (1), 286–301 (2018)

Review of Fuzzy Multi-criteria Decision Making Methods

663

44. Wang, C.N., Nguyen, V.T., Kao, J.C., Chen, C.C., Nguyen, V.T.: Multi-criteria decisionmaking methods in fuzzy decision problems: a case study in the frozen shrimp industry. Symmetry 13(3), 370 (2021) 45. Liu, M., Wang, X., Li, Y.: Service supplier selection under fuzzy and stochastic uncertain environments. J. Intell. Fuzzy Syst. 42(3), 1301–1315 (2022) 46. Liu, G., Fan, S., Tu, Y., Wang, G.: Innovative supplier selection from collaboration perspective with a hybrid MCDM model: a case study based on NEVs manufacturer. Symmetry 13(1), 1–28 (2021)

Pythagorean Fuzzy Sets

Domination in Pythagorean Neutrosophic Graphs with an Application in Fuzzy Intelligent Decision Making D. Ajay(B) , S. John Borg , and P. Chellamani Sacred Heart College (Autonomous), Tirupattur 635601, Tamilnadu, India [email protected]

Abstract. Pythagorean Neutrosophic set is regarded as an advancement of neutrosophic set representing incomplete, imprecise and uncertain details. The notable significance of the Pythagorean neutrosophic graphs (PNG) in comparison with fuzzy graphs is its resilient fuzziness than other models. This paper studies the domination in Pythagorean Neutrosophic graphs by assigning indeterminacy values independently and whereas the membership and non-membership values are dependent. The ideas of the minimal, maximal dominating set are defined on Pythagorean Neutrosophic graphs with their characterizations and examples. Few definitions and properties related to domination in Pythago-rean neutrosophic graphs are presented in this article. A novel technique on decision-making for Pythagorean neutrosophic graphs is demonstrated with an application. Keywords: Pythagorean neutrosophic set · Pythagorean neutrosophic graphs · Domination · Decision making · Fuzzy graphs

1

Introduction

Graph theory is a developing field in mathematics with applications in different domains like biochemistry, electrical engineering, biology, operations research, astronomy and computer science. In modelling the problems of a real-life situation, it is a drop in using crisp values because of the imprecise and vagueness in the problems. To solve these uncertain and vague information, Fuzzy set theory concept developed by Zadeh in [1] plays a crucial role. The elements of the fuzzy set has membership ranging between 0 and 1. In consideration of the imprecise information in human information, the fuzzy set added with some more restrictions leads to the extensions as intuitionistic and neutrosophic fuzzy sets [2,3]. Neutrosophic set, which is a development of the fuzzy set assigns a truth, indeterminacy, and false degree to the elements, holding the condition that the sum of the degrees is between 0 and 3. Intuitionistic sets has the elements with membership (μ) and non-membership (σ) grade satisfying c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 667–675, 2022. https://doi.org/10.1007/978-3-031-09176-6_74

668

D. Ajay et al.

the condition μ+σ ≤ 1. Yager [4] developed the notion of Pythagorean sets which has relaxation in the condition as μ2 + σ 2 ≤ 1. Likewise, picture and spherical sets have been developed from neutrosophic sets, and to these concepts, the Pythagorean Neutrosophic sets were introduced. Pythagorean neutrosophic set is a fusion of the remarkable concepts pythagorean and neutrosophic sets, which allows the element to posses the membership (μ), indeterminacy (β) and nonmembership (σ) grades with the constrain μ2 + β 2 + σ 2 ≤ 2. The concept of PN graphs was developed by the fusion of the concepts Pythagorean Neutrosophic set and graph theory [5], and further theoretical concepts were developed in [6–8]. Graphs portrays the relationship between the object and respective information in a pictorial form. Fuzzy graphs comes into play when there is occurrence of vagueness and uncertainty in the relation expressed as a graphical model. Fuzzy graphical model is same as a crisp graph in structure but deals with vague data. Idea of graph with fuzziness was developed by Kaufmann in [10] motivated from fuzzy relation in [9] by Zadeh. Several basic graph theoretical concepts in the fuzzy graph was developed by Rosenfeld in [11]. Intuitionistic fuzzy relations were instituted in [12] and intuitionistic fuzzy graphs with their properties were explored in [13]. Various concepts such as operations, strong intuitionistic graphs, and intuitionistic fuzzy hypergraphs were developed in [14–16]. Naz et al. in [17] illustrated the hypothesis of PFG. Some results related to PFG were discussed in [18,19] explored the energy of PFG introduced by Naz and Akram, consequently following the proposition of some operations of PFGs in [20]. In [21], strong neutrosophic graphs were introduced and single-valued neutrosophic graphs were proposed by Broumi et al. [22]. Domination in graphs has attracted the attention of a range of scientists, software engineers due to their applications in the field of communication and traffic systems. The works on domination in graphs have been published since 1960. Authors in [23] introduced domination concept of fuzzy graphs using their effective edges. Further, results on the dominating sets, dominating number, independent number, and total dominating number for intuitionistic fuzzy graphs was dealt by authors in [24,25] and dominating energy in the product of these graphs originated from [26]. M. Mullai in [27] introduced the idea of dominations in neutrosophic graphs. Domination in Pythagorean neutrosophic graphs are studied due to their improved fuzziness in comparison with the neutrosophic fuzzy graphs. Inspired from the above articles in the literature of fuzzy graphs, the authors have proposed a new concept of domination for Pythagorean neutrosophic graphs with independent indeterminacy values and dependent membership and nonmembership grades. The objective of the research is to introduce the interesting concept of domination in Pythagorean Neutrosophic graphs and to explore the properties and results of the domination of Pythagorean Neutrosophic graphs. The work will provide an insight into minimal and maximal dominating sets for Pythagorean neutrosophic graphs. The article is structured with main results

Domination in Pythagorean Neutrosophic Graphs

669

on Pythagorean neutrosophic graphs in Sect. 2 followed by application of the obtained results in Sect. 3. The scope for the future work is presented in Sect. 4.

2

Domination in Pythagorean Neutrosophic Graphs

Definition 1. Let PNG on G∗ = (ΦV , ΦE ) and ,  ∈ ΦV be denoted by G=(A,B). Then, (1) μ2 − strength of connectedness (SoC) between  and  is n   μ∞ 2 ( ) = sup {μ2 ( )/n = 1, 2, ...k}, and n  μ2 ( ) = min {μ2 (p1 ), μ2 (p1 p2 ), ..., μ2 (pn−1  )/, p1 , ...pn−1 ,  ∈ ΦV , n = 1 − k}. (2) β2 − SoC between  and  is β2∞ ( ) = sup {β2n ( )/n = 1 − k}, and β2n ( ) = min {β2 (p1 ), β2 (p1 p2 ), ..., β2 (pn−1  )/, p1 , p2 , ...pn−1 ,  ∈ ΦV , n = 1 − k}. (3) σ2 − SoC between  and  is σ2∞ ( ) = inf {σ2n ( )/n = 1 − k}, and σ2n ( ) = max {σ2 (p1 ), σ2 (p1 p2 ), ..., σ2 (pn−1  )/, p1 , p2 , ...pn−1 ,  ∈ ΦV , n = 1 − k}. Definition 2. Let PNG on G∗ = (ΦV , ΦE ) be denoted by G = (A,B). An arc pw ∈ ΦE said to be a PN strong arc, if ∞ ∞ μ2 (pw) ≥ μ∞ 2 (pw), β2 (pw) ≥ β2 (pw), σ2 (pw) ≤ σ2 (pw). Definition 3. Let a PNG denoted G=(A,B). Then, (1) The PN order of G is,   2 + μ1 ( ) + β1 ( ) − σ1 ( )  i i i  | | = , 2  i ∈V

(2) The PN size of G is,   2 + μ2 (i j ) + β2 (i j ) − σ2 (i j )  , |ΦE | = 2   i j ∈E

(3) The PN cordinality of G is |G| = |V | + |E |. Definition 4. Let there be a vertex   in PNG. G, then PN() = { :  ∈ ΦV & (,  ) is a PN strong arc} is said to be neighbourhood 0. Definition 5. A vertex  ∈ ΦV of a PNG G, is an isolated vertex if μ2 (,  ) = 0, β2 (,  ) = 0 and σ2 (,  ) = 0 for all  ∈ ΦV . (PN() = φ). An isolated vertex does not dominate any other vertex in G. Definition 6. Let (A, B) represent a PNG on ΦV .  dominates  in G if a strong arc exists between the 2 vertices ,  .

670

D. Ajay et al.

Definition 7. A subset Q of ΦV is called a dominating set (DS) in G if for each  ∈ ΦV − Q, ∃.  ∈ Q such that  dominates  . Definition 8. A DS Q of a PNG is termed as minimal DS (MiDS) if Q has no proper subset that is DS. Definition 9. Minimum and maximum cardinality of all MiDS, is lower and upper domination number of G, represented by ld(G) and ud(G) correspondingly. Definition 10. In a PNG 2 vertices are independent (IND) if there is no strong arc between them. Definition 11. A subset Q of ΦV is an independent set (INDS) of, G if   ∞   ∞   μ2 (,  ) < μ∞ 2 (,  ), β2 (,  ) < β2 (,  ) and σ2 (,  ) < σ2 (,  ) ∀ ,  ∈ Q. Definition 12. An INDS Q of G in a PNG is a maximal independent (MaIND), if for each vertex  ∈ ΦV − Q, the set Q ∪  is not (IND). Definition 13. The minimum, maximum cardinality of all MaINDS is called lower, upper independence number & symbolized by ui(G), ui(G) correspondingly. Definition 14. Let G = (A,B) with non isolated vertices be a PNG. A set Θ is a total DS TDS if for each vertex  ∈ V , ∃ a vertex  ∈ Q and the vertices are not equal, such that  dominates  . Definition 15. Let minimum and maximum cardinality of a TDS is called lower and upper total domination number, denoted as ltd(G) and utd(G), respectively. Theorem 1. A DS Θ of a PNG, G = (A , B) is a MiDS iff for each d ∈ Θ one of the following holds: (1) No vertex in the set Θ has a strong neighborhood of the vertex d. (2) a vertex  ∈ ΦV − Θ exists, such that N ( ) ∩ Θ = d. Proof. Consider that Θ is a MiDS of G. Then for each vertex d ∈ Θ, Θ − d is not a DS and thus there exists no vertex in Θ − d that dominates the vertex  ∈ ΦV − (Θ − d). If  = d, it is clear that no vertex in Θ has a strong neighborhood of the vertex  . If  = d,  is not dominated by Θ −  , but is dominated by Θ, then the vertex  is strong neighbour only to d in Θ. N ( ) ∩ Θ = d. Conversely, consider a DS, Θ and assume that one of two conditions hold for every vertex d ∈ Θ. Suppose Θ is not a MiDS, then there exists a vertex d ∈ Θ, such that Θ − d is a DS. Thus, we get that atleast one vertex in Θ − d has a strong neighborhood of d, thus, the condition (1) fails. If Θ − d is a DS then each vertex in ΦV − Θ is a strong neighbour to atleast one vertex in Θ − d, the second condition fails contradicting the assumption that atleast of the condition holds. So Θ is a MiDS. Theorem 2. Let G = (A,B) with non isolated vertices be a PNG and Θ is a MiDS. Then ΦV − Θ is a DS of G.

Domination in Pythagorean Neutrosophic Graphs

671

Proof. Let Θ be a MiDS containing any vertex  . Since no vertices are isolated from G, there is d ∈ N ( ), aleast one vertex of the set Θ− dominates the vertex  . Then, it is evident that Θ −  is a DS. By Theorem 2, Θ ∈ ΦV − Θ. Thus every vertex in Θ is dominated by atleast one vertex in ΦV − Θ, and ΦV − Θ is a DS. Theorem 3. An INDS is a MaINDS of PNG, G = (A,B) iff it is IND and DS. Proof. Let Θ be a MaINDS in a PNG, hence for every vertex  ∈ ΦV − Θ, the set Θ ∪  is not independent. For each vertex  ∈ ΦV − Θ, there is a vertex  ∈ Θ such that  is a strong neighbour to  . Thus Θ is a DS. Hence Θ is both DS and INDS. Conversely, assume Θ is both DS and IND. Suppose Θ is not maximal independent, then ∃  ∈ ΦV − Θ, Θ ∪  is IND. If Θ ∪  is IND then no vertex in Θ is strong neighbour to  . Hence Θ cannot be a DS, which is a contradiction. Hence Θ is a MaINDS. Theorem 4. Each MaINDS in a PNG G, is a MiDS. Proof. Let Q be a MaINDS in a PNG. By Theorem 3, Q is a DS. Suppose Q is not a MiDS, then ∃ atleast one vertex  ∈ Q for which Q −  is a DS. But if Q −  dominates ΦV − {s − { }} then atleast one vertex in Q −  must be strong neighbour to  . Thus contradicts that Q is an INDS of G. Q must be a MiDS. Theorem 5. For a PNG, ltd(G) = O(G) iff the neighbour of every vertex of G is unique. Proof. If a unique neighbourhood exists for each vertex of G then, the vertex set ΦV is the only DS of G, then ltd(G) = O(G). Conversely, consider ltd(G) = O(G). If ∃ a vertex  with neighbours  and  then ΦV −  is a TDS of G, so that t(G) < O(G) which contradicts. Therefore, neighbourhood of every vertex of G is unique.

3

Application

This section is devoted to construction of a decision-making process on the Pythagorean Neutrosophic sets called the Pythagorean Neutrosophic decisionmaking method. A new decision making algorithm employing the concept of Pythagorean neutrosophic is also proposed. 3.1

Algorithm

1. Input the attributes H = {1 , 2 , ...k } and criteria C = {q1 , q2 , ...qn }. 2. Construct the matrix by using the attributes and criteria.

672

D. Ajay et al.

3. Use the score function S(c) =

μ + (1 − β)(1 − σ) 2

calculate the matrix of score values for each attribute. t  4. Use the choice function δ = qρ where δ = 1, 2, ...m and ρ = 1, 2, ..., t ρ=1

5. Now arrange the attributes according to their choice values in ascending or descending order. 6. Choose the attribute which has the maximum value as the optimum attribute. 3.2

Numerical Example

In a hill station, a family is opting to buy a home within their budget. Let us consider this decision-making problem and using the proposed Pythagorean Neutrosophic decision-making method can choose the best option. Let us consider the attributes as the 4 houses in the area of that hill station. And the criteria to choose be q1 - the cost of the house, q2 - be the facilities and transport available near the house, and q3 - the environment around the house. Step: 1 The attributes are H = {1 , 2 , 3 , 4 } and the criteria as C = {q1 , q2 , q3 }. Where q1 = {(1 , .6, .4, .4), (2 , .3, .4, .2), (3 , .7, .5, .1), (4 , .8, .6, .2)} q2 = {(1 , .8, .4, .3), (2 , .9, .3, .1), (3 , .7, .3, .2), (4 , .5, .3, .1)} q3 = {(1 , .7, .5, .1), (2 , .8, .2, .1), (3 , .6, .5, .2), (4 , .5, .4, .3)} Step: 2 Construct the matrix using attributes and criteria (Table 1). Table 1. Relations of attributes in criteria

q1

q2

q3

1 (.6, .4, .3) (.8, .4, .3) (.7, .5, .1) 2 (.3, .4, .2) (.9, .3, .1) (.8, .2, .1) 3 (.7, .5, .3) (.7, .3, .2) (.6, .5, .2) 4 (.8, .6, .2) (.5, .3, .1) (.5, .4, .3)

Step: 3 The score matrix is obtained by applying score function (Table 2).

Domination in Pythagorean Neutrosophic Graphs

673

Table 2. Score matrix of attributes H

q2

q3

1 .39

q1

.6

.575

2 .39

.765 .76

3 .525 .63 4 .56

.5

.565 .46

Step: 4 By using the choice function, we have the choice values as (Table 3), Table 3. Choice values of attributes H 1

2

3

4

Choice Value 1.565 1.915 1.655 1.585

Step: 5 By arranging the attributes in descending order, we get the order  2 > 3 > 4 > 1 Step: 6 The optimum attribute is 2 Clearly, the maximum value is 1.915 for 2 .

4

Conclusion

The authors in this article have developed a new concept for domination in Pythagorean neutrosophic graphs and discussed its applicability in decisionmaking real world problem. The fuzziness for the graphs are provided with independent indeterminacy value and membership and non-membership values are chosen such that they are dependent. The article also investigates the minimal and maximal dominating sets for Pythagorean neutrosophic graphs. Further the work on this article can be extended to Pythagorean neutrosophic bipolar graphs, energy of Pythagorean Neutrosophic graphs and Pythagorean Neutrosophic soft graphs, interval valued Pythagorean Neutrosophic graphs and can be applied in real life situations.

References 1. Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965) 2. Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986) 3. Smarandache, F.: A Unifying Field in Logics: Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability, pp. 1–141. American Research Press, Rehoboth (1999)

674

D. Ajay et al.

4. Yager, R.R.: Pythagorean fuzzy subsets. In: Proceedings of the Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada, pp. 57–61 (2013) 5. Ajay, D., Chellamani, P.: Pythagorean neutrosophic fuzzy graphs. Int. J. Neutrosophic Sci. 11(2), 108–114 (2020) 6. Ajay, D., Chellamani, P.: Pythagorean neutrosophic soft sets and their application to decision-making scenario. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 308, pp. 552– 560. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85577-2 65 7. Chellamani, P., Ajay, D.: Pythagorean neutrosophic dombi fuzzy graphs with an application to MCDM. Neutrosophic Sets Syst. 47, 411–431 (2021) 8. Ajay, D., Chellamani, P., Rajchakit, G., Boonsatit, N., Hammachukiattikul, P.: Regularity of Pythagorean neutrosophic graphs with an illustration in MCDM. AIMS Math. 7(5), 9424–9442 (2022) 9. Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3(2), 177–200 (1971) 10. Kaufmann, A.: Introduction a la Theorie des Sour-Ensembles Flous; Masson et Cie 1. France, Paris (1973) 11. Rosenfeld, A.: Fuzzy graphs. In: Zadeh, L.A., Fu, K.S., Shimura, M. (eds.) Fuzzy Sets and Their Applications to Cognitive and Decision Processes, pp. 77–95. Academic Press, Cambridge (1975) 12. Shannon, A., Atanassov, K.T.: A first step to a theory of intuitioistic fuzzy graphs. In: Lakov, D. (ed.) Proceedings of the Fuzzy Based Expert Systems, Sofia, Bulgaria, pp. 59–61 (1994) 13. Shannon, A., Atanassov, K.T.: Intuitioistic fuzzy graphs from α−, β−, (α, β)-levels. Notes Intuit. Fuzzy Sets. 1, 32–35 (1995) 14. Parvathi, R., Karunambigai, M.G., Atanassov, K.T.: Operations on intuitioistic fuzzy graphs. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, pp. 1396–1401 (2009) 15. Akram, M., Dudek, W.A.: Intuitionistic fuzzy hypergraphs with applications. Inf. Sci. 218, 182–193 (2013) 16. Akram, M., Davvaz, B.: Strong intuitionistic fuzzy graphs. Filomat 26, 177–196 (2012) 17. Naz, S., Ashraf, S., Akram, M.: A novel approach to decision-making with Pythagorean fuzzy information. Mathematics 6, 1–28 (2018) 18. Verma, R., Merigo, J. M., Sahni, M.: Pythagorean fuzzy graphs: some results. arXiv preprint arXiv:1806.06721v1. (2018) 19. Akram, M., Naz, S.: Energy of Pythagorean fuzzy graphs with applications. Mathematics 6, 136 (2018) 20. Akram, M., Habib, A., Ilyas, F., Dar, J.M.: Specific types of Pythagorean fuzzy graphs and application to decision-making. Math. Comput. Appl. 23, 42 (2018) 21. Dhavaseelan, R., Vikramaprasad, R., Krishnaraj, V.: Certain types of neutrosophic graphs. Int. J. Math. Sci. Appl. 5(2), 333–339 (2015) 22. Broumi, S., Talea, M., Bakali, A., Smarandache, F.: Single-valued neutrosophic graphs. J. New Theory 10, 86–101 (2016) 23. Cockayne, E.J., Hedetnieme, S.T.: Towards a theory of domination in graphs. Networks 7, 247–261 (1977) 24. Parvathi, R, Thamizhendhi, G.: Domination in intuitionistic fuzzy graphs. In: Fourth International Conference on IFSs, Sofia, vol. 16, no. 2, pp. 39–49 (2010) 25. Somasundaram, A., Somasundaram, S.: Domination in fuzzy graphs-1. Pattern Recogn. Lett. 19(9), 787–791 (1998)

Domination in Pythagorean Neutrosophic Graphs

675

26. Vijayraghavan, R., Kalimulla, A., Basha, S.S.: Dominating energy in products of Intuitionistic fuzzy graphs. J. Global Pharma Technol. 6(9), 58–71 (2017) 27. Mullai, M.: Domination in neutrosophic graphs. In: Neutrosophic Graph Theory and Algorithms, pp. 131–147. IGI Global Publications (2019)

Game Level Design in Mobile Gaming Industry: Fuzzy Pythagorean Similarity Approach Ahmet Tezcan Tekin(&) Soft Towel Games Ltd., Valetta, Malta [email protected]

Abstract. The mobile game industry has reached an enormous volume due to the increasing use of mobile phones and this volume is increasing day by day. Depending on this situation, many game developer companies release new games and generate income from the advertisements shown in these games. Users need to play the game longer to increase revenue in this context. Game companies also attach great importance to the design of various data analyses and in-game sections to increase users’ time consumed in the game. But finding the optimum sequence of in-game sections is quite a challenging process. Because this optimum sequence may vary from user to user, analyses made in this direction also show that user-based dynamization may be more effective than static in-game section designs since users have different characteristics. In this study, Using the user-based in-game activities of a crossword puzzle game of a mobile game company, the fuzzy similarity of the users most similar to themselves was calculated. Although many methods are suggested for the similarity calculation in the literature, since the similarities of the users have fuzzy characteristics, this similarity calculation was made with the Pythagorean Fuzzy Membership Degree method on the Pythagorean Fuzzy Set. In this way, it is aimed to increase the time spent in the game by enabling users to progress through the pattern in which the users most similar to them completed the game. Keywords: Pythagorean user similarity The mobile gaming industry


Pythagorean membership degree


1 Introduction The mobile game industry has overgrown in recent years and has had a severe share in the economy. Generally, users download games for free to their mobile phones, watch in-game advertisements and purchase products from the market for the game. In this direction, game companies are making many developments that attract users’ attention and aim to increase the time they spend in the game. While these improvements are design improvements, they are also in the form of in-game editing changes. Since the ads that users watch in the game constitute the primary source of income for the games, the user needs to complete more chapters in the game and spend more time in the game. Although the in-game sections progress from easy to difficult, concepts such as ease and difficulty differ from user to user. At this point, game © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 676–683, 2022. https://doi.org/10.1007/978-3-031-09176-6_75

Game Level Design in Mobile Gaming Industry

677

development companies need to switch from a static game flowchart to a user-based dynamic scheme. But this is a challenging and costly process. This development aims to segment users and create a separate flow for each segment. But segmenting users means accepting that users in each segment will have the same behaviour. Simulating the users with the most similar features to them and configuring the game so that users who played the game for a long time have the same game flow will increase the time spent in the game. There are many similar studies in the literature. However, it is more appropriate for the method to be simulated by calculating the membership degree, which forms the basis of fuzzy logic, over the positive and negative approaches of the users. This study used mobile crossword game data with users playing the game in 30 languages in more than 40 countries. This study proposes a method for detecting a degree of matching between users based on their in-game behaviour that uses Pythagorean fuzzy relations. Users who behave positively with long-time usage and users who agree negatively about the game are identified using triangular compositions. Pythagorean fuzzy sets allow us to examine both positive and negative elements of assessments to locate users who behave positively and negatively at least the same items as a given user. In the paper, the second section deals with the literature review of fuzzy logic and Pythagorean fuzzy sets used in this study and similar applications in the literature. The third section describes the dataset used in this study and the proposed method. Finally, conclusions of the study are briefly described and future works are presented in the last section.

2 Literature Review Depending on the developing technology and the increase in the use of mobile content, the content on the internet is increasing day by day. Identifying and recommending content that may interest users has become an important literature research topic with the increasing number of content. There are many studies in the literature, especially on recommendation systems. Recommenders systems can be evaluated in three headings: content-based filtering, collaborative filtering, and hybrid filtering [1]. Both of them are based on similarity measurement of items and users. The techniques used to find the similarity between two items or users are explained below. • Cosine Similarity: Cosine similarity is a metric for comparing two numerical sequences. The cosine similarity is defined as the cosine of the angle between the sequences, which is defined as the dot product of the vectors divided by the product of their lengths. As a result, the cosine similarity is determined by the angle of the vectors rather than their magnitudes. The cosine similarity always occupies the interval [−1, 1]. For example, the cosine similarity of two proportional vectors is 1, the similarity of two orthogonal vectors is 0, and the similarity of two opposing vectors is −1. The cosine similarity is beneficial in a positive space, where the result is cleanly limited in [0, 1]. Cosine similarity is described in Eq. 1.

678

A. T. Tekin

Pn A:B i¼1 Ai Bi ffiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi cosine similarity ¼ cosðhÞ ¼ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P Pn n A B jj jjjj jj A2 B2 i¼1

i

i¼1

ð1Þ

i

where Ai and Bi are components of vectors A and B, respectively. • Euclidean Distance: Euclidean distance is the distance between two locations in coordinate geometry [2]. The length of a segment connecting the two places is measured to find the two points on a plane [3]. The Pythagoras theorem is used to derive the Euclidean distance formula. Euclidean Distance is described in Eq. 2. d¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx2  x1 Þ2 þ ðy2  y1 Þ2

ð2Þ

where ðx1 ; y1 Þ denotes the coordinate of one point and ðx2 ; y2 Þ are the coordinates of another point. d is the distance between these two points. • Manhattan Distance: Manhattan Distance is the distance between two places when measured at right angles along axes. It is |x1 − x2| + |y1 − y2| in a plane with p1 at (x1, y1) and p2 at (x2, y2) [4]. It’s simple to extrapolate this to higher dimensions. In integrated circuits, the Manhattan distance is frequently employed when wires are only parallel to the X or Y-axis. • Minkowski Distance: The Minkowski distance generalizes the Euclidean and Manhattan distances. It is a distance/similarity measurement between two locations in normed vector space (N-dimensional real space). It is described in Eq. 3. ð

Xn i¼1

jX i  Y i jp Þ

1=p

ð3Þ

If p equals 1, it is equivalent to the Manhattan Distance and if p equals 2, it is equivalent to the Euclidean Distance. • Jaccard Distance: It’s also known as the Jaccard similarity coefficient, and it’s a statistic for comparing sample sets’ similarity and diversity. T. Tanimoto [5] independently formulated it after Paul Jaccard [6] developed it. It is described in Eq. 4. J ð X; Y Þ ¼ jX \ Yj=jX [ Yj

ð4Þ

It is the proportion of shared members to the total number of members in both sets. Fuzzy Logic, which holds a prominent place in the literature, is frequently used to machine learning challenges. One of the main reasons for this is that real-world problems do not have simply 0 and 1 unique values, which is the case with machine learning approaches to real-world problems. Lofti Zadeh discovered fuzzy logic in 1965 [7–9]. Zadeh felt that analytical and computer-based solutions could solve all real-world problems [10]. He introduced the “Fuzzy Set Theory” in 1964. Fuzzy Logic provides a wide range of strategies for analyzing data uncertainty. It’s beneficial for dealing with data with inaccuracies and user preferences that change over time [11]. Fuzzy Logic has an essential place in the literature and it has been studied in different areas like in intelligent systems [12], engineering applications [13], medicine [14], renewable energy systems [15], software project management [16], e-learning [17] etc.

Game Level Design in Mobile Gaming Industry

679

It is also used in recommender systems [18–20]. Because of using Fuzzy Logic in these areas, the use of fuzzy sets was utilized to create a system that could adapt to changing user behavior. The model gains dynamicity due to this changing behavior (over time). To deal with data vagueness and imprecision, fuzzy set-theoretic methods were employed in a study by Zenebe and Norcio [21]. Also, the Pythagorean Fuzzy Sets (PFS) are used in the literature for both recommender systems and user similarity. Yager first introduced the Pythagorean fuzzy sets (PFS) in [22, 23]. It’s a novel type of fuzzy set that isn’t standard. For each x from the domain X, a PFS membership grade can be stated as rðxÞ called the strength of commitment, and d ð xÞ called the direction of commitment. Both of them are in the 0.0 to 1.0 range. Here, the membership grades AY ðxÞ and AN ðxÞ are related to rðxÞ and dðxÞ AY ðxÞ denotes support for x’s membership in A, whereas AN ð xÞ denotes opposition to x’s participation in A. The following are the relationships between all of these variables: A2Y ð xÞ þ A2N ð X Þ ¼ r 2 ðxÞ

ð5Þ

AY ð xÞ ¼ r ð xÞ  CosðhÞ

ð6Þ

A;N ð xÞ ¼ r ð xÞ  SinðhÞ

ð7Þ

where h ¼ arccosð

AY ðxÞ Þ rðxÞ

and h ¼ ð1  d ð xÞÞ 

ð8Þ p 2

ð9Þ

The value of r(x) provides a certain amount of flexibility. It has a value in the unit interval, and the larger r(x), the stronger the commitment and the lower the uncertainty. On the other hand, the value of the direction of commitment dðxÞ can provide an interesting insight into the relationships between AN ðxÞ and AY ðxÞ. The value of dðxÞ = 1 for h = 0, i.e. when there is no AN ðxÞ portion, indicating that there are no negative comments and the commitment direction is totally positive.

3 Proposed Methodology and Modelling In the mobile game industry, the user who downloads the game to his phone must continue to play the game for a long time. Because marketing activities are carried out for users to download the game to their phone, a cost is incurred accordingly. For a profitable activity, the user must continue playing the game and watching advertisements or making in-game purchases. In this way, the user first pays for himself and then brings profit. For this purpose, game companies carry out various design and software activities to increase the time the user will spend in the game. One of the most critical issues is the design and configuration of the sections in the game. Generally, the

680

A. T. Tekin

sections in a game go from easy to difficult, thus aiming to attract more attention from the user. However, the concepts of convenience and difficulty in the mobile game industry, which has a vast volume, vary from user to user. Since it will be almost impossible to design a different section for each user, it is advantageous to develop alternative sections and play the appropriate section according to the section’s performance completed by the user in the game. The dataset used in this study is the user data of a mobile crossword puzzle game published in the Google Play Store and App Store. This dataset contains information about the in-game behavior of the users in the first 12 levels. The dataset consists of 8 base features and nearly 400000 users who play the game in 30 different languages. In this study, the in-game flow was tried to be dynamic by calculating the similarity score to other users in the language in which the user who completed the first 12 levels played the game. The referenced user set consists of users who have played the game for a long time and are profitable to watch advertisements and purchase in-app products. The base features used in this study are shown in Table 1. The in-game behavior of users who play the game includes fuzziness rather than precision. The primary purpose of this study is to calculate the similarity of users based on this Fuzzy Pythagorean approach instead of traditional similarity calculation methods. In this way, the in-game behaviors of the users will be calculated separately in positive and negative directions, and these values will be taken into account individually in the similarity value to be calculated at the end of the study. As a result, a recommendation system will be established by making section suggestions to the users according to the in-game behaviors of the most similar users. In the first step, the directions of the features to be used in the study were determined according to their positive or negative weights. Then the intervals of the data were divided into five different groups. After that, these groups are mapped into 1 to 5. The details of these processes are shown in Table 2. Table 1. Base features used in user similarity. Feature Session count Gameplay duration Bonus count Hint count Repeat count Banner count Interstitial count Rewarded video count

Description The number of sessions started for the user in the first 12 levels The total gameplay duration for the users in the first 12 levels The number of bonuses in the game users used in the first 12 levels The number of hints in the game users used in the first 12 levels The number of repeating levels that users did in the first 12 levels The number of banner-type ads users watched in the first 12 levels The number of interstitial type ads users watched in the first 12 levels The number of rewarded video type ads users watched in the first 12 levels

Type Numeric

Range [1,20]

Numeric

[1,1476]

Numeric

[0,23]

Numeric

[0,11]

Numeric

[0,34]

Numeric

[0,54]

Numeric

[0,18]

Numeric

[0,11]

Game Level Design in Mobile Gaming Industry

681

Table 2. Base features mapping details. Feature

Direction

Session count Gameplay duration

Negative Positive

Mapping classes 5-1 1-5

Bonus count Hint count Repeat count

Negative Negative Negative

5-1 5-1 5-1

Banner count

Positive

1-5

Interstitial count

Positive

1-5

Rewarded video count

Positive

1-5

Detail Minimum session count is preferable Maximum gameplay duration is preferable A minimum bonus count is preferable Minimum hint count is preferable The minimum repeat count is preferable The maximum banner count is preferable A maximum interstitial count is preferable Maximum rewarded video count is preferable

Then rating differences for the users and reference users are handled in two groups which are positive and negative ratings. Positive ratings include gameplay duration, banner count, interstitial count, and rewarded video count. If the difference for each feature is 0, it is mapped into 1 and if the difference is 1, it is mapped into 0.5. The rest of the differences for the positive group are mapped into 0, respectively, and they are assigned to AY ðxÞ. The negative ratings are session count, bonus count, repeat count, and hint count. If the difference for each feature is 0, it is mapped into 1 and if the difference is 1, it is mapped into 0.5. The rest of the differences for the negative group are mapped into 0, respectively, and they are assigned to AN ðxÞ. In the second step, based on the values which are AY ð xÞ and AN ðxÞ, rðxÞ value is calculated. The normalization operation is performed to calculate rðxÞ value and r max values are calculated based rðxÞ values. r max range is [0.0, 1.0]. Also, dðxÞ value which shows the direction of the commitment for each user comparison, is calculated. In the final step, r max and dðxÞ values are used for the final similarity score. Its formulation is shown in Eq. 10. scorex ¼ r max  d ð xÞ 

p 2

ð10Þ

Because of p2 is a constant in the equation, it is discarded in the final similarity score calculation. Illustrated version of the proposed method for calculating the similarity of users is shown in Table 3.

682

A. T. Tekin Table 3. Illustrated version of the proposed method for calculating similarity.

Ratings Positive ratings

Ref user positive ratings

Negative ratings

Ref user negative ratings

AY

AN r

rnormalized d

Score

Rank

User1 User2 User3 User4 User5

5,4,3,5 5,4,3,5 5,4,3,5 5,4,3,5 5,4,3,5

3,2,1,5 4,3,5,1 2,4,3,4 4,1,1,5 3,4,3,2

4,2,1,5 4,2,1,5 4,2,1,5 4,2,1,5 4,2,1,5

2,5 2,5 2 3,5 1,5

3,5 1,5 0,5 3,5 0,5

0,869 0,589 0,417 1,000 0,319

0,3431 0,3864 0,3515 0,5000 0,2540

4 2 3 1 5

4,2,3,5 2,4,3,4 1,3,3,4 4,4,3,5 2,3,4,4

4,301 2,916 2,062 4,950 1,581

0,39 0,66 0,84 0,50 0,80

User 4 and the reference user have the most robust commitment in the illustrated version of the proposed method. So, the game level design for User 4 will be the same as the reference user because it has the most similarity, which is based on both positive and negative direction features.

4 Conclusion and Future Work Many studies are being carried out to increase the time users spend on the content platforms they use. One of the most popular of these is recommendation systems. In this study, it was tried to calculate the similarity ratios to the users who have played the game before by using the in-game behavior data of the users of a mobile crossword puzzle game. Since the problem is not precise due to its nature, the problem was taken with the fuzzy approach and the Fuzzy Phytagorean approach was used for the similarity calculation. In the current game, while the level transitions were progressing in a static structure, the users started to continue with the game patterns of the users most similar to them, thanks to the proposed method. The proposed system has been subjected to 50% A/B tests with the existing structure, and a period of 1 month has been expected for the first results to occur. According to the preliminary results, the proposed system has improved the user’s stay in the game by 7–10%. Since the commercial evaluation of a game is in the long run, testing is ongoing to make the results more precise. During this time, many steps are being considered for future work, such as increasing the number of features used in the method, changing the scaling, specifying shorter or longer boundaries instead of the first 12 sections.

References 1. Singh, R.H., Maurya, S., Tripathi, T., Narula, T., Srivastav, G.: Movie recommendation system using cosine similarity and KNN. Int. J. Eng. Adv. Technol. 9(5), 556–559 (2020) 2. Smith, K.J.: Precalculus: A Functional Approach to Graphing and Problem Solving. Jones & Bartlett Publishers, Burlington (2011) 3. Cohen, D.: Precalculus: A Problems-Oriented Approach, p. 698. Cengage Learning (2004). ISBN 978-0-534-40212-9

Game Level Design in Mobile Gaming Industry

683

4. Black, P.E.: Manhattan distance. Dictionary of algorithms and data structures (2006). http:// xlinux.nist.gov/dads// 5. Tanimoto, T.T.: Elementary mathematical theory of classification and prediction (1958) 6. Jaccard, P.: The distribution of the flora in the alpine zone. 1. New Phytologist 11(2), 37–50 (1912) 7. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965). https://doi.org/10.1016/S00199958(65)90241-X 8. Zadeh, L.A.: Fuzzy algorithms. Info. Ctl. 12, 94–102 (1968) 9. Zadeh, L.A.: Outline of a New Approach to the Analysis of Complex Systems and Decision Processes (1973) 10. Kumar, M., Misra, L., Shekhar, G.: A survey in fuzzy logic: an introduction. IJSRD Int. J. Sci. Res. Dev. 3(06) (2015) 11. Yager, R.R.: Fuzzy logic methods in recommender systems. Fuzzy Sets Syst. 136(2), 133– 149 (2003) 12. Yager, R.R., Zadeh, L.A. (eds.): An Introduction to Fuzzy Logic Applications in Intelligent Systems, vol. 165. Springer, New York (2012). https://doi.org/10.1007/978-1-4615-3640-6 13. Ross, T.J.: Fuzzy Logic With Engineering Applications. Wiley, Chichester (2005) 14. Phuong, N.H., Kreinovich, V.: Fuzzy logic and its applications in medicine. Int. J. Med. Informatics 62(2–3), 165–173 (2001) 15. Suganthi, L., Iniyan, S., Samuel, A.A.: Applications of fuzzy logic in renewable energy systems–a review. Renew. Sustain. Energy Rev. 48, 585–607 (2015) 16. Colomo-Palacios, R., González-Carrasco, I., López-Cuadrado, J.L., García-Crespo, Á.: ReSySTER: a hybrid recommender system for Scrum team roles based on fuzzy and rough sets. Int. J. Appl. Math. Comput. Sci. 22(4), 801–816 (2012) 17. Ferreira-Satler, M., Romero, F.P., Menendez-Dominguez, V.H., Zapata, A., Prieto, M.E.: Fuzzy ontologies-based user profiles applied to enhance e-learning activities. Soft Comput. 16(7), 1129–1141 (2012) 18. Queiroz, S.R.D.M., de Carvalho, F.D.A., Ramalho, G.L., Corruble, V.: Making recommendations for groups using collaborative filtering and fuzzy majority. In: Bittencourt, G., Ramalho, G.L. (eds.) Advances in Artificial Intelligence, SBIA 2002. LNCS (LNAI), pp. 248–258. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36127-8_24 19. Martínez, L., Barranco, M.J., Pérez, L.G., Espinilla, M.: A knowledge based recommender system with multigranular linguistic information. Int. J. Comput. Intell. Syst. 1(3), 225–236 (2008) 20. Jain, A., Gupta, C.: Fuzzy logic in recommender systems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, vol. 749, pp. 255–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71008-2_20 21. Zenebe, A., Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst. 160(1), 76–94 (2009) 22. Yager, R.R.: Pythagorean fuzzy subsets. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 57–61. IEEE (2013) 23. Yager, R.R.: Pythagorean membership grades in multicriteria decision making. IEEE Trans. Fuzzy Syst. 22(4), 958–965 (2013)

A Decision Making Approach Using Linear Diophantine Fuzzy Sets with Dombi Operations J. Aldring1(B) , S. Santhoshkumar2 , and D. Ajay3 1

2

3

Department of Mathematics, Panimalar Institute of Technology, Chennai 600123, India [email protected] Department of Mathematics, Patrician College of Arts and Science, Adyar, Chennai 600020, India Department of Mathematics, Sacred Heart College (Autonomous), Tirupattur 635601, Tamilnadu, India

Abstract. A Linear Diophantine fuzzy set with the addition of references parameters paves way for an advanced approach to handle uncertainties in multicriteria decision making. In this paper, the linear Diophantine fuzzy sets (LDFSs) have been employed to develop a multi criteria decision making method with the help of Dombi mean aggregation operators. In addition, the weighted dice similarity measures with their properties are studied for LDFSs. An illustration is provided to validate the proposed decision making approach.

Keywords: Linear Diophantine fuzzy sets operations · Dice similarity measures

1

· Decision making · Dombi

Introduction

The statements of opinion or linguistic terms of experts/people have been quantified by L A Zadeh [1] in 1965 when he defined fuzzy set theory. Zadeh’s theory led researchers to step down in a novel research world and they developed various concepts using fuzzy set theory [2–4]. The key concepts of fuzzy set theory are grades of membership and grades of non membership and these theories are restricted due to their limitations. To overcome this complexity researchers Riaz and Hashmi [5] introduced a new theory using Linear Diophantine Fuzzy Set (LDFS) and also have discussed the applications of the above concept in multi-attribute decision-making problems in 2019. In the year 2021 Riaz and Hashmi et al. [6] have developed their research as Spherical linear Diophantine fuzzy sets along with modeling uncertainties which are involved in multi criteria decision making. In 2021, Huseyin Kamaci [7] made an attempt to extend the concept of LDFS by the structural extension of the ranges of grades of membership, non-membership, and reference parameters from the interval [0, 1] to unit c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 684–692, 2022. https://doi.org/10.1007/978-3-031-09176-6_76

A Decision Making Approach Using Linear Diophantine Fuzzy Sets

685

circle in the complex plane. Researchers Parimala, Jafari et al. [8] proposed a work to solve a linear diophantine fuzzy environment by applying the dijkstra algorithm in 2021. Aggregation operator is a pivot tool to analyze the multi criteria decision making environment. In 2021, Iampan et al. [9] proposed a novel aggregation operator called Linear diophantine fuzzy Einstein aggregation operators for MCDM problems. Based on the researches, we have proposed a decision making approach using linear diophantine fuzzy sets (LDFSs) with Dombi mean operations. The rest of the sections in this article are structured as follows. Section 2 describes the basic definitions and notations which are fuzzy sets and their operations and used for this research. The arithmetic operation of linear diophantine fuzzy set (LDFS) are defined using Dombi t-norm in Sect. 3 and weighted dice similarity measure of LDFSs is discussed in Sect. 4. Followed by this, Sect. 5 makes an attempt to construct algorithm for weighted dice similarity measure (WDSM) in decision making model using Linear Diophantine Fuzzy Dombi Weighted Arithmetic (LDFDWA) and Linear Diophantine Fuzzy Dombi Weighted Geometric (LDFDWG) aggregation operators. Section 6 discusses the illustration of medical diagnosis using LDFS decision making model.

2

Preliminaries

˜ is defined on a universe of discourse  as the Definition 1. [1] A fuzzy set IF form    ˜ = r, IF ˙ ξ˜ (r) ˙ |r˙ ∈  F

where, ξF˜ (r) ˙ ∈ [0, 1]. Here ξF˜ (r) ˙ denotes membership function to each r. ˙ Definition 2. Let  be a universal discourse set. Then, linear diophantine fuzzy ´ in the universal discourse set  is an object form set (LDFS) L ´ = {(r, L ˙ ΔL (r), ˙ ΦL (r) ˙ , αL (r), ˙ βL (r)) ˙ |r˙ ∈ } where, ΔL (r), ˙ ΦL (r), ˙ αL (r), ˙ βL (r) ˙ ∈ [0, 1] respectively, represent the degree of ´ membership, non-membership and reference parameters of r˙ ∈  into the set L ˙ ˙ ≤ 1 and 0 ≤ ΔL (r)α ˙ L (r)+Φ ˙ ˙ L (r) ˙ ≤ 1. with the conditions 0 ≤ αL (r)+β L (r) L (r)β ´ = {(r, Also, the LDFS L ˙ 1, 0 , 1, 0) |r˙ ∈ } is called the absolute LDFS in  ´ = {(r, and the LDFS L ˙ 0, 1 , 0, 1) |r˙ ∈ } is called the null LDFS in . Definition 3. A linear diophantine fuzzy number (LDFN) can be written in the ´ = (ΔL ), ΦL  , αL , βL ) satisfying the following conditions: form L 1. ΔL , ΦL , αL , βL ∈ [0, 1] 2. 0 ≤ αL + βL ≤ 1 3. 0 ≤ ΔL αL + ΦL βL ≤ 1 ´ = (ΔL ), ΦL  , αL , βL ) be a LDFN. Then the score and Definition 4. Let L accuracy functions respectively as follows:

686

J. Aldring et al.

  ´ = 1 [(ΔL − ΦL ) + (αL − βL )] 1. Sc L   2  ´ = 1 (ΔL +ΦL ) + (αL + βL ) 2. Ac L 2 2 Definition 5. Let L´i = (ΔLi ), ΦLi  , αLi , βLi ) ∀i = 1, 2 be two LDFNs. Then, 1. 2. 3. 4. 5.

3

L´1 ⊕ L´2 = (ΔL1 + ΔL2 − ΔL1 ΔL2 , ΦL2 ΦL2  , αL1 + αL2 − αL1 αL2 , βL2 βL2 ) L´1 ⊗ L´2 = (ΔL2 ΔL2 , ΦL1 + ΦL2− ΦL1 ΦL2  , αL2 αL2 , βL1 + βL2 − βL1 βL2 , ) η η η L´1 = 1 − (1 − ΔL1 ) , ΦηL1 , 1 − (1 − αL1 ) , βLη1

 η η   η  L´1 = ΔηL1 , 1 − (1 − ΦL1 ) , , αLη 1 , 1 − (1 − βL1 ) , L´1 ≤ L´2 ⇔ ΔL1 ≤ ΔL2 , ΦL1 ≥ ΦL2 , αL1 ≤ αL2 , βL1 ≥ βL2

Dombi Operational Laws for LDFNs

This section recalls the definitions of Dombi t-norm (TN) and t-conorm (TCN) and defines the arithmetic operations of LDFS using Dombi TN and TCN with their arithmetic and geometric aggregation operators. Definition 6. Let ζ and ϑ be two real numbers. Then, the Dombi TN and TCN operational laws are defined by (i). D∗ (ζ, ϑ) = ζ ⊕ ϑ = 1 − (ii). D (ζ, ϑ) = ζ ⊗ ϑ =

1 κ

κ

ζ ϑ 1+{( 1−ζ ) +( 1−ϑ ) 1 κ

κ

1−ϑ 1+{( 1−ζ ζ ) +( ϑ )

1

}κ

1

}κ

where, κ ≥ 1 and ζ, ϑ ∈ [0, 1]. Definition 7. Let L´i = (ΔLi , ΦLi  , αLi , βLi ) ∀i = 1, 2 be two LDFNs on . Then the Dombi operational laws on LDFSs are given as follows: 1. Algebraic sum of LDFS: L´1 ⊕ L´2 ⎛

 ⎜ 1 1 ⎜ =⎝ 1− κ  Δ κ 1 ,  Δ  1−Φ κ  1−Φ κ 1 , κ κ L1 L L1 L2 1+ + 1−Δ2 1+ + 1−ΔL1 ΦL1 ΦL2 L2 ⎞ 

⎟ 1 1 ⎟ 1− κ  α κ 1 ,  α  1−β κ  1−β κ 1 ⎠ κ κ L1 L2 L1 L2 1+ + 1 + + 1−α 1−α β β L1

L2

L1

L2

2. Algebraic product of LDFS: L´1 ⊗ L´2 ⎛

 ⎜ 1 1 ⎜ =⎝  Δ  1−Φ κ  1−Φ κ 1 , κ  Δ κ 1 , 1 − κ κ L1 L L1 L2 1+ + 1−Δ2 1+ + 1−ΔL1 ΦL1 ΦL2 L2 ⎞ 

⎟ 1 1 ⎟  α κ  α κ 1 , 1 −  1−β κ  1−β κ 1 ⎠ κ κ L1 L2 L1 L2 1+ + 1 + + 1−α 1−α β β L1

L2

L1

L2

A Decision Making Approach Using Linear Diophantine Fuzzy Sets

687

3. Scalar multiplication of LDFS:

⎛

 ⎜ η L´1 = ⎝ 1 − 1+

 

1 Δ L1 1−ΔL1

κ  κ1 ,

1− 1+



1+

1 αL 1 1−αL1





1 1−ΦL1 ΦL 1

κ  κ1 ,

,

κ  κ1

1+



1 1−βL1 βL1

⎞  ⎟ κ  κ1 ⎠

4. Power operation of LDFS:

⎛

⎜ L´1 = ⎝



η

1+



1 Δ L1 1−ΔL1

κ  κ1 , 1 −



1+



1 αL 1 1−αL1

1+





1 1−ΦL1 ΦL 1

κ  κ1 , 1 −

,

κ  κ1

1+



1 1−βL1 βL1

⎞  ⎟ κ  κ1 ⎠

Theorem 1. Let L´i = (ΔLi , ΦLi  , αLi , βLi ) ∀i = 1, 2, 3, . . . , n be a collection LDFNs on . Then the aggregated value of them, using Linear Diophantine Fuzzy Dombi Weighted Arithmetic (LDFDWA) and Linear Diophantine Fuzzy Dombi Weighted geometric (LDFDWG) operators is also LDFN and it is defined as follows, n    ´ ´ ´ ´ LDF DW A L1 , L2 , L3 , . . . , Ln = i L´i

⎛  ⎜ ⎜ =⎜ 1− ⎝

i=1

 1+

n 

i

1 

i=1

 1−

 1+

n  i=1

i

1 

Δ Li 1−ΔLi

αL i 1−αLi

κ  κ1

,

 1+

n 

i

1 

i=1

, κ  κ1

 1+

n 

i=1

i

1 

 1−ΦLi ΦL i

1−βLi βLi

κ  κ1

,

⎞  ⎟ ⎟ ⎟ κ  κ1 ⎠ (1)

688

J. Aldring et al.

n    i ´ ´ ´ ´ L´i LDF DW G L1 , L2 , L3 , . . . , Ln =

⎛  ⎜ ⎜ =⎜ ⎝

i=1



n 

1+

i

1 

i=1

  1+

n 

i=1

i

1 

Δ Li 1−ΔLi

αL i 1−αLi

κ  κ1

,1 −

 1+

n 

i

1 

i=1

,1 − κ  κ1

 1+

T

n 

i=1

i

1 

 1−ΦLi ΦL i

1−βLi βLi

κ  κ1

,

⎞  ⎟ ⎟ ⎟ 1  κ κ ⎠

where  = (1 , 2 , . . . n ) be the weight vector of L´i , i > 0 and

n  i=1

(2) i = 1.

The properties of the developed LDFDWA and LDFDWG aggregation operators are mentioned as follows: Theorem 2. Let L´i = (ΔLi , ΦLi  , αLi , βLi ) ∀i = 1, 2, 3, . . . , n be a collection ´ Then LDFNs which are identical, i.e. L´i = L∀i.   ´ – LDF DW A L´1 , L´2 , L´3 , . . . , L´n = L   ´ – LDF DW G L´1 , L´2 , L´3 , . . . , L´n = L. Theorem 3. Let L´i = (ΔLi , ΦLi  , αLi ,βLi ) ∀i = 1, 2, 3, . . . , n be a collection LDFNs. Let L´+ = max L´1 , L´2 , L´3 , . . . L´n . Then   – L´− ≤ LDF DW A L´1 , L´2 , L´3 , . . . , L´n ≤ L´+   – L´− ≤ LDF DW G L´1 , L´2 , L´3 , . . . , L´n ≤ L´+ . Theorem 4. Let L´i = (ΔLi , ΦLi  , αLi , βLi ) and P´i = (ΔPi , ΦPi , αPi , βPi ) ∀i = 1, 2, 3, . . . , n be two collection of LDFNs, and L´i ≤ P´i . Then,     – LDF DW A L´1 , L´2 , L´3 , . . . , L´n ≤ LDF DW A P´1 , P´2 , P´3 , . . . , P´n     – LDF DW G L´1 , L´2 , L´3 , . . . , L´n ≤ LDF DW G P´1 , P´2 , P´3 , . . . , P´n

A Decision Making Approach Using Linear Diophantine Fuzzy Sets

4

689

Weighted Dice Similarity Measure of LDFSs

Theorem 5. Let L´i = (ΔLi , ΦLi  , αLi , βLi ) and P´i = (ΔPi , ΦPi  , αPi , βPi ) ∀i = 1, 2, 3, . . . , n be two collection of LDFNs. Then a weighted dice similarity measure (WDSM) of LDFS is given by ´ ´ dw LDF S (Li , Pi )

⎞ ⎛   n 2 Δ L i Δ P i + ΦL i ΦP i + α L i α P i + β L i β P i 1   ⎠ = wi ⎝  2 2 n i=1 Δ2L + Φ2L + α2L + βL + Δ2P + Φ2P + α2P + βP i

i

i

i

i

i

i

i

(3) which satisfy the conditions: ´ ´ (i). 0 ≤ dw LDF S (Li , Pi ) ≤ 1. ´i , P´i ) = dw ´ ´ (ii). dw ( L LDF S LDF S (Pi , Li ) w ´ ´ ´ (iii). dLDF S (Li , Pi ) = 1 if Li = P´i Proof. The proof of the above axioms as follows: – From the Eq. (3), it is clear that

 ´ ´ dw ≤ 0, and Δ2Li + Φ2Li + αL2 i + βL2 i + (Δ2Pi + Φ2Pi + LDF S (Li , Pi ) 2 + βP2 i ) ≥ 2 (ΔLi ΔPi + ΦLi ΦPi + αLi αPi + βLi βPi ). Thus, according to αP i ´ ´ the ineqality A2 + B2 ≥ 2AB, we get 0 ≤ dw LDF S (Li , Pi ) ≤ 1. – The Eq. (3) also proves with axiom (ii). – If L´i = P´i i.e. ΔLi = ΔPi , ΦLi = ΦPi , αLi = αPi , βLi = βPi . We have n

1 ´ ´ dw wi LDF S (Li , Pi ) = n i=1 Remark 1. If we take wi = reduced to DSM for LDFSs.



 2 Δ2Li + Φ2Li + αL2 i + βL2 i  = 1.

2 Δ2Li + Φ2Li + αL2 i + βL2 i



T

1 1 1 1 w1 , w2 , w3 , . . . wn ´ ´ i.e. dw LDF S (Li , Pi ) =

, then the WDSM for LDFS dLDF S (L´i , P´i ).

Remark 2. The DSM and WDSM for LDFS are stated as follow: – dLDF S (L´i , P´i ) = 1 − dLDF S (L´i , P´i ). w ´ ´ ´ ´ – dw LDF S (Li , Pi ) = 1 − dLDF S (Li , Pi ).

5

Decision Making Model Using LDFSs

The developed LDFDWA and LDFDWG aggregation operators are used to construct a decision making model based on dice similarity measure. Consider m number of alternatives (Ri ) and n number of attributes (Cj ) with the weight vectors of criteria αi where i = 1, 2, 3, . . . , m and j = 1, 2, 3, . . . , n. Then the decision making approach can described as follows.  Step 1. Construct linear diophantine fuzzy decision matrix DM = eκij m×n where ekij denotes the LDFNs of alternative Ri on Cj given by experts Eκ .

690

J. Aldring et al.

Step 2. Find the fused values of DM = [eij ]m×n using either Eq. (1) or (2). Step 3. Fix the primary reference LDFNs to each criteria that is ∇j (r). ˙ ˙ and Step 4. Calculate the WDSM of each eij with corresponding ∇j (r) determine Dice Similarity Fuzzy Decision Matrix (DSFDM). Step 5. Find out the alternatives and their most influence criteria based on the WDSM values.

6

Illustration of Medical Diagnosis Using LDF Decision Making Model

A group of (Ri ; i = 1, 2, 3, 4) covid-19 affected patients have been taken and their main symptoms such as C1 -Body pain, C2 -Breathing problem, C3 -Cough are considered as criteria. The treatment has to be given to the patients according to their symptoms level. So, patients have been evaluated by symptoms in two interval of time. Then the information is converted in to LDFNs and the two LDF decision matrices that are given in step 1 are constructed. Also, the chance of high risk parameters values are taken as primary reference parameters that are also changed in to LDFNS and given in step 3. Then, we proceed to decision making process to find the high risk of patients based on the chosen criteria.  Step 1. The linear diophantine fuzzy decision matrix M κ = eκij m×n where ekij denotes the LDFNs of alternative Ri on Cj given by experts E1 and E2 whose weight vector is 0.5, 0.5 respectively. C1

⎛

C2

R1 [0.5, 0.3], [0.3, 0.4] ⎜ R2 ⎜ 1 ⎜ [0.4, 0.6], [0.4, 0.5] M = ⎜ R3 ⎝ [0.6, 0.4], [0.5, 0.4] R4 [0.5, 0.6], [0.6, 0.4]

[0.3, 0.6], [0.2, 0.8] [0.2, 0.9], [0.4, 0.6] [0.3, 0.5], [0.7, 0.3] [0.4, 0.6], [0.4, 0.5]

C1

⎛

C2

R1 [0.4, 0.6], [0.4, 0.5] ⎜ R2 ⎜ 2 ⎜ [0.5, 0.6], [0.3, 0.7] M = ⎜ R3 ⎝ [0.5, 0.6], [0.6, 0.4] R4 [0.4, 0.6], [0.5, 0.4]

[0.2, 0.8], [0.3, 0.7] [0.3, 0.7], [0.4, 0.5] [0.5, 0.7], [0.4, 0.4] [0.6, 0.5], [0.5, 0.4]

C3

⎞ [0.7, 0.2], [0.7, 0.3] ⎟ [0.5, 0.7], [0.5, 0.4] ⎟ ⎟ ⎟ [0.8, 0.3], [0.7, 0.2] ⎠ [0.6, 0.5], [0.4, 0.4] C3

⎞ [0.5, 0.4], [0.4, 0.5] ⎟ [0.6, 0.5], [0.5, 0.3] ⎟ ⎟ ⎟ [0.6, 0.5], [0.5, 0.4] ⎠ [0.4, 0.5], [0.4, 0.4]

Step 2. Then the fused values of M • = [eij ]m×n using Eq. (1) is given as follows: M• R1

⎛

C1

C2

C3

[0.46, 0.37], [0.36, 0.44]

[0.26, 0.67], [0.26, 0.74]

[0.64, 0.25], [0.63, 0.36]

⎜ R2 ⎜ ⎜ [0.46, 0.60], [0.36, 0.57] ⎜ R3 ⎝ [0.56, 0.46], [0.56, 0.40] R4

[0.46, 0.60], [0.40, 0.54]

⎞

[0.43, 0.57], [0.63, 0.34]

⎟ [0.56, 0.57], [0.50, 0.34] ⎟ ⎟ ⎟ [0.75, 0.36], [0.64, 0.25] ⎠

[0.54, 0.54], [0.46, 0.44]

[0.54, 0.50], [0.40, 0.40]

[0.26, 0.76], [0.40, 0.54]

A Decision Making Approach Using Linear Diophantine Fuzzy Sets

691

Step 3. The primary reference LDFNs to each criteria i.e. ∇j (r). ˙ ˙ : [0.6, 0.2], [0.5, 0.3] , ∇1 (r)

∇2 (r) ˙ : [0.4, 0.6], [0.5, 0.4] ,

˙ : [0.7, 0.5], [0.8, 0.3] ∇3 (r) ˙ are Step 4. The WDSM of each elements M • = [eij ] with corresponding ∇j (r) calculated and framed dice similarity fuzzy decision matrix (DSFDM). R1 DSF DM = [xij ]m×n =

⎛

∇1 /C1

∇2 /C2

∇3 /C3

0.9380

0.9039

0.9603

⎞

⎜ R2 ⎜ ⎜ 0.8466 ⎜ R3 ⎝ 0.9516

0.9628 0.9878

⎟ 0.9533 ⎟ ⎟ ⎟ 0.9809 ⎠

R4

0.9867

0.9156

0.8917

Step 5. The alternatives (Ri ) and their most influence criteria Cj based on the WDSM values are listed as follows: R1 ≈ ∇3 /C3 , R2 ≈ ∇2 /C2 , R3 ≈ ∇2 /C2 , R4 ≈ ∇2 /C2 .

7

Conclusion

This paper has defined the arithmetic operation of LDFS using Dombi t-norm and t-conorm. In addition the weighted dice similarity measure of LDFSs are discussed. Then, Dombi mean aggregation operators of LDFSs is used to construct a decision making method. Also weighted dice similarity measures and their properties are examined and validated with remarks. Finally, the proposed method has been implemented to medical diagnosis problem. In future research, Einstien and Hamy mean operations are to be explored in LDFSs in order to develop novel decision making techniques.

References 1. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965) 2. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986) 3. Yager, R.R.: Pythagorean fuzzy subsets. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Edmonton, Canada, pp. 57–61 (2013) 4. Senapati, T., Yager, R.R.: Fermatean fuzzy sets. J. Ambient. Intell. Humaniz. Comput. 11(2), 663–674 (2019). https://doi.org/10.1007/s12652-019-01377-0 5. Riaz, M., Hashmi, M.R.: Linear Diophantine fuzzy set and its applications towards multi-attribute decision-making problems. J. Intell. Fuzzy Syst. 37(4), 5417–5439 (2019) 6. Riaz, M., Hashmi, M.R., Pamucar, D., Chu, Y.M.: Spherical linear Diophantine fuzzy sets with modeling uncertainties in MCDM. Comput. Model. Eng. Sci. 126(3), 1125–1164 (2021) 7. Kamacı, H.: Complex linear Diophantine fuzzy sets and their cosine similarity measures with applications. Complex Intell. Syst., 1–25 (2021)

692

J. Aldring et al.

8. Parimala, M., Jafari, S., Riaz, M., Aslam, M.: Applying the Dijkstra algorithm to solve a linear diophantine fuzzy environment. Symmetry 13(9), 1616 (2021) 9. Iampan, A., Garc´ıa, G.S., Riaz, M., Athar Farid, H.M., Chinram, R.: Linear diophantine fuzzy Einstein aggregation operators for multicriteria decision-making problems. J. Math. 2021 (2021)

IoT Platform Selection Using Interval Valued Intuitionistic Fuzzy TOPSIS Sezi Çevik Onar, Cengiz Kahraman, and Başar Öztayşi(&) Industrial Engineering Department, Istanbul Technical University, 34367 Maçka, Istanbul, Turkey {cevikse,kahramanc,oztaysib}@itu.edu.tr

Abstract. The Internet of Things (IoT) platform selection is one of the important steps that leads to digital transformation. The companies should consider multiple factors such as return of investment, the flexibility to change the IoT platform, the future performance, sustainability of the platform, the maturity level of IoT platform, security of the IT platform, the support services provided by the platform, previous relations with the IoT platform provider company, the strength of IoT ecosystem, the scope of the services, and usability of the IoT platform. Evaluating these factors is a complex process that requires ambiguous and subjective judgments. Interval valued intuitionistic fuzzy sets enable dealing with ambiguous and subjective evaluations. In this study we developed an interval valued intuitionistic fuzzy TOPSIS method for evaluating IoT Platforms. In order to show the applicability of the proposed methodology, an illustrative example is provided. Keywords: IoT platform selection
MCDM fuzzy sets
TOPSIS
Digital transformation


Interval valued intuitionistic

1 Introduction The role of the IoT in business in the world is increasing, the market value of IoT is growing more than two digits every year with an acceleration. The advances in IoT technologies makes it easier for the firms to utilize IoT technologies. The main challenge for an IoT system adoption is to use the solutions that are safe and adaptive to the other systems of the firm. While considering an IoT solution, not only problem specific issues but also the future challenges such as adaptation and scalability should be taken into account. In the world, many companies are starting their first IoT projects. The projects can be a part of many subjects such as designing a smart plant, autonomous car, or smart energy system. In most of these companies using a modular IoT platform for managing the data is considered for supporting the application development process and providing analytics. IoT platforms usually provide lower costs, a fast application opportunity with professional support. The different layers of IoT platforms provide various solutions to the customers. The layers of IoT platforms are Connectivity Service, Device Management Services, Data Storage Services, Data Processing Services, Visualization Services, Integration Services, and Security Services. Although it may © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 693–701, 2022. https://doi.org/10.1007/978-3-031-09176-6_77

694

S. Ç. Onar et al.

provide these befits, some IoT projects are not successful due to the problems such as lack of match with the platform and the companies’ needs, problems due to scaling and adaptation to the other systems. In the market there not only big IoT platform providers such as Microsoft Azure IoT, Amazon AWS IoT, PTC Thingworx, or Siemens Mindsphere. There are also the small players, new startups that provide various solutions to the companies. Thus, despite its potential benefits selecting the right IoT platform is a complex process that involves multiple criteria and necessitates subjective evaluations of the alternatives based on these criteria. Interval valued intuitionistic fuzzy sets enable us to define membership and nonmembership values independently as an interval (Kahraman et al. 2019, 2016; Oztaysi et al. 2019a, 2019b, 2017). This flexibility in defining the uncertainty and imprecision enable decision makers represent their hesitancy in the evaluation of a concept. Especially, when the decision makers struggle evaluating subjective aspects of complex problems interval valued intuitionistic fuzzy sets can be utilized. In this study, we have used interval valued intuitionistic fuzzy TOPSIS approach for evaluating IoT platforms. The rest of the paper is organized as follows: In Sect. 2, a literature review on IoT platforms and IoT platform evaluation criteria based on the literature review is give. Section 3 summarizes the utilized methodology. In Sect. 4 an illustrative case study is provided. Last section concludes and gives further suggestions.

2 Literature Review and the IoT Platform Evaluation Criteria IoT Platforms does not only attract industry but also the academicians. In the Scopus database we have search “IoT Platforms” in the abstract, keywords and title, a total of 2667 paper have focused on IoT platforms. Figure 1 shows the number of studies that focus on IoT per year.

600 500 400 300 200 100 0 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Fig. 1. The number of studies that focus on IoT (based on Scopus Database)

IoT Platform Selection

695

Figure 2 shows the top 5 articles that publish studies based on IoT platforms. The papers in Sensors Switzerland, Sensors, IEEE Access, and Electronics Switzerland are the articles that mainly focus on technical aspects of IoT Platforms. In IEEE Internet of Things Journal not only just the technical aspects the other implications of IoT are also evaluated.

Fig. 2. Top 5 articles that publish studies based on IoT platforms

In the literature, several studies focus on the IoT project evaluation. Some of the studies analyzed just one of the aspects. For instance, Hasan et al. (2022) try to reveal the threats that could weaken the integrity, privacy, and security of internet of medical things systems. The results show that of internet of medical things systems are vulnerable to the security attacks. Bharathi et al. (2019) evaluate the security risks of IoT platforms by using analytical hierarchy process. The second group tries to evaluate IoT platforms with a holistic perspective. De Nardis et al. conduct a review on the existing IoT platforms. The platforms are evaluated based on communication protocols, data visualization, data processing, integration with external services and security. Based on this review, authors develop a framework for IoT project selection by using seven criteria. Famideh et al. (2021) develop a framework for analyzing IoT platforms. They applied this framework in the IoT platform selection for smart cities. Mijuskovic et al. (2021) design a comparison frameworks for IoT platforms by considering both functional and non-functional requirements. The authors applied a AHP based methodology for evaluating five different IoT platforms namely, Azure, AWS, SaS; Thingworx; Kaa IoT are studied to evaluate the performance of the framework. Mijuskovic et al. (2021) claim that while selecting IoT platforms comparative analysis methods should be used. In this study, five IoT platforms namely Azure, AWS, SaS, ThingWor and Kaa IoT are evaluated by using analytical hierarchy process. Mijac et al. (2021) utilized Promethee method for selecting an IoT platform for smart cities. Lin et al. (2020) develop a new probabilistic linguistic best-worst method for defining the criteria weights for IoT platform evaluation. They combined this methodology with probabilistic linguistic TODIM method for evaluating IoT platforms. Kondratenko et al. (2019) utilize

696

S. Ç. Onar et al.

Mamdani-type fuzzy rule based system for evaluating IoT platforms. The criteria are selected as reliability, dependability, safety, and security of IoT platforms. Based on the literature review the criteria for evaluating IoT alternatives can be gathered as return of investment (RIO), the flexibility to change the IoT platform (flexibility), the future performance (future), sustainability of the platform (sustainability), the maturity level of IoT platform (maturity), security of the IT platform (security), the support services provided by the platform (support), previous relations with the IoT platform provider company (relations), the strength of IoT ecosystem (strength) the scope of the services (scope), usability of the IoT platform (usability).

3 Interval Valued Intuitionistic Fuzzy TOPSIS ~IVIF be Interval-valued intuitionistic fuzzy set with a membership function lX~ ð xÞ Let X and non-membership function vX~ ð xÞ are closed intervals where the starting and ending þ þ  points are shown by l ~ ð xÞ; lX ~ ð xÞ; tX ð xÞ and tV ~ ð xÞ, respectively (Cevik Onar et al. X 2015). n h i h i o ~IVIF ¼ \x; l~ ð xÞ; l ~þ ð xÞ ; t~ ð xÞ; t ~þ ð xÞ [ jx 2 X ; X X X X X

ð1Þ

 where 0  lX~þ ð xÞ þ tX~þ ð xÞ  1; l ~ ð xÞ can ~ ð xÞ  0; tX ~ ð xÞ  0: The hesitancy degree pX X be obtained as in Eq. (2).

   pX~ ð xÞ ¼ 1  lX~ ð xÞ  vX~ ð xÞ ¼ 1  lX~þ ð xÞ  tX~þ ð xÞ; 1  l ð x Þ  t ð x Þ ~ ~ X X   ¼ plX~ ð xÞ; puX~ ð xÞ ~¼ Let X

ð2Þ

h i h i h i h i þ  þ ~ ¼ l~ ; l þ ; v~ ; v þ ; l ; v l ; v and Y be two interval~ ~ ~ ~ X X X Y Y X Y~ Y~

~ and Y~ can be defined as valued intuitionistic fuzzy numbers. The operations on X follows: ~  Y~ ¼ X ~  Y~ ¼ X

h i h i    þ þ þ þ   þ þ l ; ~ lY~ ; lX ~ vY~ ; vX ~ þ lY~  lX ~ lY~ ; vX ~ vY~ X þ lY~  lX h

i h i  þ þ     þ þ þ þ l l ; l l þ v  v v ; v þ v  v v ; v : ~ Y~ ~ ~ Y~ ~ Y~ ~ ~ Y~ X X Y~ X X X Y~ X

ð3Þ ð4Þ

~IVIF can be given in Eq. (5). The score function of X þ   l þ lX~þ  v ~  vX ~ X ~IVIF ¼ X~ S X 2

ð5Þ

IoT Platform Selection

697

~IVIF can be given in Eq. (6). The accuracy function of X þ   l þ lX~þ þ v ~ þ vX ~ X ~IVIF ¼ X~ : A X 2

ð6Þ

Interval valued intuitionistic fuzzy weighted aggregation operator can be given in Eq. (7) IVIFWAw ð~x1 ; ~x2 ; . . .; ~xn Þ ¼

Dh

1

Yn  n

1  l i

w i

;1 

Yn  i¼1

1  liþ

iE Yn w i i h Y n ðv Þwi ; i¼1 ðviþ Þwi ; i¼1 i

ð7Þ

    þ þ where ~xi ¼ l ði ¼ 1; 2; . . .; nÞ and the weights are w ¼ ðw1 ; w2 ; . . .; i ; li ; vi ; vi n P wn ÞT , wi 2 ½0; 1, wi ¼ 1: i¼1

The steps of Interval Valued Intuitionistic Fuzzy TOPSIS can be given as follows (Oztaysi et al. 2017):   ~ k for each decision maker (k = 1, ... , K) Step 1. Define the decision matrix DM by using the scale in Table 1.

C1 ~ k ¼ C2 DM .. . Cn

A1       þ þ ; v11k ; v11k l11k ; l11k      þ þ ; v l21k ; l21k 21k ; v21k .. .      þ þ ; v ln1k ; ln1k n1k ; vn1k

A2     þ þ l 12k ; l12k ; v12k ; v12k      þ þ l22k ; l22k ; v 22k ; v22k .. .      þ þ ln2k ; ln2k ; v n2k ; vn2k 






Am       þ þ


l1mk ; l1mk ; v1mk ; v1mk      þ þ


l2mk ; l2mk ; v 2mk ; v2mk .. .. . .      þ þ


lnmk ; lnmk ; v nmk ; vnmk

ð8Þ

where n denotes the number of criteria (i = 1, … , n) and m denotes the number of alternatives (j = 1, … , m).

Table 1. Interval valued intuitionistic fuzzy linguistic scale Linguistic terms Absolutely Low (AL) Very Low (VL) Low (L) Medium Low (ML) Approximately Equal (AE) Medium High (MH) High (H) Very High (VH) Absolutely High (AH)

Membership & Non-membership values ([0.10, 0.25], [0.65, 0.75]) ([0.15, 0.30], [0.60, 0.70]) ([0.20, 0.35], [0.55, 0.65]) ([0.25, 0.4]), [0.50, 0.60]) ([0.45, 0.55], [0.30, 0.45]) ([0.50, 0.60], [0.25, 0.40]) ([0.55,0.65], [0.20, 0.35]) ([0.60,0.70], [0.15,0.30]) ([0.65,0.75], [0.10,0.25])

Step 2. Define the fuzzy positive ideal solution ( g PIS xk ) and fuzzy negative ideal g solution ( NIS xk ) for each decision maker by using Eq. (5) and (6).

S. Ç. Onar et al.

698

g PIS xk ¼ g xk ¼ NIS

            þ       þ   þ þ þ l1 k ; l1þ k ; v 1 k ; v1 k ; l2 k ; l2 k ; v2 k ; v2 k ;


; ln k ; ln k ; vn k ; vn k



                þ þ þ þ þ þ : l 1 k ; l1 k ; v1 k ; v1 k ; l2 k ; l2 k ; v2 k ; v2 k ;


; ln k ; ln k ; vn k ; vn k

ð9Þ ð10Þ

    þ  þ is the interval valued intuitionistic fuzzy evaluation of where l i k ; li k ; vi k ; vi k the ith criterion associated with the highest score and accuracy functions.      þ li k ; liþ k ; v ; v is the interval valued intuitionistic fuzzy evaluation of the ith   i k i k criterion associated with the minimum score and accuracy functions. Step 3. Calculate the distances between the jth alternative to positive and negative ideal solutions by using Eq. (11) and (12).

k

Dj ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  n 2  2  2  2  2  2

1X þ þ   wT l  þ lijk  liþ k þ v þ vijk  viþ k þ plijk  pli k þ puijk  pui k ijk  li k ijk  vi k 2 i¼1 i

ð11Þ 

k

Dj ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  n 2  2  2  2  2  2

1X þ þ   wT l  þ lijk  liþ k þ v þ vijk  viþ k þ plijk  pli k þ puijk  pui k ijk  li k ijk  vi k 2 i¼1 i

ð12Þ Step 4. Aggregate the distances for the decision makers by using Eq. (13) and (14) for each alternative j. D j ¼

 XK  k k D k j k¼1

ð13Þ

D j ¼

 XK   k k D k j k¼1

ð14Þ

where j = 1, 2, … , m; k = 1, 2, … , K, and kk is the weight of decision maker k and P 0  kk  1, Kk¼1 kk ¼ 1. Step 5. Obtain the CCj closeness coefficient of each alternative by using Eq. (15) and rank the alternatives based on closeness coefficient values. CCj ¼

D j ; j ¼ 1; 2; . . .; m D j þ Dj

ð15Þ

4 Evaluation of IoT Platforms by Using Interval Valued Intuitionistic Fuzzy TOPSIS In this study, in order to illustrate the applicability of the proposed methodology, we have evaluated five IoT platforms by using the criteria namely, return of investment (RIO), the flexibility to change the IoT platform (flexibility), the future performance (future), sustainability of the platform (sustainability), the maturity level of IoT

IoT Platform Selection

699

platform (maturity), security of the IT platform (security), the support services provided by the platform (support), previous relations with the IoT platform provider company (relations), the strength of IoT ecosystem (strength) the scope of the services (scope), usability of the IoT platform (usability). Table 2. Shows the linguistic evaluations of IoT platform alternatives. Table 2. Linguistic evaluations of IoT platform alternatives RIO Flexibility Future Sustainability Maturity Security Support Relations Strength Scope Usability

Alt 1 MH ML MH VH VL VL VL L VH AE ML

Alt 2 VH VL VL L L H AH L H H MH

Alt 3 H H EE AH AL VL VH EE L AE L

Alt 4 AE AL MH H VH VH AE AL VL AH VH

Alt 5 AH ML VH MH AH H EE AE VL MH VH

Table 3 shows the interval valued intuitionistic fuzzy evaluations of IoT platform alternatives. Table 3. Interval valued intuitionistic fuzzy evaluations of IoT platform alternatives Alt 1

Alt 2

Alt 3

Alt 4

Alt 5

RIO

([0.5,0.6],[0.25,0.4])

([0.6,0.7],[0.15,0.3])

([0.55,0.65],[0.2,0.35])

([0.45,0.55],[0.3,0.45])

([0.65,0.75],[0.1,0.25])

Flexibility

([0.25,0.4],[0.5,0.6])

([0.15,0.3],[0.6,0.7])

([0.55,0.65],[0.2,0.35])

([0.1,0.25],[0.65,0.75])

([0.25,0.4],[0.5,0.6])

Future

([0.5,0.6],[0.25,0.4])

([0.15,0.3],[0.6,0.7])

([0.5,0.5],[0.5,0.5])

([0.5,0.6],[0.25,0.4])

([0.6,0.7],[0.15,0.3]) ([0.5,0.6],[0.25,0.4])

Sustainability

([0.6,0.7],[0.15,0.3])

([0.2,0.35],[0.55,0.65])

([0.65,0.75],[0.1,0.25])

([0.55,0.65],[0.2,0.35])

Maturity

([0.15,0.3],[0.6,0.7])

([0.2,0.35],[0.55,0.65])

([0.1,0.25],[0.65,0.75])

([0.6,0.7],[0.15,0.3])

([0.65,0.75],[0.1,0.25])

Security

([0.15,0.3],[0.6,0.7])

([0.55,0.65],[0.2,0.35])

([0.15,0.3],[0.6,0.7])

([0.6,0.7],[0.15,0.3])

([0.55,0.65],[0.2,0.35])

Support

([0.15,0.3],[0.6,0.7])

([0.65,0.75],[0.1,0.25])

([0.6,0.7],[0.15,0.3])

([0.45,0.55],[0.3,0.45])

([0.5,0.5],[0.5,0.5])

Relations

([0.2,0.35],[0.55,0.65])

([0.2,0.35],[0.55,0.65])

([0.5,0.5],[0.5,0.5])

([0.1,0.25],[0.65,0.75])

([0.45,0.55],[0.3,0.45])

Strength

([0.6,0.7],[0.15,0.3])

([0.55,0.65],[0.2,0.35])

([0.2,0.35],[0.55,0.65])

([0.15,0.3],[0.6,0.7])

([0.15,0.3],[0.6,0.7])

Scope

([0.45,0.55],[0.3,0.45])

([0.55,0.65],[0.2,0.35])

([0.45,0.55],[0.3,0.45])

([0.65,0.75],[0.1,0.25])

([0.5,0.6],[0.25,0.4])

Usability

([0.25,0.4],[0.5,0.6])

([0.5,0.6],[0.25,0.4])

([0.2,0.35],[0.55,0.65])

([0.6,0.7],[0.15,0.3])

([0.6,0.7],[0.15,0.3])

The results of the analysis and the ranking of alternatives are given in Table 4. Table 4. Evaluation results for IoT platform alternatives D(PIS) D(NIS) CC Rank

Alt 1 0.37 0.33 0.47 4.00

Alt 2 0.52 0.12 0.18 5.00

Alt 3 0.38 0.42 0.53 3.00

Alt 4 0.31 0.42 0.57 2.00

Alt 5 0.2 0.49 0.71 1.00

700

S. Ç. Onar et al.

The results show that Alternative 5 is the best alternative whereas Alternative 2 is the worst alternative.

5 Conclusion and Further Suggestions Among various IoT platforms, it is very hard for the companies to select the appropriate IoT platform. The success of these projects effects the digital transformation journey of the firms. IoT platform selection process involve many criteria that can only be evaluated by human judgements. Thus, evaluating IoT alternatives based on subjective judgements is a complex process. In this study, we have proposed an interval valued intuitionistic TOPSIS approach for evaluating IoT platforms. We have utilized eleven criteria for evaluating the alternatives. In the illustrative example we show how the proposed methodology can be applied for evaluating IoT platforms. For the future studies, the proposed methodology should be applied to a real case problem. A sensitivity analysis and comparing the methodology with other MCDM methods will be beneficial. Other extensions of fuzzy sets such as Type 2, hesitant, Pythagorean, spherical and picture fuzzy TOPSIS methods can be applied and the results can be compared.

References Bharathi, S.V.: Forewarned is forearmed: assessment of IoT information security risks using analytic hierarchy process. Benchmarking 26(8), 2443–2467 (2019) De Nardis, L., Mohammadpour, A., Caso, G., Ali, U., Di Benedetto, M.-G.: Internet of things platforms for academic research and development: a critical review. Appl. Sci. (Switzerland), 12(4) (2022). Art. no. 2172 Fahmideh, M., Yan, J., Shen, J., Mougouei, D., Zhai, Y., Ahmad, A.: A comprehensive framework for analyzing iot platforms: a smart city industrial experience Smart. Cities 4(2), 588–622 (2021) Hasan, M.K., et al.: A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet-of-Medical-Things. IET Commun. 16(5), 421–432 (2022) Kondratenko, Y., Kondratenko, G., Sidenko, I.: Multi-criteria decision making and soft computing for the selection of specialized IoT platform. Adv. Intell. Syst. Comput. 836, 71– 80 (2019) Lin, M., Huang, C., Xu, Z., Chen, R.: Evaluating IoT platforms using integrated probabilistic linguistic MCDM method. IEEE Internet Things J. 7(11), 11195–11208 (2020). Art. no. 9099246 Mijač, T., Pašalić, I.N., Tomat, L.: Selection of IoT platforms in smart cities: multicriteria decision making. In: Proceedings of the 16th International Symposium on Operational Research in Slovenia, SOR 2021, pp. 35–40 (2021) Mijuskovic, A., Ullah, I., Bemthuis, R., Meratnia, N., Havinga, P.: Comparing apples and oranges in IoT context: a deep dive into methods for comparing IoT platforms. IEEE Internet Things J. 8(3), 1797–1816 (2021). Art. no. 9169714 Kahraman, C., Oztaysi, B., Cevik Onar, S.: Interval-valued intuitionistic fuzzy confidence intervals. J. Intell. Syst. 28 (2), 307–319 (2019)

IoT Platform Selection

701

Oztaysi, B., Onar, S.C., Kahraman, C.: Performance measurement model for software development teams using interval-valued intuitionistic fuzzy analytic hierarchy process. J. Multiple-Valued Logic Soft Comput. 33(4–5), 321–339 (2019) Oztaysi, B., Onar, S.C., Goztepe, K., Kahraman, C.: A multi-expert interval-valued intuitionistic fuzzy location selection for the maintenance facility of armoured vehicles. J. Multiple-Valued Logic Soft Comput. 32(1–2), 149–173 (2019) Oztaysi, B., Cevik Onar, S., Kahraman, C., Yavuz, M.: Multi-criteria alternative-fuel technology selection using interval-valued intuitionistic fuzzy sets. Transp. Res. D Transp. Environ. 53, 128–148 (2017) Oztaysi, B., Onar, S.C., Goztepe, K., Kahraman, C.: Evaluation of research proposals for grant funding using interval-valued intuitionistic fuzzy sets. Soft. Comput. 21(5), 1203–1218 (2015). https://doi.org/10.1007/s00500-015-1853-8 Kahraman, C., Onar, S.C., Oztaysi, B.: A comparison of wind energy investment alternatives using interval-valued intuitionistic fuzzy benefit/cost analysis. Sustainability (Switzerland) 8 (2) (2016). Art. no. 118 Cevik Onar, S., Oztaysi, B., Otay, I., Kahraman, C.: Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy 90, 274–285 (2015)

Interval-Valued Pythagorean Fuzzy AHP&TOPSIS for ERP Software Selection Tuğba Dalyan1(&)

, Irem Otay2

, and Mehmet Gülada1

1

Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Eski Silahtarağa Elektrik Santrali, Istanbul Bilgi University, Eyüpsultan, 34060 Istanbul, Turkey [email protected], [email protected] 2 Faculty of Engineering and Natural Sciences, Department of Industrial Engineering, Eski Silahtarağa Elektrik Santrali, Istanbul Bilgi University, Eyüpsultan, 34060 Istanbul, Turkey [email protected]

Abstract. The selection of an Enterprise Resource Planning (ERP) system is considerably important since it has effects on the productivity of companies. This paper aims to choose the best decision for ERP software over many conflicting criteria by using Pythagorean fuzzy (PF) Analytic Hierarchy Process (AHP) that integrated with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed model includes a hierarchical structure with four main criteria which are finance, technology, usability and corporate, twenty two sub-criteria and several alternatives. In this study, the evaluations are expressed using interval-valued PF sets that consist of membership and non-membership values, where the square sum of these degrees is at most “1”. The weights of the criteria are computed using interval-valued PF AHP. Then, fuzzy TOPSIS method is utilized to evaluate alternatives by considering distances of alternatives to negative and positive ideal solutions (NIS, PIS), respectively. This attempt using interval-valued PF AHP-TOPSIS is deemed to be a significant contribution toward ERP selection problem. Keywords: Pythagorean fuzzy AHP MCDM
ERP software selection


Pythagorean fuzzy TOPSIS
Fuzzy

1 Introduction Organizations seek out any technological advantages over their competitors in marketplace through a smart integration of digital technologies, processes and competencies across all functions. Nowadays, Enterprise Resource Planning (ERP) software which is an essential component of business or organization, provides to leverage a suite of integrated core business processes such as sales, production, finance, purchasing, logistics, and human resources and to streamline and automate these processes. Statista reports that revenue in the ERP Software segment is reached to US $47,252.15M in 2022 and it is projected to be closer to US$58,420.29M by 2025. It shows that selecting the most suitable ERP has gained considerable importance with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 702–710, 2022. https://doi.org/10.1007/978-3-031-09176-6_78

Interval-Valued Pythagorean Fuzzy AHP&TOPSIS

703

the wide usage of ERP since it improves accuracy and productivity, eliminates redundancies with integrating processes, provides reports of real-time data from single database, facilitate synchronization and access information for all parties and improves satisfaction with quick response rates. On the other hand, implementation of ERP systems seems a risky and important investment because of the high cost, adaptation difficulties and many complexities. Many companies have spent millions of dollars and employed a lot of man-hours for implementing and adapting to the ERP system. For these reasons, selection of the most appropriate ERP software by scrutinizing criteria provided by different ERP providers is very crucial for many companies. ERP software selection is a multiple-criteria decision-making (MCDM) problem. There have been several studies on ERP selection based on different learning mathematical methods such as AHP, fuzzy AHP, fuzzy Analytic Network Process (ANP), both AHP and TOPSIS method in literature. In this study, we proposed a model to choose the best decision for ERP software over many conflicting criteria by using Interval-valued PF AHP that integrated with PF TOPSIS method. The model is designed as a hierarchical structure with four main criteria, twenty two sub-criteria and four alternatives. The weights of the criteria are calculated using interval-valued PF AHP. Then, PF TOPSIS method is utilized to evaluate alternatives by considering distances of alternatives to negative and positive ideal solutions (NIS, PIS). As final step, we computed the closeness coefficient ratios for each alternative and made the final decision based on the descending values. To the best of our knowledge, it is the first attempt for selecting ERP software by using interval-valued PF AHP-TOPSIS methodology. The rest of this paper is as follows: In Sect. 2, previous studies are described. In Sect. 3, preliminaries are presented. In Sect. 4, the PF methodology for ERP system evaluation is described. Section 5 includes application part while in Sect. 6 we summarize and conclude the paper.

2 Literature Review Various studies have focused to address the ERP selection problem. The well-known method is the AHP method [1] is applied to solve the ambiguities in the assessment of ERP alternatives and relative importances of the attributes to select a suitable ERP system for an electronics company in Taiwan [2]. In study [3], the authors proposed integrated Fuzzy AHP and TOPSIS for evaluating and selecting the most suitable ERP software as a real world problem in Switzerland. The authors utilized triangular fuzzy numbers in all pairwise comparison matrices through Fuzzy AHP and in experts’ judgments. In other study [4], the authors presented a framework integrating the following methodologies: ANP, Choquet integral and Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) with vendor related, customer related, and software related criteria. A hybrid AHP-TOPSIS methodology is proposed for ERP software selection and implementation to achieve most impact on supply chain performance with 12 criteria and six alternatives [5]. Same methodology is applied to select the most qualified ERP system in Bangladesh [6]. The authors are using MCDM

704

T. Dalyan et al.

for selecting qualified ERP system with seven criteria and four alternatives. Yet another study [7], the author’s utilized interval-valued Pythagorean uncertain linguistic aggregation operators to solve a multiple attribute ERP system selection problem.

3 Preliminaries ~ in which l ~ ð xÞ : X ! ½0; 1 pointing Let X be a fixed set. A Pythagorean fuzzy set Q Q out the membership degree as vQ~ ð xÞ : X ! ½0; 1 showing the non-membership degree of the element x to P, can be represented as in Eq. (1) [8, 9]: ~ffi Q

nD E o x; lQ~ ð xÞ; vQ~ ð xÞ ; xX where 0  lQ~ ð xÞ2 þ vQ~ ð xÞ2  1

ð1Þ

The hesitancy degree can be written as follows: pQ~ ð xÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  lQ~ ð xÞ2 vQ~ ð xÞ2

ð2Þ

~ ¼ l; v; Q ~ 1 ¼ l1 ; v1 , Q ~ 2 ¼ l2 ; v2 are three Pythagorean Definition 1. Assume that Q Fuzzy Numbers (PFNs). Some PF arithmetic operations are as follows [10, 11]: ~1  Q ~2 ¼ Q

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  l21 þ l22  l21 l22 ; v1 v2

ð3Þ

~1  Q ~2 ¼ Q

 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi l1 l2 ; v21 þ v22  v21 v22

ð4Þ

~ ¼ð kQ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  ð1  l2 Þk ; vk Þðk [ 0Þ

ð5Þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1  ð1  v2 Þk ; ðk [ 0Þ

ð6Þ

~ k ¼ ðlk ; Q

~ ¼ h½lL ; lU ; ½vL ; vU i be an interval-valued Pythagorean fuzzy Definition 2. Let Q number (IVPFN). The hesitancy degrees (pL and pU Þ are:     p2L ¼ 1  l2U þ v2U ; p2U ¼ 1  l2L þ v2L

ð7Þ

~ ¼ ½lL ; lU ; ½vL ; vU  is disDefinition 3. The defuzzification formula for an IVPFN Q played in Eq. (8) [10]. def ðg QÞ ¼

h      1 i l2L þ l2U þ 1  p4L  v2L þ 1  p4U  v2U þ lL lU þ 1  p4L  v2L 1  p4U  v2U ð4Þ 6

ð8Þ

Interval-Valued Pythagorean Fuzzy AHP&TOPSIS

ðw1 ; w2 ; . . .; wn ÞT

~p ¼ Q

   

lLp ; lUp ; vLp ; vUp

an IVPFN and w ¼ n P ~ p (wp 0; be the weight vector of Q wp ¼ 1). Interval-valued

4. Let

Definition

705

be

p¼1

Pythagorean fuzzy weighted average (IVPFWA) and geometric (IVPFWG) operators are given in Eqs. (9–10) [12]: 

2



~ 1; Q ~ 2 ; . . .Q ~ n ¼ \4 1  IVPFWA Q "

1

l2Lp

wp

!1=2 ; 1

p¼1 n Y

w vLpp ;

p¼1



n  Y

n Y

~ 1; Q ~ 2 ; . . .Q ~n ¼ \ IVPFWG Q 2

wp

!1=2 3 5;

ð9Þ

[

p¼1

"



1

l2Up

p¼1

#

w vUpp

n  Y

n Y

w lLpp ;

p¼1

4 1

n  Y

n Y

# w lUpp

p¼1

1  v2Lp

wp

; !1=2 ; 1

p¼1

n  Y

1  v2Up

wp

!1=2 3 5[

p¼1

ð10Þ

4 Proposed PF Methodology Step 1. Determining the problem, main and sub-criteria and possible ERP products. Step 2. Collecting all pair-wise comparison matrices of criteria using the linguistic scale as in Table 1.

Table 1. Linguistic scale with corresponding IVPFNs. Linguistic terms CLI: Certainly Low Importance VLI: Very Low Importance LI: Low Importance BAI: Below Average Importance AI: Average Importance EI: Equal Importance AAI: Above Average Importance HI: High Importance VHI: Very High Importance CHI: Certainly High Importance

lL 0.00 0.10 0.20 0.35 0.45 0.50 0.55 0.65 0.80 0.90

lU 0.00 0.20 0.35 0.45 0.55 0.50 0.65 0.80 0.90 1.00

vL 0.90 0.80 0.65 0.55 0.45 0.50 0.35 0.20 0.10 0.00

vU 1.00 0.90 0.80 0.65 0.55 0.50 0.45 0.35 0.20 0.00

706

T. Dalyan et al.

Step 3. Checking consistency of all pair-wise comparison matrices using Saaty’s consistency analysis concept. Step 4. Calculating weights of the criteria by using PF AHP method. Step 4.1. Finding the differences matrix utilizing Eqs. (11–12): dijL ¼ l2ijL  v2ijU

ð11Þ

dijU ¼ l2ijU  v2ijL

ð12Þ

Step 4.2. Creating interval multiplicative matrix by using Eqs. (13) and (14). sijL ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1000dijL

ð13Þ

sijU ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1000dijU

ð14Þ

Step 4.3. Calculating the indeterminacy value employing Eq. (15).   hij ¼ 1  l2ijU  l2ijL  v2ijU  v2ijL

ð15Þ

Step 4.4. Multiplying the indeterminacy degrees with Interval Multiplicative matrix to obtain the matrix of unnormalized weights. 

sijL þ sijU tij ¼ hij

2

 ð16Þ

Step 4.5. Computing the weights of criteria. Step 4.6. Following the same steps for calculating sub-criteria weights. Step 5. Evaluation of alternatives utilizing PF TOPSIS method. Step 5.1. Collecting Pythagorean fuzzy decision matrices from the expert. (D ¼ ðCj ðXi ÞÞmxn ) employing Table 1. Step 5.2: Obtaining the aggregated weighted IVPF decision matrix 0

Dwagg ¼ ðCj ðXiw ÞÞmxn

ðlw11 ; vw11 ; pw11 Þ B .. ¼@ . ðlwm1 ; vwm1 ; pwm1 Þ

1


ðlw1n ; vw1n ; pw1n Þ C .. .. A . .


ðlwmn ; vwmn ; pwmn Þ

ð17Þ

Step 5.3. Employing score function for estimating the positive and negative ðX PIS ; X NIS Þ ideal solutions (PIS, and NIS), respectively.

X

PIS

¼ ¼

 Cj ; max \ScoreðCj ðXiw ÞÞ [ jj i

¼ 1; 2:::n ; X NIS  w Cj ; min \ScoreðCj ðXi ÞÞ [ jj ¼ 1; 2:::n i

ð18Þ

Interval-Valued Pythagorean Fuzzy AHP&TOPSIS

707

Step 5.4. Calculating the distances from each judgment and PIS and NIS values. 0    2  1 2 þ    C B  lij  lj  C B n  X 2 C B  2   1   þ PIS þ   C; d  xi ; X NIS B wj B þ  vij  vj d xi ; X ¼ C 2 j¼1 C B  @  2  2  A þ   þ  pij  pj  0    2  1 2    B  lij  lj  C C B  n B  2   2  C 1X  vij  v C ð19Þ þ ¼ wj B j C B  2 j¼1 C B   @  2  2  A  þ  pij  p j  Step 5.5: Computing the closeness coefficient ratios ðCCRðXi ÞÞ for each alternative by Eq. (20), and make the final decision based on the descending values of CCR(Xi). CCRðxi Þ ¼

d  ðx

d  ðxi ; X NIS Þ NIS Þ þ d þ ðx ; X PIS Þ i; X i

ð20Þ

5 Application This study analyzes ERP software evaluation problem of a private company. The researchers conducted interviews with the experts working at the company. Based on hierarchical structure and extensive literature review, a hierarchy of the problem designed with four criteria and 22 sub-criteria, is illustrated in Fig. 1. Table 2 lists the pairwise comparison matrix of criteria. When the steps of the IVPF AHP-TOPSIS methodology are followed, the difference and the interval multiplicative matrices are obtained. Then, the criteria weights are computed as illustrated in Table 3. Table 2. Pairwise comparison matrix of main criteria. Criteria C1 C2 C3 C4 C1 EI AAI AAI HI C2 EI AAI HI C3 EI AI C4 EI

708

T. Dalyan et al. Table 3. Results of PF AHP method. Criteria C1 C2 C3 C4

C1 1.000 0.402 0.402 0.152

C2 1.600 1.000 0.402 0.152

C3 1.600 1.600 1.000 0.800

C4 3.402 3.402 0.800 1.000

Priority weights 0.406 0.342 0.139 0.112

Fig. 1. Hierarchy of the problem

As seen in the Table 4, C1 is the most important criterion, the remainings are ranked as C2 > C3 > C4. The same steps are employed to compute the weights of subcriteria. The sub-criteria weights and decision matrix are given in Table 4. Table 4. Linguistic decision matrix. Sub-criteria Weights A1

A2

A3

A4

Sub-criteria Weights A1

A2

A3

A4

C1.1 C1.2 C1.3 C1.4 C1.5 C2.1 C2.2 C2.3 C2.4 C2.5 C3.1

VLI AAI LI AAI HI AAI AAI AAI AI AAI VHI

VHI VHI BAI HI AAI VHI HI AI AAI BAI AAI

AAI HI VHI AAI AI AI AAI BAI AI HI HI

C3.2 C3.3 C3.4 C3.5 C4.1 C4.2 C4.3 C4.4 C4.5 C4.6 C4.7

AAI AI AI VHI HI HI AI AAI AI AI AAI

LI HI VHI HI HI AI HI HI HI LI AI

AI LI AAI BAI AI BAI BAI BAI BAI AAI LI

0.475 0.057 0.209 0.100 0.158 0.515 0.199 0.115 0.104 0.067 0.250

LI BAI VLI AI AI LI HI CHI HI VHI LI

0.071 0.181 0.310 0.189 0.059 0.139 0.370 0.139 0.070 0.084 0.139

HI LI LI AI AI VHI AAI VHI AAI HI CHI

Interval-Valued Pythagorean Fuzzy AHP&TOPSIS

709

In IVPF TOPSIS, initially the linguistic values are transformed into their corresponding IVPFNs. The score function is used to determine PIS and NIS values for each alternative. Afterwards, by using Eq. (19) distances between an alternative and PIS and NIS values are computed as listed in Table 5. Table 5. The distances between each alternative and PIS & NIS values. Sub- Criteria d  ðxi ; X NIS Þ A1 A2 C1.1 0.194 0.000 C1.2 0.000 0.200 C1.3 0.000 0.194 C1.4 0.000 0.110 C1.5 0.000 0.279 C2.1 0.000 0.369 C2.2 0.169 0.000 C2.3 0.743 0.200 C2.4 0.279 0.000 C2.5 0.563 0.200 C3.1 0.000 0.644 C3.2 0.450 0.369 C3.3 0.000 0.279 C3.4 0.000 0.279 C3.5 0.110 0.563 C4.1 0.000 0.279 C4.2 0.563 0.369 C4.3 0.200 0.110 C4.4 0.563 0.200 C4.5 0.200 0.110 C4.6 0.450 0.279 C4.7 0.824 0.369

A3 0.700 0.563 0.363 0.279 0.110 0.644 0.169 0.110 0.110 0.000 0.369 0.000 0.450 0.644 0.369 0.279 0.110 0.369 0.369 0.369 0.000 0.279

A4 0.563 0.369 0.700 0.110 0.000 0.279 0.000 0.000 0.000 0.369 0.450 0.279 0.000 0.369 0.000 0.000 0.000 0.000 0.000 0.000 0.369 0.000

d þ ðxi ; X PIS Þ A1 A2 0.644 0.700 0.563 0.363 0.700 0.644 0.279 0.169 0.279 0.000 0.644 0.363 0.000 0.169 0.000 0.543 0.000 0.279 0.000 0.363 0.644 0.000 0.000 0.169 0.450 0.279 0.644 0.473 0.473 0.000 0.279 0.000 0.000 0.194 0.169 0.279 0.000 0.363 0.169 0.279 0.000 0.279 0.000 0.543

A3 0.000 0.000 0.563 0.000 0.169 0.000 0.000 0.653 0.169 0.563 0.363 0.450 0.000 0.000 0.194 0.000 0.473 0.000 0.194 0.000 0.450 0.653

A4 0.363 0.194 0.000 0.169 0.279 0.473 0.169 0.743 0.279 0.194 0.194 0.279 0.450 0.363 0.563 0.279 0.563 0.369 0.563 0.369 0.169 0.824

Finally, the C CR ðxi Þ scores of the alternatives are derived using Eq. (20) and the final decision is made based on decreasing order of the CCR values as given in Table 6. The results highlight that A3 is the most appropriate ERP software and it is followed by A2, A4 and A1, respectively. Table 6. Results of the proposed PF methodology. Alternatives A1 A2 A3 A4

CCR ðxi Þ 0.325 0.421 0.710 0.376

Rank 4 2 1 3

710

T. Dalyan et al.

6 Conclusion In this paper, PF TOPSIS based on PF AHP has been presented and proposed for solving an ERP selection problem. The proposed methodology has solved the problem successfully considering expert’s opinion. The results show that A3 outperformed with the highest CCR value over the other ERP software alternatives. Consequently, proposed PF methodology is found to be more realistic and providing appropriate results. Moreover, other fuzzy set extensions such as hesitant fuzzy sets, neutrosophic sets, and fuzzy multi-sets can be also used for the further analysis. The results of the study can be compared with other techniques such as fuzzy best-worst method.

References 1. Saaty, T.L.: Decision Making with Dependence and Feedback: The Analytic Network Process. PWS Publications, Pittsburgh (1996) 2. Wei, C.C., Chien, C.F., Wang, M.J.J.: An AHP-based approach to ERP system selection. Int. J. Prod. Econ. 96(1), 47–62 (2005) 3. Kara, S.S., Cheikhrouhou, N.: A multi criteria group decision making approach for collaborative software selection problem. J. Intell. Fuzzy Syst. 26(1), 37–47 (2014) 4. Gürbüz, T., Alptekin, S.E., Işıklar Alptekin, G.: A hybrid MCDM methodology for ERP selection problem with interacting criteria. Decis. Support Syst. 54(1), 206–214 (2012) 5. Rouyendegh, B.D., Baç, U., Erkan, T.E.: Sector selection for ERP implementation to achieve most impact on supply chain performance by using AHP-TOPSIS hybrid method. Tehnicki Vjesnik-Technical Gazette 21(5), 933–937 (2014) 6. Uddin, M.R., Al Noman, A., Tasnim, F., Nafisa, N., Hossain, S.: A hybrid MCDM approach based on AHP, and TOPSIS to select an ERP system in Bangladesh. In: 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), pp. 161–165 (2021) 7. Gao, H., Wei, G.-W.: Multiple attribute decision making based on interval-valued Pythagorean uncertain linguistic aggregation operators. Int. J. Knowl. Based Intell. Eng. Syst. 22(1), 59–81 (2018) 8. Otay, I., Jaller, M.: Multi-criteria & multi-expert wind power farm location selection using a pythagorean fuzzy analytic hierarchy process. In: Kahraman, C., Cebi, S., Onar, S.C., Oztaysi, B., Tolga, A.C., Sarı, İ.U. (eds.) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making, vol. 1029, pp. 905–914 (2019) 9. Yager, R.R.: Properties and applications of Pythagorean fuzzy sets. In: Angelov, P., Sotirov, S. (eds.) Imprecision and Uncertainty in Information Representation and Processing. Studies in Fuzziness and Soft Computing, vol. 332, pp. 119–136. Springer, Cham (2016). https://doi. org/10.1007/978-3-319-26302-1_9 10. Karasan, A., Ilbahar, E., Kahraman, C.: A novel Pythagorean fuzzy AHP and its application to landfill site selection problem. Soft. Comput. 23(21), 10953–10968 (2018) 11. Zhang, X., Xu, Z.: Extension of TOPSIS to MCDM with Pythagorean fuzzy sets. Int. J. Intell. Syst. 29(12), 1061–1078 (2014) 12. Garg, H.: New exponential operational laws and their aggregation operators for intervalvalued Pythagorean fuzzy multicriteria decision-making. Int. J. Intell. Syst. 33(3), 653–683 (2018)

Type-2 Fuzzy Sets

Stabilization of a Fuzzy Controller Using an Interval Type-2 Fuzzy System Designed with the Bee Colony Optimization Algorithm Leticia Amador-Angulo and Oscar Castillo(&) Tijuana Institute of Technology, Tijuana, Mexico [email protected], [email protected]

Abstract. A Bee Colony Optimization algorithm (BCO) for stabilization of a benchmark controller is presented. The main goal of BCO is finding the optimal design of the Membership Functions (MFs) in the Interval Type-2 Fuzzy Logic System (IT2FLS). The results demonstrated that the BCO algorithm shows good stabilization when a IT2FLS is evaluated in the Fuzzy Logic Controller (FLC). A comparative analysis is implemented to demonstrated and verified that BCO algorithm presents excellent results in comparison with the Type-1 Fuzzy Systems (T1FLS). Keywords: Bee Colony Optimization
Fuzzy controller
Interval Type-2 Fuzzy Logic System
Benchmark problem
Stabilization

1 Introduction In the last decade, the Bee Colony Optimization (BCO) algorithm has demonstrated to be a meta-heuristic with an excellent methodology to solve complex problems. The BCO algorithm has been analyzed by a several authors, to mention some authors: Chong et al. in [11] implemented this algorithm in the Optimization of job shop scheduling, Lučić et al. in [19] implement a BCO algorithm applied to a vehicle routing, Nabavi et al. in [23] presents a study case called TRACTOR: traffic‐aware and power‐efficient virtual machine using an Artificial BCO algorithm, and Kumar et al. in [17] presents a Fuzzy logic-based Load Frequency Control using Artificial BCO algorithm. The benchmark control used in this paper has the main objective of controlling the temperature in a shower based on the water flow factor, some works implemented this study case similarly; Khalilakbar et al. use a design and implementation of an Intelligent System to Control use of Water Shower in [16], and Saniei et al. study a Temperature-Decreasing Shower in [24]. This control problem is widely studied by several researchers, the new contribution is that it has not been applied using an Interval Type-2 Fuzzy Logic System (IT2FLS) as a tool to control of the temperature, the main objective of the IT2FLS in this problem is to be able to evaluate uncertainty, in this car regulates the temperature according to the speed of the water flow.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 713–721, 2022. https://doi.org/10.1007/978-3-031-09176-6_79

714

L. Amador-Angulo and O. Castillo

An important contribution in this paper is to demonstrate the performance and efficiency of the BCO algorithm in the stabilization of a shower controller, as this algorithm has to find the optimal design of the MFs of the IT2FLS applied to the FLC in the minimization of the error in the simulation of this study case. This paper is arranged as the following. In Sect. 2 some relevant related works are mentioned. Section 3 describes the BCO algorithm. Section 4 describes the proposed IT2FLS. Section 5 illustrates the simulation results and comparative with T1FLS. Finally, Sect. 6 gives the conclusions and future directions of this research.

2 Related Works Several research works in the last years have demonstrated that BCO algorithm is an excellent tool to solve complete problems; for example, in [1] a BCO algorithm is implemented to Optimization applied to complex non-linear plants, in [2] the BCO algorithm is applied using IT2FLS for tuning fuzzy controllers, in [13] A BCO algorithm applied in the Fixed-Time Traffic Control is presented, Long et al. in [18] a Dynamic Self-Learning Artificial BCO Algorithm for Flexible Job-Shop Scheduling Problem with Job Insertion is presented, in [25] a Design and Development of an Intelligent Control by Using BCO algorithm Technique is presented, in [8] a BCO based-fuzzy logic-PID control design of electrolyzer for microgrid stabilization is presented, in [10] a Research on Motion Behavior and Quality-of-Life Health Promotion Strategy Based on BCO is presented, and in [28] an Improved Artificial Bee Colony Algorithm Guided by Experience is presented. The problems in the field of the computer engineering are every day more complex, this is because several factors must be analyzed, therefore, the uncertainty is better to evaluate with the IT2FLS. This Fuzzy Sets better stabilization in Fuzzy Logic Controller (FLC) compared to the T1FLS, some relevant research are: Castillo et al. shows a review on the design and optimization of IT2FLS fuzzy controllers in [4], Cara et al. study a Multiobjective optimization and comparison of nonsingleton type-1 and singleton IT2FLS in [5], Castillo et al. analyzed a Type-2 fuzzy logic: theory and applications in [6], Castro et al. presents an IT2FLS Toolbox for Control Applications in [7], and Cervantes et al. implemented an Intelligent Control of Nonlinear Dynamic Plants Using a Hierarchical Modular Approach and IT2FLS in [9].

3 Bee Colony Optimization Teodorović et al. in 2009 creates the new bio-inspired algorithm based on behavior of bees to find food called Bee Colony Optimization (BCO) algorithm [27]. This algorithm is based on the creation of multi agent system capabilities to successfully solve problems. Every artificial bee proposes one possible solution in the problem. The algorithm has two phases: the forward pass and backward pass [26, 27]. The characteristic in the BCO algorithm consists in simulating the behavior real to solve complex problems, in this case, the goal for the BCO algorithm is to find the optimal values in the MFs of the IT2FLS for the stabilization of the temperature in the FLC with the

Stabilization of a Fuzzy Controller

715

minimum error. The graphical idea in this paper is illustrated in Fig. 1, and Fig. 2 shows the basic steps.

Fig. 1. Graphical idea BCO algorithm applied to IT2FLS.

Fig. 2. Basic steps of the BCO algorithm.

716

L. Amador-Angulo and O. Castillo

The dynamics of this algorithm is shown by Eqs. (1–4):

Pij;n

h ib dqij;n ea
d1ij ¼  a h 1 ib P q
dij ij;n jAi;n Di ¼ K


Pf i ¼

Pf i Pf colony

1 ; Li ¼ Tour Length LI

Pf colony ¼

1 XN Bee Pf i i¼1 N Bee

ð1Þ

ð2Þ

ð3Þ ð4Þ

where Eq. (1) represents the probability of a bee k located on a node i selects the next node denoted by j, where, N ki is the set of feasible nodes (in a neighborhood) connected to node i with respect to bee k, the probability to visit the following node is represented by b. qij is inversely proportional to the city distance. d ij represents the distance of node i until node j. Finally, a is a binary variable that is used for to find better solutions. Equation (2) represents the fact that a waggle dance will last for a certain duration, determined by a linear function, where K denotes the waggle dance scaling factor, Pf i denotes the profitability scores of bee i as defined in Eq. (3) [3, 12], and Pf colony indicates the bee colony’s average profitability as in Eq. (4) and is updated after each bee completes its tour.

4 Interval Type-2 Fuzzy Logic System The first idea of the fuzzy logic system (FLS) is created by Zadeh in 1965 [29, 30]. An extension for the type-2 fuzzy sets (IT2FLS) is presented by Mendel et al. In 1999 [14, 15, 22, 23]. The first idea in the implementation of the FLS through of a Fuzzy Logic Control is presented by Mamdani in 1974 [20]. Figure 3 illustrates the architecture of the FLC using an IT2FLS.

Fig. 3. Architecture of a FLC in the implementation of IT2FLS.

Stabilization of a Fuzzy Controller

717

~ is represented by Eq. (5) [21, 22]. The first characteristic in an IT2 fuzzy set, A,   ~ ¼ ð x; uÞ; u ~ ð x; uÞj8xX; 8uJ x ½0; 1 A A

ð5Þ

The study case in this paper consists in controlling the temperature in a shower. BCO algorithm to turning the Membership Function (MFs) values in an IT2FLS to minimize the control problem is presented in the follows: 4.1

Interval Type-2 Fuzzy Logic System

The proposed structure for the IT2FLS has two inputs to the fuzzy system: called Temp and Flow, and two outputs called Cold and Hot. The type of MFs is illustrates in Fig. 2, and the linguistics values are shown on Table 2. An empirical analysis allowed to identify the names of the linguistic labels. The representation of the proposed IT2FLS is illustrated in the Fig. 4, and the rules are shown in Table 1.

Fig. 4. Proposed structure for the IT2FLS.

Table 1. Rules for the IT2FLS. # Rules Input 1 Input 2 Output 1 temp flow cold 1 cold soft openSlow 2 cold good closeSlow 3 cold hard closeFast 4 good soft openSlow 5 good good steady 6 good hard closeSlow 7 hot soft openFast 8 hot good openSlow 9 hot hard closeSlow

Output 2 hot openFast openSlow closeSlow openSlow steady closeSlow openSlow closeSlow closeFast

718

L. Amador-Angulo and O. Castillo

In the BCO algorithm every bee (artificial bee) has a size of 119 values (44 values (inputs) and 72 values (outputs)) for the MFs of the IT2FLS.

5 Simulation Results The settings parameters for the simulation are; a Population (N) of 50, Follower Bee of 25, b of 2.5, a of 0.5 and Iterations of 20 values. The metric to evaluate the performance in the BCO algorithm (called function fitness) is known as Mean Square Error (MSE) of the reference, which is the behavior of the speed in the simulations and is shown by Eq. (6). MSE ¼

2 1 Xn  Yi  Yi i¼1 n

ð6Þ

A total of 25 experiments were realized in the case study with T1FLS and IT2FLS. Table 2 shows the experimental results for the metrics such as; best, worst, average and standard deviation found by BCO algorithm. Table 2. Experimental results. Metrics Best Worst Average Standard Deviation

IT1FLS 2.45E−01 4.15E−01 2.74E−03 3.10E−01

IT2FLS 2.65E−03 5.32E−01 3.07E−01 1.01E−03

Table 2 shows a better stabilization when an IT2FLS is implemented on the model, for example; the best results for the T1FLS is of 2.45E−01 and for the IT2FLS is of 2.65E−03. Other important analysis is the standard deviation the value that IT2FLS is of 1.01E−03 compared to T1FLS is of 3.10E−01, in this case the results that BCO algorithm to find implemented a IT2FLS is more stable than T1FLS. Figure 5 illustrates a comparative of the best results to find by each Fuzzy Sets using in this paper.

Fig. 5. Comparative results in the best convergence for the T1FLS and IT2FLS.

Stabilization of a Fuzzy Controller

719

6 Conclusions An important contribution consists in the demonstration of the performance of BCO algorithm in this case applied to a benchmark problem called Shower Controller. The results demonstrate that a stabilization on the controlling of the temperature when an IT2FLS is implemented is achieved and this is because the uncertainty is better to analysis. With the goal to observe if the results of the BCO algorithm are good, a comparative with T1FLS is presented. IT2FLS demonstrates a better convergence on the results (See Fig. 5). The initial proposal with the implementation of IT2FLS consists of analyzing an efficient management of uncertainty. With this paper, the convergence of the BCO algorithm is faster when IT2FLS is used, and better errors are found with the proposal method. The demonstration of an excellent performance with IT2FLS is a main contribution in this research. Some future works to be considered are to explore more with perturbations in the model, and another important implementation for BCO algorithm is to explore the optimization with Generalized Type-2 FLS to observe the performance of the BCO algorithm. In addition, it would also be interesting to be able to change the types of MFs, such as Gaussians and generalized bells because in this paper, trapezoids and triangles MFs are used. Another important future work would be to change the inference method in the FLS to Takagi-Sugeno-Kang.

References 1. Amador-Angulo, L., Castillo, O.: A new algorithm based in the smart behavior of the bees for the design of Mamdani-style fuzzy controllers using complex non-linear plants. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and NatureInspired Optimization, pp. 617–637, Springer, Cham (2015). https://doi.org/10.1007/978-3319-17747-2_47 2. Amador-Angulo, L., Castillo, O.: A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers. Soft. Comput. 22(2), 571–594 (2016). https://doi.org/10.1007/s00500-016-2354-0 3. Biesmeijer, J.C., Seeley, T.D.: The use of waggle dance information by honey bees throughout their foraging careers. Behav. Ecol. Sociobiol. 59(1), 133–142 (2005) 4. Castillo, O., Melin, P.: A review on the design and optimization of interval Type-2 fuzzy controllers. Appl. Soft Comput. (ASC) 12, 1267–1278 (2012) 5. Cara, A.B., Wagner, C., Hagras, H., Pomares, H., Rojas, I.: Multiobjective optimization and comparison of nonsingleton type-1 and singleton interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 21(3), 459–476 (2012) 6. Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W.: Type-2 fuzzy logic: theory and applications. In: 2007 IEEE International Conference on Granular Computing (GRC 2007), p. 145. IEEE (2007) 7. Castro, J.R., Castillo, O., Melin, P.: An interval type-2 fuzzy logic toolbox for control applications. In: Fuzzy System Conference, pp. 1–6 (2007) 8. Chaiyatham, T., Ngamroo, I.: A Bee Colony Optimization based-fuzzy logic-PID control design of electrolyzer for microgrid stabilization 8(9), 6049–6066 (2012)

720

L. Amador-Angulo and O. Castillo

9. Cervantes, L., Castillo, O., Melin, P.: Intelligent control of nonlinear dynamic plants using a hierarchical modular approach and type-2 fuzzy logic. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011. LNCS (LNAI), vol. 7095, pp. 1–12. Springer, Heidelberg (2011). https://doi. org/10.1007/978-3-642-25330-0_1 10. Chen, R.: Research on motion behavior and quality-of-life health promotion strategy based on bee colony optimization. J. Healthc. Eng. 2022 (2022) 11. Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 2006 Winter Simulation Conference, pp. 1954–1961. IEEE (2006) 12. Dyler, F.C.: The biology of the dance language. Annu. Rev. Entomol. 47, 917–949 (2002) 13. Jovanović, A., Teodorović, D.: Fixed-time traffic control at superstreet intersections by bee colony optimization. Transp. Res. Rec. (2021). 03611981211058104 14. Karnik, N.N., Mendel, J.M.: Operations on Type-2 fuzzy sets. Int. J. Fuzzy Sets Syst. 122, 327–348 (2001) 15. Karnik, N.N., Mendel, J., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7 (6), 643–658 (1999) 16. Khalilakbar, M., Nasresfahani, S.: Design and fabrication of an intelligent system to control use of water shower and chlorine pond by swimmers in pools according to the hygiene protocols. J. Adv. Sport Technol. 5(1), 130–138 (2021) 17. Kumar, N.K., et al.: Fuzzy logic-based load frequency control in an island hybrid power system model using artificial bee colony optimization. Energies 15(6), 2199 (2002) 18. Long, X., Zhang, J., Zhou, K., Jin, T.: Dynamic self-learning artificial bee colony optimization algorithm for flexible job-shop scheduling problem with job insertion. Processes 10(3), 571 (2022) 19. Lučić, P., Teodorović, D.: Vehicle routing problem with uncertain demand at nodes: the bee system and fuzzy logic approach. In: Verdegay, J.L. (ed.) Fuzzy Sets in Optimization, pp. 67–82. Springer, Heidelberg (2003b). https://doi.org/10.1007/978-3-540-36461-0_5 20. Mamdani, E.H.: Applications of fuzzy algorithms for simple dynamic plant. Proc. IEEE 121 (12), 1585–1588 (1974) 21. Mendel, J.M., Mouzouris, G.C.: Type-2 fuzzy logic system. IEEE Trans. Fuzzy Syst. 7(6), 642–658 (1999) 22. Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002) 23. Nabavi, S.S., Gill, S.S., Xu, M., Masdari, M., Garraghan, P.: TRACTOR: traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization. Int. J. Commun. Syst. 35(1), e474 (2022) 24. Saniei, N., et al.: Exploring a temperature-decreasing shower concept to conserve water and energy. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 85413, p. V005T05A009. American Society of Mechanical Engineers (2021) 25. Tiacharoen, S., Chatchanayuenyong, T.: Design and development of an intelligent control by using bee colony optimization technique. Am. J. Appl. Sci. 9(9), 1464–1471 (2012) 26. Teodorović, D., Davidović, T., Šelmić, M., Nikolić, M.: Bee colony optimization and its applications. In: Handbook of AI-Based Metaheuristics, pp. 301–322 (2021) 27. Teodorović, D.: Bee colony optimization (BCO). In: Lim, C.P., Jain, L.C., Dehuri, S. (eds.) Innovations in Swarm Intelligence, vol. 248, pp. 39–60. Springer, Heidelberg (2009). https:// doi.org/10.1007/978-3-642-04225-6_3

Stabilization of a Fuzzy Controller

721

28. Wang, C., Shang, P., Liu, L.: Improved artificial bee colony algorithm guided by experience. Eng. Lett. 30(1) (2022) 29. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Part I. Inf. Sci. 8, 199–249 (1975) 30. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Part II Inf. Sci. 8, 301–357 (1975)

Optimal Design and Internet of Things Implementation of a General Type-2 Classifier for Blood Pressure Levels Oscar Carvajal

, Patricia Melin(&)

, and Ivette Miramontes

Tijuana Institute of Technology, Tijuana, México {oscar.carvajal17, cynthia.miramontes}@tectijuana.edu.mx, [email protected]

Abstract. Recently, soft computing techniques, such as Type-2 Fuzzy logic and bio-inspired algorithms have been utilized in many areas. In this work, we propose a medical diagnosis application using type-2 fuzzy logic and the Ant Lion Optimizer to classify the blood pressure levels. We utilized the Framingham database for the optimization of the classifier. The main contribution of this work is to develop an intelligent model to classify the blood pressure levels and its Hardware Implementation using the Internet of Things concept on the Jetson Nano Board. This implementation utilized a blood pressure level monitor to obtain the systolic and diastolic blood pressure of the patients, which are the inputs of the classifier. The classification results and the input parameters can be monitored locally in a Human Machine Interface display or can be monitored in any other place through the internet of things concept. The results demonstrate that Type-2 Fuzzy logic achieves better results than Type-1 Fuzzy logic. Keywords: Blood pressure
Hypertension
Optimization
Internet of Things (IoT)
Ant Lion Optimizer (ALO)
General type-2 fuzzy logic
Embedded systems
Jetson Nano

1 Introduction Recently, soft computing techniques have been utilized widely to solve problems and satisfy needs in areas, such as: control [1], industry [2, 3], home [4], and medicine [5, 6]. This work presents the optimization and implementation of a general type-2 fuzzy classifier for blood pressure levels. This classifier is optimized utilizing the Ant Lion Optimizer (ALO) [7] and implemented in the Jetson Nano Development Board [8] utilizing the Framingham database [9]. The world is constantly changing for getting better; nowadays, there is an increasing number of applications that utilize the Internet of Things (IoT) concept [10]. IoT means that every device is connected to the internet, and this has the advantage of communication between two or more devices remotely to monitor or control the process. The recent increment of IoT applications is because people have been working remotely in their homes due to the recent pandemic of COVID-19. The internet of things offers mechanisms to work in this way. In this work, we use the concept of IoT © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 722–729, 2022. https://doi.org/10.1007/978-3-031-09176-6_80

Optimal Design and Internet of Things Implementation

723

for the blood pressure remote monitoring; also, the user of the application can see the result in a local form with the results obtained from the Jetson Nano Development Board. On the other hand, type-2 fuzzy logic has the advantage of handling the uncertainty better than type-1 in many applications [11]. The main contribution of this work is to develop an intelligent model to classify the blood pressure levels and its Hardware Implementation using the Internet of Things concept on the Jetson Nano Board. In Sect. 2, the methodology of the proposed work is presented, in Sect. 3, we present the results of this work, and finally, in Sect. 4, the conclusions and future work of this study are presented.

2 Proposed Method The main methodology of this work is shown in Fig. 1. First, the offline optimization of the general type-2 fuzzy classifier is done utilizing the ALO. The data used to optimize the classifier is obtained from the Framingham database. The 760 patients from the database were selected using the hold-out method. Then the implementation in the Jetson Nano Development Board is done as previously described in [9]. We have also included in the implementation the concept of IoT to monitor the diagnosis and measurements of the patient.

Fig. 1. General methodology

Figure 2 illustrates the architecture and the optimized membership functions utilizing the ALO for the blood pressure classifier using general type-2 fuzzy logic of the Mamdani type. This classifier has two inputs: systolic and diastolic blood pressure, and an output that is the blood pressure levels. It can be observed that all the membership functions are of trapezoidal type.

724

O. Carvajal et al.

Fig. 2. Blood pressure general type-2 fuzzy classifier architecture

A sample of the rules of the general type-2 classifier are listed as follows and were previously defined and tested in [12].

In Fig. 3, we can observe the architecture of the Internet of Things utilized in this work, which is based on the publish-subscribe messaging utilizing Message Queueing Telemetry Transport (MQTT). This lightweight network protocol runs mainly over TCP/IP. The Jetson Nano Board is the MQTT Client that publishes the information obtained from the patient to the web page; this is done to a channel of communication through a specific topic. This topic is an identifier where the client will publish in this case; the server MQTT also commonly known as Broker MQTT is responsible for handling all the communication of the IoT Devices in a smart way. This mechanism does not utilize the polling method as the HTTP does. So, it is the main advantage of utilizing MQTT over HTTP; this means that the Broker MQTT does not ask about the values of each device; it knows when the information comes from IoT devices in a public-subscribe manner. The webserver acts as a translator of the MQTT information from the Jetson Nano Board. The information received is sent to the HTTP client or web page through WebSockets. They allow real-time communication.

Optimal Design and Internet of Things Implementation

725

Fig. 3. IoT architecture

In Fig. 4, we present the risk factors of the general medical diagnosis hybrid model previously described in [13]. They are obtained from Nextion Human Machine Interface (HMI) touch display where the patient can give their personal information such as Age, Gender, parents with Hypertension, Weight, and Height to obtain the person's body mass index.

Fig. 4. HMI risk factors

For the type-2 fuzzy classifier proposed in this work; the inputs are obtained directly from the blood pressure meter OMRON M3 IT via USB communication to the Jetson Nano Development Board as illustrated in Fig. 5, the reading of the device can be the last or the average of the last three measurements.

726

O. Carvajal et al.

Fig. 5. Measurements from the OMRON M3 IT blood pressure meter

Once the patient's risk factors and blood pressure signals are obtained, the results of the general type-2 classifier are displayed in the HMI in a local manner, as can be observed in Fig. 6. Also, the percentage of developing hypertension and pulse levels previously described [9, 14].

Fig. 6. HMI general diagnosis

In Fig. 7, remote monitoring is illustrated. The diagnosis is sent to the HTTP client with the before explained IoT architecture; additionally, the systolic and diastolic are sent to the web page with the actual date.

Optimal Design and Internet of Things Implementation

727

Fig. 7. HTTP client

3 Results Table 1 shows 30 experiment results of the three optimized fuzzy classifiers for the blood pressure levels using general type-2 fuzzy logic, interval type-2 fuzzy logic and type-1 fuzzy logic. The experiments for the three classifiers utilized 760 patients of the Framingham database. Table 1. Experiment results for 760 patients ALO results for the Framingham database Patients 760 Technique T1 FL IT2 FL GT2 FL Average 87.22 89.55 89.382 Standard deviation 1.94 1.136 0.823

In Table 2, the results with all the database patients are presented. The comparison is between the three fuzzy classifiers. In this case, the best solution found in the earlier experiments with 760 patients is tested with the 4239 patients of the Framingham database. Table 2. Experiment results for 4239 patients ALO results for the Framingham database Patients 4239 Technique T1 FL IT2 FL GT2 FL Average 87.23 89.76 89.442 Standard deviation 2.09 1.257 1.014

728

O. Carvajal et al.

In order to compare results between the fuzzy classifiers, a statistical z-test is done, with a level of significance of 95%. In this case, as a claim and null hypothesis we establish that µ1 > µ2 and as the alternative hypothesis µ1 < > :

1; if lA ð xÞ  a 0; if lA ð xÞ  b ½0; 1; if b  lA ð xÞ  a

ð1Þ

732

C. Peraza et al.

The intervals are viewed as regions, the elevated region for membership degree (MD) of 1, the reduced region for MD of 0 and for the shadowed area the MD is in [0, 1]. Pedrycz then proposed that the optimal a and b values can be computed by Eq. (2). evaluated areaða;bÞ ðlA Þ þ reduced areaða;bÞ ðlA Þ ¼ shadowed areaða;bÞ ðlA Þ

ð2Þ

The explanation of Eq. (2) can be graphically illustrated as in Fig. 1.

Fig. 1. The graphical view of ST2

Finally, the a and b values are computed by optimizing the V(a, b) function expressed in Eq. (3):    Z  Z Z   V ða; bÞ ¼  ð3Þ lA ð xÞdx þ ð1  lA ð xÞÞdx  dx x2A  x2A x2S r

e

4 Proposed Methodology The creators of harmony search (HS) resorted to the simile of improvisation in Jazz music to explain how the algorithm works. According to them, there are three possible actions in this regard: (a) playing a previously learned piece from memory; (b) play something similar to that familiar piece and gradually adjust it to the desired key while keeping the musical intervals fixed (transpose a piece from one key to another); (c) compose something new based on previous knowledge and/or select new notes at random, Harmony Memory Accepting (HMR), Pitch Adjustment (PArate) and Random Selection, the HS is postulated by Eqs. (4–6): HMR 2 ½0; 1

ð4Þ

Xnew ¼ Xold þ bpð2 rand  1Þ

ð5Þ

Parameter Adaptation in Harmony Search

PArate ¼ PLowerlimit þ PRange rand Where PRange ¼ PUpperLimit  PLowerLimit

733

ð6Þ

The adaptation of parameters is carried out through a FIS which contains an input and an output. This FIS is of the Mamdani type and encapsulates three rules. The variant of harmony search with the fuzzy parameter adaptation using ST2 is called ST2FHS. The structure of the ST2FHS is observed in Fig. 2, and the rules with the knowledge for changing HMR during execution of HS are presented in Table 1. Basically, the HMR is monotonically increasing as the iterations are also increasing.

(a) Input

(b) Output

Fig. 2. Membership functions and structure of the ST2FHS

Table 1. Rules of the ST2FHS HMR iteration Small Medium Big Small Small − − Medium − Medium − Big − − Big

4.1

Simulation and Statistical Results

The case study used for the simulations of the proposed method is a controller to stabilize the position of a direct current motor. This controller was implemented with interval type-2 fuzzy logic (IT2FLC see this in Fig. 3). The type-2 membership functions (IT2MF) used are described in Eqs. (7) and (8). Table 2 contains the values used for the construction of the IT2MFs of each input and output and Table 3 describes the rules for motor position controller.

734

C. Peraza et al.

Fig. 3. IT2FLC structure for the motor position

8 > > > > > > > > > > >
> maxðl1 ð xÞ; l2 ð xÞÞ 8x 62 ðb1; c2Þ > > lð xÞ ¼ > > > 1 8x 2 ðb1; c2Þ > > > > : lð xÞ ¼ minða; minðl1 ð xÞ; l2 ð xÞÞÞ 8 > > > > > > > > > >
 > > maxðl1 ð xÞ; l2 ð xÞÞ 8x 62 ðb1; b2Þ > > lð xÞ ¼ > > > 1 8x 2 ðb1; b2Þ > > : lð xÞ ¼ minðl1 ð xÞ; l2 ð xÞÞ

ð7Þ

ð8Þ

Parameter Adaptation in Harmony Search Table 2. IT2 MF Input Err MF a1 b1 NV −1.96 −1.35 CV −0.58 −0.14 PV 0.01 0.54 Input C_Err EN −1.66 −1.05 ENM −0.42 −0.28 SE −0.13 −0.03 EMM 0.01 0.16 EM 0.34 0.71 Output Voltage D −1.85 −1.45 DM −0.48 −0.27 M −0.14 −0.03 AM 0 0.10 A 0.16 0.56

c1 −0.95 0.28 1.04

d1 −0.34 — 1.65

a2 −1.62 −0.26 0.37

b2 −1.01 0.04 0.98

c2 −0.62 0.53 1.37

d2 0 — 1.98

−0.67 −0.09 0.08 0.33 0.71

−0.29 — — — 1.1

−1.33 −0.29 −0.05 0.11 0.04

−0.72 −0.06 −0.002 0.21 0.4

−0.34 0.03 0.18 0.44 1.25

0 — — — 1.25

−0.84 −0.10 0.07 0.31 1.16

−0.23 — — — 1.56

−1.65 −0.30 −0.02 0.11 0.36

−1.25 −0.20 0.03 0.22 0.76

−0.64 0.04 0.17 0.43 1.36

−0.06 — — — 1.76

Table 3. IT2FLC rules for the motor position Rule_Number Inputs Err C_Err 1 NV EN 2 NV SE 3 NV EM 4 CV EN 5 CV EM 6 PV EN 7 PV SE 8 PV EM 9 CV SE 10 NV ENM 11 CV ENM 12 PV ENM 13 PV EMM 14 CV EMM 15 NV EMM

Output Voltage D D DM AM DM AM A A M D AM A A DM D

735

736

C. Peraza et al.

The objective function used for measuring performance in this control case is the RMSE error. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 XN ðx  ^xt Þ2 RMSE ¼ t¼1 t N

ð9Þ

The harmony generated for the construction of the parameters of the IT2MFs type-2 has a total of 90 parameters because the trapezoidal type-2 IT2MF is made up of 8 parameters while the triangular IT2MF consists of 6 parameters. These 90 parameters will be optimized using the proposed method to minimize the RMSE error of the motor position. The 30 simulations were performed without noise and applying a Gaussian random number of 0.5. The results obtained with the method can be observed in Table 4 and a comparison with another method is presented in Table 5. The following tables present the best, worst, mean, and standard deviation obtained with the method ST2FHS-IT2FLC without noise and with noise. Additionally, the Z-value obtained by applying the z-test statistical test is shown, where the significant evidence will be when obtaining values less than -1.645 when comparing the proposal with respect to ST2FHS-FLC and ST2FDE-FLC without noise and with noise. Table 4. IT2FLC simulation result for the motor position controller Method

HS- FLC without noise

ST2FHS-FLC without noise

HS- FLC ST2FHS-FLC ST2FHSST2FHSwith noise FLC with noise IT2FLC without IT2FLC with noise noise

Best Worst Average Std. Z-value

7.86e−03 5.16e−01 1.65e−01 1.37e−01

7.32e−03 5.66e−02 9.22e−03 3.45e−03

4.22e−02 1.09e+00 5.90e−01 3.07e−01

1.95e−02 9.07e−01 4.62e−01 2.83e−01

2.25e−03 5.67e−03 4.38e−04 3.54e−04 −13.86

2.16e−04 2.40e−02 2.10e−03 1.70e−03 −8.90

Table 5. Comparison with another method Method

DE- FLC without noise

ST2FDE-FLC without noise

DE -FLC ST2FDE with noise FLC with noise

ST2FHSIT2FLC without noise

ST2FHSIT2FLC with noise

Best Worst Average Std. Z-value

7.34E−03 2.19E−02 1.71E−02 2.81E−03

4.35E−03 7.43E−03 7.24E−03 5.35E−04

2.24E−02 4.85E−01 2.44E−01 1.36E−01

2.25e−03 5.67e−03 4.38e−04 3.54e−04 −5.65

2.16e−04 2.40e−02 2.10e−03 1.70e−03 −58.07

5.89E−04 7.47E−02 2.18E−02 1.90E−02

5 Conclusion and Future Work This article puts forward a ST2FHS for achieving parameter adaptation in HS, with the goal of improving the performance of the method and avoid using fixed parameters throughout the iterations. A case is considered of controlling the motor position and the

Parameter Adaptation in Harmony Search

737

theory of IT2FLC is implemented in the fuzzy controller. The objective of the method was to optimize the IT2MF parameters of the motor controller. The results presented demonstrate the effectiveness of the method and the significant evidence when applying a statistical test. As future work, the ST2FHS method can be applied to new and more complex control plants.

References 1. Hewei, G., Sadiq, A.S., Tahir, M.A.: Adaptive fuzzy-logic traffic control approach based on volunteer IoT agent mechanism. SN Comput. Sci. 3(1), 1–15 (2021). https://doi.org/10. 1007/s42979-021-00956-3 2. Voskoglou, M.G.: Fuzzy logic in control theory. In: Magdi, D.A., Helmy, Y.K., Mamdouh, M., Joshi, A. (eds.) Digital Transformation Technology, pp. 217–228. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2275-5_13 3. Rodriguez, R., Trovão, J.P.F., Solano, J.: Fuzzy logic-model predictive control energy management strategy for a dual-mode locomotive. Energy Convers. Manage. 253, 115111 (2022) 4. Taghvaei, M., Gilvanejad, M., Sedighizade, M.: Cooperation of large-scale wind farm and battery storage in frequency control: an optimal Fuzzy-logic based controller. J. Energy Storage 46, 103834 (2022) 5. Ochoa, P., Castillo, O., Melin, P., Soria, J.: Differential evolution with shadowed and general type-2 fuzzy systems for dynamic parameter adaptation in optimal design of fuzzy controllers. Axioms 10(3), 194 (2021) 6. Castillo, O., Valdez, F., Peraza, C., Yoon, J.H., Geem, Z.W.: High-speed interval type-2 fuzzy systems for dynamic parameter adaptation in harmony search for optimal design of fuzzy controllers. Mathematics 9(7), 758 (2021) 7. Yang, J., Zhang, T., Hong, J., Zhang, H., Zhao, Q., Meng, Z.: Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle. Energy 233, 121221 (2021) 8. Wu, T., Qi, Y., Liao, L., Ji, F., Chen, H.: Research on equalization strategy of lithium-ion batteries based on fuzzy logic control. J. Energy Storage 40, 102722 (2021) 9. Mohanaselvi, S., Shanpriya, B.: Application of fuzzy logic to control traffic signals. In: AIP Conference Proceedings, vol. 2112, no. 1, p. 020045. AIP Publishing LLC (2019) 10. Castillo, O., Aguilar, L.T.: Type-2 Fuzzy Logic in Control of Nonsmooth Systems. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03134-3 11. Pedrycz, W.: Shadowed sets: representing and processing fuzzy sets. IEEE Trans. Syst. Man Cybernet. B (Cybernet.) 28(1), 103–109 (1998) 12. Wijayasekara, D., Linda, O., Manic, M.: Shadowed type-2 fuzzy logic systems. In: IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), pp. 15–22. IEEE (2013) 13. Melin, P., Ontiveros-Robles, E., Castillo, O.: Background and theory. In: New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic, pp. 5–28. Springer, Cham (2021). https://doi.org/10.1007/978-3-319-28862-8_2 14. Patel, H.R., Shah, V.A.: General type-2 fuzzy logic systems using shadowed sets: a new paradigm towards fault-tolerant control. In: Australian & New Zealand Control Conference (ANZCC), pp. 116–121. IEEE (2021)

738

C. Peraza et al.

15. Kumar, P., Dudeja, C.: Shadowed type 2 fuzzy-based Markov model to predict shortest path with optimized waiting time. Soft. Comput. 25(2), 995–1005 (2020). https://doi.org/10. 1007/s00500-020-05194-y 16. He, S., Pan, X., Wang, Y.: A shadowed set-based TODIM method and its application to large-scale group decision making. Inf. Sci. 544, 135–154 (2021) 17. Pedrycz, W.: Shadowed sets: representing and processing fuzzy sets. IEEE Trans. Syst. Man Cybern. B Cybern. 28, 103–109 (1998)

Diagnosis of Diabetes Using Type-2 Fuzzy System Hamit Altıparmak

, Rahib Abiyev(&)

, and Murat Tüzünkan

Applied Artificial Intelligence Research Centre, Near East University, Nicosia, North Cyprus, Turkey {hamit.altiparmak,rahib.abiyev, murat.tuzunkan}@neu.edu.tr

Abstract. Diabetes is one of the serious chronic diseases that need to be diagnosed early for the healthy life of people. The diagnosis of diabetes is based on a set of input symptoms, some of which are acquired through laboratory analysis. Computer systems with artificial intelligence can be created to enable rapid diagnosis and treatment. The uncertain character of the medical data of patients and complexity of diagnosis causes uncertainty of decision. One of the best methodologies used for diagnosing diabetes is fuzzy logic. The paper proposes a type-2 fuzzy neural network (T2-FNN) for the diagnosis of diabetes. The architecture of the T2-FNN model is developed. Neural network learning is employed for the construction of the type-2 fuzzy rule base. The presented T2FNN is simulated using two different versions of diabetes datasets: the Pima dataset and its extended version. The system’s modeling was implemented using a different number of rules. The system accuracy has been achieved as 99%. Comparative results show the reliability of the study. Keywords: Diabetes


Type-2 fuzzy system
Neural networks
Diagnosis

1 Introduction Diabetes is a serious disease that occurs when the pancreas cannot produce enough insulin. As a result of insufficient production of insulin, the concentration of glucose is increased in the blood. Diabetes is one of the serious health problems and can be fatal if it is detected late. There are different types of diabetes, they are divided into type-1-, type-2- and gestational diabetes, [1]. When the pancreas is not producing the hormone insulin or producing too little insulin then type-1 diabetes occur. It usually occurs in the childhood or teenage years and diabetes patients have to take insulin throughout their lives. Type-2 diabetes occurs when a small amount of insulin is produced. Poor lifestyle, not doing sports, malnutrition and genetic factors can cause type-2 diabetes. It usually occurs in middle and older ages. Depending on healthy nutritional conditions, Type-2 diabetes can also be seen in obese children. Gestational diabetes occurs during pregnancy due to hormonal changes. Gestational diabetes is most likely to disappear after pregnancy, but these people become prone to type-2 diabetes later on. Diagnosis of diabetes is difficult that needs high-level expertise. The number of people who lost their lives due to the late diagnosis of diabetes is increasing day by day. If the high © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 739–747, 2022. https://doi.org/10.1007/978-3-031-09176-6_82

740

H. Altıparmak et al.

sugar concentration is not controlled, organ loss and blindness may occur, so early detection of diabetes is very important [2]. In addition, early detection of diabetes greatly reduces treatment costs [3, 4]. In this study, two different datasets consisting of the results of the medical analysis of type-2 diabetes patients were used. Type-2 diabetes patients can recover. Type-1 diabetes patients must take the insulin hormone throughout their lives, and there is no other solution. If type-2 diabetes is detected early, it is very easy to control the disease. With a healthy diet, physical activity and drug use, type-2 diabetes patients can continue their lives without any problems. People with type-2 diabetes have a chance of recovery. Today, artificial intelligence has offered solutions to such complex diseases. Artificial intelligence has contributed with high accuracy to many complex problems such as fuzzy logic systems that detect skin diseases [5], rule-based diabetes diagnosis systems [6] and classification of cancerous images [7]. Many studies on the diagnosis of diabetes have been presented in the literature. Diabetes diagnosis systems have been created using many different methods such as support vector machines [8], decision trees [9, 10], artificial neural networks [11], swarm optimizations [12] and deep neural networks [13]. These models were designed using statistical data. These models use various input symptoms and signs in order to make an appropriate diagnosis. By dividing input ranges and analyzing them physician-doctors make a decision about the diseases. In more cases, these input ranges are characterised by uncertainty. Some of the medical data carry noises. Due to the uncertainty of input information, imprecision of data sometimes, the decision about the disease may be not accurate. The uncertain character of the medical data of patients and complexity of diagnosis causes uncertainty of decision. Also, existing noise in input data, the complexity of datasets did not provide high enough accuracy in the decision. Fuzzy logic is one efficient approach that can handle uncertainty and presents appropriate decisions about diseases. In this study, a Type-2 fuzzy logic system is proposed for diabetes diagnosis. Type-2 fuzzy logic can handle high-order uncertain data and provide high accuracy in the decision of complex problems [14–16]. Type-2 fuzzy systems are designed for solving many engineering problems [17–21]. Several architectures that diagnose various medical diseases using a type-2 fuzzy system were presented in [22–25]. One of the effective ways to design type-2 fuzzy systems is to use neural network structure. Type-2 fuzzy neural network (T2-FNN) is proposed by integrating an artificial neural network into Type-2 fuzzy sets. In this study, T2-FNN diagnostic system is designed using two versions of Pima datasets. The main problem, in the emergence of this work, is the design of a high-accuracy system. The contributions of this study are: the design of T2-FNN architecture for diabetes’ diagnosis; the learning of T2-FNN using cross-correction techniques and gradient descent algorithm. The paper is structured as follows. Section 2 describes the T2-FNN model designed to diagnose diabetes. In Sect. 3 the modelling of the T2-FNN system is presented. Section 4 gives the conclusions.

Diagnosis of Diabetes Using Type-2 Fuzzy System

741

2 T2-FNN for Diagnosis of Diabetes Diabetes is characterised by a set of symptoms. These are a number of pregnancies, blood pressure, Hourly Serum Insulin value, Diabetes Pedigree Function, 2 h of plasma glucose concentration in the oral glucose tolerance test, Triceps Skin Fold Thickness, Body mass index and age. These symptoms are input for the designed T2-FNN. The ~ design of output of the model is healthy and diabetes. The type-2 rules used for the A 1j the system is presented below. P ~ 1j and … and xm is A ~ THEN y1 ¼ m wi1 xi ; …; IF x1 is A mj i¼1 yr ¼

Xm i¼1

ð1Þ

wir xi

~ are type-2 membership functions (MFs), xi and yj are input and output signals where A ij respectively, wij are weight coefficients, i = 1, .. , m, j = 1, .. , r. The Gaussians are used to describe the fuzzy values of A. lj ð x i Þ ¼ e



ðxi cij Þ2 r2 ij

ð2Þ

Here cij and rij are the center and widths of membership functions. In the paper, Gaussian MFs with uncertain mean cij 2 [c1ij ; c2ij  are used to describe type-2 membership functions. Figure 1 presents interval type-2 MF with an uncertain mean. In Fig. 1 each point is represented by upper lð xÞ and lower lð xÞ MFs

Fig. 1. Gaussian interval type-2 MF

h i h i lA~ i ðxk Þ ¼ lA~ i ðxk Þ; lA~ i ðxk Þ ¼ li ; li k

k

k

ð3Þ

The inference process is implemented using the t-norm “min” operation. Then f ¼ lA~ ðx1 Þ  lA~ ðx2 Þ  . . .  lA~ ðxn Þ; 1

2

n

f ¼ lA~ 1 ðx1 Þ  lA~ 2 ðx2 Þ  . . .  lA~ n ðxn Þ The T2-FNN output is calculated using formulas (5) and (6)

ð4Þ

742

H. Altıparmak et al.

uk ¼ yj ¼

Xm

p

Pr

j¼1 f j yj vjk

Pr

x w ;i i¼1 i ij

q

Pr

j¼1 f j yj vjk

Pr

ð5Þ

¼ 1; ::; m; j ¼ 1; ::; r; k ¼ 1; . . .; n

ð6Þ

j¼1 f j

þ

j¼1 f j

Here p and q are used to update the lower and upper portions of output signal. After finding output signals the learning of T2-FNN parameters start. Training includes adjusting the values of c1ij, c2ij, rij, wij and vjk parameters. Gradient descent algorithm is implemented for learning the parameters of T2-FNN [18, 19].

3 Modelling of T2-FNN for Diabetes’ Diagnosis In this study, the T2FNN-based diabetes diagnosis system is designed using the extended Pima dataset [26, 27], which includes the data of 2000 patients. There are 8 entries in both versions of the Pima dataset. In the datasets, the first input is the number of pregnancies. As the number of pregnancies increases, the susceptibility to diabetes increases. Type 2 diabetes is higher in women who have given birth more than 3 times. The next input of our datasets is glucose concentration. A value below 140 reduces the risk of diabetes, while a high value increases the risk of diabetes. Blood pressure is the third input parameter. Diabetes patients may experience high blood pressure. A higher than normal blood pressure may be a sign of diabetes, but it does not necessarily mean diabetes. In healthy people, blood pressure is below 90. Skinfold thickness is the fourth entry in our dataset. Skinfold thickness is increasing day by day in diabetic patients. A skinfold thickness greater than 24 may increase diabetes’ risk. The 5th input in our dataset is the insulin value. The 2-h insulin value is less than 116 in healthy people. Normally high insulin levels are one of the strongest signs of diabetes. The sixth entry in the datasets is the body mass index, this value is below 30 in healthy people. The higher this value, the higher the risk of diabetes. Our seventh entry is the diabetes pedigree function, a value greater than 0.5 means diabetes risk. The last input is the age of the patients. There are two outputs in our datasets, these are two classes diabetic and healthy. The initial version of the Pima dataset includes 768 people, and the extended advanced version includes 2000 people. In order to be a fair approach to the studies in the literature, the data sets were not combined. Table 1 describes the statistical measurements for the second version of diabetes’ datasets. The table presents the maximum value, standard deviation, mean and correlation values for all features. The correlation score shows us how valuable each input is for the diagnosis of diabetes. Table 1 shows that the most valuable attribute contributing to the diagnosis of diabetes is glucose.

Diagnosis of Diabetes Using Type-2 Fuzzy System

743

T2FNN architecture has been developed for the diagnostic system. The first problem in the development phase of the architecture was to find the appropriate values for the parameters to be used. Appropriate values were found as a result of testing many different parameters. Cross-validation was implemented during the adjustment of the parameters. The results were simulated with 2000 epochs using a 10-fold crossvalidation technique. The training performance of the T2-FNN system is evaluated using root mean square error (RMSE). Different numbers of rules are used in the designed system, these are 16, 32, 40, 48, 64, 80 and 100. The system performance was estimated by accuracy, sensitivity, specificity, and precision. Tables 2 and 3 present the results obtained from the T2FNN system using two versions of the Pima dataset. In both tables, the system accuracy is increased by increasing the number of rules. The results obtained with 100 rules in both data sets are the highest accuracy results. In the data set consisting of 2000 samples, the precision, specificity, sensitivity and accuracy values were obtained as 99.26, 99.6, 100 and 99.75, respectively. In the data set consisting of 768 samples, the same performances were obtained as 98.13, 99.00, 99.24 and 99.06, respectively. Table 1. Statistics for extended data sets Attributes Mean Std Blood pressure 69.1455 19.1883 Glucose 121.182 32.0686 Pregnancies 3.70 3.3060 Insulin 80.254 111.18 Skin thickness 20.935 16.103 Diabetes pedigree function 0.471 0.32355 BMI 32.193 8.1499 Age 33.09 11.7864 Outcome 0.342 0.474498

Max 122 199 17 744 110 2.42 80 81 1

Correl. Scores 0.07 0.45 0.22 0.12 0.07 0.15 0.27 0.23 –

Table 2. T2-FNN results using extended data sets

Rules 16 32 40 48 64 80 100

Accuracy 0.806000 0.870000 0.903000 0.924500 0.959000 0.991000 0.997500

SensiƟvity 0.769091 0.850993 0.913851 0.914463 0.954683 0.986842 1.000000

Specificity 0.820000 0.878223 0.898438 0.929256 0.961136 0.993161 0.996215

Precision 0.618421 0.751462 0.790936 0.859649 0.923977 0.986842 0.992690

744

H. Altıparmak et al. Table 3. T2-FNN results using PIMA diabetes Rules 16 32 40 48 64 80 100

Accuracy 0.825545 0.869792 0.904948 0.924479 0.955729 0.968750 0.990685

Sensitivity 0.804545 0.85000 0.904564 0.944915 0.930147 0.984127 0.992453

Specificity 0.878788 0.878788 0.905123 0.915414 0.969758 0.961240 0.990060

Precision 0.761194 0.761194 0.813433 0.832090 0.944030 0.945372 0.981343

Comparative results of different diabetes diagnosis models in the literature are presented in Table 4. Table 4 depicts the results of the T2FNN model with 32, 80 and 100 rules. In order to make the comparison fair, the results of the data set consisting of 768 examples used by the articles in the literature were compared. As seen in Table 4, T2FNN systems offered higher accuracy than other models. The results obtained with the architecture presented in this study show the effectiveness of the T2FNN system in diagnosing diabetes. Table 4. Comparative results (PIMA datasets with 768 data samples) Authors Methodology Sreedevi and Padmavathamma [28] Genetic Algorithm Bozkurt et al. [29] ANN Sa'ddi et al. [30] Naïve Bayes Mercaldo et al. [31] Deep Learning Rabina and Chopra [32] MLP Choubey and Paul [3] MLP NN Polat et al. [8] SVM Nabi et al. [33] 4 Machine Learning Combination Tand and Tseng [34] Genetic Algorithm Maniruzzaman et al. [35] Quadratic Discriminant Analysis Bashir et al. [36] HM-BagMoov Aslam et al. [37] 3 Steps Genetic programming The paper T2-FNN (with 32 rules) The paper T2-FNN (with 80 rules) The paper T2-FNN (with 100 rules)

Accuracy 0.72 0.76 0.76 0.77 0.77 0.79 0,79 0.80 0.81 0.82 0,86 0.87 0.87 0.969 0.991

4 Conclusions This study proposes a T2-FNN model for the diagnosis of diabetes. The combination of neural networks and type-2 fuzzy logic were presented for the construction of the T2FNN model. Pima datasets were used to simulate the presented system. The training of

Diagnosis of Diabetes Using Type-2 Fuzzy System

745

the presented integrated model is simulated by cross-validation and gradient descent algorithm. Different numbers of rules were used in the simulation phase. The results obtained for all simulations were presented in Table 2 and Table 3. The obtained results show that the obtained accuracy increases as the number of rules increases. The results for both datasets showed that using 100 rules gave the highest accuracy. With the original Pima dataset consisting of 768 samples, precision, specificity, sensitivity and accuracy values were 98.1, 99, 99.25 and 99.1. With the second extended version of the dataset consisting of 2000 samples, precision, specificity, sensitivity and accuracy values were 99.3, 99.6, 100, and 99.75, correspondingly. The T2-FNN results were compared with the results of other existing systems. The comparisons indicate the suitability of the T2-FNN model in diabetes’ diagnosis. Further research includes the learning improvement of the T2-FNN system using evolutionary computation for the diagnosis of other medical diseases.

References 1. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. World Health Organization (2006). https://apps.who.int/iris/handle/ 10665/43588 2. Contreras, I., Vehi, J.: Artificial intelligence for diabetes management and decision support: literature review. J. Med. Internet Res. 20(5) (2018). https://doi.org/10.2196/10775 3. Choubey, D.K., Paul, S.: GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. Int. J. Intell. Syst. Appl. 8(1), 49–59 (2016). https://doi.org/10.5815/ ijisa.2016.01.06 4. Rang, H.P., Dale, M., Ritter, J.M., Flower, R.: Rang & Dale’s Pharmacology. https://www. uk.elsevierhealth.com/rang-dales-pharmacology-e-book-9780702040740.html. Accessed 22 Aug 2021 5. Bush, I.J., Abiyev, R., Ma’aitah, M.K.S., Altıparmak, H.: Integrated artificial intelligence algorithm for skin detection. In: ITM Web of Conferences, vol. 16, pp. 1–6. EDP Sciences (2018). https://doi.org/10.1051/itmconf/20181602004 6. Imanov, E., Altıparmak, H., Imanova, G.: Rule based intelligent diabetes diagnosis system. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F.M. (eds.) ICAFS 2018. AISC, vol. 896, pp. 137–145. Springer, Cham (2019). https://doi.org/10.1007/978-3030-04164-9_20 7. Altiparmak, H., Nurçin, F.V.: Segmentation of microscopic breast cancer images for cancer detection. In: Proceedings of the 2019 8th International Conference on Software and Computer Applications, pp. 268–271 (2019) 8. Polat, K., Güneş, S., Arslan, A.: A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine. Expert Syst. Appl. 34(1), 482–487 (2008). https://doi.org/10.1016/J.ESWA.2006.09.012 9. Singh, A.A.G., Leavline, E.J., Baig, B.S.: Diabetes prediction using medical data. J. Comput. Intell. Bioinform. 10(1), 1–8 (2017) 10. Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., Tang, H.: Predicting diabetes mellitus with machine learning techniques. Front. Genet. 9 (2018). https://doi.org/10.3389/FGENE.2018. 00515

746

H. Altıparmak et al.

11. Erkaymaz, O., Ozer, M., Perc, M.: Performance of small-world feedforward neural networks for the diagnosis of diabetes. Appl. Math. Comput. 311, 22–28 (2017). https://doi.org/10. 1016/J.AMC.2017.05.010 12. Christopher, J.J., Nehemiah, H.K., Kannan, A.A.: Swarm Optimization approach for clinical knowledge mining. Comput. Methods Programs Biomed. 121(3), 137–148 (2015). https:// doi.org/10.1016/J.CMPB.2015.05.007 13. Kannadasan, K., Edla, D.R., Kuppili, V.: Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clin. Epidemiol. Glob. Heal. 7, 530–535 (2019). https://doi.org/10.1016/J.CEGH.2018.12.004 14. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. (Ny) 8, 199–249 (1975). https://doi.org/10.1016/0020-0255(75) 90036-5 15. Mendel, J.M.: Uncertain Rule-Based Fuzzy Systems. Introduction and New Directions, 2nd edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51370-6 16. Karnik, N.N., Mendel, J.M.: Applications of type-2 fuzzy logic systems to forecasting of time-series. Inf. Sci. (Ny) 120, 89–111 (1999). https://doi.org/10.1016/S0020-0255(99) 00067-5 17. Abiyev, R.H.: A type-2 fuzzy wavelet neural network for time series prediction. In: GarcíaPedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010. LNCS (LNAI), vol. 6098, pp. 518–527. Springer, Heidelberg (2010). https://doi.org/10.1007/9783-642-13033-5_53 18. Abiyev, R.H., Kaynak, O., Alshanableh, T., Mamedov, F.: A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Appl. Soft Comput. 11(1), 1396–1406 (2011). https://doi.org/10.1016/J.ASOC. 2010.04.011 19. Abiyev, R.H., Kaynak, O.: Type 2 fuzzy neural structure for identification and control of time-varying plants. IEEE Trans. Ind. Electron. 57(12), 4147–4159 (2010). https://doi.org/ 10.1109/TIE.2010.2043036 20. Abiyev, R., Abizada, S.: Type-2 fuzzy wavelet neural network for estimation energy performance of residential buildings. Soft. Comput. 25(16), 11175–11190 (2021). https:// doi.org/10.1007/s00500-021-05873-4 21. Abiyev, R.H.: Credit rating using type-2 fuzzy neural networks. Math. Probl. Eng. (2014). https://doi.org/10.1155/2014/460916 22. Shafaei, B.N., Vahidian, K.A., Nasli, E.E., Zare, A.: Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model. Eur. J. Oper. Res. 264, 859–869 (2018). https://doi.org/10.1016/J.EJOR.2017.07.046 23. Mohammed, H.A., Hagras, H.: Towards developing type 2 fuzzy logic diet recommendation system for diabetes. In: Proceedings of the 10th Computer Science and Electronic Engineering Conference, CEEC 2018, pp. 56–59 (2018). https://doi.org/10.1109/CEEC. 2018.8674186 24. Abiyev, R.H., Altiparmak, H.: Type-2 fuzzy neural system for diagnosis of diabetes. Math. Probl. Eng. 2021 (2021). https://doi.org/10.1155/2021/5854966. Article ID 5854966 25. Goharimanesh, M., Lashkaripour, A., Akbari, A.: A comparison of fuzzy types 1 and 2 in diabetics control, based on augmented minimal model. J. World Electric. Eng. Tech 4(2), 70–75 (2015) 26. Dataset of diabetes. https://www.kaggle.com/johndasilva/diabetes 27. Pima Indians Diabetes Database. https://www.kaggle.com/uciml/pima-indians-diabetesdatabase

Diagnosis of Diabetes Using Type-2 Fuzzy System

747

28. Sreedevi, E., Padmavathamma, M.A.: A threshold genetic algorithm for diagnosis of diabetes using Minkowski distance method. Int. J. Innov. Res. Sci. Eng. Technol. 4(7), 1–6 (2015). https://doi.org/10.15680/IJIRSET.2015.0407103 29. Bozkurt, M.R., Yurtay, N., Yilmaz, Z., Sertkaya, C.: Comparison of different methods for determining diabetes. Turkish J. Electr. Eng. Comput. Sci. 22, 1044–1055 (2014) 30. Sa’di, S., Maleki, A., Hashemi, R., Panbechi, Z., Chalabi, K.: Comparison of data mining algorithms in the diagnosis of type II diabetes. Int. J. Comput. 5(5), 1–12 (2015) 31. Mercaldo, F., Nardone, V., Santone, A.: Diabetes mellitus affected patients classification and diagnosis through machine learning techniques. Procedia Comput. Sci. 112, 2519–2528 (2017). https://doi.org/10.1016/J.PROCS.2017.08.193 32. Rabina, E., Anshu, C.: Diabetes prediction by supervised and unsupervised learning with feature selection. Int. J. Adv. Res. 2(5), 1–6 (2016) 33. Nabi, M., Wahid, A., Kumar, P.: Performance analysis of classification algorithms in predicting diabetes. Int. J. Adv. Res. Comput. Sci. 8(3), 456–460 (2017) 34. Tang, P.H., Tseng, M.H.: Medical data mining using BGA and RGA for weighting of features in fuzzy k-NN classification. In: Proceedings of the 2009 International Conference on Machine Learning Cybernetics, vol. 5, pp. 3070–3075 (2009). https://doi.org/10.1109/ ICMLC.2009.5212633 35. Maniruzzaman, M., et al.: Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm. Comput. Methods Programs Biomed. 152, 23–34 (2017). https://doi.org/10.1016/J.CMPB.2017.09.004 36. Bashir, S., Qamar, U., Khan, F.H.: IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J. Biomed. Inform. 59, 185–200 (2016). https://doi.org/10.1016/J.JBI.2015.12.001 37. Aslam, M.W., Zhu, Z., Nandi, A.K.: Feature generation using genetic programming with comparative partner selection for diabetes classification. Expert Syst. Appl. 40, 5402–5412 (2013). https://doi.org/10.1016/J.ESWA.2013.04.003

Hesitant Fuzzy Sets

Neutrosophic Hesitant Fuzzy Optimization Approach for Multiobjective Programming Problems Firoz Ahmad(B)

and M. Mathirajan

Department of Management Studies, Indian Institute of Science, Bangalore 560012, India [email protected]

Abstract. This paper presents the multiobjective programming problems (MOPPs) while considering the neutrosophic hesitant fuzzy situation. The indeterminacy and hesitancy degrees are incorporated in MOPPs and introduce the neutrosophic hesitant fuzzy multiobjective programming problems (NHFMOPPs). Three different membership functions determine the marginal evaluation for each objective function. Additionally, a new solution approach for solving the NHFMOPPs is investigated. The proposed method is applied to the manufacturing system problem. At last, conclusions along with a comparative study are discussed in the paper. Keywords: Neutrosophic set · Hesitant fuzzy set · Indeterminacy members · Hesitant fuzzy members · Multiobjective optimization

1

Introduction

The multiobjective modeling and formulations of real-life problems are ubiquitous in day-to-day life. The structure of mathematical programming models depends on the characteristic features of the optimizing criteria, such as deterministic framework, vague and random uncertainties, etc. Getting a single optimal solution is quite tricky for multiple objectives, but a set of compromise solutions can be obtained. Hence, the efforts are being made in the direction of obtaining the best possible solution to the MOPPs under the desired optimization environment. Zadeh et al. (1996) was the first who introduced the fuzzy set (FS), and afterward, the researchers popularly adopted it in developing the multiple criteria, multiple attributes, and multiobjective decision-making solution techniques. The first who introduced the idea of the fuzzy programming approach (FPA) was (Zimmermann 1978) for solving the MOPPs, containing the membership function (degree of Supported by Indian Institute of Science Bangalore India. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 751–762, 2022. https://doi.org/10.1007/978-3-031-09176-6_83

752

F. Ahmad and M. Mathirajan

belongingness) of each element. The generalization of the fuzzy set is presented by Atanassov (1986) and named an intuitionistic fuzzy set (IFS). The characteristic features of the IFS include membership and non-membership functions of the components or parameters. Using the IFS concept, (Angelov 1997) first explored the optimization technique, named; the intuitionistic fuzzy programming approach (IFPA) for MOPPS. In practice, the decision-makers may not be specific about the single known values of membership grades but can depict it as a set of values. To tackle such cases, (Torra and Narukawa 2009) proposed a hesitant fuzzy set (HFS). The HFS allows representing the membership degree (degree of belongingness) by replacing the unique values with membership grades. Moreover, more flexibility or versatility in the FS and IFS has been introduced by incorporating indeterminacy degree or neutral thoughts (Smarandache 1999) and termed as neutrosophic set (NS). The structure of NS complies with triplets members in the set. Three different members are depicted in the NS, which can be revealed as truth, indeterminacy, and falsity membership grades. By utilizing the NS, the neutrosophic fuzzy programming approach came into existence and was drastically used in multi-criteria decision-making problems. Many existing decision sets and corresponding optimization approaches are not capable enough to represent the different scope of expert/decision-maker/manager perceptions, such as the existence of indeterminacy and hesitations degrees during decision-making processes. By considering the two crucial aspects, indeterminacy and hesitations degree, this study has been fully benefited or accomplished with a single-valued neutrosophic hesitant fuzzy (SVNHF) set and, simultaneously, NHFMOPP has been investigated. The developed NHFMOPP provides the most promising optimization scenario by reducing risk-reliability violations and ensuring the best compromise solution for MOPPs. Furthermore, a new solution is suggested for solving the obtained NHFMOPPs and termed as neutrosophic hesitant fuzzy Pareto optimal solutions (NHFPOSs). The rationality and robustness of NHFPOS are discussed by applying the different tests for optimality. The ample opportunity to interact with various experts will be fruitful in discrete decisionmaking scenarios. Thus, the proposed structure of NHFMOPPs will be a helpful technique and widely been accepted for solving NHFMOPPs. The rest of the portion of this research article is organized as follows: In Sect. 2, some Literature reviews are discussed. In contrast, Sect. 3 represents the formulation of neutrosophic hesitant fuzzy MOPPs. A computational example taken from the manufacturing system is elaborately discussed in Sect. 4. Some comparative study is highlighted in the context of the developed NHFPOSs of NHFMOPPs with different approaches. Finally, concluding remarks and future extended scope is discussed in Sect. 5.

2

Literature Review

Multiobjective optimization techniques have widely been discussed in different situations, and a vast amount of literature is dedicated to various MOPPs and the related optimization method. Recently, Ahmad et al. (2022) discussed a

Neutrosophic Hesitant Fuzzy Optimization Approach

753

solution approach for the MOPPs using a hesitant fuzzy aggregation operator. The different weighted operators (geometric) have been used for evaluating the most promising compromise solution set. Ahmad and Smarandache (2021) performed the study on MOPPs by presenting the robust mathematical programming models and the corresponding optimization approach in the neutrosophic environment. Mahmoodirad et al. (2018) also discussed the relevant approach for solving the transportation using the MOPP in the IF situation. Ahmad and John (2021) introduced an efficient intuitionistic fuzzy solution approach for multiobjective optimization problems and applied it to the pharmaceutical supply chain. Ahmad et al. (2018) also attempted to develop the neutrosophic hesitant fuzzy algorithm for nonlinear MOPPs. Bharati (2018) suggested the fuzzy-based solution approach for solving the MOPPs. Moreover, Ahmad (2021a, 2021b) presented the neutrosophic optimization approach for MOPPs. Zhou and Xu (2018) investigated a novel algorithm that is based on the hesitant fuzzy set for optimizing the multiobjective portfolio problems. Ahmad et al. (2020) discussed the modified neutrosophic methods for modeling and optimizing the multiobjective closed-loop supply chain. Abdel-Basset et al. (2018) investigated a fully neutrosophic linear programming problem for dealing with the MOPPS. Ahmad et al. (2019) also optimized the water resource allocation problem arising in the shale gas extraction processes using the neutrosophic programming approach. All the above-discussed studies target the optimization method for MOPPs under fuzzy and the extended fuzzy environment. A narrow portion of research work is oriented to the optimization approach under the neutrosophic hesitancy scenarios. Of course, dealing with the MOPPs is quite tricky and challenging under the diverse neutrosophic hesitancy scenarios. The two aspects (indeterminacy and hesitancy) of uncertainty make the problem more complex. Handling such complexity leads to the computational burden and iterative procedures being lengthy. In short, the neutrosophic hesitant fuzzy set can be a handy tool for tackling situations. Thus, we have developed the NHFMOPPs and NHFPOSs to get better solution results, and the performance measures are also depicted by tuning the different parameters.

3

Neutrosophic Hesitant Fuzzy MOPPs

A mathematical programming problem having more than one objective function (commensurable/conflicting) can be formulated as MOPPs. The classical structure of the MOPPs can be depicted as follows: M inimize (F1 (¯ x), F2 (¯ x), · · · , Fp (¯ x)) s.t. R(¯ x)(≤ or = or ≥)0 x ¯≥0

(1)

¯) represents real x) depicts the pth objective functions. The term R(x where Fp (¯ valued function and the possible set decision variable is represented by x ¯. In the MOPP Eq. (1), it is well-known that decision-makers are interested in achieving the neutrosophic fuzzy goals of each objectives simultaneously. Dealing

754

F. Ahmad and M. Mathirajan

with this circumstance, the MOPP Eq. (1) can be converted into neutrosophic fuzzy multiobjective programming problem (NFMOPP) which can be furnished as follows: ˜ (N F M OP P ) M inimize (F˜1 (¯ x), F˜2 (¯ x), ..., F˜p (¯ x)) s.t. R(¯ x)(≤ or = or ≥)0 x ¯≥0

(2)

˜ depicts a flexible neutrosophic fuzzy notion of (·), which has the where symbol (·) literal meaning of “minimizing the functions as much as possible with respect to the neutrosophic fuzzy scenario”. A neutrosophic fuzzy approach is executed by determining a set of x ¯ possible solutions and neutrosophic fuzzy goals Gi , i = 1, 2, · · · , p, along with a set of neutrosophic fuzzy Hence, the fuzzy decision set is defined by D = G ∩ C. Consequently, the neutrosophic fuzzy decision set DN , along with neutrosophic objectives and constraints, may be furnished as follows: DN = (∩pi=1 Gi )(∩m j=1 Cj ) = (x, μD (x), λD (x), νD (x) ) 

where μD (x) = min

μG1 (x), μG2 (x), ..., μGi (x) μC1 (x), μC2 (x), ..., μCj (x)

 ∀ x∈X

 λG1 (x), λG2 (x), ..., λGi (x) λD (x) = max ∀ x∈X λC1 (x), λC2 (x), ..., λCj (x)   νG1 (x), νG2 (x), ..., νGi (x) νD (x) = max ∀ x∈X νC1 (x), νC2 (x), ..., νCj (x) 

(3) (4) (5)

The truth, indeterminacy and a falsity membership functions can be represented by μD (x), λD (x) and νD (x). The lower and upper bounds Li and Ui for each objectives can be depicted as below: Ui = max [Fi (x)] and Li = min [Fi (x)]

∀ i = 1, 2, 3, · · · , p.

(6)

Similarly, pth objective function bound can be given as below: Uiμ = Ui , Uiλ = Lμi + yi , Uiν

=

Uiμ ,

Lμi = Li Lλi = Lμi Lνi

=

Lνi

f or truth membership f or indeterminacy membership

+ zi

f or f alsity membership

where yi and zi ∈ (0, 1) represents the real numbers which are known in advance. Thus, the different membership functions can be defined as follows: ⎧ if Fi (x) < Lμi ⎪ ⎨1 F fi (x)−LT μGi (x) = 1 − U μ −Lμ i if Lμi ≤ Fi (x) ≤ Uiμ (7) i i ⎪ ⎩ 0 if Fi (x) > Uiμ

Neutrosophic Hesitant Fuzzy Optimization Approach

⎧ ⎪ ⎨1 λGi (x) = 1 − ⎪ ⎩ 0 ⎧ ⎪ ⎨1 νGi (x) = 1 − ⎪ ⎩0

Fi (x)−Lλ i Uiλ −Lλ i

Uiν −Fi (x) Uiν −Lν i

755

if Fi (x) < Lλi

if Lλi ≤ Fi (x) ≤ Uiλ if Fi (x) > Uiλ

(8)

if Fi (x) > Uiν if Lνi ≤ Fi (x) ≤ Uiν if Fi (x) < Lνi

(9)

Once the neutrosophic decision DN is defined, then one can summarize x∗ ∈ X as an optimal decision with the following condition: μD (x∗ ) = maxx∈X μD (x), λD (x∗ ) = minx∈X λD (x) and νD (x∗ ) = minx∈X νD (x)

Afterward, by determining the different membership functions μGi (x), λGi (x) and νGi (x) in the neutrosophic decision set (Ahmad et al. 2019), the NFMOPP (2) can be transformed into the following problem: M aximize M inimize M inimize s.t.

min (μG1 (x), μG2 (x), · · · , μGp (x)) max (λG1 (x), λG2 (x), · · · , λGp (x)) max (νG1 (x), νG2 (x), · · · , νGp (x)) x ∈ X.

(10)

Now the above problem (10) can be transformed into the following (11): M aximize φ (φ = α − β − γ) s.t. μGi (x) ≥ α, λGi (x) ≤ β νGi (x) ≤ γ, μGi (x) ≥ λGi (x) μGi (x) ≥ νGi (x), 0 ≤ α, β, γ ≤ 1 x ∈ X, ∀ i = 1, 2, · · · , p.

(11)

The problem (11) can be considered as neutrosophic optimization model, see; Ahmad (2021b), Ahmad et al. (2018). With the help of different membership functions (7), (8) and (9), the problem (11) should have a unique solution. Elsewhere, Pareto optamality test can be examined by summarizing the following model (12): p M ax i=1 ηi s.t. μGi (x) − ηi = μGi (x∗ ) λGi (x) + ηi = λGi (x∗ ) (12) νGi (x) + ηi = νGi (x∗ ) x ∈ X, ∀ i = 1, 2, · · · , p. where η = (η1 , η2 , · · · , ηp )T and x∗ is an optimal solution of problem (10) and (12). For presenting the neutrosophic hesitant fuzzy MOPPs, we recall the neutrosophic (Smarandache 1999) and hesitant fuzzy (Torra and Narukawa 2009)

756

F. Ahmad and M. Mathirajan

concept simultaneously. The extended version of NFMOPP (2) can be represented as NHFMOPP (13) (Ahmad et al. 2018). ˜˜ ˜2 (x), ..., F˜ ˜p (x)) (N HF M OP P ) M inimize (F˜˜1 (x), F˜ s.t. R(¯ x)(≤ or = or ≥)0 x ¯≥0

(13)

˜˜ The notions (·) depicts a flexible neutrosophic hesitant fuzzy notion of (·), which has the literal meaning of “minimizing the functions as much as possible with respect to the neutrosophic hesitant fuzzy scenario”. A neutrosophic hesitant fuzzy decision set DhN can be represented as below: G˜i = {x, μG˜i (x), λG˜i (x), νG˜i (x) | x ∈ X} In a simple way, the set of various membership functions in neutrosophic hesitant situation can be written as below: ⎧ ⎪ ⎨ μG˜i (x) = {μG1i (x), μG2i (x), · · · , μGlii (x)} hG˜ i (x) = λG˜i (x) = {λG1i (x), λG2i (x), · · · , λGlii (x)} (14) ⎪ ⎩ ν (x) = {ν 1 (x), ν 2 (x), · · · , ν l (x)} ˜i Gi Gi G Gi i

Remark 1: One should note that the different membership functions μGki (x), λGki (x) and νGki (x) for all i = 1, 2, · · · , p and ki = 1, 2, · · · , li should i i i be decreasing (or increasing) functions similar to Eqs. (7), (8) and (9); where li is the number of experts or decision makers who assigns the aspiration levels for the objective functions O˜˜i (x) for all i = 1, 2, · · · , p, in neutrosophic fuzzy environment. We proceed towards the proposed optimization technique in the next Subsect. 3.1. 3.1

Proposed Optimization Approach

Assuming that the decision-maker is planning to obtain the optimal solution for NHFMOPP (13). For this purpose, let us define the neutrosophic hesitant fuzzy decision set DhN (Ahmad et al. 2021; Ahmadini and Ahmad 2021) as below: DhN = G˜1 ∩ G˜2 ∩ · · · ∩ G˜p = {x, hDhN (x)} according to the neutrosophic hesitant degree hDhN (x).

⎧ μDN (x) = ∪μ ˜ (x)∈h ˜ (x),··· ,μ ˜ (x)∈h ˜ (x) | min {μG˜1 (x), · · · , μG˜p (x)} ⎪ ⎪ G1 Gp h ⎪  Gp  G1 ⎪ ⎪ ⎪ ⎪ = min {μG˜ (x)}pi=1 ⎪ i ⎪ ⎪ ⎪ ⎨ λDN (x) = ∪λ ˜ (x)∈h ˜ (x),··· ,λ ˜ (x)∈h ˜ (x) | max {λG˜ (x), · · · , λG˜ (x)} p 1 G1 Gp h  Gp  G1 hDN (x) = p ⎪ h (x)} = max {λ ⎪ ˜i i=1 G ⎪ ⎪ ⎪ ⎪ νDN (x) = ∪ν ˜ (x)∈h ˜ (x),··· ,ν ˜ (x)∈h ˜ (x) | max {νG˜1 (x), · · · , νG˜p (x)} ⎪ ⎪ G G Gp h ⎪ 1  Gp  1 ⎪ ⎪ ⎩ = max {νG˜ (x)}pi=1 i

(15)

Neutrosophic Hesitant Fuzzy Optimization Approach

757

for each x ∈ X. The members of μDhN (x) can be regarded as the lowest truth hesitant membership set whereas λDhN (x) and νDhN (x) can be considered as the highest indeterminacy and falsity hesitant membership sets. Additionally, μDhN (x), λDhN (x) and νDhN (x) exhibits the truth, indeterminacy and a falsity hesitant degrees of acceptance for the NHFMOPPs. The score function for μDhN (x), λDhN (x) and νDhN (x) can be determined using arithmetic-mean and consequently the following problem can be obtained (16): max (χ(μG˜1 (x)), χ(μG˜2 (x)), · · · , χ(μG˜p (x))) − min (χ(λG˜1 (x)), χ(λG˜2 (x)), · · · , χ(λG˜p (x))) − min (χ(νG˜1 (x)), χ(νG˜2 (x)), · · · , χ(νG˜p (x))) (16) l i

where χ(μG˜i (x)) = l i

j=1

ν

j=1

l i

μ li

˜j (x) G i

j=1

, χ(λG˜i (x)) =

λ li

˜j (x) G i

and χ(νG˜i (x)) =

˜j (x) G i

are the arithmetic-mean score function of μG˜i (x), λG˜i (x) and νG˜i (x), respectively. For solving the above problem (16), the weighted sum method is used. In simple manner, the model (16) can be transformed into (17):

p M aximize ζ = ˜i (x)) − χ(λG ˜i (x)) − χ(νG ˜i (x) i=1 wi χ(μG s.t. μG˜p (x) ≥ α, λG˜p (x) ≤ β νG˜p (x) ≤ γ, μG˜p (x) ≥ λG˜p (x) (17) μG˜p (x) ≥ νG˜p (x), 0 ≤ α, β, γ ≤ 1 x ∈ X, ∀ i = 1, 2, · · · , p. p where w = (w1 , w2 , · · · , wp ) ( i=1 wi = 1) is the set of non-negative weights which is usually assigned by the decision maker to each objectives. Now, we succesfully transform the NHFMOPP (13) into single-objective programming with the help of Theorem 1 discussed below. p Theorem 1. Let us consider w = (w1 , w2 , · · · , wp ) ( i=1 wi = 1) is a nonnegative set of weights assigned to each objectives. If the optimal solution is x∗ for the problem (17) then NHFPOS for the NHFMOPP will satisfy < x∗ , Hp (x∗ ) | x∗ ∈ X >. li

Proof. By assuming < x∗ , Hp (x∗ ) | x∗ ∈ X > will not satisfy NHFPOS and simultaneously will not be optimal for the NHFMOPP. Hence, there must be existence of < x, Hp (x) | x ∈ X > in such a way that μG˜ ki (x) ≥ μG˜ ki (x∗r ), λG˜ ki (x) ≤ λG˜ ki (x∗r ) and νG˜ ki (x) ≤ νG˜ ki (x∗r ) for all i i i i i i i = 1, 2, · · · , p, ki = 1, 2, · · · , li , and μ ˜ kj (x) > μ ˜ kj (x∗r ), λ ˜ kj (x) < λ ˜ kj (x∗r ) Gj

Gj

Gj

Gj

and ν ˜ kj (x) < ν ˜ kj (x∗r ) for at least one j ∈ {1, 2, · · · , p} and kj = 1, 2, · · · , lj . Gj

Gj

We have all the non-negative weight vectors, then p p wi χ(μG˜i (x)) > i=1 wi χ(μG˜i (x∗)) i=1 p p wi χ(λG˜i (x)) <  i=1 wi χ(λG˜i (x∗)) i=1 p p ˜i (x)) < ˜i (x∗)) i=1 wi χ(νG i=1 wi χ(νG

(18)

758

F. Ahmad and M. Mathirajan

All the inequalities in Eq. (18) contradict the optimality of x∗ for the problem (17). Thus, the proof is over. 3.2

Solution Algorithm

1. Firstly define the various membership functions such as μ  ki (x), λ  ki (x) and Gi

Gi

 ν i (x), λG i (x), νG i (x) | ki (x) ∀ ki = 1, 2, · · · , li DMs and develop Gi = {x, μG Gi

x ∈ X} such that μG i (x) = {μG1i (x), μG2i (x), · · · , μGli (x)}, λG i (x) = i {λG1i (x), λG2i (x), · · · , λGli (x)} and νG i (x) = {νG1i (x), νG2i (x), · · · , νGli (x)} for i i all goals Oi ∀ i = 1, 2, · · · , p. 2. Determine the score function using the arithmetic-mean approach χ(μG˜i (x)), χ(λG˜i (x)) and χ(νG˜i (x)) for each μG˜i (x), λG˜i (x) and νG˜i (x). 3. The different positive weights should be assigned as wi for each i-th objectives Oi (x) corresponding to the preference of the different experts’ opinion. Formulate the model (17) for getting the optimal values of x∗r , defined on < x∗ , Hp (x∗ ) >.

4

Numerical Example: Manufacturing System Problem

The discussed numerical example is adopted from Ahmad et al. (2018), Singh and Yadav (2015). Based on the given data, the unit cost of each product P1 , P2 and P3 are c1 = 8, c2 = 10.125 and c3 = 8, and the respective sale’s price are; 119.875 and s3 = 95.125 s1 = 99.875 −1/2 , s2 = −1/2 −1/3 . The decision maker intends and shows x1

x2

x3

the interest for maximum profit and minimum total time needed. With the help of available data (Singh and Yadav 2015), the formulations and modeling of MOPPs take the form of nonlinear MOPPs (19), represented as follows: 1

1

1

M in F1 (x) −99.875x12 + 8x1 − 119.875x22 + 10.125x2 − 95.125x33 + 8x3 M in F2 (x) 3.875x1 + 5.125x2 + 5.9375x3 s.t. 2.0625x1 + 3.875x2 + 2.9375x3 ≤ 333.125 3.875x1 + 2.0625x2 + 2.0625x3 ≤ 365.625 2.9375x1 + 2.0625x2 + 2.9375x3 ≥ 360 x1 , x2 , x3 ≥ 0. (19) The above formulated nonlinear MOPPs (19) are written in AMPL language, and the CONOPT solver is used to solve the final optimization model; see Server (2016). The individual maximum and minimum values for each objectives (19) are U1 = −180.72, L1 = −516.70, L2 = 599.23 and U2 = 620.84 respectively. In the first phase, only one expert’s opinion about the first and second objectives was taken to determine the aspiration levels.

Neutrosophic Hesitant Fuzzy Optimization Approach

759

With the aid of (11), the corresponding nonlinear neutrosophic hesitant MOPP is furnished follows (20): M aximize φ (φ = α − β − γ) s.t.

1

1

1

1

1

1

1 (99.875x12

1 −8x1 +119.875x22

(99.875x12 −8x1 +119.875x22 −10.125x2 +95.125x33 −8x3 )t −(−180.72)t (−516.70)t −(−180.72)t

≥α

(99.875x12 −8x1 +119.875x22 −10.125x2 +95.125x33 −8x3 )t −(−180.72)t (y1 )t

≤β

1 −10.125x2 +95.125x33 −8x3 )t −(−180.72)t t t (−516.70) −(−180.72) −(z1 )t (620.84)t −(3.875x1 +5.125x2 +5.9375x3 )t (620.84)t −(599.23)t (620.84)t −(3.875x1 +5.125x2 +5.9375x3 )t (y2 )t (3.875x1 +5.125x2 +5.9375x3 )t −(599.23)t −(t2 )t (620.84)t −(599.23)t −(z2 )t

≤ γ (20)

≥α ≤β

≤γ

constraints (19) The nonlinear neutrosophic MOPP (20) is solved at t = 2 and the compromise solution is obtained x = (60.60, 4.90, 58.50), F1 = 409.70, F2 = 607.28 along with the satisfaction level φ∗ = 0.62. One can easily interprate φ∗ = 0.62 as an overall percentage of decision makers’ satisfaction level which is 62%. Suppose that first objective F1 has a preference over the second F2 , for which we assign some weights such as w1 = 0.65 and w2 = 0.35. By applying the developed optimization approach, the following weighted nonlinear neutrosophic problem (21) can be formulated: max

0.65

μ

+0.35 s.t.

G1 1

μ

(x)+μG2 (x)−λG1 (x)−λG2 (x)−νG1 (x)−νG2 (x)

G1 2

1

1

1

1



1

2 (x)+μG2 (x)+μG3 (x)−λG1 (x)−λG2 (x)−λG3 (x)−νG1 (x)−νG2 (x)−νG3 (x) 2

2

2

2

2

3

2

2

2

x ∈ X.

(21) The weighted nonlinear neutrosophic programming (21) is solved and got the optimal values as x∗ = (60.48, 5.26, 58.37), and φ∗ = 0.99. The corresponding values of each objective is determined as F1 = 416.58, F2 = 607.88. The summary of solution results using NHFPOSs is furnished in Table 1. Table 1. Manufacturing system: Optimal outcomes using proposed optimization approach. Solution method

Objective values Deviations Satisfaction level Max. O1 Min. O2 (U1 − Z1 ) (Z2 − L2 )

Zimmerman’s technique (Singh and 409.70 Yadav 2015)

λ = 0.62

607.28

107

8.05

γ-operator (Singh and Yadav 2015) 288.86

599.64

227.84

0.41(min.) δ(x) = 0.96

Min. bounded sum operator (Singh 416.58 and Yadav 2015)

607.88

100.12

8.65

ψ(x) = 0.97

Proposed optimization approach

607.88

100.12

8.65

ζ = 0.99 (max)

416.58

760

F. Ahmad and M. Mathirajan

From the logical, technical, and contribution point of view, it should be made clear about the performances and superiority of the proposed NHFPOSs for the NHFMOPPs. It should be justified or clarify how the discussed approach outperforms the existing algorithms and what the scope for implementing the proposed approach is? Moreover, efficiently, to address these questions, as we all know, the literal meaning of neutral thoughts and opinions varies based on human ideology and perceptions. Incorporation of the neutral thoughts and indeterminacy degrees of different decision-makers and the distinguished experts in any problem is quite inter-related and inter-connected with real-life situations. It leads toward the reality closer and generates more adorable and rational outcomes that enhance the reliability of getting a better solution under the neutrosophic hesitancy condition. Even though it is not claimed in the proposed approach that after incorporating the various experts’ neutral thoughts, the outcomes of the proposed quantitative approach would result in a better alternative under the same neutrosophic hesitancy condition; however, the proposed approach entirely relies on the neutrosophic hesitant degrees provided by the different decisionmakers or experts. As the developed solution approach (NHFPOSs) and NHFMOPPs exhibit the dynamic structure while modeling the real-life problems, anyone can independently introduce or incorporate their neutral thoughts and hesitation degrees togetherly without taking care of others’ satisfaction criteria at the time. Thus, dealing with the independent, inconsistent, and incomplete information during the optimization process creates a wide range of utility and acceptability of the proposed approach for solving the MOPPs. Using the proposed approach, the different weightage can be assigned while dealing with a single programming problem, although the preferences of various experts are different. Hence, the proposed approach would also be beneficial for applying in the existing real-life problems having the structure of MOPPs and, of course, when priorities among the objectives have a significant and indispensable role in the neutrosophic hesitancy scenarios. One more advantage of the proposed approach is that it can aggregate the experts’ or decision-makers opinions/perceptions unanimously under indeterminacy and hesitancy thoughts, only by solving a single programming problem. In determining the solution, there may be the chances of losing some critical information of indeterminacy and hesitancy thoughts.

5

Conclusions

This paper presents the study of a new multiobjective mathematical programming problem under the neutrosophic hesitancy scenarios. A robust approach for formulating and optimizing the MOPPs has been effectively designed and elaborately discussed. The proposed NHFMOPPs are developed by considering unquantified human perceptions such as indeterminacy and hesitancy degrees while making the optimal decisions. The indeterminacy and hesitancy degrees are integrated simultaneously, corresponding to the optimal decision policies closer to reality. A new solution approach called NHFPOSs is investigated by

Neutrosophic Hesitant Fuzzy Optimization Approach

761

integrating the indeterminacy and hesitancy degrees. A numerical example of a manufacturing system is presented, revealing the working efficiency of the suggested solution approach. The performance measure of the propounded approach is analyzed by considering the various experts’ satisfaction levels and yielding a set of compromise solutions. Hence, it would be much more efficient and convenient to tools while dealing with indeterminacy and hesitancy degrees. Due to the space restrictions, some well-known aspects of research are still untouched and can be explored as a future research scope. The presented NHFMOPPs can be extended for bi-level and multi-level optimization problems. The stochastic programming approach can also be incorporated by assuming the random parameters in the NHFMOPPs. Furthermore, using the presented research, it would be more worth modeling real-life problems, such as transportation problems, supplier selection problems, inventory control, portfolio optimization, etc. Some heuristic and meta-heuristic approaches can be introduced for solving the NHFMOPPs.

References Abdel-Basset, M., Gunasekaran, M., Mohamed, M., Smarandache, F.: A novel method for solving the fully neutrosophic linear programming problems. Neural Comput. Appl. 31(5), 1595–1605 (2018). https://doi.org/10.1007/s00521-018-3404-6 Ahmad, F.: Interactive neutrosophic optimization technique for multiobjective programming problems: an application to pharmaceutical supply chain management. Ann. Oper. Res., 1–35 (2021a) Ahmad, F.: Robust neutrosophic programming approach for solving intuitionistic fuzzy multiobjective optimization problems. Complex Intell. Syst. 7(4), 1935–1954 (2021b). https://doi.org/10.1007/s40747-021-00299-9 Ahmad, F., Adhami, A.Y., John, B., Reza, A.: A novel approach for the solution of multiobjective optimization problem using hesitant fuzzy aggregation operator. RAIRO-Oper. Res. 56(1), 275–292 (2022) Ahmad, F., Adhami, A.Y., Smarandache, F.: Single valued neutrosophic hesitant fuzzy computational algorithm for multiobjective nonlinear optimization problem. Neutrosophic Sets Syst. 22, 76–86 (2018) Ahmad, F., Adhami, A.Y., Smarandache, F.: Neutrosophic optimization model and computational algorithm for optimal shale gas water management under uncertainty. Symmetry 11(4), 544 (2019) Ahmad, F., Adhami, A.Y., Smarandache, F.: Modified neutrosophic fuzzy optimization model for optimal closed-loop supply chain management under uncertainty. In: Optimization Theory Based on Neutrosophic and Plithogenic Sets, pp. 343–403. Elsevier (2020) Ahmad, F., Ahmad, S., Zaindin, M.: A sustainable production and waste management policies for Covid-19 medical equipment under uncertainty: a case study analysis. Comput. Ind. Eng. 157(3), 107381 (2021) Ahmad, F., John, B.: A fuzzy quantitative model for assessing the performance of pharmaceutical supply chain under uncertainty. Kybernetes (2021) Ahmad, F., Smarandache, F.: Neutrosophic fuzzy goal programming algorithm for multi-level multiobjective linear programming problems. In: Smarandache, F., Abdel-Basset, M. (eds.) Neutrosophic Operational Research, pp. 593–614. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57197-9 27

762

F. Ahmad and M. Mathirajan

Ahmadini, A.A.H., Ahmad, F.: Solving intuitionistic fuzzy multiobjective linear programming problem under neutrosophic environment. AIMS Math. 6(5), 4556–4580 (2021) Angelov, P.P.: Optimization in an intuitionistic fuzzy environment. Fuzzy Sets Syst. 86(3), 299–306 (1997) Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986) Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manage. Sci. 17(4), B-141–B-164 (1970) Bharati, S.K.: Hesitant fuzzy computational algorithm for multiobjective optimization problems. Int. J. Dyn. Control 6(4), 1799–1806 (2018). https://doi.org/10.1007/ s40435-018-0417-z Mahmoodirad, A., Allahviranloo, T., Niroomand, S.: A new effective solution method for fully intuitionistic fuzzy transportation problem. Soft. Comput. 23(12), 4521– 4530 (2018). https://doi.org/10.1007/s00500-018-3115-z Rizk-Allah, R.M., Hassanien, A.E., Elhoseny, M.: A multi-objective transportation model under neutrosophic environment. Comput. Electr. Eng. 0, 1–15 (2018) Server, N.: State-of-the-Art Solvers for Numerical Optimization (2016) Singh, S.K., Yadav, S.P.: Modeling and optimization of multi objective non-linear programming problem in intuitionistic fuzzy environment. Appl. Math. Model. 39(16), 4617–4629 (2015) Smarandache, F.: A Unifying Field in Logics: Neutrosophic Logic (1999) Torra, V., Narukawa, Y.: On hesitant fuzzy sets and decision. In: IEEE International Conference on Fuzzy Systems, pp. 1378–1382 (2009) Zadeh, L.A., Klir, G.J., Yuan, B.: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers, vol. 6. World Scientific (1996) Zhou, W., Xu, Z.: Portfolio selection and risk investment under the hesitant fuzzy environment. Knowl.-Based Syst. 144, 21–31 (2018) Zimmermann, H.J.: Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst. 1(1), 45–55 (1978)

How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets? Bartlomiej Kizielewicz1 , Andrii Shekhovtsov1 , and Wojciech Salabun1,2(B) 1 Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information ˙ lnierska 49, Technology, West Pomeranian University of Technology in Szczecin, ul. Zo 71-210 Szczecin, Poland {bartlomiej-kizielewicz,wojciech.salabun}@zut.edu.pl 2 National Institute of Telecommunications, Szachowa 1, 04-894 Warsaw, Poland

Abstract. More and more often, we have to deal with uncertain data while making decisions. One popular way to model uncertain data is to use one of the many generalizations of fuzzy sets. In this paper, we would like to draw attention for the use of Hesitant fuzzy sets (HFSs) in solving decision-making problems. The main challenge is the complex algorithms that can guarantee high accuracy and operate on HFSs. The HFS COMET approach is known in the literature but is rarely used due to its complexity. The main contribution of our work is the simplification of the HFS COMET algorithm to make it more applicable. For this purpose, we make comparisons of different score functions, which are used to infer based on a hybrid algorithm that combines the advantages of TOPSIS and COMET methods. Finally, we have shown the efficiency of the proposed approach by using reference rankings and similarity coefficients. Keywords: Uncertainty · Hesitant FuzzySets decision-analysis · MCDA

1

· HFS · Multi-criteria

Introduction

The increasing number of tools for dealing with uncertainty in multi-criteria decision-making leads to the high demand for their various functionalities. The primary tool for dealing with uncertainty is fuzzy logic, characterized by the flexibility and simplicity needed to understand it. Over the last years, many extensions have been developed based on it, such as Intuitionistic fuzzy sets (IFS), Hesitant fuzzy sets (HFS), Pythagorean fuzzy sets (PFS), or Spherical fuzzy sets (SFS) [4,7,15]. Hesitant fuzzy sets were created to fill in the gaps concerning the difficulty of determining the degree of membership of a given element [14]. This difficulty did not arise, as in IFS, from the margin of error or, as in the case of type 2 fuzzy sets c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 763–771, 2022. https://doi.org/10.1007/978-3-031-09176-6_84

764

B. Kizielewicz et al.

(T2FS), from the distribution of possibilities, but from specific possible values that could have been omitted during inference. Hesitant fuzzy sets also have many extensions in the form of dual hesitant fuzzy sets [18], or hesitant fuzzy subsets [1,2]. In multi-criteria decision-making in a Hesitant fuzzy sets environment, point functions are often used to approximate the values of hesitant fuzzy numbers. There are many approaches related to point functions. For example, Narayanamoorthy et rest proposed to use the Hesitant fuzzy sets approach for the problem of selecting an underground hydrogen storage site [11]. Ali et al. used a Weighted interval-valued dual-hesitant fuzzy sets-based approach based on a scoring function to evaluate the quality of learning [3]. Due to their high popularity and diversity, it is not easy to evaluate them accurately. In addition, existing hybrid techniques that combine the advantages of HFS scoring functions with multi-criteria decision-making methods are often prone to the ranking reversal paradox. Therefore, this paper compares the use of six scoring functions in the TOPSIS-COMET approach, which is free from the rankings reversal paradox [9,10]. Our proposed approach is compared using ranking similarity coefficients [12,13]. The paper structure is as follows: In Sect. 2, we presented the initial assumptions about Hesitant fuzzy sets and hesitant fuzzy sets score functions. Section 3 discussed the methods we used and the rank similarity coefficients. In Sect. 4, we showed the use of score functions to approximate the decision matrix and evaluate it using the TOPSIS-COMET approach. Then, we compared the obtained rankings among themselves by the rankings’ similarity coefficients. Finally, in Sect. 5, we presented conclusions and future directions.

2 2.1

Preliminaries Hesitant Fuzzy Sets

This section is devoted to describing the basic definitions and notions of fuzzy set (FS) and its new generalization which are referred to as the hesitant fuzzy set (HFS). An ordinary fuzzy set (FS) A in X is defined as A = {x, A(x) : x ∈ X}, where A : X → [0, 1] and the real value A(x) represents the degree of membership of x in A [5]. Definition 1 [16]. Let X be the universe of discourse. Then, a hesitant fuzzy set (HFS) on X can be write as H = {x, h(x) : x ∈ X}

(1)

where h(x), referred to the hesitant fuzzy element (HFE), is a set of some values in [0, 1] denoting the possible membership degree of the element x ∈ X to the set H.

How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets?

2.2

765

Hesitant Fuzzy Sets Score Functions

This section presents the scoring functions used in the study to approximate the values of Hesitant fuzzy sets. The HFS scoring functions presented are based on Hesitant fuzzy elements functions, a detailed description of which can be found in [6]. The scoring functions used are: 1. The arithmetic-mean score function: 1 S (h (xi )) n i=1 n

S1 (H) =

(2)

2. The geometric-mean score function:  S2 (H) =

n 

 n1 S (h (xi ))

(3)

i=1

3. The minimum score function: S3 (H) = min {S (h (x1 )) , S (h (x2 )) , . . . , S (h (xn ))}

(4)

4. The maximum score function: S4 (H) = max {S (h (x1 )) , S (h (x2 )) , . . . , S (h (xn ))}

(5)

5. The product score function: S5 (H) =

n 

S (h (xi ))

(6)

i=1

6. The fractional score function: S6 (H) = n

n i=1

i=1 S (h (xi )) +

3 3.1

S (h (xi )) n i=1 (1 − S (h (xi )))

(7)

Methods The COMET Method

The Characteristic Objects Method (COMET) is an approach proposed for creating nonlinear models [8]. The decision models created by the COMET method are robust to the ranking reversal paradox due to the use of characteristic objects. The steps of the COMET method are as follows: Step 1. Establishing the problem space. The decision expert determines the problem space by specifying r number of criteria, C1 , C2 , . . . , Cr . Next, a collection is determined for each criterion that includes fuzzy numbers Ci , e.g.,   C˜i1 , C˜i2 , ..., C˜ici according to Eq. (8).

766

B. Kizielewicz et al.

  C1 = C˜11 , C˜12 , . . . , C˜1c1  C2 = C˜21 , C˜22 , . . . , C˜2c2 ...   Cr = C˜r1 , C˜r2 , . . . , C˜rc

(8)

r

where C1 , C2 , . . . , Cr are the fuzzy number orders for all criteria taken into account. Step 2. The characteristic object collection (COs) is formed by using the Cartesian product of the cores of fuzzy numbers at each criterion, as shown in the following Eq. (9). CO = C(C1 ) × C(C2 ) × ...C(Cr )

(9)

This procedure gives us an ordered set of COs, represented by (10). CO1 = C(C˜11 ), C(C˜21 ), ..., C(C˜r1 ) CO2 = C(C˜11 ), C(C˜21 ), ..., C(C˜r2 ) ... COt = C(C˜1c1 ), C(C˜2c2 ), ..., C(C˜rcr )

(10)

where t means the number of COs and can be calculated using Eq. (11). t=

r 

ci

(11)

i=1

Step 3. Calculation of characteristic object preferences COs. The procedure for determining the preference values of the characteristic objects is to compare them pairwise with each other and to assign one of the possible values αij for the Expert Judgment Matrix (M EJ), which is represented by Eq. (12). ⎞ ⎛ α11 α12 . . . α1t ⎜ α21 α22 . . . α2t ⎟ ⎟ (12) M EJ = ⎜ ⎝ ... ... ... ... ⎠ αt1 αt2 . . . αtt where αij is the result of comparing COi and COj . If an object is preferred, it receives 1 point, while a less preferred object receives zero points. In the case of identical preference, each object gets 0.5 points. This step in the classical version of COMET is subjective because it depends entirely on the expertise of the decision-maker and is expressed as (13). However, for this work, the focus is on the TOPSIS-COMET approach, where the TOPSIS method acts as an expert to evaluate the characteristics objects according to [10]. ⎧ ⎨ 0.0, fexp (COi ) < fexp (COj ) αij = 0.5, fexp (COi ) = fexp (COj ) (13) ⎩ 1.0, fexp (COi ) > fexp (COj )

How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets?

767

When all comparisons of characteristic objects have ended, a vertical vector containing the Summed Judgment SJ is computed from the M EJ matrix. The values of the vertical vector SJ can be calculated using the Eq. (14). SJi =

t 

αij

(14)

j=1

Finally, the preference values for each characteristic object are calculated. As a result, a vertical vector P is obtained in which i -th row contains an approximation of the preference value calculated for COi . Step 4. According to the Eq. (15), all Characteristic Objects COs and their values are converted to fuzzy rules.     (15) IF C C˜1i AND C C˜2i AND . . . THEN Pi Therefore, a complete base of fuzzy rules is acquired. Step 5. Fuzzy inference and evaluation of decision alternatives. A set of values represents each decision option, e.g., Ai = {αi1 , αi2 , . . . , αri }. It is also associated with the criteria C1 , C2 , ..., Cr . The ratings for the i−th decision option are obtained using the Mamdani fuzzy inference technique. Due to the recommendation of an invariant rule base, the obtained ratings are unambiguous, and the COMET method is resistant to the rank reversal paradox [8].

4

Study Case

This section will present research on comparing HFS scoring functions for the obtained ratings and rankings from the TOPSIS-COMET approach. The decision matrix presented using Table 1 from Yang and Hussain’s work on ratings of transportation service companies was used to conduct the research [17]. Table 1. The decision matrix used in the study was derived from [17]. Ai Q1

Q2

Q3

Q4

A1 {0.212, 0.365, 0.536}

{0.435, 0.518,0.614, 0.805, 0.912}

{0.158, 0.314, 0.536}

{0.312, 0.509}

A2 {0.356, 0.525, 0.612}

{0.365, 0.405, 0.714}

{0.405, 0.514, 0.615, 0.712, 0.914} {0.212, 0.408, 0.514}

A3 {0.165, 0.255, 0.455, 0.500}

{0.275, 0.320, 581}

{0.311, 0.473, 0.518, 0.806}

A4 {0.720, 0.914}

{0.419, 0.516, 0.612, 0.714, 0.905} {0.239, 0.405, 0.518}

A5 {0.203, 0.314, 0.545, 0.603, 0.814} {0.104, 0.318, 0.455}

{0.302, 0.543, 0.662}

{0.527,0.644,0.725, 0.833, 0.914} {0.455, 0.509, 626} {0.608, 0.716, 0.815, 0.923}

The above decision matrix was approximated to ordinal numerical values using HFS scoring functions (2), (3), (4), (5), (6), (7). Using the scoring functions on the decision matrix is shown using Fig. 1. Then, the values thus obtained were used to define TOPSIS-COMET models. The weights for each criterion were assigned as equal, and the characteristic values for each criterion were defined as (0, 0.5, 1).

768

B. Kizielewicz et al.

The obtained ratings for the given scoring functions are presented using Table 2. The largest differences in ratings are seen between alternatives A1 , A2 , A3 , A5 and alternative A4 . Alternative A4 achieved the highest rating for all scoring functions using the TOPSIS-COMET method.

Fig. 1. Decision matrix values obtained using S1 −S6 scoring functions. Table 2. Obtained evaluations of alternatives A1 −A5 for the proposed HFS TOPSISCOMET approach. Ai S1

S2

S3

S4

S5

S6

A1 0.42691 0.17583 0.29265 0.52923 0.22927 0.37883 A2 0.51663 0.17434 0.35427 0.64374 0.21341 0.52529 A3 0.49858 0.12187 0.32911 0.65040 0.22082 0.46501 A4 0.64543 0.35687 0.53029 0.70257 0.44203 0.72330 A5 0.52367 0.13876 0.32734 0.66025 0.24226 0.48010

Table 3 shows the obtained rankings for the proposed HFS scoring function approaches with the TOPSIS-COMET method. Among all the obtained rankings, the highest position was obtained by alternative A4 . However, the rest of the positions among the rankings varied, and none of the proposed approaches achieved the same ranking.

How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets?

769

Table 3. Rankings of A1 −A5 alternatives for the HFS TOPSIS-COMET approach. HFS TOPSIS-COMET Si Ranking S1

A4 > A5 > A2 > A3 > A1

S2

A4 > A1 > A2 > A5 > A3

S3

A4 > A2 > A3 > A5 > A1

S4

A4 > A5 > A3 > A2 > A1

S5

A4 > A5 > A1 > A3 > A2

S6

A4 > A2 > A5 > A3 > A1

A visualization of the preferences and rankings from the proposed approaches is presented using Fig. 2. Both preference visualization and rankings point to alternative A4 as the best choice. When visualizing the preferences, the apparent distribution of preferences is most uniform for the scoring function S4 and S1 . On the other hand, the most significant divergence of the obtained ratings is visible for the scoring functions S5 and S2 . In the case of the visualization of the rankings, a significant discrepancy concerning the scoring functions can be seen here.

Fig. 2. Evaluation and ranking visualization for the HFS TOPSIS-COMET approach of various scoring functions.

Figure 3 presents the correlation matrices for the considered rankings from the HFS TOPSIS-COMET approach are presented, where matrix (a) shows the values for the similarity coefficient of the rankings W S and matrix (b) for the weighted Spearman correlation coefficient rw described in [8]. Of the values obtained, the highest similarity was achieved by rankings S3 , S6 and S1 , S4 , where the value of W S and rw was 0.92. Conversely, the lowest similarity was

770

B. Kizielewicz et al.

achieved by rankings S2 , S4 and S3 , S6 , which obtained a value of rw of 0.25 and values of W S of 0.63, 0.66.

Fig. 3. W S and rw correlation heat maps for different HFS score functions.

5

Conclusions

Methods based on Hesitant fuzzy sets are often based on point functions. Therefore, the presented work has formulated a new approach based on point functions and the TOPSIS-COMET method. The study on grade comparisons and rankings for the new approach based on scoring functions shows significant differences between the results obtained. However, each method used obtained reliable results. Therefore, future work should consider extending the proposed approach to include a compromise ranking aggregating rankings or scoring functions to determine a single ranking. In addition, studies on the impact of significance of weights in the proposed approach would also need to be conducted.

References 1. Alcantud, J.C.R., Torra, V.: Decomposition theorems and extension principles for hesitant fuzzy sets. Inf. Fusion 41, 48–56 (2018) 2. Alcantud, J.C.R., de Andr´es Calle, R., Torrecillas, M.J.M.: Hesitant fuzzy worth: an innovative ranking methodology for hesitant fuzzy subsets. Appl. Soft Comput. 38, 232–243 (2016) 3. Ali, J., Bashir, Z., Rashid, T.: Weighted interval-valued dual-hesitant fuzzy sets and its application in teaching quality assessment. Soft. Comput. 25(5), 3503–3530 (2020). https://doi.org/10.1007/s00500-020-05383-9 4. Faizi, S., Salabun, W., Nawaz, S., ur Rehman, A., Watr´  obski, J.: Best-worst method and hamacher aggregation operations for intuitionistic 2-tuple linguistic sets. Expert Syst. Appl. 181, 115088 (2021) 5. Farhadinia, B.: Distance and similarity measures for higher order hesitant fuzzy sets. Knowl.-Based Syst. 55, 43–48 (2014) 6. Farhadinia, B.: A series of score functions for hesitant fuzzy sets. Inf. Sci. 277, 102–110 (2014)

How to Make Decisions with Uncertainty Using Hesitant Fuzzy Sets?

771

7. Gandotra, N., et al.: New pythagorean entropy measure with application in multicriteria decision analysis. Entropy 23(12), 1600 (2021) 8. Kizielewicz, B., Salabun, W.: A new approach to identifying a multi-criteria decision model based on stochastic optimization techniques. Symmetry 12(9), 1551 (2020) 9. Kizielewicz, B., Shekhovtsov, A., Salabun, W.: Application of similarity measures for triangular fuzzy numbers in modified TOPSIS technique to handling data uncertainty. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 307, pp. 409–416. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85626-7 48 10. Kizielewicz, B., Shekhovtsov, A., Salabun, W.: A new approach to eliminate rank reversal in the MCDA problems. In: Paszynski, M., Kranzlm¨ uller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2021. LNCS, vol. 12742, pp. 338–351. Springer, Cham (2021). https://doi.org/10.1007/978-3030-77961-0 29 11. Narayanamoorthy, S., Ramya, L., Baleanu, D., Kureethara, J.V., Annapoorani, V.: Application of normal wiggly dual hesitant fuzzy sets to site selection for hydrogen underground storage. Int. J. Hydrogen Energy 44(54), 28874–28892 (2019) 12. Salabun, W., Urbaniak, K.: A new coefficient of rankings similarity in decisionmaking problems. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12138, pp. 632–645. Springer, Cham (2020). https://doi.org/10.1007/978-3-03050417-5 47 13. Salabun, W., Watr´  obski, J., Shekhovtsov, A.: Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry 12(9), 1549 (2020) 14. Sultan, A., Salabun, W., Faizi, S., Ismail, M.: Hesitant fuzzy linear regression model for decision making. Symmetry 13(10), 1846 (2021) 15. Thakur, P., et al.: A new entropy measurement for the analysis of uncertain data in MCDA problems using intuitionistic fuzzy sets and COPRAS method. Axioms 10(4), 335 (2021) 16. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010) 17. Yang, M.S., Hussain, Z.: Distance and similarity measures of hesitant fuzzy sets based on Hausdorff metric with applications to multi-criteria decision making and clustering. Soft. Comput. 23(14), 5835–5848 (2019) 18. Zhu, B., Xu, Z., Xia, M.: Dual hesitant fuzzy sets. J. Appl. Math. 2012 (2012)

Author Index

A Abbood, Ahmed Oudah, 336 Abiyev, Rahib, 739 Abraham, Ajith, 242, 463, 558 Abu Bakar Sedek, Amirah Nabilah Sedek, 272 Adilov, Farukh, 308, 446 Agaoglu, Mustafa, 348 Ahmad, Firoz, 751 Ajay, D., 667, 684 Akburak, Dilek, 655 Akın, Erhan, 317 Akkaya, Selen Burçak, 568 Akkus, Fatih, 148 Aksaç, Kaan, 24 Aksaç, Selin, 208 Akshara, A. S., 156 Aldring, J., 684 Algin, Ramazan, 348 Ali, Adila, 505 Aliev, Elmar, 505 Aliyev, Elchin, 505 Alkaya, Ali Fuat, 348 Alptekin, S. Emre, 426 Altıparmak, Hamit, 739 Amador-Angulo, Leticia, 713 Amroush, Fady, 540 Antonijevic, Milos, 3 Ari, Emre, 124 Aslan, Mustafa Kaan, 524 Astafurov, Maksim, 308 Aswathy, S. U., 463, 558 Atalma, Umut, 472 Atici, Ugur, 263 Avci, Umut, 417

Aydın, Ilhan, 317 Aysoysal, Batuhan, 190 B Bacanin, Nebojsa, 3 Badaoui, Fatima-ezzahra, 646 Baratian, Samira, 242 Bennawy, Mohamed, 514 Beyca, Omer Faruk, 263 Biçer, Ezgi, 86 Birim, Sule ¸ Öztürk, 606 Boulmakoul, Azedine, 646 Bouziri, Adil El, 646 Bozyigit, Fatma, 173 Bulut, Önder, 417, 497 Burnaz, Sebnem, ¸ 140 C Ça˘gatay, Emine, 438 Caglar, Gozde, 190 Cakir, Altan, 148 Candar, Mert, 131 Carvajal, Oscar, 722 Castillo, Oscar, 713, 730 Castro, Javier, 62 Cebi, Selcuk, 225 Challenger, Moharram, 173 Chellamani, P., 667 Chen, Chen-Tung, 579, 617 Chen, Chien-Wen, 617 Chen, Fei, 44 Cherradi, Ghyzlane, 646 Chiclana, Francisco, 272 Choi, Jaeho, 44

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Kahraman et al. (Eds.): INFUS 2022, LNNS 505, pp. 773–775, 2022. https://doi.org/10.1007/978-3-031-09176-6

774

Author Index

Christidou, Athina Ntiana, 70 Çiçekli, Sevra, 531

John Borg, S., 667 Jovanovic, Luka, 3

D Dalyan, Tu˘gba, 702 Damien, Hanyurwimfura, 325 Demircan, Murat Levent, 24 Demirkale, Huseyin, 190 Demirta¸s, Tuba, 101 Derelioglu, Volkan, 190 Donyatalab, Yaser, 250, 296 Drakaki, Maria, 70

K Kadir, Norhidayah A, 272 Kahraman, Cengiz, 225, 693 Kamis, Nor Hanimah, 272 Karaba˘g, Oktay, 497 Karalar, Erem, 524 Kara¸sahin, Emre, 36 Karim, Lamia, 646 Kaya, Tolga, 86, 140, 166, 372, 409, 438 Khalilipour, Alireza, 173 Kilercik, Burak, 480 Kim, Sumi, 44 Kizielewicz, Bartłomiej, 763 Kocas, Burcu, 190 Kucukbas, Ezgi, 148 Kumar, Sangaraju V., 44

E Eliiyi, U˘gur, 355 el-Kafrawy, Passent, 514 Er, Orhan, 263 Erdebilli, Babak Daneshvar Rouyendegh, 400 Erkartal, Bu˘gra, 626 Eskiocak, Defne ˙Idil, 480, 524 Espínola, Rosa, 62 F Farid, Fariba, 250 Farrokhizadeh, Elmira, 639 G Gökçe, Mahmut Ali, 364, 568 Gómez, Daniel, 62 Gönül, Tansel, 77 Güçlü, Emre, 317 Guevara, Juan Antonio, 62 Gülada, Mehmet, 702 Gürbüz, Anil, 86 Gürbüz, Tuncay, 472, 531 Gurcan, Omer Faruk, 263 Gutiérrez, Inmaculada, 62 Güven, Ceyhun, 355 H Henge, Santosh Kumar, 117, 456 Henry, Jérôme, 380 Hung, Wei-Zhan, 579 I Ilbahar, Esra, 225 ˙Ilgün, Dilhan, 426 Imamguluyev, Rahib, 93 Incel, Ozlem Durmaz, 77 Isiklar Alptekin, Gulfem, 77 Ivanyan, Arsen, 308, 446 J Januário, Alexandre, 380 Jiang, Yuchen, 216, 233

L Lazarev, Alexey, 391 Lbath, Ahmed, 646 Li, Minglei, 216, 233 Li, Xiang, 216, 233 Linardos, Vasileios, 70 Luo, Hao, 216, 233 M Madhushree, T., 156 Marjanovic, Marina, 3 Mathews, Arun B., 463 Mathirajan, M., 751 Maurice, Mwizerwa, 325 Melin, Patricia, 722 Mert, Buse, 480, 524 Miramontes, Ivette, 722 Mohamad, Daud, 272 Moiseev, Sergey, 391 N Namlı, Özge H., 53 O Ochoa, Patricia, 730 O˘gul, ˙Iskender Ülgen, 524 Öner, Erdinç, 355, 364 Önal, Merve, 409 Onar, Sezi Çevik, 693 Oral, Furkan, 480 Orbay, Selin, 438 Örnek, Mustafa Arslan, 548 Otay, Irem, 702 Ova, Alper, 579 Özcan, Tuncay, 409

Author Index Özdemir, Yavuz Selim, 400 Ozkaya, Emre, 148 Öztay¸si, Ba¸sar, 639, 693 Öztürkmeno˘glu, Okan, 36 P Paldrak, Mert, 548 Parlak, Ismail Burak, 101 Peker, Serhat, 280 Peraza, Cinthia, 730 Polat, Muhammed Fatih, 190 Priya, K. Yoga, 156 Priyadharshini, R., 156 Q Qadri, Syed Shah Sultan Mohiuddin, 364 R Rouyendegh, Babak Daneshvar, 288 S Safaei, Abdul Sattar, 242 Sałabun, Wojciech, 763 Saniso˘glu, Meltem, 140 Santhoshkumar, S., 684 Santos, Daniel, 62 Sarı, Cem, 14 Seker, Sukran, 199 Senvar, Ozlem, 190 Sertbas, Salih, 190 Shekhovtsov, Andrii, 763 Singh, Bhupinder, 117, 456 Sobhani, Mozhgan, 166 Sogukkuyu, Derya Yeliz Cosar, 190 Sönmez, Filiz Erata¸s, 606 Soygüder, Servet, 288 Strumberger, Ivana, 3 Suleymanov, Abil, 93 Sünnetci, Bahar Y., 438 T Tan, Aylin, 288 Ta¸sdemir, Funda Ahmeto˘glu, 182

775 Tasoglu, Kartal, 190 Tekin, Ahmet Tezcan, 14, 676 Thami, Rachid Oulad Haj, 646 Toy, Ayhan Özgür, 417, 497 Tüzünkan, Murat, 739 U Ulku, Eyup Emre, 109 Ulku, Ilayda, 109 Ulukan, H. Ziya, 208 Ulutagay, Gözde, 336 Ünal, Ay¸segül, 409 Üstünda˘g, Alp, 131 Utku, Can, 173 Utku, Semih, 36, 540 V Varol, Mehmet Ali, 190 Varshini, R. Sanjjushri, 156 Venkatesh, J., 156 Vesic, Ana, 3 Y Yaman, Tutku Tuncalı, 488 Yanık, Seda, 53 Yaylalı, Serdar, 488 Yazici, Ibrahim, 124 Ye¸sil, Ercem, 372 Yigit, Emre, 190 Yılmaz, Atınç, 626 Yilmaz, Okan, 148 Yilmaz, Selin, 372 Yin, Shen, 216, 233 Yusupbekov, Nodirbek, 446 Yusupbekov, Nodirdek, 308 Z Zaenchkovski, Artur, 391 Zakieh, Abdul Razak, 540 Zeybek, Ömer, 480 Zhang, Yinsheng, 588 Zivkovic, Miodrag, 3