Key Digital Trends Shaping the Future of Information and Management Science: Proceedings of 5th International Conference on Information Systems and Management Science (ISMS) 2022 3031311523, 9783031311529

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Lecture Notes in Networks and Systems 671

Lalit Garg · Dilip Singh Sisodia · Nishtha Kesswani · Joseph G. Vella · Imene Brigui · Sanjay Misra · Deepak Singh   Editors

Key Digital Trends Shaping the Future of Information and Management Science Proceedings of 5th International Conference on Information Systems and Management Science (ISMS) 2022

Lecture Notes in Networks and Systems

671

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, Türkiye 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]).

Lalit Garg · Dilip Singh Sisodia · Nishtha Kesswani · Joseph G. Vella · Imene Brigui · Sanjay Misra · Deepak Singh Editors

Key Digital Trends Shaping the Future of Information and Management Science Proceedings of 5th International Conference on Information Systems and Management Science (ISMS) 2022

Editors Lalit Garg Faculty of Information and Communication Technology University of Malta Msida, Malta Nishtha Kesswani Central University of Rajasthan Tehsil Kishangarh, Rajasthan, India Imene Brigui EMLYON Business School Écully, France

Dilip Singh Sisodia Department of Computer Science and Engineering National Institute of Technology Raipur Chhatisgarh, India Joseph G. Vella CIS Department University of Malta Msida, Malta Sanjay Misra Østfold University College Halden, Norway

Deepak Singh Computer Science and Engineering National Institute of Technology Chhattisgarh, India

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-31152-9 ISBN 978-3-031-31153-6 (eBook) https://doi.org/10.1007/978-3-031-31153-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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

This multidisciplinary book delves into information systems’ concepts, principles, methods and procedures and their innovative applications in management science and other domains, including business, industry, health care and education. It will be valuable to students, researchers, academicians, developers, policymakers and managers thriving to improve their information and management systems, develop new strategies to solve complex problems and implement novel techniques to utilize the massive data best. This book Key Digital Trends Shaping the Future of Information Systems and Management Science (proceedings of ISMS 2022) is intended to be used as a reference by scholars, scientists and practitioners who collect scientific and technical contributions concerning models, tools, technologies and applications in the field of information systems and management science. This book shows how to exploit information systems in a technology-rich management field. Lalit Garg Dilip Singh Sisodia Deepak Singh Nishtha Kesswani Imene Brigui Joseph G. Vella Sanjay Misra

Organization

PC Members Program Committee Chairs Brigui, Imene G. Vella, Joseph Garg, Lalit Kesswani, Nishtha Misra, Sanjay Singh, Deepak

Sisodia, Dilip Singh

EMLYON Business School, Écully, France University of Malta, CIS Dept, Msida, Malta University of Malta, Department of Computer Information Systems, Msida, Malta Central University of Rajasthan, Kishangarh (Ajmer), Rajasthan, India Ostfold University College, Halden, Norway National Institute of Technology Raipur, Dept. of Computer Science and Engg., Chhattisgarh, India National Institute of Technology Raipur, Department of Computer Science & Engg., Chhattisgarh, India

Contents

Improvisation of Predictive Modeling Using Different Classifiers for Predicting Thyroid Disease in Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sini Shibu and Dinesh Sahu Application of IoT for Proximity Analysis and Alert Generation for Maintaining Social Distancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mopuru Bhargavi, Anurag Sinha, G. Madhukar Rao, Yash Bhatnagar, Shubham Kumar, and Shila R. Pawar Analysis and Optimization of Fault - Tolerant Behaviour of Motors in Electric Vehicular Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shivani Jitendra Khare, Shubham Singh, Siddharth Roy, Yogesh B. Mandake, and Deepak S. Bankar Transfer Learning of Mammogram Images Using Morphological Bilateral Subtraction and Enhancement Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Marline Joys Kumari, N. Thirupathi Rao, Debnath Bhattacharyya, Lalit Garg, and Megha Bhushan Deep Learning Based Bengali Image Caption Generation . . . . . . . . . . . . . . . . . . . Sayantani De, Ranjita Das, and Krittika Das Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pushpendra Kumar, Kanojia Sindhuben Babulal, Dashrath Mahto, and Zaviya Khurshid

1

12

23

39

49

60

Smart Energy Saver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aishwarya Angeleena, SR. Bhavithra, and N. Sabiyath Fatima

71

Agile Helmet-A Smart Secure System for Motorbike . . . . . . . . . . . . . . . . . . . . . . . K. Gayathri, Ch. Raga Madhuri, Ch. Ganesh Karthik, and N. Raja Reddy

84

Decentralized Digital Identity: A New Form of Secured Identity Using Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanjay Kumar Jena and Ram Chandra Barik

93

Object Detection with YOLO Version 3 for Big Data . . . . . . . . . . . . . . . . . . . . . . . 103 Rajat Saxena, Shatendra Dubey, and Vishma Kumar Karna

x

Contents

Efficient Approach for Virtual Machine and Resource Allocation in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Rajat Saxena, Shatendra Dubey, and Rajesh Sada A Novel Approach for Service Selection and Ranking in Federated Cloud . . . . . 129 Rajat Saxena, Shatendra Dubey, and Upanshu Kumar A Review on IoT Based Wireless Sensor Network and Security . . . . . . . . . . . . . . 143 Shabnam and Manju Pandey Automated Spoken Language Identification Using Convolutional Neural Networks & Spectrograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Hari Shrawgi, Dilip Singh Sisodia, and Piyush Gupta A Software Quality Characteristics Optimization Model to Reduce Evaluation Overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Kamal Borana, Meena Sharma, and Deepak Abhyankar Smart Home Using Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Ghaliya Al Farsi and Maryam AlSinani ADAPT- Automated Defence TrAining PlaTform in a Cyber Range . . . . . . . . . . 184 Muhammad Mudassar Yamin, Ankur Shukla, Mohib Ullah, and Basel Katt An Improved Recommender System for Dealing with Data Sparsity Using Autoencoders and Neural Collaborative Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 R. Devipreetha and Anbazhagan Mahadevan Neural Network Based Algorithm to Estimate the Axial Capacity of Corroded RC Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Yogesh Kumar, Harish Chandra Arora, Aman Kumar, Krishna Kumar, and Hardeep Singh Rai ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers . . . . . . . . . . . . . 231 Sarvanshdeep Singh Sahota, Harish Chandra Arora, Aman Kumar, Krishna Kumar, and Hardeep Singh Rai Indian Sign Language Digit Translation Using CNN with Swish Activation Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Seema Sabharwal and Priti Singla

Contents

xi

Prognosis of Viral Transmission in Naturally Ventilated Office Rooms Using ML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Nishant Raj Kapoor, Ashok Kumar, and Anuj Kumar Impact of Organization Justice on Organizational Citizenship Behavior and Employee Retention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Bhawna Chahar Adopting Metaverse as a Pedagogy in Problem-Based Learning . . . . . . . . . . . . . . 287 Riya Baby, Amala Siby, Jerush John Joseph, and Prabha Zacharias Design of Fuzzy Based Controller for Temperature Process in Fire Tube Boiler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 K. Srinivasan, V. Rukkumani, T. Anitha, and V. Radhika Accelerating the Prediction of Anti-cancer Peptides Using Integrated Feature Descriptors and XGBoost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Deepak Singh, Pulkit Garg, and Anurag Shukla Intelligent Door Locking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Fathima Ismail, Jennifer Julien Felix, and N. Sabiyath Fatima Automated Generation of Multi-tenant Database Systems Based upon System Design Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Rebecca Camilleri and Joseph G. Vella Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Blessing Guembe, Ambrose Azeta, Sanjay Misra, and Lalit Garg A Multi Objective Hybrid Collision-Free Near-Optimal Dynamic Path Planner for Autonomous Robots in Dynamic Environments . . . . . . . . . . . . . . . . . . 364 Neeraja Kadari and G. Narsimha How Does Background Music at Stores Impact Impulse Buying Behavior of Young Customers in Vietnam? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Cuong Nguyen, Nguyen Le, and Chau Huynh A Deep Convolutional Neural Network for Remote Life Activities Detection Using FMCW Radar Under Realistic Environments . . . . . . . . . . . . . . . 400 A. Helen Victoria, V. M. Gayathri, and Anirudh Vasudevan A Tool to Aid Households in Investments Decision . . . . . . . . . . . . . . . . . . . . . . . . . 413 Christian Bonanno, Vijay Prakash, and Lalit Garg

xii

Contents

Cloud Computing in Upstream Oil and Gas Industry: Aspirations, Trends and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 Shaqeeq Baluch, Vijay Prakash, and Lalit Garg Plant Disease Detection Using Deep Learning Techniques . . . . . . . . . . . . . . . . . . . 441 Ambuja Behera and Somya Goyal Sentiment Analysis for Depression Detection and Suicide Prevention Using Machine Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 Sunny Singh and Saroj Kumar Chandra Actor Model Frameworks: An Empirical Performance Analysis . . . . . . . . . . . . . . 461 Carl Camilleri, Joseph G. Vella, and Vitezslav Nezval Electric Sports Bicycle Motor Testing in a Laboratory Jig Using Cloud Computing and Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Swaroopa Bhosale, Datta. S. Chavan, Anupama Singh, Satwik Dalvi, Guruprasad Kulkarni, Atharva Kulkarni, Meena Chavan, and Shashank Saindanvise MLTPDFS: Design of Machine Learning Model for Temporal Analysis of Progressive Diseases via Facial Scans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Pranali Rahul Dandekar and Yoginee Surendra Pethe CNN with Transfer Learning for Skin Lesion Identification Using Tasmanian Devil Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498 Vineet Kumar Dubey and Vandana Dixit Kaushik A Comprehensive Study of Crop Disease Detection Using Machine Learning Classification Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 Sanjeela Sagar and Jaswinder Singh Hybrid ANT Colony Optimization Routing Algorithm for AODV Protocol Improvement in FANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 Tripti Gupta, Ajay Kumar Dadoria, and Laxmi Shrivastava Estimation of Implied Volatility for Ethereum Options Using Numerical Approximation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 S. Sapna and Biju R. Mohan Semi Supervised Approach with Entity Embeddings for Heart Disease Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 M. D. Harsha Prada, A. S. Sri Saila, B. Subbulakshmi, and M. Nirmala Devi

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xiii

Machine Learning Model to Predict Mortality Due to Cardiovascular Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 Megha Bhushan, Manimit Haldar, Rishi Dwivedi, Tanya Rajpoot, Priyanka Garg, and Shreya Umrao A Novel PCA-Logistic Regression for Intrusion Detection System . . . . . . . . . . . . 575 Roseline Oluwaseun Ogundokun, Modupe Odusami, Dilip Singh Sisodia, Joseph Bamidele Awotunde, and Damodar Prasad Tiwari Diagnosis Expert System on Breast Cancer Using Fuzzy Logic and Clustering Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 Joseph Bamidele Awotunde, Dilip Singh Sisodia, Peace Ayomide Ayodele, Roseline Oluwaseun Ogundokun, and Virendra Singh Chouhan Effective Teaching Aids for People with Dyslexia . . . . . . . . . . . . . . . . . . . . . . . . . . 602 Jaya Banerjee, Durbar Chakraborty, Baisakhi Chakraborty, and Anupam Basu The Numerical Estimation of Spectral Energy Density in Electroencephalogram (EEG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 Eliazar Elisha Audu and Lalit Garg Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627

Improvisation of Predictive Modeling Using Different Classifiers for Predicting Thyroid Disease in Patients Sini Shibu(B) and Dinesh Sahu Computer Science and Engineering, SRK University, Hoshangabad Road, Bhopal 462024, Madhyapradesh, India [email protected]

Abstract. In this era of big data and machine learning, a lot of emphasis is on gathering useful insights from available data. This is facilitated with the availability of low cost computing facilities and programming languages that support a lot of libraries, packages and modules for machine learning and artificial intelligence. If data is used for predictive modelling, it can result in models that can effectively predict future outcomes. This paper aims to efficiently predict the occurrence of Thyroid disease in patients based on the predictive modelling on the dataset available through Kaggle. An in-depth Exploratory Analysis on the dataset is carried out and the results are presented in this paper. The major concern is that while predicting the chances of having a disease in the patient mis-classification can result into lower reliability of the model. Hence, the models are developed such that the mis-classification rate is nearly negligible. The main aim of study is to explore the various classification algorithms like k-NN, Random Forest Classifier, XGBoost Classifier and CatBoost Classifier and improvise the models using hyper-parameter optimization to obtain optimum accuracy in prediction of Thyroid disease. In the experimental set-up it has been observed that the model using Random Forest Classifier is resulting in the best classification accuracy of 98.83%. Keywords: Data analytics · Predictive analytics · Machine Learning · Neural Networks · Classification · Exploratory Data Analysis

1 Introduction 1.1 Data Analytics Today’s world is a data driven world. Every second huge amounts of data is being generated. This data is analyzed and used for yielding insights for businesses which can improve productivity and profitability too. This process generally begins with descriptive analytics which aims at describing historical trends in the data. Descriptive analytics is used to answer the question “what happened?” This comprises of measuring traditional indicators from the data. The indicators used for analysis will be different for each

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 1–11, 2023. https://doi.org/10.1007/978-3-031-31153-6_1

2

S. Shibu and D. Sahu

industrial application developed. Descriptive analytics is not intended to make any predictions or decisions. It focuses on summarizing the data in a descriptive and meaningful way which provides a good overview of the data. The next important part of data analytics is advanced analytics which involves extracting data, making predictions and discovering trends in the data. Statistics and machine learning technologies such as neural networks, natural language processing, sentiment analysis etc. can be used in this area. This information provides new insight from existing data. Advanced analytics primarily answers “what if?” questions. The easy availability and implementation of machine learning techniques, massive data sets and inexpensive computing power has enabled the use of these techniques in many industries. The analysis performed on big data sets is instrumental in enabling these techniques to improve decision making thus enhancing business performances. 1.2 Prediction and Predictive Analysis Prediction can be defined as the estimation of unknown data on the basis of current knowledge and perceptions. This definition gives an understanding that prediction relates to futuristic data like weather predictions, disease prediction, stock value estimation etc. Predictive algorithms are used in Machine Learning to determine an unknown variable such as the price of an apartment or a used vehicle based on previous data available in the dataset. In order to perform predictive analysis the following steps as shown in Fig. 1 have to be followed: 1. 2. 3. 4.

Data gathering Data Analysis Data Monitoring Predictive Analysis

Fig. 1. Phases of Predictive Analysis

2 Literature Review In [1] the authors have researched about hyperthyroidism and hypothyroidism. These are the two common diseases of the thyroid gland that releases thyroid hormones and helps

Improvisation of Predictive Modeling

3

in regulating the rate of body’s metabolism. Data cleaning was applied in their work to make the data ready for performing analytics so as to show the risk of patients getting thyroid. Machine learning plays a decisive role in the process of disease prediction. The paper elaborates the analysis and classification models that are being used in the thyroid disease prediction. Their work is based on the information gathered from the dataset taken from UCI machine learning repository. In their paper, the authors also proposed different machine learning techniques and diagnosis for the prevention of thyroid. Support Vector Machine (SVM), Machine Learning Algorithms, K-NN and Decision Trees were used to predict the risk of a patient getting thyroid disease based on the parameters of study. In [2] the authors investigate the machine learning classification problems and present several methods of feature selection and classification for thyroid disease diagnosis. The thyroid gland releases thyroid hormones for regulating the rate of body’s metabolism. The two common diseases of the thyroid gland are hyperthyroidism and hypothyroidism. Classification of these thyroid diseases is a considerable task that requires great accuracy too. The problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. The proposed methods of feature selection are Univariate Selection, Recursive Feature Elimination and Tree Based Feature Selection. Three classification techniques have been used in the research work namely Naïve Bayes, Support Vector Machines and Random Forest. The results show that the Support Vector Machines provide the most accurate classification and hence it was used as a classifier to separate the symptoms of thyroid diseases into 4 classes namely Hypothyroid, Hyperthyroid, Sick Euthyroid and Euthyroid (negative). In [3] the authors have presented that classification based Machine learning can be used in various medical services. In medical field, the most demanding task is to diagnose the patient’s health conditions and to provide proper care and treatment of the disease at the initial stage. For the Thyroid disease, the normal and traditional methods of thyroid diagnosis involve a thorough inspection of the patient and also various blood tests. The main goal of this paper is to recognize the disease at the early stages with a very high accuracy. Machine learning techniques is applied in medical field for making correct decisions, proper disease diagnosis and it also saves cost and time of the patient and his attendants. The purpose of this paper is prediction of thyroid disease using classification Predictive Modelling followed by binary classification using Decision Tree ID3 and Naive Bayes Algorithms. The Thyroid Patient dataset with proper attributes were fetched and using the Decision Tree algorithm the presence of thyroid in the patient is tested. Further, if thyroid is present then Naïve Bayes algorithm is applied to check for the thyroid stage in the patient. Paper [4] was written to provide a source of reference for the research scholars who want to work in the area of prediction of thyroid disease. From the different machine learning techniques, the authors have used three algorithms namely logistic regression, decision trees and k-nearest neighbor (k-NN) algorithms to predict and evaluate their performance in terms of accuracy. Their study has shown how to predict the thyroid disease and highlighted how to apply logistic regression, decision trees and k-NN as a tool for the classification. For this, thyroid data set of machine learning repository has been used from UC Irvin knowledge discovery in databases archive.

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S. Shibu and D. Sahu

3 Exploratory Data Analysis This paper is based on the study of the dataset available at Kaggle in hypothyroid.csv file. The dataset consists of 1999 rows of data with 25 features recorded for various patients. A detailed Exploratory data analysis was carried out using Python, the results of which are presented below: The features of the data set are Age, Sex, on_thyroxine, query_on_thyroxine, on_antithyroid_medication, thyroid_surgery, query_hypothyroid, query_hyperthyroid, pregnant, sick, tumor, lithium, goiter, TSH_measured, TSH, T3_measured, T3, TT4_measured, TT4, T4U_measured, T4U, FTI_measured, FTI, TBG_measured and Result. Out of the 25 features, there are 5 float64 values, 1 int64 value and 19 object type values in the dataset. There are no missing values in the dataset. Table 1 shows the various statistical measures of the numeric data in the dataset. Table 1. Statistical measures of numeric data Age

TSH

T3

TT4

T4U

FTI

count

1999

1999

1999

1999

1999

1999

mean

53.634817

6.094632

1.925313

107.376838

0.988074

110.128214

std

19.299538

24.548877

0.970592

45.055034

0.2316

41.111126

min

1

0

0.05

2

0

0

25%

37

0

1.3

82

0.85

90

50%

57

0.7

1.8

103

0.96

107

75%

70

2.3

2.3

126

1.07

127

max

98

530

9.8

450

2.03

450

The graph in Fig. 2 summarizes the analysis performed on the ages of various patients and the top 10 ages according to their frequencies are plotted:

Improvisation of Predictive Modeling

5

Fig. 2. Top 10 ages in the data set with their frequencies

The graph in Fig. 3 shows that the dataset comprises mainly of elderly people. Dividing the Age feature into three parts – “Young”, “Middle” and “Elder” where young ages belong to the age range ≥29 and 30 °C (The air conditioning system is turned ON) If the room temperature < 20 °C (The air conditioning system is turned OFF) A Photoresistor Sensor is also known as LDR (Light Dependent Resistor) is added to the entire circuit. The purpose of this sensor is to detect the amount of light intensity in a particular room. Daylight is measured in Illuminance (lux level) and determined by this LRD. In the presence of daylight, the use of lights or LEDs may be cut off by switching them on only whenever needed. The working condition of this circuit is such that when the illuminance of the room is below 300 lux (SI Unit of Illuminance) the lights in the classrooms are automatically turned ON. Likewise, when the lux meter shows a reading above 350 lux, the lights are turned OFF. Figure 5 shows the working of the LDR. Step 01: The Light Sensor (Photoresistor LDR) is placed in the middle of the classroom. Step 02: The LDR works in such a way that it measures the intensity of light in the particular area. Hence the ideal position of the LDR is the middle of the classroom. Step 03: The LDR is connected to the Arduino UNO and the Relay Split (12 V) Step 04: The intensity of light is measured by the unit lux. Hence the conditions are as follows:

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A. Angeleena et al.

Fig. 5. Working of the Light Sensor

If Daylight recorded by the LDR < 300 lux unit (The LED lights in the classroom are turned ON with the help of Arduino UNO and Relay Split) Else the LEDs stays off Temperature and lux level is determined by, For temperature we can use, dT dr = α( ) dt dt = change in temperature, α = first order temperature coefficient of resistance, = change in temperature. The temperature sensor (LM35) gives output voltage 0 °C if the temperature is 0 °C. And also there will be a rise of 0.01 volts (10 mV) for every degree rise in temperature. Voltage can be converted into temperature by using this method, dR dt dT dt

Vout =

10mV ×T ◦C

For lux level we can use, E=

F × UF × MF A

E = illuminance (lux, lumens), F = average lumen from light source, UF = coefficient of utilization, MF = maintenance factor for light source, A = area per lamp (m2 ) The room daylight lux level is sensed by LDR. The threshold lux is calculated by the above equation. If the room daylight lux level is lesser than threshold lux then the sufficient light is provided and optimum threshold lux level is obtained.

Smart Energy Saver

Pseudo Code: Start void IN() Begin count++; lcd.clear(); lcd.print(“Person in room:”); lcd.setcursor(0,1); lcd.print(count); delay(1000); End if(digitalRead( irsensor1)) IN(); Endif void OUT() Begin count--; lcd.clear(); lcd.print(“Person in room:”); lcd.setcursor(0,1); lcd.print(count); delay(1000); End if(digitalRead( irsensor2 )) OUT(); Endif End

Hardware Output:

Fig. 6. SES Hardware Setup

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The Fig. 6 depicts the project hardware setup.

5 Result and Analysis The following table describes about the comparison between other existing system. The smart energy saver has the dominance among the comparison table (Table 1). Table 1. Comparison with Existing System

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Fig. 7. Performance comparison on overall power wastage

The (Fig. 7) explains that Smart Energy Saver has the lowest power wastage among the other products. It examines that in other products namely alexa enabled energy saving system (Alexa has the second most least power wastage due to the absence of temperature detection, which needs voice control to control all the fans and lamps and other appliances. Sometimes, the voice command given as the input could be confused with homophones these are the drawbacks of the alexa and consumes power in these cases. Thirdly, Energy saving system using PIR sensor for classroom monitoring (ESUPCM) has the higher power wastage comparing to alexa. It uses PIR sensors to ON/OFF the lamp and fans. It sets the temperature range to switch ON the fans but the limitation is it takes hardly 10 min to switch OFF the fan and light when motion not detected which leads to power consumption. Lastly, Automatic room lightening control system with temperature detection(ARLCS),it has the more power wastage comparing to others as it results due to the temperature fluctuations could be detected and hence this might lead to changes in the display of the same. SES system has the advantage of other product’s limitations and it is better than all other products based on the parameters explain.

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Fig. 8. Overall accuracy based on Temperature consideration and Intensity control

In the Fig. 8 Temperature consideration and Intensity control graph, there are two main parameters taken into consideration - Temperature consideration and Intensity control. If the appliances running even without a person or with a person in a room but not in the particular place of fans or light or AC, there is electric power waste, to overcome this SES came with the light intensity sensor, that compare the daylight and switch off/on based on the requirement and temperature sensor is detected the range of temperature outside according to cool/hot the AC or fans turned on/off. Due to this SES becomes best among other product. Alexa, does not have the temperature consideration so it becomes less accuracy compared to SES. ESUPCM has the temperature consideration but takes 10 min for turning off the switch when the motion not detected and it also does not have intensity control which makes least accuracy comparing to alexa. Lastly, ARLCS, it does not have the intensity control and uses PIR sensor to detect heat which leads to temperature fluctuations could be detected. Hence, SES stands in upper hand among the other products.

6 Conclusion and Future Work Dealing with only a few components and changing state of the loads can be done in an efficient manner. This prototype functions as a key system that’s needed in every home since energy saving has also serves as great attention as a global issue. If this issue has to be addressed, distribution of energy saving systems becomes mandatory. Nowadays, individual households are implying systems of these types at home as well, considering the fact that it also has a control over the costs. This system could be further developed enabling a smart messaging system, based on the number of persons entering the room.

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References 1. Twumasi, C., Dotche, K.A., Banuenumah, W., Sekyere, F.: Energy saving system using a PIR sensor for classroom monitoring. In: 2017 IEEE PES-IAS PowerAfrica (2017) 2. Vijai, S., Deeparagavi, M., Nivetha, B., Bhuvaneswari, A., Balachandran, M.: An energy conservation using 360 degree PIR sensor by arduino microcontroller. Int. J. Eng. Res. Technol. (IJERT) 3. Muskan, S., Nanditha, N., Mangala, M., Ranjitha, N.R., Swetha, K.T.: Arduino based automated energy saver in it and domestic load. Int. J. Eng. Res. Technol. (IJERT) (2019) 4. Rohith, M., Fatima, N.S.: IoT enabled smart farming and irrigation system. In: Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021). IEEE Xplore (2021) 5. Srikanth, V., Vinith, V., Vimal Raj, V., Navaneetha Krishnan, B., Rajan Babu, S.: Intelligent energy saving and voice control system. Int. Res. J. Eng. Technol. (IRJET) (2019) 6. Saranya, S.S., Fatima, N.S.: IoT Information Status Using Data Fusion and Feature Extraction Method. Computers, Materials & Continua. Tech Science Press (2021) 7. Philip, A.A., Thomas, A.A., Philip, A.L.A.A.M., SA, M.F.: Smart campus power controller. IJSTE Int. J. Sci. Technol. Eng. 8. Mohamed Rayaan, A., Rhakesh, M.S., Sabiyath Fatima, N.: Detection of EMCI in Alzheimer’s disease using lenet-5 and faster RCNN algorithm. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds.) Image Processing and Capsule Networks. ICIPCN 2022. LNNS, vol. 514, pp. 433–447. Springer, Cham (2022) 9. Harikrishnan, R., Sivagami, P.: Intelligent power saving system using pir sensors. In: International Conference on Electronics, Communication and Aerospace Technology ICECA (2017) 10. Rashid, A.T., Rashid, O.T.: Design and implementation a smart energy saving system using an arduino and RF module. Int. J. Comput. Appl. 975, 8887 (2018)

Agile Helmet-A Smart Secure System for Motorbike K. Gayathri(B) , Ch. Raga Madhuri, Ch. Ganesh Karthik, and N. Raja Reddy Department of CSE, VR Siddhartha Engineering College, Kanuru, India [email protected], [email protected]

Abstract. Agile helmet is basically a smart helmet. The core concept of an agile helmet reduces the risk of riding a motorcycle. The most important objective is to keep bike drivers safe. The engine will turn off deliberately if the motorist wears no helmet. Using this helmet whenever an accident occurs, GPS and GSM modules send the location information to the respective families. This is done by introducing the IR and vibratory sensor to the helmet. These sensors are set to certain threshold values. This project aims to save the lives of drivers and reduce the number of people killed in road accidents. It is one of the most advanced road safety systems projects ever undertaken. There are many smart helmets available in the market but this helmet is advanced as it is integrated with many sensors. Keywords: Helmet Detection · Accident Detection · Alcohol Detection · Micro Controller · Location Tracking · GPS · GSM · Vibration sensor

1 Introduction According to a WHO research, motor vehicle accidents are primary cause of fatality among 15–29-year-olds worldwide. Each year, almost 1.35 million people die and 50 million are injured. Road accidents are the ninth most common cause of mortality worldwide. Road collisions are expected to kill around 4 lakh people in India by 2020. Experts say that drug or alcohol abuse, speeding, failure to wear a helmet, and a lack of enforcement are among the leading causes of road accidents in India. As we are concerned about safety of motorcycle driver, we need to think of smart solution to decrease the rate of death due to road accidents. As the name of our title is AGILE HELMET, which means a clever way to use helmet and drive bike. The major cause of road accident is due to failure to wear helmet. AGILE HELMET will not permit the rider to begin the motorcycle without a helmet.by smartly turning off the ignition switch [1] and it also have some techniques to save the driver when accident occurs. We are having mainly three aspects to be deployed in this project. Motorbike won’t get started if the motorist did not kept helmet while going out for ride [2]. This can be implemented by turning off the ignition switch. If the alcohol level consumed by the rider is more than the precise limits, bike will not start. It can be checked by using alcohol sensor. Whenever an accident occurs, immediately a message will be sent to respective families through GPS and GSM modules. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 84–92, 2023. https://doi.org/10.1007/978-3-031-31153-6_8

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2 Literature Survey In the literature survey, we have obtained numerous smart ways for helmet systems with several methodologies and proposed plannings. In [1], an efficient methodology is used to elucidate the problem by making use of smart helmet band. It is an innovative concept that makes motorcycle riding safer than ever before. The operation of this band is very simple; a limit switch is installed inside the helmet, which determines whether or not the rider kept helmet; if the rider doesn’t wear helmet, then motorcycle will not set into motion. Using GSM and GPS technologies, this smart helmet band gives aid in the event of an accident. In [2], Smart Helmet Controlled Vehicle is a project commenced to rise the ratio of road safety among bikers. When an accident happened, the vibration levels are communicated to the processor, which is continually checking for uneven variations, and the appropriate information is delivered to the emergency contacts via SMS alert. The vehicle’s location is determined via the Global Positioning System. In [3], the smart helmet system is made up of two primary components: the helmet and the bike. A microcontroller unit is attached to switches on the helmet. The helmet is equipped with sensors such as an alcohol sensor, a speed sensor, and an RFID tag. An accelerometer, RF decoder microcontroller unit, relay, GPS module, and an IOT system are included in the bike module. One of the two switches in the helmet continuously monitors the helmet’s position and sends the information to the microcontroller, which subsequently delivers it to the radio frequency transmitter. The comparator verifies data given to the RF encoder by the alcohol sensor and the speed sensor. The bike ignition starts if the rider has no alcoholic breath; else, the engine remains turned off. In [5], the goal of this initiative is to keep motorcyclist safe. Riders while riding, if an accident occurs unexpectedly, the Microprocessor will use GSM and GPS to send a message to the phone number provided. The GSM module confirms this. SIM808 is the GSM module they are utilizing. Tilt sensors detect vibration and transmit an indicator to the processor when an accident occurs. The processor may then track a place using GPS and relay the information in the form of latitude and longitude, which a typical client cannot work out. As a result, they put system in place to transmit the Google Maps link. The ambulance and family members can take particular actions to preserve the life of the rider after clicking on the Google map link in maps. In [7], whenever a rider with a Smart PPM helmet makes a positive gesture suggesting the presence of a pothole, the helmet sends an acknowledgment packet to the mobile application through a Wi-Fi link, when a user equipped with the mobile application approach towards the pothole, the user is notified through rhythmic phone vibrations if within the 150 m range.

3 Proposed System Our agile helmet blueprint is split into two units specifically helmet and vehicle module.

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Fig. 1. Schematic representation of helmet module

3.1 Helmet Module The above Fig. 1, exemplifies the schematic representation for transmitting the various inputs obtained from the helmet module. The helmet module comprises of The Arduinouno is a microcontroller-based board that runs on the ATmega328. The board contains a set of digital and analogue i/o pins to which you can link expansion boards, breadboards, and other circuits. This micro-controller, in addition to employing typical compiler tool chains, In general, programmed with features from the programming languages c and c++. Serial communications interfaces are included on the boards, including USB (universal serial bus) on some models, which are also used to load programmers from personal computers, and an alcohol sensor is used to determine whether or not the driver has ingested alcohol. It is directly fixed inside the helmet. IR sensors are fixed inside the helmet to ensure whether the helmet is put on by the rider or not. The data of the sensors is transmitted with the help of nrf24l01 transceiver to vehicle unit. If the predetermined circumstances are met, then bike will start.

Fig. 2. Schematic representation of vehicle module

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3.2 Vehicle Module The above Fig. 2 represents the block diagram for detecting the signal sent from the helmet module. The vehicle unit consists of GSM & GPS module to send location and message to the relatives if the rider met an accident or he or she is found to have consumed alcohol. These sensors are controlled by Arduino UNO, also a vibration sensor is fixed inside the bike to detect accident. The vibration sensor value is fixed to a threshold condition, if accident occurs and the sensor value exceed threshold value, a message is send to the relatives along with riders location.

4 Implementation The two modules of the smart helmet has two arduino boards which have different sensors connected to them. The helmet module consists of IR, Vibratory, MQ3 sensors and NRF transmitter and NRF receiver, LCD display, GPS and GSM modules as shown in Fig. 3.

Fig. 3. Smart helmet circuitry

Helmet Module The NRF transmitter use nrf24L01 and SPI libraries. The IR sensor detects whether the driver wore the helmet. The IR sensor emits infrared radiations and detect them. If the IR sensor did not detect any object within its range the sensor returns HIGH else LOW. The vibratory sensor returns HIGH then there are no vibrations detected else vibrations are detected. For the alcohol sensor the threshold value set by the government of India is 300 ppm for legal alcohol level in the body. The NRF transmitter is used to send the information collected from helmet module to the bike module. The NRF transmitter uses radio frequencies for the purpose of communication with the bike module. The messages sent are the following:

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• “ENGINE OFF” if the IR sensor returns value HIGH. • “ENGINE ON” if the IR sensor returns value LOW and the vibration sensor returns value HIGH and the analogue value of the alcohol senor is less than 300. • “VIBRATION DETECTED” if the IR sensor return value LOW and the vibratory sensor return value LOW. • “ALCOHOL DETECTED” if the IR sensor return value LOW and the analogue value of the alcohol sensor is greater than 300. Bike Module The NRF receiver uses display uses nrf24L01 and SPI libraries. The LCD display uses wire and LiquidCrystal libraries. Both the addresses of the transmitter and receiver are declared. The NRF receiver always listens for the radio signal. If it senses any radio signal it will take in the message in form of bites which are converted to strings later. If the received message is: • “ENGINE OFF” the LCD will display “WEAR HELMET” and the ignition will be automatically turned off. • “ALCOHOL DETECTED” the LCD will display “ALCOHOL DETECTED” and the ignition will be automatically turned off. • “VIBRATION DETECTED” the LCD will display “VIBRATION DETECTED” and the ignition will be turned off and the details regarding the location will be forwarded to the respective families. • “ENGINE ON” the vehicle ignition will be turned on. The below Fig. 4 shows the complete connection for smart helmet project (Fig. 5).

Fig. 4. Overall circuit connection

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Fig. 5. Helmet integrated with circuit

5 Results 5.1 Outputs The following results are obtained when the whole circuit is turned on. First the helmet checks for alcohol content in the exhaled air from the mouth. If alcohol is detected bike stops immediately. After alcohol detection the helmet looks whether the rider wore the helmet or not. If the helmet is not worn the bike stops immediately. If the helmet is worn then the helmets checks for vibrations continuously. If any accident occurs, then the helmet module send a message to bike module indicating that the accident has occurred. Then the bike stops immediately and the message with location as content will be sent to the indicated number in the code.

Fig. 6. Wear helmet message in LCD

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The above Fig. 6, it is the output displayed whenever the bike driver haven’t kept helmet. The output shown is WEAR HELMET. In this way we guide the bike driver not to start the bike without helmet.

Fig. 7. Engine on message in LCD

In Fig. 7, it describes Whenever bike driver wears helmet the lcd will display ENGINE ON and the driver is ready to go.

Fig. 8. Vibration detected message in LCD

In Fig. 8, Whenever the driver met with an accident, the lcd will display VIBRATION DETECTED and it automatically turn off the engine. In Fig. 9, whenever driver consumed alcohol, more than the threshold limits then it displays a message ALCOHOL DETECTED and turn off the engine.

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Fig. 9. Alcohol detected message in LCD

5.2 Performance Analysis The IR sensor has 0.8 cm maximum error obtained in the distance estimation between 10 and 14 cm. The MQ3 alcohol sensor has sensitivity that varies and can vary up to 10% both ways (plus and minus). This variation is also called the Accuracy of the Sensor. For nrf transmitter the frequency accuracy is ±60 ppm, and the power Accuracy is ±4 dBs. For the government provides the GPS signal in space with a global average user range rate error (URRE) of ≤0.006 m/s over any 3-s interval, with 95% probability (Table 1). Table 1. Represents the comparison of different smart helmets available. Helmet name

IR Sensor

Vibration Sensor

Alcohol Sensor

GSM

GPS

Nolan N70-2 GT Classic N-com Helmet

Yes

Yes

No

Yes

Yes

Smart Helmet

Yes

No

Yes

No

No

Smart Helmet Using IoT Yes

Yes

Yes

Yes

Yes

Proposed Model

Yes

Yes

Yes

Yes

Yes

6 Conclusion and Future Work As we are concerned about safety of motorcycle driver, we need to think of smart solution to decrease the rate of death due to road accidents. As the name of our title is AGILE HELMET, which means a clever and faster way to use helmet and drive motorcycle. We have implemented this idea, tested it and got results as shown in the results above. This prototype model cost is 4000 rupees but in mass production the smart helmet cost can be reduced to 2000 rupees to 2500 rupees. Finally, we have tested the model and saved the life of people. Safety is the top most priority of this project. In future the project can be extended for other purposes by making use of different types of sensors like eyeblink sensor, blood sensor, and heart beat sensor. This project can also be advanced to 3-wheeler, 4 wheelers.

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References 1. Somantri, Yustiana, I.: Smart helmet integrated with motorcycles to support rider awareness and safety based internet of things. In: 2022 International Conference on ICT for Smart Society (ICISS), pp. 01–05 (2022). https://doi.org/10.1109/ICISS55894.2022.9915262 2. Nivedetha, B., Thamaraikannan, P.: Smart accident prevention system using sensors. Int. J. Nonlinear Anal. Appl. 12 (2021) 3. Ahuja, P., Bhavsar, K.: Microcontroller-based smart helmet with GSM and GPRS. In: Second International Conference on Trends in Electronics and Information (2018) 4. Vasudevan, Bhavya Rukmini, Jana Priya, Balashivani Pal: Smart helmet controlled vehicle. IRJET 6(03) (2019) 5. Anjali Baburaj, Thasni, Reshma, Yadhu Krishnan, Deepak: Intelligent smart helmet system. IJARCEE 9(1) (2020) 6. Aatif, M.K.A.: Smart helmet based on IOT technology. Int. J. Res. Appl. Sci. Eng. Technol. 5, 409–413 (2017) 7. Vashisth, R., Gupta, S., Sahil, Rana, P.: Implementation and analysis of smart helmet. In: 4th IEEE International Conference on Signal Processing, Computing and Control (ISPCC 2017) (2017) 8. Rajput, K.Y., Chadha, G., Kanodia, B.: Smart helmet. Int. Res. J. Eng. Technol. (IRJET) (2016) 9. Immaduddin, M.: Controlling and Calibrating Vehicle-Related Issues (2015) 10. Nagakishore Bhavanam, S., Vasujadevi, M.: Automatic speed control and accident avoidance of vehicle using multi sensors. In: Proceedings of International Conference on Innovations in Electronics and Communication Engineering (ICIECE 2014) (2014) 11. Manjesh, N., Raj, S.: Smart helmet using GSM & GPS technology for accident detection and reporting system. Int. J. Electr. Electron. Res. 2(4), 122–127 (2014) 12. Rasli, M.K.A.M., Madzhi, N.K., Johari, J.: Smart helmet with sensors for accident prevention. In: ICEESE (2013)

Decentralized Digital Identity: A New Form of Secured Identity Using Blockchain Technology Sanjay Kumar Jena(B)

and Ram Chandra Barik

C. V. Raman Global University, Bhubaneswar, Odisha 752054, India [email protected], [email protected]

Abstract. Decentralized Digital Identity is the recent research focus for all identity management policy making authorities. The demand for the development robust digital identity solution is constantly rising. Conjugation of AI and biometric authentication at one side makes the identity process more streamlined to the users but at other side opens up ample of opportunities for cybercrime and identity theft also. Hence, the centralized process for maintaining the identity has become vulnerable both for the identity holder and the identity maintaining organization as well. This paper proposed a secure blockchain based generic provable claim model to be adopted for the benefit of identification of general public. The proposed model depicts secure decentralized ledger for issuer and verifier in identity model to mitigate the risk of exploitation over digital identity by the hackers to protect from duplication with fake identification. MD5 and SHA-512 driven secure hashing function is applied in a blockchain for identity verification in two application domains in Biomedical and Transport blockchain as per recent state-of-art. Keywords: Digital Identity · Blockchain · Security and integrity

1 Introduction Need of personal identity in the growing internet age is thriving day by day. Digital Identity fraud is multiplexing its versatile techniques with the level as the recent development in AI and biometrics are getting enjoyable. But security is the primary concern for any development sector, so it should be maintained. In today’s mutual business distrust ecosystem, the identity is getting fragmented and duplicated at the servers of the organizations. It is provided with a username and password to store all the identities by different websites and also with some plastic cards that is used to identify somebody [1]. This creates a mystification on the mind about the integrity of the data stored in the central server. The dark web news reveals that of having and using fake identities for credit card purchases, internet banking healthcare database theft and many other purposes. And the news continues every day [2]. As it can be observed from Fig. 1, the report provided by Statista- clearly summarizes about world-wide increase in identity theft risk amid covid-19. Including AI in managing Digital Identity can speed up the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 93–102, 2023. https://doi.org/10.1007/978-3-031-31153-6_9

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race for solution to a Decentralized Digital Identity management. And if this can be included in healthcare sector then there will be drastic change in converting hospitals to smart hospitals. So Digital Identity is spanning its area using blockchain technology. The organization of the paper is represented as Sect. 2 delivers concept of personal identity and a proposed model using blockchain technology. Section 3 elucidates the result analysis which narratively explains various cryptographic hashing techniques and a proposed model. Section 4 as conclusion.

Fig. 1. Identity Theft Risk - observed in FY May, Aug 2020 and expected risk till Aug 2021 (Source- Statista 2022)

2 The Concept of Personal Identity The analog world is using the conventional form of identity, where it generally takes out its identity card form the wallet, confirms its identity to the authorities of bank or hotel or airport or any other organizations and then after that it goes back to the wallet. Which makes the user to feel of getting full control over its identity. Similarly in internet the identity is provided digitally by means of video identification or entering data through

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online forms. However, there might be a security risk to the provided data, because the server access is maintained by some third-party organization [3]. The Digital Identity [4] is much more secure than the physical one. It overcomes the problems and reduces the level of bureaucracy and increases the speed of process within an organization by allowing for greater interoperability between departments and other institutions. But it can be serious problem if stored on a centralized server, as it becomes the target of hackers. Since 2017 alone these are more than 600 million personal details- such as address or credit card numbers have been hacked, leaked, or breached by hackers [5]. Despite the serious problem it is moving towards more secured, portable, verifiable everywhere and anytime. The user also has a major role to make its identity isolated to it would not be used by someone who is not authorized to it. Identities are always private that should be secured. Blockchain technology has started with crypto currency but it has been expanded its use in cyber-physical systems, data privacy, and digital identity in this cyber world. In this digital age, the term digital identity comes to the forefront [6]. 2.1 The model of Digital Identity Using Blockchain Technology The process of Digital Identity involves an issuer, a verifier, a decentralized ledger and digital identity holder [7]. Identity is any personal data, such as name or date of birth (DOB) that can be attested by a trusted authority. So, we can understand the process in a way like entities that issue credentials such as department of motor vehicles, election commissioner, hospitals, income tax department etc., are known as issuers. Owners of the credentials are known as users or holders. Any entity that the owner presents a claim to, so that the owner can establish some aspects of its identity, is a verifier. As we go through the Fig. 2 the issuer issue’s Digital Identity to the holder which gets verified by the verifier and send to distributed ledger, that is accessed any time. Here the decentralized ledger provides outmost security than the centralized server, that have more chance of getting attacked. The trust on the decentralized ledger is the fiduciary relationship of the person for its digital identity. Privacy of the person is its digital identity maintained untouched by any unauthorized one and the same is presented at the time of use. Keeping privacy as vital part of the model, the hyper ledger decentralized technique is used. Simplicity in the model gives a better understanding to someone who is unknown or new to the system. But is should be complex in the deeper of the part [8].

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Fig. 2. Decentralized Digital Identity process

3 Materials and Methods Need of blockchain technology for identity is to eradicate the current identity issues. Organizations need to understand the blockchain technology and its importance for the security of digital identity. There are many governments that have adopted the crypto currency to be the part of future secured digital world and fulfil the digital demands of their people [9]. And also, for MD5 the message digest algorithm that produces 128-bit hash value. As showed in Fig. 3 It is divided into four 32-bits, denoted as A, B, C and D. The main algorithm uses 512 message blocks (denoted as M) into four similar stages termed as rounds. Each round is term as 16 similar operations, which will be in total 64 operations at final step of the algorithm. In detail it can be said that first round will have 16 operations (Sequential order S1-S16). The second round will start from 17th operation (Sequential order S17-S32), and the process continues similarly. On conclusion of the fourth round and its 64th operation, the outputs added to original initialization vectors that we listed above. The result of this calculation is the final MD5 hash of the input. MathematicallyF(B, C, D) = (BC) V (¬BD)

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Fig. 3. MD5 hashing algorithm function

G (B, C, D) = (B  D) V (C ¬D) H (B, C, D) = B ⊕ C ⊕ D I (B, C, D) = C ⊕ (B V ¬D) ⊕, , V, ¬ denote XOR, AND, OR, and NOT operations. A single block that keeps all the data, hash (SHA-256) of current block and hash(h) of previous block is moved to the chain. For an example of a text block is undergone for block hash (SHA-256) and its corresponding hash code generated. A minute deviation in the original text (. is removed) leads to different hash generation as follows. V

“Blockchain can be used for issuing and verification of birth certificates, national ID cards, passports, or driving licenses.” SHA256hash: c3628eb784e20163a9865fa294ce4ed4705c92b22203fe5b4c3ecd4c09cca4b9.

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“Blockchain can be used for issuing and verification of birth certificates, national ID cards, passports, or driving licenses” SHA256hash: e6fa58195e99d3117a9da5a6ff609b6e4f0d454789709977ddff88999712a12e. Similarly for block hash (SHA-512) and its corresponding hash code generated. And with a minute deviation it can be seen the difference. “Blockchain can be used for issuing and verification of birth certificates, national ID cards, passports, or driving licenses.” SHA512hash: 953c2f21c50d15f4384cac802109f1b0aa13feee6b9b2e722bdaaeba83fb65bcf49ef7a088aa 377e4d1faa56a63afeccbf15f9933cdb82f0753277efbf2ba2ea. “Blockchain can be used for issuing and verification of birth certificates, national ID cards, passports, or driving licenses” SHA512hash: d1f238d95f063b336f15c6183f276e8384f21f8ed9a57afeb69b582ea17fa8200c00fffa758dd17 2cfea1cfe88cdb0d9066373805b2386787ce58abadcaa3cca.

4 Result and Analysis The Digital Identity model uses distributed Hyper ledger for maintaining the information that can be used at any organisation by the person with a total secured environment and no chance for the attacker for any unauthorised access. Each block in the chain is locked that can only be accessed by biometric authentication of the person. The Fig. 4 gives us a clear view about when a person when gives its biometric identity then it is the work of identifier and verifier to make it verify of the credentials by its registration and DID issuance and confirmation and after that issuing a digital identity which is then transferred to an empty block that is mined by the miner. It has been described clearly in the Fig. 5 about the identity creation process using AI technology [10]. Here PU key and PV key is referred as public key and private key. The user generates PV key by the issuer and then the DID is generated based upon the PU key by the issuer which is using AI based technology. Then only the hash is generated of the DID document which is stored at a decentralized distributed database with the DID Doc_Addr. The Digital Identity model can best fit at healthcare sector, because health data that are generally stored in hospital database or cloud that is managed by some third party. And users may lose control on their information. So, Fig. 5 which is a part of the proposed model that describes the process of identity creation. As we are proposing the model for the demanding healthcare sector so in-general the user is patient in the figure. It describes how a patient can get connected to a smart hospital and then to its favourite doctor and also have its health information in the secured blockchain technology. Once the identity generated then the wearable device or IoMT can be the part to have patient information to the doctor using IPFS in blockchain [11].

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Fig. 4. Blockchain based decentralised ledger verification process

Note: PUKey = Public Key PVkey = Private

Store DID, Hash, DID Doc_ Addr.

based on

Store the hash into the distributed database and obtain the address document

DID

Obtain the hash of DID document

Generate PUkey

Generate PVkey AI based Identifying Management (Issuer)

Generate PUkey based on PRkey

Patient

Decentralized Distributed Database

Fig. 5. Identity Identifier Creation Process using AI

The flow process of acquiring identity is well shown in Fig. 6. Here our research model is based upon the self-sovereign Digital Identity [12] so the entity or the user is sole owing control of its identity. To validate the identity certificate of the user at level 1 after receiving the DID the process of encryption and decryption is being carried out using public key (PU key and PV key ) at level 2 and 3 after it goes through a process of encryption and decryption and verification by using PV key and PU key at the level 4 to 10. Which goes as follows

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if step 3 is true then PUkey is obtained and then using the PUkey a random number is generated which is termed as encrypted R, here R denotes random number. And then the the encrypted R is sent to the user by the verifier at level 6. The patient or user decrypts it by using a private key using an AI based issuer, which is again encrypted using public key and sent to the verifier. Now the verifier decrypts it and verifies with previously sent R and at level 11 only one symmetric key is generated that leads to issue the certificate to the corresponding user and storing it at the decentralized database.

Decentralized Distributed Database

Note: PUKey = Public Key PVkey = Private Key

12- Issue Corresponding certificates to user

10- verify two Rs

11- verifier interact to user by symmetric key algo-

13- store corresponding certificates at decentralized ledger

ih

9-Decrypt Encrypt_R PVkey of verifier and obtain R

8-Encrypt the R, Encrypt_R using PUkey and obtain R

6-Encrpt R sent to User

4-If Step 3 is true then Obtain PUkey

5-random number Encrpt_R is generated using obtained PUkey generated

2- Obtains the Hash of DID document,

1-User Requests for identity certificates

The Verifier

7-Decrypt Encrypt R by PVkey

Patient

3- Obtain DID doc based on Hash of DID doc Arrdand make its hash and check whether Hash1 is eual to Hash

AI based Identifying Management (Issuer)

Fig. 6. Flow process of Acquiring Identity and bundling with the DID

4.1 Biomedical Blockchain For better Medicare and also prior diagnosis of patient emerges secure patient data sharing to other high-end hospitals. The sharable health care data is purely risker in case of cyber-attacks. Hence sharable data uses decentralized architecture with the

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blockchain enabled data communication. The hash functions depict unique hash value using SHA512, MD5 etc. Our case study for making smart hospitals that can run robustly against any failure and date exposure using blockchain can be a solution. The structure of blockchain is outlined in the Table 1. Table 1. Structure of Blockchain Field

Size

Magic Number

4 bytes

Block size

4 bytes

Block header (80 bytes) Version

4 bytes

Previous Block Hash

32 bytes

Merckle Tree

32 bytes

Time stamp

4 bytes

Difficulty rating in bits

4 bytes

Nonce

4 bytes

Transaction counter

1 to 9 bytes

Transactions

depends on transaction size

4.2 Transport Blockchain Smart transportation management system also requires blockchain to be involved in making decentralized and autonomous. May it be ride sharing, logistics, asset management, vehicle identity, drivers identity or any other that can be managed securely using blockchain. Blockchain can be used for lifetime management and surveillance of devices. Right from the assembly of the vehicle it gets registered to a universal blockchain and within its lifetime that can include maintenance, resale or accident, traffic violation and all other realtime data gets recorded in the ledger of blockchain very secure and accessible [13].

5 Conclusions This paper depicts a distinct state-of-art and recent research focus towards digital identity issues, challenges and applications using blockchain technology with medical blockchain and transport block chain. According to security aspect, the proposed blockchain driven Digital Identity model can be implemented in any sector of this digital world where identifying user is the primary essence. The result analysis section narrates two application areas one is health care sector and anther transport sector as case studies. But most importantly it gives emphasis on health care sector because of

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its growing demand and the issues that the world faces are in the covid outbreak can be a major reason. Hence the best option will be to adapt the future security system for protecting the personal information that is available on internet. There is a lot of potential in the proposed model and it is demanding a lot more research for a privacy integrated and trusted simplified society in a secured environment.

References ˇ cko, Š, Be´cirovi´c, Š, Kamišali´c, A., Mrdovi´c, S., Turkanovi´c, M.: Towards the classification 1. Cuˇ of self-sovereign identity properties. IEEE Access 10, 88306–88329 (2022) 2. Jason, K., Ann, P.: Dark web offers passwords, personal data for sale; How can you protect yourself. WLS-TV. https://abc7chicago.com/the-dark-web-search-engine-browser-identitytheft/11535331/ 3. Dwivedi, S.K., Amin, R., Vollala, S.: Blockchain based secured information sharing protocol in supply chain management system with key distribution mechanism. J. Inf. Secur. Appl. 54, 102554 (2020) 4. White, O.,et al.: Digital Identification-A key to inclusive growth. https://www.mckinsey. com/business-functions/mckinsey-digital/our-insights/digital-identification-a-key-to-inclus ive-growth 5. Sharma, D.H., Dhote, C.A., Potey, M.M.: Identity and access management as security-as-aservice from clouds. Proc. Comput. Sci. 79, 170–174 (2016) 6. Vujicic, D., Jagodic, D., Randic, S.: Blockchain technology, bitcoin, and ethereum: a brief overview, pp.1–6 (2018) 7. Takemiya, M. Vanieiev, B.: Sora identity: secure, digital identity on the blockchain. In: 42nd IEEE Annual Computer Software and Applications Conference, pp. 582–587. Tokyo, Japan (2018) 8. Guo, H., Yu, X.: A survey on blockchain technology and its security. Blockchain: Res. Appl. 3(2), 100067 (2022) 9. Song, Z., Yu, Y.: The digital identity management system model based on blockchain. In: International Conference on Blockchain Technology and Information Security, pp. 131–137 (2022) 10. Zhao, W., Yang, N., Li, G., Zhang, K: Research on digital identity technology and application based on identification code and trusted account blockchain fusion. In: 2nd IEEE International Seminar on Artificial Intelligence, Networking and Information Technology, pp.405–409 (2021) 11. Wang, W., et al.: A Privacy protection scheme for telemedicine diagnosis based on double blockchain. J. Inf. Secur. Appl. 2214–2126 (2021) 12. Stockburger, L., Kokosioulis, G., Mukkamala, A., Mukkamala, R. R., Avital, M.: Blockchainenabled decentralized identity management: the case of self-sovereign identity in public transportation. Blockchain: Res. Appl. 2(2), 100014 (2021) 13. Yuan, Y., Wang, F.-Y.: Towards blockchain-based intelligent transportation systems. In: IEEE 19th International Conference on Intelligent Transportation Systems, pp.1–4 (2016)

Object Detection with YOLO Version 3 for Big Data Rajat Saxena1(B) , Shatendra Dubey2 , and Vishma Kumar Karna2 1

Big Data Lab, Department of Computer Science and Engineering, Shri Vaishnav Institute of Information Technology, Shri Vaishnav Vidyapeth Vishwavidyalaya, Indore, India [email protected] 2 Big Data Lab, Department of Information Technology, NRI Institute of Information Science and Technology, Bhopal, India

Abstract. Object detection is the most vital task for the application of computer vision. The You only Look Once (YOLO) version3 is the most promising technique used for deep learning-based object detection. It is the k-means cluster method, which estimates initial weight and height of the predicting bounding boxes that are sensitive to the initial cluster centers. This paper focuses on study of approaches for efficient object detection techniques in the field of computer vision. The improvement in the accuracy had been studied based on the deep learning techniques. It aims to develop an online tool for a better study about the object detection techniques as a platform to review the object detection in both online and offline images. A popular state of art object detection algorithm called YOLOv3 which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world. Keywords: Artificial Neural Networks · Convolutional Neural Networks · Residual Network · You Only Look Once (YOLO)

1

Introduction

Object detection can take part a vital role in automation of vehicles, industries, and computer vision. Object detection with deep learning is better approach than traditional object detection systems. People are ready to distinguish what sort of articles are there before us, where they are, and how they cooperate. It does not take one moment to distinguish the kind of article present before us. To cope up with the Convolutional Neural Systems, if performing calculation are quick and exact enough for picture handling, the Personal Computers (PCs) are automatically drives without assisting gadgets and sensors for partial extract data to the clients. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 103–116, 2023. https://doi.org/10.1007/978-3-031-31153-6_10

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Fig. 1. Working of YOLO Version 3

In this custom, it uses genuine Artificial Intelligent (AI), if these calculations could finish Deep Learning (DL) spending with high productive and fantastic execution like individuals do. In these experiments, acknowledgment, arrangement, confinement, and item discovery are sequential tasks. These tasks are used for picture handling, in which exactness, speed, cost and intricacy are the key difficulties. In next future technologies just like R-CNN, distinct proposition strategies are used to initiate potential bounding boxes and thereafter run a classifier on these boxes in a picture. After grouping, anchor box is used for handling the refinement, copy discovery, and rescore the case dependency of the picture [15]. This mind overwhelming way is modest for independently prepared individual segments. It invokes us for object identification, which solitary relapse picture pixels to class probabilities and anchor box facilitates. Our framework uses You Just Look Once (YOLO) to predict a picture in comparison with existing research work. The working of YOLO algorithm is shown in Fig. 1. YOLO is based on full images and it improves the location execution of images. Amazing quickness and mind boggling pipeline is the biggest advantage of YOLO algorithm. It brought together many advantages for item location. To anticipate recognitions, we run and test a neural system on another image. A top identification technique quick R-CNN [1] botches foundation fixes in a picture and requires higher configuration setting. YOLO overcomes a large portion of the quantity of foundation mistakes, which appears in Fast R-CNN. When regular pictures are prepared and tried over the fine art, the performance of

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Fig. 2. The example of image classification, localization and object detection

YOLO is much better than top discovery strategies like DPM and R-CNN. It requires less inclined resources because YOLO is exceptionally generalizable. With YOLO V3, we are able to detect objects from live videos and pictures. In YOLO V3, object recognition task is performed after using the image classification and object localization. Thereafter, we invoked the object detection and object segmentation. In Fig. 2, all basic processes that involved in the objection segmentation is presented.

2

Literature Review

In this section, we discuss about Artificial Neural Network, Convolutional Neural Networks, Residual Network, and some related object detection techniques. 2.1

Artificial Neural Networks

In ANN, there are lots of hubs, which replicate the natural neurons of human brain. The association and communication of these neurons is similar to simulation of input information gathering and processing of different natural neurons. These activities are passed through different neurons. The connection of these neurons is called as Hub and yield of every hub is called as hub stream. In hub, a weight value is associated with each connection. With the estimation and value change in weights of each connection, ANN s is simulated as similar to natural brain. 2.2

Convolutional Neural Networks

Fundamentally Convolutional Neural Networks are same basic Neural Networks. They have neurons which are enable adjustable loads and bias values [15]. It is

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just like plug and play of few sources of information and spot item with nonlinearity. In Convolutional Neural Networks, a solitary differentiable score work communicates to a class score from crude picture pixels. Figure 3 demonstrates Convolutional Neural Networks. We learns standard Neural Networks to apply in every traps/traps and Support Vector Machine (SVM)/Softmax has applied on the last associated layer.

Fig. 3. Convolutional Neural Networks

Fig. 4. Residual Network

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Residual Network

ResNet or a Residual Network [4] is a neural system engineering which takes care of the issue of disappearing gradients [14] as basic as could be expected under the circumstances. It is very inconvenience and off chance to send the angle signal in reverse. So an easy and alternate option is chosen to done all process at each layer. In this modified system, characterization and enactment at a layer as given by following Eq. [3] (Fig. 4): y = f (x)

(1)

where f(x) is our convolution, grid duplication, or group standardization. Whenever the reverse sign is sent, the inclination constantly should go through f(x). It raises the ruckus due to nonlinearities inclusion. Then, the ResNet executes at each layer with following equation: y = f (x) + x

(2)

At the end, the “+ x”enotes us the alternate way of enabled inclination, which is passed in reverse direction. It is a hypothetical slope which skips over all middle layers by stacking these layers and achieves the base without being decrement. It is an unpredictable and genuine instinct, which implicates the most recent manifest. With the help of above description, f(x) + x takes the structure. Batch Normalization is used in ResNet Unit architecture. Weight can be assigned for all full connected convolutional layers. We build and execute a system organized by these layers with TensorFlow. To at long last work on the article detection, we have to experience a few works of others that will be useful for our work. Before really jump into ResNet how about we experience how a basic CNN (Convolutional Neural Network) works. If there should arise an occurrence of an ordinary CNN we pass our info x through conv-ReLu-conv arrangement and we get our yield F(x) [3]. Be that as it may, if there should be an occurrence of a ResNet how about we perceive how it functions. From the above diagram we can clearly understand that our input x go through conv-relu-conv layer first but, in addition to that we add our input x to the final output, let’s call it h(x) = f(x) + x and that is called skip connection and by doing so ResNet was able to optimize the error rate. Since, from the hypothetical piece of CNN we came to realize that the more profound the neural system goes precision of the CNN increments. However, as a general rule it isn’t valid. In the time of 2015 Microsoft research group has acquainted another thought with make an a lot further system called ResNet [3] or lingering learning and, in [3] they imagined with the assistance of a chart that on the off chance that we increment the quantity of layers in a neural system the preparation mistake just as test blunder increments (Fig. 5).

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Fig. 5. Residual learning: A Building Block

2.4

Some Related Object Detection Techniques

Joseph Redmon et al. [6] presents a fast and simple YOLO approach for detecting real time images. This method fast and accurate in differentiate between art and real time images. This method is better than R-CNN in aspect of regression and class probabilities for each bounding boxes. Chengji Liu et al. [5] developed a generalized object detection method, which is based on training set like blurring, cropping, noise, and rotating of images. Higher ability of robustness is shown by training set in this method. The experiment on trained standard sets have poor robustness with degraded images. After some modifications, same set of degraded images performs better in terms of average precision. Venbo Lan et al. [4] proposed a new YOLO structure with adding small pedestrian features in pass through layers. They used INRIA data set and It increases the number of detection frames up to 25 frames per second. Rumin Zhang et al. [9] recommended a light field camera to categorize and classify objects into the images. It helps them in labeling and training the object filter. The effectiveness of YOLO algorithm is tested in different types of scenarios. Zhimin Mo et al. [6] used solder joint of automotive doors for working in real time. Consequences and accuracy of YOLO algorithm improves the results for real time object detections. We proposed a new approach with the observation of above research works and their advantages, which is described in following Section.

3

Proposed Method: You Only Look Once (YOLO)

You Only Look Once (YOLO) [6,9] is the most progressive framework for item recognition continuously. In Pascal Titan X it forms the pictures at 30 outlines for each second and has a guide at 57.9% COCO test-dev. YOLO is a model for the

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discovery of items and is equipped for working continuously. It has 3 renditions are accessible up until this point. YOLO utilizes a solitary CNN arrange for order, and for the area of an article by methods for cradle squares. YOLO [7] predicts progressively prohibitive squares on the lattice cell. Yield of every framework cell of YOLO. The yield tensor of every framework cell contains ‘5 + class number’ values in it where ‘5 = 1’ certainty score esteem + 2 esteem for the middle co-ordinate + 2 esteem for tallness and width of the bounding box. How about we consider a circumstance where we have ‘3 * 3’ framework cell and 3 class along these lines yield y would be of shape ‘3 * 3 * 8’. Likewise, YOLO v3 uses anchor boxes for foreseeing more than one article allotted to a similar network cell. On the off chance that we consider 2 grapple boxes for our above characterized model. At that point our yield y would be of shape ‘3 * 3 * 2 * 8’. Here worth 2 speaks to the quantity of anchor boxes. Since for YOLO v3 it utilizes 9 grapple boxes in this manner the worth would be 9. General Formula: (N ∗ N ) ∗ [num of anchors ∗ (5 + num of anchors)]

(3)

where, N = brace cell shape. Non-Max suppression is a calculation utilized for tidying up when different lattice cell predicts a similar article. There are 3 phases for Non- Max suppression 1. Discard all anchor boxes with certainty score ≤ 0.6 (for example ≤ 60%). 2. Pick the crate with biggest certainty score yield as a forecast. 3. Discard any residual box with IoU ≥ 0.5 (i.e. ≥ 50%). Since YOLO and YOLO v2 had some issue to identify little items and thus YOLOv3 [8] utilizes three screening layer initial one having ‘13 * 13’ lattice cell giving us a yield highlight guide of size ‘13 * 13 *255’ and again one screening layer having ‘26 * 26’ framework cell gives us a component guide of size ‘26 * 26 * 255’ and in conclusion a screening layer having ‘52 * 52’ gives us an element guide of size ‘52 * 52 * 255’ and it’s the last yield of the network(the 106th layer). YOLO can just recognize objects that have a place with the classes that are available in the informational index utilized for system training [11]. These loads were gotten from the set up COCO system learning information, and in this manner we can distinguish 80 classes of items. YOLO [2,10] results are promising. The mind boggling informational index to recognize VOC Pascal, YOLO conceivable to accomplish a mean normal exactness, or mapping, of 63.4 out off 100 during the utilization of 45 outlines for every second. For examination, the further developed model R-CNN VGG 16 quicker achieves MAP 73.2, however just works with up to 7 outlines for every second, which is multiple times diminishes productivity. In this way, the fundamental selling point for YOLO is its guarantee of good execution in article recognition at ongoing rates. That permits its utilization in

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frameworks, for example, robots, self-driving vehicles, and automatons, where being time basic are absolutely critical. Table 1 shows different values for various levels of YOLO Version 3 Algorithm. Table 1. Values for Various Levels OF YOLO Version 3 Algorithm Detection Framework

mAP FPS

YOLO

61.8

SSD 500

47

73.8

23

YOLO V2 (416 × 416 image size) 74.8

62

YOLO V2 (480 × 480 image size) 75.8

56

Faster RCNN - VGG16

71.2

9

Faster RCNN - ResNet

74.4

6

YOLO v3 [8,13] executes 320 × 320 pixels size object detection in 21 ms at 29.3 mAP, which is much precise and quicker than SSD. To compare with YOLOv3 [8], we take a gander at the old .5 IOU mAP discovery metric. RetinaNet approach accomplished in 198 ms with 57.5 AP50. On the other side, Titan X executes in 52ms with 57.9 AP50, which is 3.8× quicker. Our proposed YOLO v3 algorithm estimated at .5 IOU mAP with about 4x quicker Focal Loss. With the change in size of the model, we get a stretch trade-off among speed and exactness, in which no retraining is required. The following is the most noteworthy and least FPS. However, the outcome beneath can be very one-sided specifically they are estimated at various mAP. 3.1

Prediction of Each Grid Cell

We should consider a circumstance where we are attempting to foresee three classes utilizing a 3 * 3 framework cell at that point, for every matrix cells YOLO predicts the accompanying. Y = [Pc , bx , by , bh bw , c1 , c2 , c3 ]

(4)

where, Pc = Confidence Score or Confidence esteem; bx =x co-ordinate of the inside point; by = y co-ordinate of the inside point; bh = stature of the anchor box; bw = width of the anchor box; c1 = class 1; c2 = class 2; c3 = class 3. Pc is one when an a network cell contains an item and it is zero when a lattice cell doesn’t contain an article. At the point when confidence score is zero we couldn’t care less about the remainder of the qualities. bx , by , bh , bw are the bounding boxes of the item. These worth gives the co-ordinates of the bounding box of the article. bx and by are the inside co-ordinates and bh and bw are the stature and width of the bounding box. Lastly we have the quantity of classes. c1 , c2 , c3 ..........cn .

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Necessity of Anchor-Boxes

In the past area [12], it is said that each cell organize in charge of the forecast of the item. However, imagine a scenario where the cell system is in charge of estimating a few articles. Next, the idea of grapple box becomes an integral factor. On the off chance that there is just one sort of prohibitive field, YOLO won’t most likely anticipate more than one article related with that specific cell of the system. When all is said in done, what really YOLO predicts the article related with a particular cell of the system, which predicts the points of confinement related with said cell arrange. Along these lines, the grapple box it is just a prohibitive boxes all things considered and different parts of the race. What’s more, they are commonly good with an article. Presently, in the event that we consider the past circumstance, where a phone system was utilized and 3 * 3 Class 3, and on the off chance that we consider n tying down boxes in a cell of the lattice, at that point y is an impression of the size of 3 * 3 * 8 * n. Subsequently, the general equation for computing elements of the size card: (N ∗ N ) ∗ [num of anchors ∗ (5 + number of classes)]

(5)

where, N ∗ N - the size of the lattices. Furthermore, 5 here in light of the fact that each securing field 5 explicit qualities that have, 4 point of confinement arranges certainty score + 1 = 5. 3.3

Prediction of Anchor Boxes in YOLOv2 and Later

YOLO v3 utilizes 9 anchor boxes per lattice cell and they utilizes K-mean clustering to produce 9 grapple boxes per network cell. In the prior subjects we have just clarified that what YOLO predicts as a yield y; there we came to realize that bx , by , bw , bh are the x and y co-ordinates and with and high estimations of the inside point and anchor box individually. However, how these qualities are get anticipated. It may make sense to foresee the width and the tallness of the bounding box, however by and by, that prompts insecure slopes during preparing. Rather the greater part of the cutting edge objects indicator including Faster R-CNN uses balances. What are counterbalances? Balance is only what amount we should move the anticipated bounding box so as to get the ideal anchor box. At that point, these changes are connected to the anchor boxes to get the forecast. YOLOv3 has three anchor boxes, in this way it predicts three anchor boxes per cell (Fig. 6). YOLO v2 and YOLO v3 utilize these following recipes to compute the estimations of co-ordinate of the bounding boxes. bx = σ(tx ) + cx ; by = σ(ty ) + cy ; bw = pw ∗ etw ; bh = ph ∗ eth

(6)

Here tx , ty , th , tw are what the system yields c and c are the upper left coordinates of the lattice. pw and ph are stays measurements for the container. What’s more, we have passed our inside co-ordinates expectation through a

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Fig. 6. YOLO v3 Network Architecture

Fig. 7. Visualization of YOLO v3

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sigmoid capacity in this way we get yield somewhere in the range of 0 and 1. Following Fig. 7 is the visualization of what we have explained: The resultant forecasts, bw and bh are standardized by the stature and the width of the picture. In this way, if the expectations bw and bh for the case containing a pooch are (0.3, 0.8) at that point the genuine width and stature on a 13 * 13 highlight guide is (13 * 0.3, 13 * 0.8). 3.4

The Total Number of Bounding Box that YOLOv3 Can Predict

As we as a whole realize that YOLOv3 predicts at 3 unique scales; they are 13 * 13, 26 * 26 and 52 * 52. Hence all out number of bounding box that YOLOv3 can foresee is: (13 * 13 * 3) + (26 * 26 * 3) + (52 * 52 * 3) = 10,647 bounding boxes; that implies all out 10,647 forecasts. Confidence loss: If an object is detected in the box, the confidence loss is: 2

S  B  i=0 j=0

ˆ 2 1obj ij (Ci − Ci )

(7)

th 1obj boundary box in cell i is responsible for detecting the ij = 1 ; if the j object, otherwise 0.

If no object is detected in the box, the confidence loss is: 2

λnoobj ∗

S  B  i=0 j=0

1noobj (Ci − Cˆi )2 ij

(8)

where 1noobj is the complement of 1obj ij ij and λnoobj = 0.5

4

Implementation

Our technique is partitioned into two subsections: 1. Model Advancement 2. API Advancement 4.1

Model Advancement

1. Required Dependencies: 1. TensorFlow (GPU adaptation liked) 2. NumPy 3. Pad/PIL 4. IPython 2. Batch Norm and Fixed Padding: We will characterize a capacity for group standard. Since we realize that model uses clump standards that are same as resents. What’s more, YOLO v3 utilizes convolution with fixed cushioning.

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3. Features Extraction: Darknet-53:- For highlight extraction YOLOv3 utilizes Darknet-53, a neural system pre-prepared on ImageNet. Darknet-53 has lingering associations. We will expel the last three layers of Darknet-53 since we just need the highlights. The last three layers of Darknet-53 comprises of Averagepool, completely associated and SoftMax layers. 4. Convolution Layers: Since we definitely realize that YOLOv3 is a Fully Convolutional Network (FCN). In this manner, clearly YOLO v3 has huge quantities of convolution layers. For us it is useful to assemble them in a solitary capacity. 5. Detection Layers:YOLO v3 contains three location layers and in every three identification layers distinguish at various scales. For every cell in the element map, identification layer predicts ’num of anchors * (5 + num of class)’ values utilizing ‘1 * 1’ convolution. YOLO v3 utilizes 3 grapple boxes for every identification layers. Therefor 3 stays for every discovery layers and 3 identification layers produce ’3 * 3 = 9’anchor boxes altogether. Also, for every 3 anchor boxes it predicts 4 directions of the crate (bx , by , bw , bh ), certainty score(pc )and class probabilities (c1 , c2 , c3 .....cn ). 6. Up sample Layers: Since we have to connect with the remaining yields of the Darknet-53 preceding applying recognition on an alternate scale we will utilize the example of the component guide utilizing closest neighbour introduction. 7. Non-max suppression: Since the model will create a great deal of boxes, so we will dispose of all undesirable boxes with low certainty score (pc). Likewise, we will maintain a strategic distance from different boxes comparing for one article. We will dispose of all cases with high cover utilizing non-max suppression for each class. 8. Final Model: At long last, we will characterize the model class by utilizing every one of the layers that are going to use in the YOLOV3 model. 9. Converting weights to TensorFlow Format: Since we have just conveyed the YOLO v3[1] model in TensorFlow so now its opportunity to stack the official loads. What we will do now is that, we will repeat through the first weight record and bit by bit make ‘tf.assign’ tasks. 10. Running the Model and Mapping Weights to The TensionFlow Model: To start with, we will stack the first weight document and guide it to the TensorFlow model and we at that point pass an example picture to create yield of it. 4.2

API Advancement

Since, goal of our venture is to advancement of an online framework to perceive protests in a scene picture, in this way we have to send an API that can deal with every one of the undertakings performed by our model sent in Subsect. 4.1. There are 4 required dependencies for API Advancement, which are following:

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1. 2. 3. 4.

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Conclusions and Future Work

In this paper, we proposed and implement an efficient object detection technique in the field of computer vision. We also improve the accuracy in object detection based on the deep learning techniques. It aims to develop an online tool for a better object detection in both live videos and images. A popular state of art object detection algorithm called YOLOv3 is modified which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world. This technique may be broadly deployed in applications that used drones, quad-copters, and soon service robots for capturing the images. This research work is performed well in many domains where much assistance is needed for using automotive machines. It is very useful in structured, semi-structured and unstructured data processing of Big Data. The growth of this big data is expected to be even faster in coming decades, which is getting from social media, sensors, transactions, pictures, videos and so on. It fetches high applicability of this proposed work. This approach is extremely fast as compared to other real time detectors. In this work, no complex pipeline is needed for maintaining neural network on images. It successfully differentiates natural images against the art work.

References 1. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010) 2. Fu, C.Y., Shvets, M., Berg, A.C.: Retinamask: learning to predict masks improves state-of-the-art single-shot detection for free. arXiv preprint arXiv:1901.03353 (2019) 3. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) 4. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998) 5. Liu, C., Tao, Y., Liang, J., Li, K., Chen, Y.: Object detection based on yolo network. In: 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 799–803. IEEE (2018) 6. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

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7. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017) 8. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018) 9. Saxena, R., Dey, S.: Cloud shield: effective solution for DDoS in cloud. In: Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (eds.) IDCS 2015. LNCS, vol. 9258, pp. 3–10. Springer, Cham (2015). https://doi.org/10.1007/ 978-3-319-23237-9 1 10. Saxena, R., Dey, S.: Light weight access control mechanism for mobile-based cloud data storage. Int. J. Next Gener. Comput. 9(2) (2018). http://perpetualinnovation. net/ojs/index.php/ijngc/article/view/424 11. Saxena, R., Dey, S.: On-demand integrity verification technique for cloud data storage. Int. J. Next Gener. Comput. 9(1) (2018). http://perpetualinnovation.net/ ojs/index.php/ijngc/article/view/425 12. Saxena, R., Dey, S.: Data integrity verification: a novel approach for cloud computing. S¯ adhan¯ a 44(3), 1–12 (2019). https://doi.org/10.1007/s12046-018-1042-4 13. Saxena, R., Dey, S.: A generic approach for integrity verification of big data. Cluster Comput. 22(2), 529–540 (2019). https://doi.org/10.1007/s10586-018-2861-0 14. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) 15. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

Efficient Approach for Virtual Machine and Resource Allocation in Cloud Computing Rajat Saxena1(B) , Shatendra Dubey2 , and Rajesh Sada2 1

Cloud Computing Lab, Department of Computer Science and Engineering, Shri Vaishnav Institute of Information Technology, Shri Vaishnav Vidyapeth Vishwavidyalaya, Indore, India [email protected] 2 Cloud Computing Lab, Department of Information Technology, NRI Institute of Information Science and Technology, Bhopal, India

Abstract. Globalization has brought many changes in modern business world. Problems of enterprise oriented software applications like distribution and configuration of resources presents challenge to the traditional software sales model. Cloud computing presents the solution for such problems. With the help of cloud-based model one can earn good return by making the QoS demands of their users (customers) satisfied. In order to fulfill the infrastructure demands (like network, storage, etc.) to their users, service providers have to maintain their own hardware or they lease it from the IaaS providers. For maintaining their own hardware SaaS providers will have to incur an extra cost, but if taken on lease, zero maintenance cost involved. Moreover, if provider wants to optimize cost and gain customer satisfaction, they will have to satisfy their customers/users by maintaining service level objectives. This paper presents a model for resource allocation in such a way as to minimize cost and maximize profit by satisfying the service level needs of the customers. The model is designed to enable the service providers with an ability to cope with the changing needs of customers, mapping customer requests to infrastructure level parameters and better handling of heterogeneous VMs. Keywords: Cloud Computing · Service Level Agreement (SLA) · Quality of Service (QoS) · Scheduling · Software as a Service (SaaS)

1

Introduction

Cloud providers allow users to access application via internet without making an investment in software and other infrastructural resources. SaaS providers either use their own resources or the rented one to serve their customers, [2,6,7,9,11]. But there are certain limitations [12] associated like hosting an in-house resource system can increase the administrative cost, on the other hand, renting may c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 117–128, 2023. https://doi.org/10.1007/978-3-031-31153-6_11

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affect the QoS as performance of IaaS varies [4]. Present work is an attempt to meet these challenges. In this paper, a new and innovative model is proposed for admission control and resource scheduling for optimum utilization of cloud resources in public domain. This will help SaaS providers to attain its objectives of cost, profit and customer satisfaction. Traditional service provider [10,14], allocates each customer with an individual virtual machine to meet service requirements that results in increased cost due to poor utilization of hardware as it is possible that customer doesn’t use the complete VM capacity as is reserved for customer’s request. Hence, multitenancy strategy could be used to reduce resource cost by keeping in view service level constraints. This would reduce SLA violation also [1]. For fulfilling customer request, earning customer loyalty, minimizing cost and hence, enlarging market share, one must answer few questions like 1. How to manage changing needs of customers like upgrading the packages bought, adding multiple accounts, etc. ?. 2. How to link customer needs with the parameters in infrastructure level ?. 3. How to cope with the heterogeneity of infrastructure level ?. Present paper is an attempt to answer above questions by proposing an innovative mathematical model to maximize profit of service provider. This paper presents two major contributions: first proposing a mathematical model for service providers (SaaS) considering customer’s QoS requirement so as to satisfy customer and earn loyalty, and secondly by scheduling resources in such a way as to minimize cost incurred and maximize revenues along with the satisfaction of customer. The proposed system model reduces the cost burden of the user by allowing them to pay according to their use of resources and hence is costeffective. Also, it allows customers to use resource on the basis of their needs which make it scalable too. These advantages have increased the scope of cloud in modern business world like commerce, trade and industry.

2

System Model

Figure 1 shows a service model for SaaS providers in a Cloud Computing Environment consisting of three layers namely, infrastructure layer, platform layer and application layer to meet customer request [15]. All services offered to customers are managed by application layer of the model. Scheduling and mapping of customer’s QoS request to infrastructure level parameters and hence, allocation of resource VM is done by platform layer. Finally, actual initiation and release of VMs is controlled by infrastructure layer [8]. Following is a detailed mathematical system model from the perspective of both the user and service provider to enhance level of customer satisfaction, better quality of service and better relationship between two parties. There are four type users in proposed model namely: individual user, government organization, private enterprise and research organization. The request for resource allocation is initiated by user via application layer of the model. This

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Fig. 1. Model for Admission Control and Scheduling in Cloud Computing Environment

request is then checked for SLA parameters and if the customer request meets provider defined SLA parameters then next checking for resource availability is done. If resource is available, request is accepted and scheduled by platform layer of the model. Finally, initiation of VM is done by infrastructure layer on the basis of scheduling done and at the end of the contract infrastructure layer releases the VM thus, allocated. If at any point of contract, customer feels a need to upgrade the contract, whole process of request initiation is repeated. However, if constraints are met and resource is available, re-scheduling is done and a new contract is formed. Otherwise, request is cancelled and old contract continues. Platform layer plays a vital role in this model as major decision-making is done in it [3]. 2.1

Actors in the Model

The actors engaged in our model along with their aims are stated below: 1. Customer: The one who actually requests for resource access to the cloud service provider and consumes them. As soon as a customer agrees to the

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pre-defined conditions of SLA like response time etc., an application request is sent to the application layer of service provider with QoS requirements like type of request, product type along with contract length, deadline of the contract etc. If a customer requests the upgrade of a service(s) in mid-way the contract, service provider has to manage this request intelligently matching the SLA requirements. SLA is a legal tender contract between Cloud Service Providers and user. Violation of terms and condition of SLA invokes penalty for both parties, which is depends upon norms defined in SLA. SLA includes QoS parameters specified by customers and pre-defined parameters of service providers 2. Cloud Broker: A cloud broker act as intermediary between user and provider. The role of broker is simply to save user time and efforts by searching required services from different vendors and also it provides information about how to use cloud computing in business to support business goals. The actual budget of serving broker is contingent with the budget of the customer and anticipated discount percentage. Broker’s profit is dependent on the provider price and the budget of the customer. To maximize the profit from the margin of negotiated price between customer and service provider is the main objective of the broker [2]. 3. Cloud Service Provider (CSP): CSP provides services to cloud customer like storage, network etc. They actually managed their own resources. Purpose of CSPs is to boost profit and expand their market share by undertaking as many requests as possible. 2.2

Mathematical Model for Resource Allocation and Profit Maximization

This section illustrates the mathematical equations used in the present research work. Here N is the service request {r1 , r2 .....rN } send by user to SaaS provider. Constraints of each request are: request deadline; user’s budget; rate of penalty for delayed work; workload; Contract length; input and output file size. In our proposed model, we defined J IaaS provider as called {j1 , j2 ....jJ }. We also defined M virtual machines {V M1 , V M2 , ..., V MM } for each IaaS provider J. M is responsible for running the physical resources. VM Attributes like VM cost, Data transfer price (in and out both), Data transfer time, and Data transfer speed (depends upon network) are calculated by CSP as per the SLA requirements. new is defined as following equation: The profit P rofijl new = Bnew − Costnew P rofijl ijl ; ∀ i ∈ I, j ∈ J, and l ∈ Nj

(1)

The total cost on V Mi of type l and IaaS provider j is given by following equation: new new new + P DCijl Costnew ijl = V M Costijl + DT Cjl

∀ i ∈ I, j ∈ J, and l ∈ Nj

(2)

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Table 1. Assumption and Acronyms for Model Symbol

Descripton

I

Number of initiated VMs

V Mil

V Mi with type l

J

Total number of IaaS providers

inP rij

Prices per GB charged for Data Transfer-In

outP rij

Prices per GB charged for Data Transfer-Out

SITij

Time taken for Initiating V Mi of Type l

Bnew

Budget to SaaS provider

DLnew

Deadline

βnew

Penalty Rate

inDSnew

Data-in required to process the user requests

outDSnew

Data-out required to process the user requests

Costnew ijl

Total cost incurred to the SaaS provider by processing the user request on V Mi of type l and resource provider j

new P rofijl

The profit gained by the SaaS provider

V M Costnew ijl

Total Cost incurred to SaaS Provider

new DT Cjl

Data Transfer Cost

new P DCijl

Penalty Delay Cost

V M Costnew ijl

VM Cost for serving the request

P riV Mjl

The Price of VM I with type l

SITij

Service Initiation Time

CL

Contract Length of Customer Request

new DT Cjl

Data Transfer Cost

new P DCijl

Penalty Delay Cost

βi

Penalty Rate

new ET Dijl

Exceeded Time Deadline period

Tijl

The time to process the request i on the virtual machine l of resource providers j

wi sjl ini +outi dtsjl

Time to process the requests

ET Dijl

Exceeded Time Deadline

Costijl

The Cost of request i

Budgeti

Budget for the profit of SaaS provider

Tijl

The Execution Time of Request i

Di

Deadline

Time to Transfer Data

tCost

Total cost of customer request

Costnew ijl

The total cost incurred to the SaaS provider by processing the upgraded user request on V Mi of type l and resource provider j

Costinitial ijl

The total cost incurred to the SaaS provider by processing the user request on V Mi of type l and resource provider j

Costup ijl

Processing Cost for Upgrading the Request

U Rijl

Upgrade Rate

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The VMCost (V M Costnew ijl ) is invoked by following equation V M Costnew ijl = P riV Mjl ∗ (SITij ∗ CL)

(3)

where SITij = Pjl ∗ ( swjli ); Data transfer cost as described in Equation includes cost for both data-in and data-out. new DT Cjl = (inDS new ∗ inP rijl ) + (outDS new ∗ outP rijl );

∀ i ∈ I, j ∈ J, and l ∈ Nj

(4)

new ) is Calculated : In Eq. (5), penalty delay cost (P DCijl new new = βi ∗ ET Dijl ; ∀ i ∈ I, j ∈ J, and l ∈ Nj P DCijl

(5)

Tijl is determined as follows: Tijl = (

wi ini + outi )+( ) + ET Dijl sjl dtsjl

(6)

The cost of request i must satisfy following Constraints: Costijl < Budgeti

(7)

The execution time of request i Tijl must meet the following Constraint : Tijl ≤ Di + βijl

(8)

Thus, to achieve the proposed goals, it must satisfy two constraints (7) and (8). Profit Model for Request Upgrade: Case 1: Request not initiated Above Model would be applicable. Case 2: Request initiated initial + Costup tCost = Costnew ijl + Costijl ijl

Costup ijl = P riV Mjl ∗ U Rijl

2.3

Mapping Strategy

Mapping of customer needs to the parameters of Infrastructure level depends on the capability of resource. In this research, focus is on the VM level and not the host level. An example of the mapping strategy between VM type and user request parameters is given in Fig. 2.

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Fig. 2. Mapping between user request to resources

3

Implementation

We have used CloudSim [5,13] as a simulator of cloud environment and have implemented the proposed Model in this environment. Perspectives of both users’ and SaaS providers were observed. Performance parameters of users’ side are: number of request accepted per unit of time and the speed of request processing i.e. average of service initiation time and that of providers side are: number of VM’s initiated and average profit earned. Table 2 describes the required software tools run on common machine and windows platform. Table 2. Experimental Setup No of PCs Processor Memory Maximum Data for Storage Development Tool Development Environment Database Server

2 Intel core i7-2600S 2.80 GHz 16 GB 1TB Net Beans 8.0.2 and CloudSim 4.0 JDK 1.8.0-40 and JRE 1.8.0-40 MySQL Server 5.1 and SQLyog Server ultimate v9.0.2.0

In our implemented system, there exists four types of service packages: Basic, Silver, Gold and Diamond. Table 3 describes about the details of these packages. Table 3. Service Packages Name of Package RAM

CPU Processor

Basic

Intel core i7-2600S 2.80 GHz Windows 10

2GB

Operating System

Silver

4 GB

Intel core i7-2600S 2.80 GHz Windows 10

Gold

8 GB

Intel core i7-2600S 2.80 GHz Windows 10

Diamond

Constant (User Request) Constant (User Request)

Variable

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Result Analysis

Synthetic workload of the proposed mathematical model has tested on CloudSim 4.0 simulator, in which random synthesized data have been simulated using suitable statistical distribution. Comparative analysis of proposed method and existing method has shown in Fig. 3, which depicts variation in request arrival rate at different time interval for individual packages. Figure 4 demonstrates variation in number of requests accepted to initiate a service. The Average profit for four product packages have displayed in Fig. 5. Comparative Analysis of proposed model and existing (StaticGreedy) model has been depicted in Fig. 6, 7, and 8. This analysis has been based on acceptance rate, service initiation time, earned profit and its variations. Proposed model has been designed as per the standard Service Level Agreement (SLA), quality constraints and efforts, which maximize the profit of Cloud Service Providers without violating the terms and conditions of SLA. From Fig. 6, it is clearly evident that the number of request arriving and being accepted per second for proposed model are greater than the existing system. This shows us that proposed model is more efficient than the present in terms of number of user request being accepted. Figure 7 shows the variation in average time taken to initiate a service. It is obvious that the proposed algorithm is more effective as it takes less time to initiate a service. It could be clearly seen from Fig. 8, that the profit earned via proposed model on the basis of cost incurred and number of user request accepted is higher than the existing one. This shows that our model has an advantage over existing model and is more beneficial.

Fig. 3. Variation in Request Arrival Rate for Product Packages

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Fig. 4. Variation in Package SIT for Number of User Request Accepted

Fig. 5. Variation in Profit for Number of User Request Accepted

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Fig. 6. Variation in Average Request Arrival Rate for Existing and Proposed System

Fig. 7. Variation in Average Service Initiation Time for Existing and Proposed System

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Fig. 8. Variation in Average Profit for Existing and Proposed System

5

Conclusions and Future Work

In this paper, we have built and formulate a mathematical model for SAAS providers to customer request scheduling. It considers dynamic business demands and quality parameters with cost optimization. Proper Virtual Machine mapping and resource scheduling have achieved the research objectives of the paper. This paper also considers the incremental customer load, upgraded requests, and profit maximization with penalty reduction for SLA violation.

References 1. Ahuja, N., Kanungo, P., Katiyal, S., Parandkar, P.: Optimum management of resources in cloud computing environment: a challenge. In: International Conference on “Computing for Sustainable Global Development” INDIACom2015organised by BVICAM, pp. 4–30 (2015) 2. Awasthi, C., Kanungo, P.: Resource allocation strategy for cloud computing environment. In: 2015 International Conference on Computer, Communication and Control (IC4), pp. 1–5 (2015). https://doi.org/10.1109/IC4.2015.7375716 3. Buyya, R., Garg, S.K., Calheiros, R.N.: Sla-oriented resource provisioning for cloud computing: challenges, architecture, and solutions. In: 2011 International Conference on Cloud and Service Computing, pp. 1–10. IEEE (2011) 4. Buyya, R., Ranjan, R., Calheiros, R.N.: InterCloud: utility-oriented federation of cloud computing environments for scaling of application services. In: Hsu, C.-H., Yang, L.T., Park, J.H., Yeo, S.-S. (eds.) ICA3PP 2010. LNCS, vol. 6081, pp. 13–31. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13119-6 2 5. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Soft. Pract. Experience 41(1), 23–50 (2011)

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6. Goudarzi, H., Pedram, M.: Maximizing profit in cloud computing system via resource allocation. In: 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 1–6 (2011). https://doi.org/10.1109/ICDCSW.2011. 52 7. Mehta, H.K., Kanungo, P., Chandwani, M.: Performance enhancement of scheduling algorithms in web server clusters using improved dynamic load balancing policies (2008) 8. Parikh, S.: A survey on cloud computing resource allocation techniques, pp. 1–5 (2013). https://doi.org/10.1109/NUiCONE.2013.6780076 9. Saxena, R., Dey, S.: Cloud shield: effective solution for DDoS in cloud. In: Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (eds.) IDCS 2015. LNCS, vol. 9258, pp. 3–10. Springer, Cham (2015). https://doi.org/10.1007/ 978-3-319-23237-9 1 10. Saxena, R., Dey, S.: A novel access control model for cloud computing. In: Li, E., et al. (eds.) IDCS 2016. LNCS, vol. 9864, pp. 81–94. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45940-0 8 11. Saxena, R., Dey, S.: Light weight access control mechanism for mobile-based cloud data storage. Int. J. Next Gener. Comput. 9(2) (2018). http://perpetualinnovation. net/ojs/index.php/ijngc/article/view/424 12. Saxena, R., Dey, S.: On-demand integrity verification technique for cloud data storage. Int. J. Next Gener. Comput. 9(1) (2018). http://perpetualinnovation.net/ ojs/index.php/ijngc/article/view/425 13. Saxena, R., Dey, S.: Data integrity verification: a novel approach for cloud computing. S¯ adhan¯ a 44(3), 1–12 (2019). https://doi.org/10.1007/s12046-018-1042-4 14. Wu, L., Garg, S., Buyya, R.: Sla-based admission control for a software-as-a-service providerin cloud computing environments (2012). https://doi.org/10.1016/j.jcss. 2011.12.014 15. Wu, L., Garg, S.K., Buyya, R.: Sla-based resource allocation for software as a service provider (SaaS) in cloud computing environments. In: 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 195–204 (2011). https://doi.org/10.1109/CCGrid.2011.51

A Novel Approach for Service Selection and Ranking in Federated Cloud Rajat Saxena1(B) , Shatendra Dubey2 , and Upanshu Kumar2 1

2

Cloud Computing Lab, Department of Computer Science and Engineering, Shri Vaishnav Institute of Information Technology, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India [email protected] Department of Information Technology, NRI Institute of Information Science and Technology, Bhopal, India

Abstract. In Federated Cloud environment, Cloud service ranking and selection is a very tedious work because of complexion involved Service Level Agreement (SLA) and Service specifications. Quality of Service (QoS) expected from Cloud users may be conflicting based on their applications. Cloud Brokers needs to be deal with Cloud users and Federated cloud like a heterogeneous interface. A Cloud Brokers also fulfils many suitable services from available resources. Cloud Broker also choose the federated cloud services based on their rank for available service. Noncommercial and conflicting demands of users evolves Cloud Service Selection a very competitive multi-criteria based service ranking and selection problem. Cloud User requests from different federated clouds based on some preference order of QoS parameters. This preference order converts into individual QoS parameters by assigning suitable weight. The weighted demands are estimated the available QoS values for each cloud service. In the next step, modified VIKOR method is applied for finding the rank of the services. This rank of service is based on preference of QoS parameters. The service with highest rank is selected and provided to the user. We used CloudSim for the testing of this method. Keywords: Quality of Service (QoS) Service Selection · Federated Cloud

1

· Service Ranking · Cloud

Introduction

The massive growth of Cloud computing [1] furnishes us satisfactory opportunities to deal with storage, computing, networking domains. The main aim of Cloud Computing [2] is to supply computing service just like we get Gas, Water and other necessary services. With Cloud computing, Small and Medium Sized Enterprises (SMEs) are encouraged to install their cloud application and reduce the cost of infrastructure production, need of skilled manpower and system maintenance. Attractive Service Level Agreements (SLAs) are offered from c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 129–142, 2023. https://doi.org/10.1007/978-3-031-31153-6_12

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different Cloud Service Provider to access the various service patterns. With similar type of services and varied performance levels, the Cloud marketplace is getting overfilled day by day. Non fulfilment of user specific requirements makes very difficult for a Cloud Service Provider to survive in the competitive market. Interconnected Cloud marketplace are necessarily required for providing us cloud services as an utility [3]. Federated Cloud [4] is a kind of interconnected Cloud in which CSPs are perform their duties under one federation and recommend their services. One Federated cloud can host homogeneous services from different CSPs. It is very hard for a Cloud user to identify and select suitable kind of services as per their requirements. Cloud Service Brokerage (CSB)[5] is a kind of act which helps cloud users in selecting services as per their QoS needs. Cloud Broker uses different CSB for access provision of various services to different CSPs. Cloud Broker acts as an interface between Cloud user and different CSPs. It offers help in various services such as service discovery, service provisioning, service selection, service delivery, and service monitoring, etc. In Federated Cloud Environment, Service Selection is very difficult task due to heterogenous environment created by different platforms, access methods and Cloud services. Cloud users are always welcome best services in less cost and without compromise in quality. Cloud Broker is acts as a solution provider who provides trade-off among QoS demands and SLA requirements. QoS request are always vary for different application. For example, medical application requires low cost, high security, and high availability. Gaming application requires high performance and high availability. In majority, users are requested low cost, high available, high secure, high reliable and high-performance solutions. In the view of above examples, we can depict that multiple-criteria is imposed for service selection. We can also conclude with conflict that low-cost solution may not provide high performance. Multi-Criteria Decision Making (MCDM) [7] methods are used for finding a solution and evaluating conflicts. The application of MCDM methods is widely used in industrial, scientific, and engineering solutions since last many decades. Service selection based on single or multiple criteria is a decision-making problem because in both cases it selects single service from various available services. We can use MCDM for cloud service selection because of properties of MCDM [8]. These criteria may be functional and nonfunctional types. User can demand these criteria in the form of QoS. It is a challenging task to fulfil all conflicting QoS demands and determine a Cloud Service. Therefore, a compromised and labeled solution is proposed to satisfy QoS requirements. We have proposed User Preference based Brokering (UPB) method designed using Ordered Weighted Averaging (OWA) [9] and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) [10] methods to provide multi-criteria guided ranking and selection of services. OWA method is used to assign weights to QoS criteria as per user preferences. VIKOR method is used to find ranks of given service alternatives within given QoS constraints.

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The rest of paper is organized as: In Sect. 2, the related work is discussed. Federated cloud service broker model is discussed in Sect. 3. Service selection problem scenario is discussed in Sect. 4. The UPB method is described in Sect. 5. Simulation environment and results are given in Sect. 6. Conclusion and future work is given in Sect. 7.

2

Related Work

The evolution of cloud services raises many challenges to both CSPs and Users. CSPs have to differ their services in terms of performance, pay-per-use, efficiency, security, etc. The users are facing challenge to select suitable service as per their requirements. A recommendation system which provides ranks to services based on their QoS parameters can be a great help to users in suitable service selection. A systematic study on recommendation system has been done by Aznoli and Navimipour [11]. They gave a detailed review of various techniques in cloud service recommender system. The methods used in recommender systems have been classified in collaborative filtering, knowledge based, demographic based and hybrid. These methods are reviewed and tested on the basis of scalability, availability, accuracy and trust attributes. A collaborative filtering technique has been proposed by ma et al. [12] to find missing QoS values. The technique uses time series analysis in finding missing QoS values. A collaborative prediction model has been proposed to predict multivalues QoS data. Collaborative filtering techniques uses values from neighboring entities. Orientation and dimension similarity based vector comparison method has been proposed to enhance similarity measures. Fuzzy analytic hierarchy process(FAHP) method is used to determine appropriate weights of applications as per their requirements. A broker based model has been proposed by Lin et al. [13] which consists of a cloud service selection algorithm for providing ranks to CSPs on the basis of users QoS demands. Every CSP is assigned an index value based on QoS characteristics which helps to retrieve values faster and arrange them quickly. A decision support system has been proposed by Gupta et al. [14] to compare and analyze various cloud services in multi-cloud environment. The dimensions like cost, risk and quality are considered for comparing various CSPs. Various CSPs can participate and collaborate in decision making. How to decide which service is best among available services is the biggest issue among CSPs. Second issue is with CSPs for improvements in services and getting more customers. These problems are addressed by Liu et al. [15]. Rough set theory [16] has been used to determine most crucial factors. These factors affects in cloud service selection. A information system is created with the help of cloud services and attributes. Rough set theory is used to reduce number of attributes. These reduced attributes help in better service selection. An Analytic Hierarchy Process [17] based ranking framework has been proposed by Garg et al. [18] to rank cloud services on QoS parameters. The framework provides methods to measure QoS of services. It also provides methods to prioritize cloud services on the basis of QoS offerings. The framework is based on SMI attributes.

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A multi-criteria based mathematical model has been proposed by Rehman et al. [19] for cloud service selection. The proposed model addresses selection of services which possesses similar characteristics. The services, differ in performance can be considered for evaluation through this model. A service ranking framework based on QoS experience of cloud consumers has been proposed Zheng et al. [20] to predict ranking of cloud services without their invocation. It uses similarity measures in service ranking. A broker based cloud service selection model has been proposed by Wang [21] to provide dynamic service selection. An index based architecture has been proposed by Sundareswaran et al. [22] to manage large number of CSPs. The index is build on the basis of similarity between CSPs. An algorithm for service selection is proposed which uses aggregation of services to provide ranking of CSPs. A Hyper-graph [23] based Computational Model and Minimum Distance-Helly Property algorithm have been proposed by Somu et al. [24] to rank CSPs. Helly property [25] has been used to reduce complexity of ranking model and assign weights to service attributes.

3

Preliminaries

This section provides us the basic concepts of Ordered Weighted Averaging and VIKOR used for designing proposed UPB method. 3.1

Ordered Weighted Averaging Method

Ordered Weighted Averaging (OWA) operator [9] is a mapping function, which maps F into R with the following equation F : RN → R

(1)

Associated weighting vector are representing with following properties: wi ∈ [0, 1]

(2)

Furthermore F (a1 , a2 , ...an ) =

i=1 

wi bj

(3)

n

Here bj is the largest elements of aggregated collection (a1 , a2 , ...an )’. Fuller and Majlender [9] are finding the associated optimal weight values with the following equation, which are used for assigning weight to the given QoS criteria. w1 [(n − 1) ∗ β + 1 − (n ∗ w1 )]n = ((n − 1) ∗ β)n−1 [(n − 1) ∗ β − n) ∗ w1 + 1] (4) In the above equation, w1 is the first weight vector and n represents number of criteria. We get optimal values of w1 for β ≥ 0.5 in the above equation.

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VIKOR Method

VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) [3] is an effective method, which is used to solve multi-criteria decision making problem. Its working is depending upon compromised ranking list, which provides ideal solution for closeness to this list. The group utility for individual regret and majority criteria for opponent makes it suitable solution. A well-known compromise programming technique is Lp -metric, which is used for obtaining compromised ranking list. To obtaining compromised ranking list, VIKOR uses Lp -metric [29]. i=1  wi ∗ (fi∗ − fij ) p p1 Lp,j = ( [ ] ) f∗ − f n

(5)

To obtain compromised ranking list and solution, VIKOR method use following steps: 1. A decision matrix D[n×m] initiate the method, in which n is the QoS criteria and m service alternatives. Aj = x1 ; x2 ; ...xm symbolizes the j th alternative and x1 ; x2 ; ... xm are values of some system variable x. fij signifies every member of matrix, in which value of ith criteria denotes j th alternative. 2. We have used following formula for calculating maximum value fi and minimum value fj of all QoS criteria. fi∗ = maxj fij = max[fij |j = 1, 2, 3.....m], i = 1, 2, 3....n

(6)

fi = minj fij = min[fij |j = 1, 2, 3.....m], i = 1, 2, 3....n

(7)

3. We have used following formula for calculating value of Sj and Rj for all alternatives. Si =

j=1  n

[wj ∗

(fj∗ − fij ) ] (f ∗ − f  )

Ri = M axj [wj ∗

(fj∗ − fij ) ] (f ∗ − f  )

(8)

(9)

Here, Si is the close group utility measure and Ri is the individual regret measure for given service alternative xi . Furthermore, The wj is the weight of j th relative criteria. The user provides his relative preference of criteria which is converted in weight value. 4. We have used following formula for computing the value of Qi from all alternatives m: (10) Qi = vQSi + (1 − v)QRi Here QSi =

(Si − S ∗ ) (S  − S ∗ ) + 1

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QRi = Here

(Ri − R∗ ) (R − R∗ ) + 1

S ∗ = minj Si S  = maxj Sj R∗ = minj Ri R = maxj Ri

We calculate the v as a weight for maximum group utility and 1 − v as a weight to individual regret. The value of v is varies between 0 and 1. It is usually set to 0.5 [31], [32]. 5. We have prepared and sorted three ranking list Q(minimum), R and S in descending order with the help of Q. In last, the values Qi , i = 1, 2, ..., m are assigned to the rank of services.

5

Federated Cloud Service Broker (FCSB) Model

In Federated Cloud Environment (FCE), A huge pool of services is created with the participation of different Cloud Service Providers (CSP). This variety of services may be have similar functionalities and possesses different characteristics. Users are getting confused with this verity of services for selecting a desired service to fulfil their requirements. Cloud Service Brokerage technique may help users in selecting Cloud Service Provider (CSP) to fulfil their needed desired service. Cloud broker can be useful in selecting cloud services such as discovery, ranking, selection, provisioning, monitoring. All CSPs are requires to provide the QoS details of their services to the cloud broker for their promotion and sustaining in market. Federated Cloud Service Broker (FCSB) model for service ranking and selection used in this paper is shown in Fig. 1. There are thee layers in this model: Cloud User, Cloud Broker and Federated Cloud Environment. 1. Cloud User: Cloud users are interact with Cloud Broker (CB) through the given User Interface (UI). This UI can be a public or private Cloud. Cloud users can submit their QoS demands with their preference to FSCB. FCSB proceeds this service or set of services as per preference and QoS demand by the cloud users.

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Fig. 1. Federated Cloud Service Broker (FCSB) Model

2. Cloud Broker: Cloud broker consists of three sub-modules: QoS Data Collector, Service Data Coordinator, and Service Ranker. QoS attributes of services are collected within QoS data Collectors and it has connected the CSP from service data mangers. This service data manager or coordinator collects and updates QoS data of cloud services. It also manages Service Level Agreements (SLA). Service Ranker module submits the ranks of available services, which are based on their QoS values and preference given to these values. These preferences have converted into weight values. Now, this weight value has assigned to the respective QoS parameter. Therefore, there exists more than one service in same order selection with same functionalities. 3. Federated Cloud Environment: Federated Cloud Environment (FCE) is created with the help of leasing the services and resources from various CSPs. Federated Cloud is fetched through Cloud Service Manager and the cloud broker. Cloud service manager is responsible for providing the information of available services. It also facilitates service registration with Cloud Broker. Access management, monitoring, utilization of services, and failure tolerance are the other services of Cloud Service Manager.

6

Service Selection Problem Scenario

Service selection is a very challenging task because FCE have multipurpose resources and services with variable characteristics. Cloud broker are implemented with efficient mechanism, which assists different CSPs for selecting QoS

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Fig. 2. Problem Scenario

as per their service need. A classical service selection scenario is depicted in Fig. 2. It is broker based system in which broker has to identify a service or set of services based on user requirements. We can write broker based system as: CB = {Ri , Si }

(11)

Here CB is any cloud broker, Ri is set of requirements, and Si is set of services. Mapping of requirements is doing over the set of services by cloud broker. The set of requirements can be specified as: R = R1 ; R2 ....Rm ; where m ≥ 2 The service set is specified as: S = S1 ; S2 ....Sn ; where n ≥ 2 We evaluate each requirement in contradiction of service’s performance criteria. Then, each performance criteria is checked and evaluated through a function. Thereafter, these performance criteria help us in evaluation of each service. A matrix is used to group these performance criteria and services into an entity, which is called Decision Matrix. A Decision Matrix D is represented as:

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⎞ a11 a12 . . . a1n ⎜ a21 a22 . . . a2n ⎟ ⎟ ⎜ ⎜ . . ... . ⎟ ⎟ ⎜ D=⎜ . ... . ⎟ ⎟ ⎜ . ⎝ . . ... . ⎠ am1 am2 . . . amn ⎛

In the above Decision Matrix, we have n QoS criteria and m cloud services. We assigned weight for each criteria. A preference is set for each pair of weight and criteria, which may be different for each user.

Algorithm 1. UPB Method Input 1: (n, β); where n = QoS Criteria and β is situation parameter Input 2: Matrix D(n×m); where n = QoS Criteria and m = cloud services Output : Ranked Services 1: if β < 0.5 then 2: β = 0.5 3: end if 4: if β > 0.5 then 5: Calculate w1 using equation number (1). 6: Calculate wn using following equation number: 1 +1] 7: wn = [((n−1)β−n)w ((n−1)β+1)−nw1 8: for i = 2 to n-1 do 9: wi = n−1 w1n−1 ∗ wni−1 10: end for 11: end if 12: Normalize Decision Matrix D. 13: For all criteria, compute fi∗ as maximum value and fi as minimum value. 14: for i = 1 to m do (fj∗ −fij ) 15: Compute Si = j=1 n [wj ∗ (f ∗ −f  ) ] 16: 17: 18: 19: 20: 21: 22:

(f ∗ −fij )

Compute Ri = M axj [wj ∗ (fj∗ −f  ) ] end for values for alternatives i = 1...m using following equation: Compute Qi index ∗ (Ri −R∗ ) i −S ) Qi = v ∗ (S(S −S ∗ )+1 + (1 − v) (R −R∗ )+1 Sort the values of R, S and Q in descending order. Assign ranks to service alternatives. Select the service with highest rank.

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User Preference Based Brokerage (UPB) Method

Algorithm 1 is used to propose and implement UPB method for service ranking and selection in federated cloud environment. Initially, UPB method takes two input parameters: Input 1 and Input 2. Input 1 is the composition of two parameters: n = QoS Criteria and β = situation parameter. Input 2 is the Decision Matrix, which is composition of QoS parameters and service alternatives. The situational parameter is responsible for evaluation of important QoS parameter. In Table 1, initial weight values, w1 is obtained using Eq. (1). If a user choose more importance to first QoS demand, then higher value is assigned for the situational parameter β. It is a clear point of notice from Table 1 that higher value of β gives higher weight value. In UPB method, initially, we assign weight to first and last QoS parameters. In Algorithm 1, line number 10 and 11 are using to obtained weight values of other parameters. It is possible that same cloud service from different cloud service provider have different QoS value. So, we have to normalize them necessarily. In the next step, we have normalized the given Decision Matrix, which is composition of QoS criteria and service alternatives. In the next step, we have computed the minimum and maximum values of given criteria. Now, the values of cost criteria have changed. Thus, Minimum cost gets highest value. Now, we have to find out positive ideal solution for calculating closeness and negative ideal solution for calculating separation from given parameters. To find out the Q index values, we have modified equation (7). It is possible that the minimum and maximum values of closeness and separation measure can be same. On the line number 17 -18, the modified equation for evaluation is given. On the basis of Q values, we have prepared the ranking of services. The services with lowest Q value are assigned to the highest rank.

8

Simulation and Result Analysis

To simulate proposed method, we have used CloudSim [32]. We have selected the data-set provided by cloudharmony.com for various QoS attributes such as availability, security, response time, accessibility, price, ease of use, etc. of cloud services is used. To evaluate the proposed method, we choose and use five QoS criteria availability, response time, price, speed, and customer service. We initialize weight values of w1 for given values of situation parameter. With the help of equation (1), we obtained the values of β, which is shown in Table 1. To evaluate proposed method, we have choose the value of β = 0.6. Then, we have obtained the values of all weights for β = 0.6 , which is shown in Table 2. To find the ranks of services, we have used a Decision Matrix shown in Table 3. These values may be uniform or non-uniform. Linear Normalization method has applied to these values for transforming them into uniform values. We have computed the values of close group utility measure, Si , individual regret measure, Ri , and Qi of all the services. All these values are shown in Fig. 3.

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Table 1. VALUES FOR VARIOUS LEVELS OF β and INITIAL WEIGHT w1 β

0.5

0.6

0.7

0.8

0.9

1.0

w1 0.2000 0.2884 0.3661 0.5307 0.7105 1.0 Table 2. WEIGHT VALUES USING β AS 0.6 Weights w1 w1

w2

w3

w4

w5

0.2884 0.2353 0.1920 0.1566 0.1278 Table 3. DECISION MATRIX

Cloud Servers QoS Criteria Availability Response Time Price Speed Customer Service Backupgenie

5

5

5

3

5

Bluehost

5

5

5

4

5

Carbonite

3

3

3

4

2

IBackup

5

5

5

5

5

Justcloud

5

5

5

5

4

Keepit

3

4

4

3

4

Livedrive

5

4

4

1

1

Mozy

2

3

2

3

3

MyPCBackup 5

5

4

5

4

Sugarsync

5

4

5

5

5

Fig. 3. S, R and Q (Minimum Values)

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Fig. 4. Relative Closeness

To compare the result, we have used the relative closeness metric. The obtained results of proposed method are compared with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Comparative analysis is shown in Fig. 4.

9

Conclusions and Future Work

Federated Cloud Environment becomes very much popular because a large number of users are benefited from services and resources of CSPs. FCE provides different cloud services with competitive prices and performance levels to the user groups. In this paper, we proposed a delegate model for service provision from different CSPs. In FCE, multi-criteria decision is a challenging task because it invokes many conflicting requirements for service selection and ranking on users. We used User Based Preference Brokerage for selecting multiple conflicting QoS criteria. It always helps in service selection and ranking of competitive order of preferences of services. CloudSim is used for simulating this UPB method. We compared the obtained results with TOPSIS method and get better results from this method. We would like to accommodate more QoS parameters to extend our method in near future. It would also be a good contribution that modified work can handle more variations in QoS attributes.

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A Review on IoT Based Wireless Sensor Network and Security Shabnam(B) and Manju Pandey National Institute of Technology NIT, Raipur, India {shabnam.phd2022.mca,mpandey.mca}@nitrr.ac.in

Abstract. Wireless sensor networks are very important for realization of Internet of things (IoT) system. IoT is giant network with connected devices. These devices gather data through the environment. Wireless sensor networks are used to track an object, measure temperature, medical purpose for healthcare and smart cities, which have become a very popular and large number of different applications of the wireless sensor network. A wireless sensor network (WSN) consists of a collection of different sensor nodes and these sensor nodes are communicated from one node to another sensor node. WSN This paper reviews the literature to reduce the problem in security and battery-powered sensor networks using some method and algorithm approaches by the researcher and developers. In WSN, a big challenge is a security, power consumption, loss communication, and low memory. Keywords: Wireless sensor network · Security · data aggregation · Internet of things

1 Introduction Internet of things generally refers to the collection of all those devices. The ability to connect to the internet and collect and share data is component of IoT. The Internet of things begins with sensors; many sensors already exist that are capable of measuring temperatures, allocating GPS position and identifying a variety of other physical conditions. With the rapid advancement of automation technology, power system automation helps to reduce unnecessary energy waste, the occurrence of accidents, and the efficiency of repair and maintenance when problems occur. A low fault tolerance rate is required for power system management, which necessitates real-time power system monitoring. Management and maintenance of a system become extremely difficult and dangerous as it becomes more complex. Wireless sensors are less susceptible to damage and provide more accurate and valuable data. The wireless sensor network is made up of many network nodes that each has a distinct ID identifier (Sadeghi et al. 9). The attackers attacks the data, so data can be lost or tampered with, so there are various challenges to implementing security mechanisms in wireless sensor networks, as well as some of the attacks that are possible in sensor network. (1) Wormhole attack (2) selective forwarding © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 143–151, 2023. https://doi.org/10.1007/978-3-031-31153-6_13

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attack (3) acknowledgement spoofing (4) compromised node attack (5) Node replication attack (6) Denial of services attack (7) Location based attack. There are various security issues that must be addressed in wireless sensor networks, as well context and design implications such as their integrity, authenticity, and confidentiality in various forms, thus protecting the data from passive attacks. So no one we know who is not authorized should be able to get access the data integrity, which is about the data being modified during transmission. WSN sensor network the security protocol is a fundamental requirement. There are many different types of protocols, one of these protocols is the security protocol in wireless sensor networks (SPINs), the second is the Tiynsec protocol, and the third is the localized encryption and authentication protocol (LEAP) (Fig. 1).

Security Protocol

SPINs

SNEP

µTESLA

Tiynsec

Authencaon Apporoach

LEAP

Authencaon & encrypon approach

Fig. 1. The security protocol

2 Related Method This review paper only discusses wireless sensor network approaches (Table 1).

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Table 1. Some approaches of wireless sensor network. Ref. No Year

Approaches

Description

[1]

2022 Data Aggregation Techniques (Gulati et al. 1)

This survey of the literature focuses on issues of wireless networking for the accumulation of data and energy conservation. The main goals of data aggregation techniques are network security at the highest level, higher Quality of service, lifetime improvement, and energy reduction (Gulati et al. 1)

[2]

2022 Genetic algorithm, (FIS) fuzzy inference system (Gaiova et al. 5)

To dynamically control the wireless node’s energy supply from the surroundings, the authors developed a fuzzy inference system with feedback learning applied (Gaiova et al. 5)

[3]

2022 Clustering algorithms, CH selections In-depth descriptions of recently methods (Al-Sulaifanie et al. 3) proposed techniques for managing energy heterogeneity, energy harvesting, fault tolerance, scalability, mobility, and data correlation in WSNs are presented in this paper (Al-Sulaifanie et al. 3)

[4]

2022 ARM controller, S3C2440 development board, Wi-Fi device driver (Chaumont-olive et al. 4)

Using MATLAB simulations, we calculate the total energy dissipated, throughput, and sensor nodes. We conduct research to improve the security of WSN systems (Chaumont-olive et al. 4)

[5]

2022 OSH mines, (RSSI) received signal strength indication algorithm (Sadeghi et al. 9)

This review paper looked into various WSN applications for improving occupational safety and health in the mining industry. However, a number of challenges and issues remain that must be addressed for the proposed WSN-based systems to improve their effectiveness, reliability, and security (Sadeghi et al. 9) (continued)

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Ref. No Year

Approaches

Description

[6]

2022 RF signal, HVAC system (Hidalgo-Leon et al. 7)

The research findings in this paper reveal that harvesters at the buildings can generate enough electricity to partially fulfill the power requirements for sensor nodes and even totally satisfy the power requirements of particular operating modes (Hidalgo-Leon et al. 7)

[7]

2022 Clustering techniques, SI, EORO, EFF-PSO, LEER (Gavali et al. 6)

The problem of void communications and packet collisions is solved by selecting four distinct parameters for the trust evaluation of underwater sensor nodes (Gavali et al. 6)

[8]

2022 Optimal Pollard Route Deviation using Bayesian (OPDB) protocol (Vanitha et al. 10)

The method determines the route deviation and ideal route by computing the conditional probability using prior knowledge The methodology focuses primarily on end-to-end delay, route deviation detection, optimal route selection, and network lifetime (Vanitha et al. 10)

[9]

2022 Field Programmable Gate Array (FPGA), Virtex-7 (xc7v585tffg1761–3) (Abdul-Karim et al. 2)

We created an efficient intrusion detection system that not only detects intrusions but also sends an alert signal to a control system that also gathers data about the resources that are accessible. We concentrate on the power usage of sensor nodes as the examined object resources (Abdul-Karim et al. 2)

[10]

2021 Data security fusion technology, Node secu rity optimization technology (Huanan et al. 8)

Wireless sensor networks use open wireless communication channels to transmit data, but without security measures, that data is particularly vulnerable to both internal and external attacks (Huanan et al. 8)

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3 Wireless Sensor Network (WSN) WSNs are frequently used to gather surveillance data as well as climatic, biological, demographic, and biological data. In order to charge wireless sensor end devices, energy harvesting from the environment is considered to be a requirement in the design of these systems. Providing electricity to these devices is the biggest issue with the installation of WSN equipment in distant areas and places with limited accessibility (Gaiova et al. 5). WSN applications can be used to deal with a variety of complex contexts and collect a variety of data and information and without regard to time or location restrictions, wireless sensor networks may provide users with the information they require at any time (Huanan et al. 8). Physical-layer technologies in WSNs are classified according to bandwidth into three types: narrowband, spreadspectrum, and ultra-wideband. Narrowband radio technology uses radio bandwidth on the order of symbol rate. Narrowband focuses on bandwidth efficiency. The band width efficiency is the data rate over the bandwidth. In the spread spectrum, the narrow signal is converted into a wideband signal. The spreading function used to calculate bandwidth has nothing to do with the message. Spread-spectrum technology can reduce power while maintaining stability. Physicallayer technologies in WSNs are classified into three types based on bandwidth: narrow band, spread-spectrum, and ultra-wideband. Narrowband radio technology makes use of radio bandwidth on the order of symbol rate. Narrowband is concerned with bandwidth efficiency. The data rate is measured by bandwidth efficiency. WSN applications are classified into several types (Fig. 2).

Fig. 2. Classification of WSN

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Wireless Sensor Network and Security. Wireless sensor network (WSN) security has emerged as a focus of WSN technology research, and the characteristics of wireless sensor networks make them vulnerable to a variety of attacks, risking data confidentiality, integrity, and availability, and when the network’s routing is attacked, the data collected by the sensor node cannot be transmitted cross to the destination sink node in a timely and accurate manner, the security threat of wireless sensor networks, similar to that of telecommunications networks. Sensor networks will be monitored, tampered with, built, and attacked all at the same time (Huanan et al. 8). To ensure the success of the sensor network in the real world, it is critical to have a diagnostic and debugging system that can evaluate and track the sensor node performance of the entire network.The lifespan of each sensor is increased by studies that address handling different kinds of hardware and software failures, which in turn increases the lifespan of the sensor network. The system can be made more effective by addressing issues as well as improving communication performance. Sympathy is one such tool that locates and recognizes failures. Node security optimization technologies can also improve the security of wireless sensor networks. This study will introduce a node security optimization technology based on the ternary key distribution method to protect the security of wireless sensor networks, which may improve the anti-attack performance of wireless sensor networks and simplify the topology structure of nodes (Huanan et al. 8) (Fig. 3).

Wireless Security Requirement

Data Confidentiality

Data Integrity

Authentication

Availability

Interaction with data aggregation process

Aggregation of encrypted data

Alteration in aggregated data

Sybil attacks against data aggregators

Availability of data aggregators

Fig. 3. Security of wireless sensor networks & the data aggregation process (Yick et al. 14).

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4 Data Aggregation WSN nodes are typically small, battery-powered devices. So, for WSN data aggregation, the network’s durability is of importance. During the process of data collection, many issues like increased energy use which results in inefficient use of energy and were discovered in a lifetime. Energy conservation, lifetime extension, improved quality of service, the primary goals of data aggregation techniques are network security at the highest level (Gulati et al. 1). Sensor nodes can only perform a limited amount of processing due to their small size and low battery capacity, and wireless sensor networks are prone to failure due to the limitation of low battery power, so data aggregation is a method that uses less energy and sensor networks with a high node density, the same data is perceived by numerous nodes, leading to redundancy and adopting the data aggregation strategy when routing packets from source nodes to the base (B.D. and Al-Turjman 12). Efficient data aggregations save energy while also removing redundant data, resulting in only useful data. When data is sent from a source node to a sink via neighboring nodes in a multi-hop fashion, the energy consumption is lower than when data is sent directly. Direct-to-sink to sink aggregation reduces data transmission when compared to direct to sink without aggregation (Randhawa and Jain 13). The most commonly used tools like MATLAB, NS2, and GlomoSim for data aggregation in WSN.

Data collected from sensor nodes

Data aggregation algorithm(LEACH, TAG, Diffusion)

Aggregated data

Sensor base station Fig. 4. Data Aggregation Process (B.D. and Al-Turjman 12)

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5 Conclusion and References Sensor networks require wireless sensor network systems built on the Internet of Things. These systems have issues with power consumption, limited bandwidth, memory, and communication loss. The main issue is that the sensor network saves energy. As a result, various algorithms and data aggregation were used to mitigate the problem. We have compiled a list of various proposed designs, algorithms, methods, and protocols. This research will concentrate on power consumption reduction, self-organization, fault tolerance, and security management, schemes and protocols, discussed data aggregation methods, and security issues techniques.

References 1. Gulati, K., Kumar Boddu, R.S., Kapila, D., et al.: A review paper on wireless sensor network techniques in Internet of Things (IoT). Mater Today Proc. 51, 161–165 (2021). https://doi. org/10.1016/j.matpr.2021.05.067 2. Abdul-Karim, M.S., Rahouma, K.H., Nasr, K.: Hardware implementation of effective framework for the trade-off between security and QoS in wireless sensor networks (2022). https:// doi.org/10.1016/j.micpro.2022.104590 3. Al-Sulaifanie, A.I., Al-Sulaifanie, B.K., Biswas, S.: Recent trends in clustering algorithms for wireless sensor networks: a comprehensive review. Comput. Commun. 191, 395–424 (2022). https://doi.org/10.1016/j.comcom.2022.05.006 4. Chaumont-olive, P., Sánchez-quesada, J., María, A., et al.: Jo ur na l P re Jo ur na l P of. Tetrahedron 110, 131932 (2009). https://doi.org/10.1016/j.measen.2022.100492 5. Gaiova, K., Prauzek, M., Konecny, J., Borova, M.: A concept for a cloud-driven controller for wireless sensors in IoT devices. IFAC-PapersOnLine 55, 254–259 (2022). https://doi.org/ 10.1016/j.ifacol.2022.06.042 6. Gavali, A.B., Kadam, M.V., Patil, S.: Energy optimization using swarm intelligence for IoT- authorized underwater wireless sensor networks. Microprocess. Microsyst. 93, 104597 (2022). https://doi.org/10.1016/j.micpro.2022.104597 7. Hidalgo-Leon, R., Urquizo, J., Silva, C.E., et al.: Powering nodes of wireless sensor networks with energy harvesters for intelligent buildings: a review. Energy Rep. 8, 3809–3826 (2022). https://doi.org/10.1016/j.egyr.2022.02.280 8. Huanan, Z., Suping, X., Jiannan, W.: Security and application of wireless sensor network. Procedia Comput. Sci. 183, 486–492 (2021). https://doi.org/10.1016/j.procs.2021.02.088 9. Sadeghi, S., Soltanmohammadlou, N., Nasirzadeh, F.:Applications of wireless sensor networks to improve occupational safety and health in underground mines.J. Safety Res. (2022).https://doi.org/10.1016/j.jsr.2022.07.016 10. Vanitha, C.N., Malathy, S., Dhanaraj, R.K., Nayyar, A.: Optimized pollard route deviation and route selection using bayesian machine learning techniques in wireless sensor networks. Comput. Netw. 216, 109228 (2022). https://doi.org/10.1016/j.comnet.2022.109228 11. Enabling secure data transmission for wireless sensor networks based IoT applications. Ain Shams Eng. J. https://doi.org/10.1016/j.asej.2022.101866

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12. Deebak, B.D., Al-Turjman, F.: A hybrid secure routing and monitoring mechanism in IoTbased wireless sensor networks. Ad Hoc Netw. 97, 102022 (2020). https://doi.org/10.1016/j. adhoc.2019.102022 13. Randhawa, S., Jain, S.: Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions. Springer, US (2017) 14. Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Comput. Networks 52, 2292–2330 (2008). https://doi.org/10.1016/j.comnet.2008.04.002

Automated Spoken Language Identification Using Convolutional Neural Networks & Spectrograms Hari Shrawgi1 , Dilip Singh Sisodia1(B) , and Piyush Gupta2 1 National Institute of Technology Raipur, Raipur, India

[email protected] 2 Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

Abstract. The automated identification of spoken languages from the voice signals is attributed to automatic Language Identification (LID). Automated LID has many applications, including global customer support systems and voice-based user interfaces for different machines. The hundreds of different languages are popularly spoken around the world and learning of all languages is practically impossible for anyone. The machine learning methods have been used effectively for automation and translation of LID. However, machine learning-based automation of the LID process is heavily reliant on handcrafted feature engineering. The manual feature extraction process is subjective to individual expertise and prone to many deficiencies. The conventional feature extraction not only leads to significant delays in the development of automated LID systems but also leads to inaccurate and non-scalable systems. In this paper, a deep learning-based approach using spectrograms is proposed. The Convolutional Neural Networks (CNN) model is designed for the task of automatic language identification. The proposed model is trained on a dataset from VoxForge on the speech from five different languages, viz. Deutsche, Dutch, English, French, and Portuguese. For this study, evaluation measures like accuracy, precision, recall, and F1-score are used. The new proposed approach has been compared against traditional approaches as well as other existing deep learning approaches for LID. The proposed model outperforms its competitors with an average F1-score of above 0.9 and an accuracy of 91.5%. Keywords: Language Identification · Machine Learning · Deep Learning · Convolutional Neural Networks · Deep Neural Networks

1 Introduction The task of automatic language identification (LID) refers to identifying a spoken language uttered by an anonymous speaker given a speech signal (in the form of audio waves) [1, 26]. It is a classical and essential task which has been tackled continuously by researchers for the better part of the last century and this century [2]. Language identification is important in today’s globally connected world with the increasing multicultural exchange in which a total of 6909 unique languages exist [3]. Speech and language are © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 152–163, 2023. https://doi.org/10.1007/978-3-031-31153-6_14

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an essential part of our daily life, and in the coming age of intelligent machines, it is inevitable that user interfaces would be majorly based on voice. We can already see that happening with the recent success of digital assistants voice recognition such as Alexa, Cortana, and Siri [4]. Thus, the importance of the LID is apparent in a voice dominated interface. We, humans, are producing data faster than ever before, and organizing such data is a laborious task in itself. LID can prove to be useful in recommending audio/video data to users accurately based on the preferred language of the users. Also, huge conglomerates such as Amazon and Walmart serve billions of people globally who speak different languages. It is imperative for their customer service to recognize the speaker’s language and connect to someone who can assist them in using their own language. Most use-cases of LID require the processing to be fast as such systems would be deployed in real-world scenarios as well as scalable and robust. Conventional methods used for LID are based on feature engineering and modeling the different characteristics of voice to identify languages. The discriminative cues particular to a language is identified using these features. Major features used in LID models are acoustic, phonotactic and prosodic features [5]. Recently Gaussian Mixture Modelling has been widely used to generate the cepstral features being used in LID systems [6]. A common issue in all these approaches remains to be the task of feature engineering and the related inflexibility due to it. The models resulting from such feature engineering is not suitable for real-world operation and can be very costly to produce. Such models also require pristine data for training. Deep learning is a technique that can tackle and solve this issue. Deep learning has been a boon to researchers as it has allowed us to tackle old problems from a different perspective and renewed interest. In recent years there has been a tremendous surge in applying Deep Learning to varied tasks with successful results [7]. There has been a dearth of effort to apply deep learning in this domain, which can solve this long-lasting problem of LID. Thus, inspired by this lack of effort, a solution to the LID task is proposed in this paper, which can prove to be accurate, effective as well as robust. The rest of the paper is organized into the following sections. In Sect. 2, the different studies related to language identification are discussed. The methodology used in this study is described in Sect. 3. Experimental results are discussed in Sect. 4. Lastly, the present work is concluded with a summary of findings with some future research directions in Sect. 5.

2 Related Work This LID has been traditionally used for telephonic speech. During the last decades of the twentieth century, with the advent of computers and telephones, research to automatically identify spoken language was trending. A comparative study done by Zissman [8] in 1996 summarises the state-of-the-art techniques used then. The area of interest at that time was feature engineering; with limited computing power, researchers focussed on obtaining a better representation of the speech in the feature space to improve results. Gaussian Mixture Model for acoustic modeling was commonly used in tandem with classifiers such as Hidden Markov Models [9]. Lipmann in 1997 published his work on comparing human performance and machine performance at the end of the last century [10]. In this study, Lipmann found that machine’s performance was underwhelming as compared to

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Fig. 1. The basic workflow of the study

a trained expert. The 1990s also saw the resurgence of a feature model proposed by House & Neuberg [11] in 1977. They proposed that Cepstral coefficients can also be used as feature vectors in a LID system. Some successful models based on the study of House & Neuberg were proposed which took acoustic as well as phonetic features into account [12]. Pellegrino et al. proposed an alternative approach to a phonetic model using only Mel-frequency cepstral coefficients (MFCC) in their study in 2000 [13]. The focus of LID researchers stayed on features with different variations and combinations of acoustic & phonetic features being continuously proposed [5, 6]. State-of-the-art models such as the MITLL language recognition system continued to use acoustic modeling [14, 15] until 2011 [16]. In 2011, Dehak et al. proposed the i-vector technique which is also based on acoustic modeling. I-vectors have been firmly established as the state-of-the-art in LID. But all of these models are from a past era, from a time when deep learning was not the norm. One of the recent work on LID systems which incorporates deep learning has been done by Montavon [17]. Another related work was performed by Graves et al. [18] which used a Recurrent Neural Network (RNN) for speech recognition task. A mixed approach combining deep network along with Hidden Markov Model was proposed by Deng & Yu [19]. In this approach, they used large-vocabulary speech recognition using the proposed technique. Context-dependent deep networks have also been used for largevocabulary speech recognition [20]. An overview of deep learning research on speech recognition was released by a team of Microsoft [21]. This survey can be referred to for most of the work conducted since 2009 in this domain. The use of deep neural networks in acoustic modeling for speech recognition is described by Hinton et al. [22]. Another

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approach using deep neural networks was proposed by Lopez-Moreno [23]. They also proposed the use of RNN for LiD in a subsequent study [24]. The present work aims to continue the legacy of research on LID by proposing a deep neural architecture that can match and improve on the previous works.

Fig. 2. Spectrograms samples extracted at frequencies up to 5.5 kHz from Audio signals

3 Methodology This section discusses the methodology followed in this study. Outline of the overall process is shown in Fig. 1. As shown in Fig. 1, there are subtle differences in the conventional approach and the deep learning approach. The first thing to catch the eye is that there is no feature engineering step in the proposed approach. As reviewed earlier, the feature extraction phase was the most important phase in the past approaches. But, in this paper spectrograms have replaced this phase. This phase is described in the next section. The main crux of this study is the architecture of the CNN model which is described in the design section. The sections following that present the results achieved by our model while comparing and contrasting it with previous work. 3.1 Data and Pre-processing For Language Identification tasks, VoxForge datasets [25] have been a standard. In this study, short audio recordings (around 5 s) from five languages viz. Deutsche, Dutch, English, French & Portuguese have been used. All the recordings have a bit rate of 768 kbps. A total of 4773 clippings are used in our model with the following breakup:

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H. Shrawgi et al. Table 1. Description of Data

Language

Number of recordings

Percentage of total recordings

Deutsche

1000

20.95

Dutch

967

20.26

English

809

16.95

French

1000

20.95

Portuguese

997

20.88

We can see in Table 1 that the breakdown of the data is pretty even. The data is distributed well among the five languages. This allows the CNN model to be trained in a balanced way. Skewed data can lead to poor testing accuracy. Thus, our data matches well to the ideal data required for training a CNN model. The data obtained from VoxForge is in the form of an audio signal. We decided to extract spectrograms from these signals (Fig. 2) which will finally be fed into our network as inputs. The advantage of using deep networks is we don’t need to define tailor-made features for our purposes; whereas, in other machine learning tasks, the whole model depends highly on the features used in training. In a CNN we can feed these spectrograms, and during the training of multiple layers, a set of features suitable for the task will be discovered by our model. 3.2 Design and Training Convolutional Neural Networks (CNNs) have been able to perform exceptionally well in recent times [7], and specifically for LID, they have been able to perform well [17]. Thus, we propose a new CNN architecture for the task of automatic language identification. A detailed description of the network is presented in Table 2 [27]. The proposed architecture is shown in Table 2. It consists of 9 bands of layers including the input layer. The input layer is nothing but a series of spectrograms which would be fed as input to the LiD system. Each subsequent band had four levels. Convolution layers are the first layer in all the bands. Separate bands use different channels, and successive filters reduce input size while preserving information. Table 2 color-codes band information density. The instance shrinks as we walk through a band, losing minimal information. While progressing into a band instance size decrease, information loss is minimized, and the amount of data available grows gradually. In this study, Rectified Linear Units (ReLu) are employed as the activation function for the second layer across all bands. Further, Max pool and Batch norm layers are exploited to aggregate models’ knowledge and preserve the consistent batch size. Incorporating the dropout layer in the proposed models’ network design mitigates the overfitting and enhances the models’ response to unseen data. Finally, the fully connected layer in the proposed hybrid CNN-FC model uses the last band input to obtain higher-level features and train the artificial neural network progressively. The hybrid CNN-FC-based classifier model produces the probabilistic output

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Table 2. Proposed architecture of CNN.

BAND

TYPE

CHANNELS

0

Input Convolution ReLu MaxPool BatchNorm Convolution ReLu MaxPool BatchNorm Convolution ReLu MaxPool BatchNorm Convolution ReLu MaxPool BatchNorm Convolution ReLu MaxPool BatchNorm Convolution ReLu MaxPool BatchNorm Fully Connected ReLu BatchNorm DropOut Fully Connected Softmax Loss

1 16 16 16 16 32 32 32 32 64 64 64 64 128 128 128 128 128 128 128 128 256 256 256 256 1024 1024 1024 1024 176 176

1

2

3

4

5

6

7

8

KERNEL SIZE/ STRIDE 7x7 / 1 3x3 / 2, pad=2 5x5 / 1 3x3 / 2, pad=2 3x3 / 1 3x3 / 2, pad=2 3x3 / 1 3x3 / 2, pad=2 3x3 / 1 3x3 / 2, pad=2 3x3 / 1 3x3 / 2, pad=2

using the SoftMax loss function. 2981 speech samples were randomly selected from the audio corpus and used to train our model. A learning rate of 0.0003 was used over 5000 epochs; 50% dropout was used to prevent overfitting while ensuring maximum usage of learned features. The remaining 1792 samples formed the test dataset.

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3.3 Evaluation of the Model Evaluation of the proposed model was performed using measures which are capable of evaluating the effectiveness as well as the robustness of the model. The measures used in the study are as follows [28]: i.

Accuracy Accuracy is the most widely used measure of a classifier’s performance. Accuracy is defined as the ratio of true outputs (true positives as well as negatives) over all the test cases used. It is defined by Eq. (1)   Tp + Tn Accuracy = (1) N

ii. Precision Precision is the percentage of time the classifier is correct when it predicts an output as true. Precision is given by Eq. (2) Tp  Precision =  Tp + Fp

(2)

iii. Recall Recall gives the chance of a classifier detecting a positive outcome. It shows that the classifier can detect a positive outcome. It is given by Eq. (3) Tp  Recall =  Tp + Fn

(3)

iv. F1-score F1-score is a score which incorporates both precision and recall in a single measure. It is a more robust method of measuring a classifier’s performance. It is given by Eq. (4). F1 score =

2 ∗ (Precision ∗ Recall) . (Precision + Recall)

(4)

All the evaluation measures used have been described in this section. The next section discusses the results achieved by the proposed model.

4 Results and Discussions The results achieved by the proposed model for automatic language identification are very promising. The model is evaluated on all the measures described in the previous section, and it performs well in each of those measures. Figure 3 presents the confusion matrix for the 5-way classification performed by the proposed CNN model. The confusion matrix is presented in Fig. 3. The color coding represents different languages, and the intensity of the shade represents the relative magnitude of the instances that the classifier has predicted. We can see clearly from Fig. 3 that the intensity of colors is

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Fig. 3. Confusion Matrix

Fig. 4. F1-score of the proposed model

highest among the diagonal and low everywhere else. This is the hallmark of a good classification model. Thus, from Fig. 3, the performance of the model seems promising. The major evaluation measures are all presented in this section, but their comparisons with existing models are not presented due to lack of available information in the corresponding articles. The measure of F1-score, precision & recall is not available in the referenced studies with which the proposed model is compared [17]. Accuracy measure is presented and compared with the existing models as the researchers of the corresponding study have provided this information. To establish the performance of this model, it was evaluated based on Precision, Recall, F1 score & Accuracy. Since the F1 score is a more inclusive and robust measure, it is presented first in this paper in the form of Fig. 4. It is clear from Fig. 4 that the proposed CNN model’s performance is excellent. It is not only accurate but also robust across all the five languages. The performance shows that the CNN layers have been successful in identifying the discriminating features of each language considered. Figure 5 makes it evident that the model is precise. The precision of the model is high across all the five languages which are considered in the experiment.

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Fig. 5. The precision of the proposed model

Fig. 6. Recall of the proposed model

This shows the consistency and scalability of the model towards more languages. Recall of the proposed model is represented in Fig. 6. The recall is high for all the considered languages being above 0.9 for all except French. This shows that the model can consistently identify the correct languages in the experiment. Having evaluated the proposed model individually, in the next paragraph the comparison of this model with the already existing model is presented. Figure 7 compares the performance of the proposed model, two existing deep learning models and also a

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Fig. 7. Accuracy comparison of various approaches to LiD

traditional model based on feature engineering which uses SVM. The comparison in Fig. 7 is based on accuracy, and it is clearly seen that the proposed model’s performance is better. It is noteworthy that the model achieves this accuracy without any feature engineering.

5 Conclusion In this paper, a deep learning approach has been applied to the LID. A new CNN architecture for the same has been proposed. The performance of this CNN model has been compared with other deep learning as well as the traditional approaches. The model was trained on the VoxForge dataset containing 4771 short audio signals of five different languages. This new model was also evaluated using standard classification evaluation measures such as accuracy, precision, recall, and F1-score. The new model’s performance is found to be excellent. It is shown to be accurate, with an accuracy of 91.5%; it is also shown to be robust with an average F1-score of above 0.9 across the five chosen languages. The model was then compared to other existing models. The proposed model outperformed the traditional approach as well as other deep learning approaches reviewed in this work. In conclusion, the proposed approach is faster to develop since it does not require feature engineering. It has been shown to be accurate as well as robust through the results. The approach defines a new solution for the LID task, which solves some long-standing issues in this field of research. In the future, the work can be extended by including more languages and also various dialects of the same languages. The work can also benefit from a stronger model, which is deeper than the currently proposed model. It would require better hardware specifications but would outperform the current model. A Long-Short Term Memory (LSTM) network can also be incorporated with the

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current model to increase the accuracy and create an ensemble model that can generalize even better across many languages.

References 1. Zissman, M.A., Berkling, K.M.: Automatic language identification. Speech Commun. 35, 115–124 (2001). https://doi.org/10.1016/S0167-6393(00)00099-6 2. Barnard, E., Cole, R.A.: Reviewing automatic language identification. IEEE Signal Process. Mag. 11, 33–41 (1994). https://doi.org/10.1109/79.317925 3. Lewis, M., Paul, G., Simons, F., Fennig, C.D.: Ethnologue: languages of the world. Ethnologue 87–101 (2016). https://doi.org/10.2307/415492 4. Hachman, M.: Battle of the digital assistants: cortana, siri, and google now. PCWorld 32, 13–20 (2014) 5. Tong, R., Ma, B., Zhu, D., Li, H., Chng, E.S.: Integrating acoustic, prosodic and phonotactic features for spoken language identification. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, pp. 205–208 (2006). https://doi. org/10.1109/ICASSP.2006.1659993 6. Torres-Carrasquillo, P.A., Singer, E., Kohler, M.A., Greene, R.J., Reynolds, D.A., Deller, J.R.: Approaches to language identification using Gaussian mixture models and shifted delta cepstral features. In: International Conference on Acoustics, Speech, and Signal Processing 2002, pp. 89–92 (2002). 10.1.1.58.368 7. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). https://doi.org/10.1016/j.neunet.2014.09.003 8. Zissman, M.A.: Comparison of four approaches to automatic language identification of telephone speech. IEEE Trans. Speech Audio Process. 4, 31–44 (1996). https://doi.org/10.1109/ TSA.1996.481450 9. Zissman, M.A.: Automatic language identification using Gaussian mixture and hidden Markov models. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 399–402 (1993). https://doi.org/10.1109/ICASSP.1993.319323 10. Lippmann, R.P.: Speech recognition by machines and humans. Speech Commun. 22, 1–15 (1997). https://doi.org/10.1016/S0167-6393(97)00021-6 11. House, A.S., Neuburg, E.P.: Toward automatic identification of the language of an utterance. I. Preliminary methodological considerations. J. Acoust. Soc. Am. 62, 708–713 (1977). https:// doi.org/10.1121/1.381582 12. Hazen, T.J.: Segment-based automatic language identification. J. Acoust. Soc. Am. 101, 2323 (1997). https://doi.org/10.1121/1.418211 13. Pellegrino, F., Andre-Obrecht, R.: Automatic language identification: an alternative approach to phonetic modelling. Signal Process. 80, 1231–1244 (2000). https://doi.org/10.1016/S01651684(00)00032-3 14. Torres-Carrasquillo, P.A., et al.: The MITLL NIST LRE 2007 language recognition system. In: Proceedings of the Annual Conference of the International Speech Communication Association INTERSPEECH, pp. 719–722 (2008) 15. Torres-Carrasquillo, P.A., et al.: The MITLL NIST LRE 2009 language recognition system. In: ICASSP, International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 4994–4997 (2010). https://doi.org/10.1109/ICASSP.2010.5495080 16. Singer, E., et al.: The MITLL NIST LRE 2011 language recognition system. In: ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, pp. 209–215 (2012)

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17. Montavon, G.: Deep learning for spoken language identification. In: NIPS Workshop on deep Learning for Speech Recognition and Related Applications, pp. 1–4 (2009) 18. Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. ICASSP 6645–6649 (2013). https://doi.org/10.1109/ICASSP.2013.6638947 19. Deng, L., Yu, D.: Deep convex net: a scalable architecture for speech pattern classification. In: Proceedings of the Annual Conference of the International Speech Communication Association INTERSPEECH, pp. 2285–2288 (2011) 20. Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio, Speech Lang. Process. 20, 30–42 (2012). https://doi.org/10.1109/TASL.2011.2134090 21. Deng, L., et al.: Recent advances in deep learning for speech research at Microsoft. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 8604–8608 (2013). https://doi.org/10.1109/ICASSP.2013.6639345 22. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 82–97 (2012). https://doi.org/10.1109/MSP.2012.2205597 23. Lopez-Moreno, I., Gonzalez-Dominguez, J., Plchot, O., Martinez, D., Gonzalez-Rodriguez, J., Moreno, P.: Automatic language identification using deep neural networks. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 5337–5341 (2014). https://doi.org/10.1109/ICASSP.2014.6854622 24. Gonzalez-Dominguez, J., Lopez-Moreno, I., Sak, H., Gonzalez-Rodriguez, J., Moreno, P.J.: Automatic Language Identification using Long Short-Term Memory Recurrent Neural Networks, Interspeech-2014, pp. 2155–2159 (2014) 25. Voxforge.org, Free Speech... Recognition (Linux, Windows and Mac) - voxforge.org (2006) 26. Sisodia, D.S., Nikhil, S., Kiran, G.S., Sathvik, P.: Ensemble learners for identification of spoken languages using mel frequency cepstral coefficients. In: 2nd International Conference on Data, Engineering and Applications (IDEA), pp. 1–5. IEEE (2020). https://doi.org/10. 1109/IDEA49133.2020.9170720 27. Shrawgi, H., Sisodia, D.S.: Convolution neural network model for predicting single guide RNA efficiency in CRISPR/Cas9 system. Chemomtr. Intell. Lab. Syst. 189, 149–154 (2019). https://doi.org/10.1016/j.chemolab.2019.04.008 28. Sisodia, D.S., Agrawal, R.: Data imputation-based learning models for prediction of diabetes. In: 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 966–970. IEEE (2020). https://doi.org/10.1109/DASA51403.2020.9317070

A Software Quality Characteristics Optimization Model to Reduce Evaluation Overhead Kamal Borana1(B) , Meena Sharma1 , and Deepak Abhyankar2 1 IET, DAVV, Indore, India

[email protected] 2 SCSIT, DAVV, Indore, India

Abstract. Software quality evaluation is one of the critical processes for delivering good quality software products. The software quality evaluation has a significant effect on the entire project. However, for software quality evaluation a number of techniques are available. Most of the models are based on the characteristics of software and relevant scores. But consideration and evaluation of software on all the quality characteristics are time is taken and complex, which is not suitable for all kinds of projects. Therefore, in order to reduce the software quality assessment effort, this paper proposes a categorization of essential quality characteristics based on software project needs. Additionally, a Particle Swarm Optimization Process is proposed for recommending the suitable software quality evaluation attributes. The simulation of the model has been carried out and their performance analysis has been done. Based on the experimental analysis the results are presented and the future scope of the proposed work is explained. Keywords: Software Quality Models · Recommendation System · Machine Learning · Testing Cost · Model Selection

1 Introduction Software quality is essential for an efficient and reliable software product development [1]. Therefore, evaluation of software is an essential step of software development. The software quality is depends on different processes and activities involved in software development life cycle (SDLC) [2]. However, there are a number of software quality measuring models available based on different attributes and scoring techniques. These quality measuring characteristics used to express the quality of the software product. Additionally, different software quality evaluation models are recommending different characteristics for product evaluation. Therefore, selection of suitable software quality evaluation models is a challenging task [3]. Therefore, the proposed work is keenly interested to explore the possibility for applying and selecting the appropriate quality measuring characteristics. The proposed technique is evaluating of the software attributes as well as the different quality measuring attributes for suggesting appropriate quality evaluation attributes. The proposed model © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 164–173, 2023. https://doi.org/10.1007/978-3-031-31153-6_15

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works as an information filter to suggest the relevant quality measuring attributes. This model includes the particle swarm optimization technique to suggest suitable software characteristic. In this context, this paper includes the following information. 1. A review on recent development on software quality estimation models 2. Categorization of software quality characteristics 3. Design of software quality characteristics optimization model for suggesting the appropriate quality matrix This section, provide an overview of the proposed work. The next section involves the study of recent development on software quality estimation models. Further, a model has been proposed and its simulation has been done. In addition, the performance will be reported. Finally, the conclusion has been made and future plan will be reported.

2 Related Study Software engineering is an approach pre-owned by the researchers and innovators to reduce the ratio of crisis in software. Therefore, the designer can design a valuable quality software, by using various approaches like Component-Based Software Engineering (CBSE). The components quality has a high impact on the quality of a whole application. Several quality models and Component-Off-The-Shelf (COTS) are available. S. Yadav et al. [4] presents various quality models with defined parameters for quality prediction. Models, like, Boehm’s, McCall’s, FURPS, ISO 9126, Dromey’s, have been developed for quality evaluation using related characteristics of quality indicators. Nowadays, researchers are showing their passion in the area of software reliability. The reliability component relies on the factors like the reliability of services, environment frequency so author presents the analysis and assessment of software quality models and their quality parameters. Y. Lefdaoui et al. [5] studies how to improve the quality of gamified systems with the software engineering. Recent studies have shown a significant gap between design and gamified products, reflected in a lack of consistency, integrity, measurement, and a comprehensive process that covers the different phases to achieve a quality gamified product. Thus, quality model software for evaluation is required. The contribution consists of making an analytical and qualitative study of the existing Gamification frameworks, with the aim of a model for quality evaluation. This proposal is based on the patterns and attributes determined during the bibliographic study and comparative studies. Evaluating software quality models based on several attributes is an essential and complex multi-criteria decision-making problem (MCDM). To tackle this issue, several studies have been presented by utilizing decision making and the fuzzy sets (FSs). M. Khan et al. [6] develops an intuitionistic fuzzy (IF) improved score function to deal with MCDM problems. Further, the efficiency and feasibility of the method is illustrated to choose the best software quality model under IF environment. The comparative study with previous developed approaches is also discussed. There are three categories of quality models, i.e., definition model, assessment model, and prediction model. Quality assessment model (QAM) is a metric-based approach. It is

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high importance for how to assess a system. The current QAM research is under limited investigation. To address this gap, M. Yan et al. [7] provides a summary of the current QAMs. They conduct a systematic mapping study (SMS) for structuring the articles. A total of 716 papers and 31 papers are selected as relevant at last. This work focuses on QAMs from the following: software metrics, quality factors, aggregation methods, evaluation methods and tool support. According to the results, this work discovers five needs: (1) new method and criteria to tailor a quality framework, (2) investigations on the effectiveness, strength, and weakness to guide the method selection, (3) investigations on evaluating QAMs in industrial cases, (4) investigations or real-world case studies, and (5) building a public and diverse software benchmark. Infrastructure-as-code (IaC) is a practice to implement continuous deployment by allowing management and provisioning of infrastructure through the definition of machine-readable files and automation, rather than physical hardware configuration. Although IaC represents an increasing practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code in a measurable fashion. Source code measurements are often computed and analyzed to evaluate the quality aspects of the software. However, unlike general-purpose programming languages (GPLs), IaC use domain-specific languages, and metrics. S. D. Palma et al. [8] proposes a catalog consisting of 46 metrics to identify IaC properties focusing on Ansible and shows how they can be used to analyze IaC scripts. The software development industry considers quality a crucial factor in its development. F. M. A. Obisat et al. [9] aims to define the dimensions of SQ, identify the requirements for enhancing SQ, and present the challenges. They also provide a review on the impact of quality and measurement in the life cycle of software development. They examine the need for a quality standard. The findings indicate an increasing need for high-quality software. Numerous methods have been evolved to validate quality-related issues. The software quality comprises of a total of eight attributes where maintainability is one of the important attributes. Few methods are available for maintainability analysis, none of the method employs fuzzy logic with Quality Model. U. Dayanandan et al. [10], the fuzzy analytic hierarchy process (FAHP) is proposed with Buckley method to evaluate the influence of maintainability and its sub-attributes. This model is tested against several versions of the MFC and OWL windows application and the results are compared. The analysis shows that the FAHP with Buckley method is superior to the other methods. Software quality models describe decompositions of quality characteristics. There is a gap between quality models, measurements, and assessment activities. As a first step, M. Schnappinger et al. [11] presents a framework to perform quality assessments. They applied this framework in two cases and present lessons learned. They found that results from automated tools can be misleading. Manual inspections need to be conducted to find quality issues, and concrete evidence of quality violations. L. Neelu et al. [12] aims to motivate a new hybrid agile methodology. The aim of hybrid agile model is the timely delivery of projects with high quality. But the difficulty is to effectively reflect the software quality attributes. The failure of a project is mainly not because of inefficiency of features but due to inefficiency of quality attributes, like performance, reliability, and effective usability. They present the introduction of Hybrid

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Agile Quality Parameter Analysis (HAQPE) that is a quality attribute driven agile development method. The outcome of HAQPE was evaluated through hybrid agile process assessment by applying it to a commercial project. The results demonstrate that the quality attribute model is more efficient. Accessing relevant data on the product, process, and usage of software as well as integrating and analyzing data is crucial for reliable and timely actionable insights. S. M. Fernández et al. [13] aimed at creating a quality model for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. They performed workshops at four companies to determine relevant metrics and product process factors. They also elicited how these metrics and factors are used and interpreted. Author specified the Q-Rapids quality model by comparing and integrating the results of the workshops. Then, they implemented Q-Rapids tool to support the usage of the QRapids model. Afterwards, they performed semi-structured interviews with product owners to evaluate the understandability and relevance. The participants of the evaluation perceived the metrics as well as the product and process factors. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies. By means of heterogeneous data sources, the QRapids quality model enables detecting problems that take time to find manually and adds transparency. Computational intelligence is also playing a crucial role in the prediction of quality characteristics. M. Gheisari et al. [14] gives a new optimal model for the prediction of the degree of stakeholder satisfaction (Q). Optimal models validate the real data using the relationship impacts of quality attributes. It uses the equations of constraints. The model gives the maximum and minimum values for Q. Constraints constitutes of software quality characteristics. The idle value of Q is 30 but using optimal model it gives the maximum optimal value of Q = 22.788020075 for xLAB IT consulting services on their project In-Reg Molecule Registration using the MATLAB. It means if there is any change in the value of any software quality characteristics, then it will decrease the value of Q. It proves that the given result is an optimal solution. Product line (PL)-based development is an area to develop software-intensive systems. Feature models (FMs) facilitate valid products from a PL by managing commonalities and variabilities. The researchers experience difficulties in quality assessment. The increasing complexity and size of FMs may lead to defects. M. Bhushan et al. [15] provides a review and key issues related to the FM defects in PL. They derive a typology of FM defects according to level of importance. The information on defects’ identification and explanations are provided. Further, corrective explanations are presented to fix defects. This information would help engineering community by 5 enabling developers to find the types of defects and their causes and to choose an appropriate technique to fix defects. Software product line engineering improves software quality and diminishes development cost and time. Its success lies in identifying the commonalities and variabilities of software products which are modeled using feature models. The success of software product lines heavily relies upon the quality of feature models. There are various defects that reduce profits. One of such defects is redundancy. The majority of work focuses on the identification of redundancies, their causes and corrections had poorly explored. M. Bhushan et al. [16] classified redundancies in the form of a typology. An

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ontological first-order logic rule-based method is proposed. A two-step process is presented for mapping model to ontology. First-order logic-based rules are developed and applied to the ontology for identifying redundancies, their causes and corrections. This method is illustrated using a case study. The results of experiments on 35 models with varied sizes as well as automatically generated models and models created via Feature IDE tool conclude that the method is accurate, efficient, and scalable.

3 Proposed Work This section introduces a model for identifying appropriate characteristics of software quality estimation. The aim is to reduce the quality estimation time. In this context, the explanation of the work is provided in two parts, first part include categorization of software characteristics and second part includes the explanation of proposed quality evaluation attributes: 3.1 Attribute Categorization The software quality attribute categorization considers both kinds of quality estimation model tailored and basic. Both kinds of evaluation matrix involve some common and essential characteristics which are described in Table 1. Thus, the proposed quality attributes considers these parameters as mandatory attributes. Table 1. Characteristics Followed by Most of the Models Properties

Tailored models

Basic models

Functionality

Y

N

Maturity

Y

N

Resource Utiliasation

Y

N

Testability

Y

N

Compliance

Y

N

Understability

Y

N

Usability

Y

N

Learnability

Y

N

Reliability

N

Y

In addition, based on the software requirements the attributes can be categorized to decide the additional characteristics. The Table 2 demonstrates the additional category of attributes according to needs:

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Table 2. Additional category of attributes Requirements

Attributes

Involves any calculations

Accuracy, Correctness, Efficiency

Involves security, privacy, and communication modules

Integrity, Fault Tolrence, Time Behaviour

Involved in data collection and analysis

Human Engg., Analyzability

Utilized by a person who is non technical

Recoverability, Sutability, Attractiveness, Operability

Needed to change, modify or scaled

Adaptability, Changeability, Flexibility, Modifiability, Reusability, Operability, Suitability

Deployed in multiple places/multiple machines/multiple clients

Flexibility, Installability, Maintanability, Portability, Transferability, Configurability, Compatibility, Reusability, Interoperability

Deployed in resource constrained scenarios Stability, Resource Utiliasation, Self Contained, Replacability, Managability Large number of modules

Supportability

3.2 Software Quality Attribute Optimization The proposed model for suitable attribute selection for software product quality estimation is demonstrated in Fig. 1. In this diagram the two essential components are project requirement and second is the set of attributes which need to be selected during the optimization process. The basic project requirements are selected based on the attributes and requirements defined in Table 2. Then these attributes are selected for initializing the population of the particle swarm optimization (PSO) technique. The PSO is a genetically inspired algorithm and used for search and optimization problems. The advantage of utilizing this algorithm is that it returns at least one optimal solution at each cycle. The PSO algorithm works in number of epoch cycles and minimizes the possible error and enhances the solution quality. Additionally, there are two stopping criteria is need to be implement for finalizing the solution. These stopping criteria are: 1. Number of optimization cycles 2. Objective satisfied In this presented work the objective function is considered as: Let the attributes of Table 1 is denoted as T1 and project requirement attributes are defined as T2 . Then the total required attributes R is given by Eq. (1): R = T1 ∪ T2

(1)

Then, the distance between required attributes R and model’s attribute M is defined by the Eq. (2):  D(R, M ) = (R − M )2 (2)

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Fig. 1. Proposed Software Quality Attribute Optimization Model

So, D(M , R) → 0

(3)

Therefore Eq. (3) is considered as objective function to suggest appropriate software quality evaluation model. This equation is trying to find out the fit model which contains the required project assessment characteristics. This process help to reduce the additional efforts made for selection of appropriate software quality measurement model.

4 Results Analysis The proposed work is simulated using a python-based script by changing the project requirements. The performance of the model for suggesting appropriate model is demonstrated in Fig. 2. The performance of the proposed software quality recommendation model in terms of time consumption is demonstrated in Fig. 2. The X axis of the figure demonstrates the number of optimization cycles and Y axis shows the time taken to provide solution by the proposed algorithm. However, in initial two experiments when the number of epoch cycles is less the accuracy of the recommendation is below then expectations but as the epoch cycles are increasing the model provide accurate results. However, the number of epoch cycles is increasing the amount of time for finding best solution, but it is acceptable in front of the time required for evaluation of software. The next parameter of the performance analysis is accurate in terms of percentage (%). Here the accuracy of the recommendation model is estimated by using the total fit solutions which have a lower distance than 50%, among the total solutions generated by the PSO algorithm. Therefore, Eq. (4) will be used for calculating the accuracy of the proposed recommendation model. accuracy(%) =

total fit solution × 100 total solutions genrated

(4)

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Time 350

Time in MS

300 250 200 150 Time

100 50 0 1

10

50

100

Opmizaon Cycles Fig. 2. Required Time to Recommend Appropriate Software Quality Estimation Model

Figure 3 contains the accuracy of the recommendation model with the increasing amount of epoch cycles. In this diagram, the X axis contains the optimization cycles, and the Y axis shows the accuracy in percentage. According to the obtained accuracy, findings shows that the lower amount of epochs is producing less accurate results as compared to higher epochs.

Accuracy (%)

Accuracy 80 70 60 50 40 30 20 10 0

Accuracy

1

10

50

100

Opmizaon Cycles Fig. 3. Accuracy (%) of Recommendation Model

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Therefore, the proposed model is effective and accurate for utilizing it for software quality model recommendation.

5 Conclusion and Future Work The aim of software engineering is to develop high-quality software products. Therefore, software quality estimation is an essential part of the software development process. In order to estimate the software quality a number of different models exist but some of the models also include attributes, which are not applicable in different software application development cases. Therefore, appropriate software quality estimation model can reduce the overhead of the development process. In this context, the proposed work provides a solution by evaluating the required attributes and model attributes for recommending the most suitable software evaluation model. The proposed solution works on the concept of recommendation system design and is developed with the help of the PSO algorithm. The experiments with different software requirements are performed and their results are described. The proposed model is a promising model for suggesting a suitable software quality estimation model. Thus, in near future, work on the scoring-based model for minimizing the quality estimation efforts and time is proposed.

References 1. Bhushan, M., Goel, S.: Improving software product line using an ontological approach. S¯adhan¯a 41(12), 1381–1391 (2016). https://doi.org/10.1007/s12046-016-0571-y 2. Sathyamoorthy, N., Magharla, D., Chintamaneni, P., Vankayalu, S.: Optimization of paclitaxel loaded poly (ε-caprolactone) nanoparticles using box Behnken design. Beni-Suef Univ. J. Basic Appl. Sci. 6, 362–373 (2017) 3. Miguel, J.P., Mauricio, D., Rodríguez, G.: A review of software quality models for the evaluation of software products. Int. J. Softw. Eng. Appl. (IJSEA) 5(6) (2014) 4. Yadav, S., Kishan, B.: Analysis and assessment of existing software quality models to predict the reliability of component-based software. Int. J. Emerg. Trends Eng. Res. 8(6) (2020) 5. Lefdaoui, Y., Azouz, O.: Towards a new software quality model for evaluation the quality of gamified systems. EAI Endorsed Trans. Creative Technol. 5(14), e2 (2018) 6. Khan, M., Ansari, M.D.: Multi-criteria software quality model selection based on divergence measure and score function. J. Intell. Fuzzy Syst. 38, 3179–3188 (2020) 7. Yan, M., Xia, X., Zhang, X., Xu, L., Yang, D., Li, S.: Software quality assessment model: a systematic mapping study. Sci. China Inf. Sci. 62, 191101:1–191101:18 (2019) 8. Palma, S.D., Nucci, D.D., Palomba, F., Tamburri, D.A.: Toward a catalog of software quality metrics for infrastructure code. J. Syst. Softw 170, 110726 (2020) 9. Obisat, F.M.A., Alhalhouli, Z.T., Alrawashdeh, T.I., Alshabatat, T.E.: Review of literature on software quality. World Comput. Sci. Inf. Technol. J. 8(5), 32–42 (2018) 10. Dayanandan, U., Kalimuthu, V.: A fuzzy analytical hierarchy process (FAHP) based software quality assessment model: maintainability analysis. Int. J. Intell. Eng. Syst. 11(4) (2018) 11. Schnappinger, M., Osman, M.H., Pretschner, A., Pizka, M., Fietzke, A.: Software quality assessment in practice: a hypothesis-driven framework. In: ESEM 2018, Oulu, Finland, 11–12 October 2018. ACM (2018) 12. Neelu, L., Kavitha, D.: Estimation of software quality parameters for hybrid agile process model. SN Appl. Sci. 3, 296 (2021)

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13. Fernández, S.M., Jedlitschka, A., Guzmán, L., Vollmer, A.M.: A quality model for actionable analytics in rapid software development. IEEE Copyright Notice. IEEE (2018) 14. Gheisari, M., et al.: An optimization model for software quality prediction with case study analysis using MATLAB. IEEE Access 7 (2019) 15. Bhushan, M., Negi, A., Samant, P., Goel, S., Kumar, A.: A classification and systematic review of product line feature model defects. Softw. Qual. J. 28(4), 1507–1550 (2020). https://doi. org/10.1007/s11219-020-09522-1 16. Bhushan, M., Duarte, J.Á.G., Samant, P., Kumar, A., Negi, A.: Classifying and resolving software product line redundancies using an ontological first-order logic rule based method. Expert Syst. Appl. 168(15), 114167 (2021)

Smart Home Using Internet of Things Ghaliya Al Farsi1,2(B) and Maryam AlSinani2,3 1 College of Graduate Studies, Universiti Tenaga Nasional, Kajang, Malaysia 2 Al Buraimi University College, Al Buraimi, Oman 3 Universiti Kebangsaan Malaysia, Bangi, Malaysia

[email protected]

Abstract. Advanced technology has been incorporated into human life. People widely use these technological applications that make their daily lives more comfortable. My favorite smart devices are devices that depend on safety and savings—related to technological progress. Technology plays a crucial character in epoch-to-epoch activities in the present eon. This paper’s implementation smart home application using a packet tracer program includes the internet of things technology (IoT) functions. In this investigation, the cores covenanted the whole residence system, which includes devices such as air-conditioning and the gate of a garage which are some of the days to epoch issues that lacked it. Critical necessity wants one of these needs is the creation of an intelligent home that controls the operation. It is done effectively by using a packet tracer program containing the IoT tasks to mimic and own the smart home. The technology of IoT can practice in various actual lifetime subjects like homework, office, etc. the research, attention is given to safety system which contains devices like air-conditioning, alarming, lights, and door of a garage which are some of the daytime concerns. The objective of this study is to derive an imitation of smart devices organized by the end-user smart device. Utilizing cisco packet tracer features smart home simulated and IoT appliances monitored. The results of the simulation showed that the intelligent things composed to the home gateway and the devices observed positively that clues to the view of the actual lifetime application. Keywords: Internet of things application · smart city · Home gateway · Cisco Packet Tracer Software

1 Introduction This investigation of labor deals with growing and enforcing construction and creating a design abode system efficiently. The design abode system in this enforcement is a unity of technology and services in abode environments to produce the service that needs improving the serenity, capability, and safeness of its inhabitants. Internet through the network and professional application of technologies such as radio degree describing devices (RFID), Wireless sensor networks, activated and built-in sensors, network facilities, and objects are mentioned by route of a mesh-labor of large-scale networks volume of electronic devices or Cyber system sensor linked. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 174–183, 2023. https://doi.org/10.1007/978-3-031-31153-6_16

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IoT stands for the internet of things to identify objects with communication on the internet [1, 2]. IoT allows remote objects and control locally by consuming network technology combinations. Internet of things connected devices around us through the internet and automates communication [3–7]. Groups of association and scientists have endeavored toward creating the internet quickly besides formulating the suggested explanation as [Haller et al.] “a planet where tangible seamlessly combined into knowledge mesh-labor and where tangible impedimenta can become life in the callings process.” As intensity emerges online, the internet of impedimenta is predictable to have a tremendous influence on the power [8]. Internet through the net and skillful utilization of technologies such as radio frequency identification devices (RFID), Wireless sensor networks, activated and built-in sensors, web facilities, and objects are regularly mentioned toward by way of a network of large-scale networks number of electronic devices or Cyber system sensor connected to the internet [4–7, 9–11]. This research deals with developing an application for building and creating a smart home system efficiently. The smart home system in this application is a combination of technology and services in home environments to produce the function that needs like improving the comfort, efficiency, and safety of its inhabitants. Internet of Things IoTs leads to bringing change in almost every significant aspect of life, including education. [12] as IoT networks allow customizable security plans that use connected devices such as color lights, digital signs, door locks, and sensors. Some schools and universities use the Internet of Things to create various programs for severe air threats, hackers, and other security risks [12]. Besides that Energy efficiency and cost savings with IoT in universities and schools, where lighting connected to IoT and other devices can be programmed and played automatically. For example, lights can be set to a schedule, or they can be connected to occupancy sensors and programmed to turn off when the classroom is empty. IoT connectivity improves building efficiency and reduces energy waste, saving costs [12]. The students can improve there learning and increase their performance by using Cisco Packet Tracer Software [16, 17, 20, 21, 25].

2 Cisco Packet Tracer Software Through the reenactment system dependent on the cisco parcel tracer, the keen home framework can actualize. Cisco Packet Tracer is an intense Cisco framework Institute organize demonstrating application that can mimic/make a system without a physical system that the student lives the real feeling [20, 21, 25]. It has an intuitive interface that, while arranging complex systems, is easy to utilize yet profoundly viable [11, 12, 26]. Also, Cisco Packet Tracer can work as a crossbreed arrange that consolidates natural systems with virtual systems [13]. This most recent variant of the cisco parcel tracer was additionally added to MCU-PT board single-boarded PCs (SBC-PT) [14], offering programming conditions to control associated gadgets. Recently discharged Packet Tracer favorable circumstances are (Fig. 1): • Provide a board for the control of creative items. • Allow understudies to investigate the ideas of IOE standards. • Provide a sensor detector

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• Provides useful IOT machine reenactments and representations. • Allows clients to design, make, and modify smart homes, and brilliant urban areas by providing them with different sh

Fig. 1. Cisco Packet Tracer Program Interface

The paper is structured hooked a numeral of units that contain relevant works of a literature review after the literature review is the method used in the development— moreover, the procedures for implementing a safe house and the outcomes discourse. Lastly, a summary is involved.

3 Literature Review The intelligent home consists of a time to update design lore to enhance design devices around this separate and some creative writing inspection has been provided. In 2018, Isa Al Shamsi wore the revised model of the tracking parcel in the intelligent home app. This secreting also provides programming surrounding dominant objects. To qualify the sensor wireless and design things, dwelling doorway. The application has connectors to merge the sensors into the gadget. Although the cisco bale tracking equipment provides programming wording help, the Java penmanship and Python finder have implemented it [3]. Yin, Ji et al. (2013) have provided a system architecture, as many applications can integrate through its interface. The tags of RFID worm toward cut proxy relation to devices. The chief issues to be solved associated with the intelligent home are also included in this delving [13]. Mollycoddle D Dayana et al., in their delving, utensil a machine-made firm. Dwelling appliances merge with the Arduino timber I/O port by relaying the wireless relation

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known between the wandering telephone and the Arduino timber. The shelter ensured with shibboleth safeguard [14]. T. Saravanan et al., In their delving gave stress on shelter for an IoT-based dwelling. Another tolerance of their drudgery was to deliver power and condition the surrounding soft for strains [15]. In their delving, Jasmeet and Punit suggested that the system gearboxes to 1 command and monitor electrical devices through the internet or without the internet using robot wandering applications and a variety of sensors. This method aims to hold up dwelling shelter, keep users learned and monitor the refuge of their homes, and supply verve and command to users in articulation or easy route [16]. In 2014, Alexandria Al-Oisi intended empathy among the network of luggage and distance cables device systems since masses of ACOAP convey framework things. In the process of accomplishment so, the first stride of the provocation controlled IOT urbanity, as well as how to merge sensors to science rallying engaging into tally the terminating tolerant of the design campus. m‚tiers worn design COAP constricted connection conduct in implementation [17]. It forecasted that by 2020, 25 billion “things” will be associated with the internet. This association will advance the volume of determined information, and information extricated from this information will connect to oversee and make smart choices independently. A few mechanical and fabricating spaces have been utilized in machine-to-machine communication for a long time, such as RFID and sensor systems. Even though IoT has existed for more than a decade, two advancements have been the essential drivers behind this innovation’s development. The primary is the massive development of versatile gadgets and applications; the moment is the wide accessibility of remote networks. Moreover, expanding knowledge-based capital (i.e., computer programs, information, mental property, firm-specific aptitudes, and organizational capital) and the advanced economy’s rise are critical components for quick IOT transformation [16]. Rapid developments in hardware, software, and communication technologies have facilitated the emergence of internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is rated that the total number of the internet there will be between 25 to 50 billion connected devices in operation. When these figures grow, and innovations ripen, the amount of data released will increase. The technology of internet-connected devices, through providing communication and connections between the physical and virtual worlds, the internet of things continues to expand the existing internet. In addition to an increased volume, the IoT produced big data distinguished by its time and position dependence velocity, with several different modalities and varying data quality. The paper points to the display of a modern concept in IT&C (Data Innovation and Communications, specifically Internetof-Things (IoT). A brief presentation on the nuts and bolts of this concept illustrates the significance of presenting IoT in higher instruction. Moreover, there are recognized a few viable strategies distinguished for coordination IoT highlights in the scholarly world, particularly within instructing and learning improvements. Need in case critical IT&C companies propelled and actualized ventures in this region, a show for “smart universities” is not well-defined. In our consideration, we illustrate that the ideal specialized arrangement for the literary world is IoT Stages with real-time, limited-area

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benefit arrangement utilizing Cloud Computing services. The internet and all its related administrations and applications have unequivocally affected communication, data, and showcasing worldwide through websites, blogs, email, and social systems. In this setting, the instructive environment has too enlisted significant changes, particularly since 2000, towards a modern introduction of teenagers’ instruction, reflected through online documentation, execution of ventures in virtual groups, online instructional exercises, and much more. In 1999, there rose a new term within the field, Web of Things (IoT) coined by Britain’s Kevin Ashton, who was working in supply chain optimization, but the express truly utilized it ten years later. There are a few perspectives concerning the definition of IoT. Still, we consider the report from Gartner Inc., the inquiry about the company, to be the foremost precise and complete: The Web of Things (IoT) is the organize of physical objects that contain implanted innovation to communicate and sense or connected with their inner states or the outside environment. The introduction of the Internet of Things (IoT) in instruction, which permits Internetbased communications to happen between physical objects, sensors, and controllers, has dramatically changed instructive teaching. By inserting sensors in objects and joining cloud computing, expanded reality, wearable advances, and enormous information in this stage, distinctive parameters of the informational environment can measure and examined to supply valuable data. It too has made a modern interaction between individuals and the environment in the instructive association. In this inquiry based on the later IoT ventures in instruction, we will categorize the application of IoT in education into four bunches: vitality [20]. Administration and genuine-time environment observing, checking students’ healthcare, classroom get to control and to move forward instructing and learning. We’ll explore and examine how this stage has changed the Instruction Commerce Show and included modern esteem suggestions in such organizations based on the Canvas Trade Demonstrate [22–24]. This paper investigates the Internet of Things (IoT) and its effect on supply chain administration (SCM) through a broad writing survey. Critical viewpoints of IoT in SCM are secured by counting IoT definitions, fundamental IoT innovation enablers, and different SCM forms and applications. We offer a few extant writing categorizations based on strategy and industry division and center on a classification based on effective supply chain forms. In expansion, a bibliometric examination of the writing is additionally displayed. We discover that most ponder centers on conceptualizing the effect of IoT with restricted explanatory models and experimental ponders. Most think about the conveyance supply chain handle and the nourishment and fabricating supply chains in expansion. Ranges of future SCM investigations that can bolster IoT usage are too distinguished [21]. The secret to designing smart IOT applications is intelligent processing and analysis of this big data. This article assesses the different machine learning methods that address the challenges posed by IoT data by considering smart cities as the critical use case. This study’s key gimmick is presenting a taxonomy of machine learning algorithms explaining how different techniques are applied to the data to extract higher-level information. It will also discuss the potential and challenges of machine learning in IoT data analysis.

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For a more comprehensive analysis, a use case for applying a support vector machine to Aarhus smart city traffic data is discussed.

4 Methodology The concept of judgment is very engaging, and an examine been conducted on comparable exploration to perceive its architecture and ideas. By way of applied by most investigators, the cisco backpack tracer was Euphemistic pre-owned to device fancy domicile. The newly released model of cisco was Euphemistic pre-owned because it supports domicile safety feels in an appendix to furnishing programming environs as well as networking feels. A variety of supported programming languages are creative Python, optical texts, and Java scripts. The mobile phone and the domicile portal are Euphemistic pre-owned to command devices such as the fancy window, fancy champion, fancy refuse, and sensors. Fancy techniques affiliated with the prevailing IOT access ports and the smartphone is Euphemistic pre-owned to make known with fancy devices.

5 Safe Home Implementation The main gateway contains a wireless access point and four Ethernet ports that are constructed through an SSID facility’s usual identifier. Wholly procedures linked to the gateway by the configuration list of options in all devices, the gateway name must enter. The repetition for all groups of devices must organize in the home gate. The IP configuration list of options shows the cap of procedures linked with the main gateway. The interface of Tracking delivers internally compressed procedures to add to the net. The start step is by choosing from the network devices the primary gateway device. For authentication and verification of wireless communication, we can also configure the gateway using WPA-PSK / WPA2 / WEP rules. The next stage is to join objects to the gateway. We include six items that connect using wireless paired with the main gate. Configured devices involved the web camera, doors, garage, air condition, fan, and motion detection as shown in the smart things linked in the method that only involved wireless objects in Fig. 2. Various conditions and rules are modified depending on the excellent article associated with the home entryway. These means must rehash for all items.

6 Discussion and Result Once you have completed all the major procedures for designing a creative home situation - add a home-based portal to the workplace. Also, add IoT devices to the leading portal in the workspace and finally add an end-user device such as a smartphone, tab, laptop, etc. The smartphone has clicked into the workspace, IoT server can specify to check the connections that have been created. Figure 3 connects the network showing the smart devices. This study uses IOT to focus on understanding the foundation and structures of smart home. The proposed Innovation in this paper points to the display of

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Fig. 2. Six devices connected with smart mobile through the gateway.

a modern concept in IT&C (Data Innovation and Communications, specifically Internetof-Things (IoT). A brief presentation on the nuts and bolts of this concept illustrates the significance of presenting IoT in higher instruction. Moreover, there are recognized a few viable strategies distinguished for coordination IoT highlights in the scholarly world, particularly within instructing and learning improvements. This allows understanding between interconnected devices. People communicate with computers and smartphones over a single global network and through the well-known traditional Internet protocol. What distinguishes the Internet of Things is that it allows a person to be free from the place, that is, the person can control the tools without having to be in a specific place to deal with a specific device.

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Fig. 3. Network registered the smart devices

7 Conclusion In this study, we utilized the most recent cisco bundle tracer form to present a ready home, as this adaptation incorporates various IOE gadgets. This practical application aims to imitate it in real life and simulate these programs to develop technological progress in our daily lives. In this study, we are investigating whether Cisco packet tracer software was very useful in the implementation process. The conclusion displayed that procedures can be regulated and observed by the end-user gadget. Cisco packet Tracking offers many facilities that fit facsimile uncomplicated. The results show that there is a chance to register this version to real vigor and upgrade the idea of the internet of impedimenta that can be applied in a variety of fields. The target of this investigation was to intelligent home. Technological novelty and stand-in smartphone use was the powering. An element behind this toil. Shelter measures are demanding, and the internet of things provides a fresh and excellent idea to fit our atmospheres smarter.

References 1. ElShafee, A., Hamed, A.K.: Design and implementation of a WiFi based home automation system. Comput. Sci. 2177–2180 (2012) 2. Pivare, R., Tazil, M.: Bluetooth based home automation system using cell phone, 192–195 (2011) 3. Al-Shemsi, I.: Implementation of smart home using cisco packet tracer simulator, 4(VII), 2349–6185 (2018)

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4. Aggarwal, R., Lal Das, M.: RFID security in the context of Internet of Things, Kerala (2012) 5. Kosmatos, E., Tselikas, N., Boucouvalas, A.: Integrating RFIDs and smart objects into a unified internet of things architecture. Adv. Internet Things 1, 5–12 (2011) 6. Want, R.: An introduction to RFID technology 5, 25–33 (2006) 7. ALFarsi, G., Jabbar, J., ALSinani, M.: Implementing a mobile application news tool for disseminating messages and events of Alburaimi university college. Int. J. Interact. Mobile Technol. (iJIM) 12(7) (2018) 8. Iova, O.-T.: Standards optimization and network lifetime maximization for wireless sensor networks in the Internet of things (2014) 9. Shahriyar, R., Hoque, E., Sohan, S.M., Naim, I., Akbar, M.M., Khan, M.K.: Remote controlling of home appliances using mobile telephony 2 37–54 (2008) 10. Sun, C.: Application of RFID technology for logistics on internet of things (2012) 11. AlFarsi, G., ALSinani, M.: Developing a mobile notification system for AlBuraimi university college students. Int. J. Inf. Technol. Lang. Stud. (IJITLS) 1(1) (2017) 12. Español, P.: Creating smarter schools: benefits and applications of IoT in education 8(1) (2020). https://www.igor-tech.com/news-and-insights/articles/creating-smarter-schools-ben efits-and-applications-of-iot-in-education 13. Jie, Y., Pei, J.Y., Jun, L., Yun, G., Wei, X.: Smart home system based on IOT technologies, Shiyang, China (2013) 14. Baby, D.D., Chinta, R.R., Pijush, M.: Smart home automation using IoT with security features. Int. Res. J. Eng. Technol. (IRJET) 5(10), 1167–1169 (2018) 15. Saravanan, T., Nagarajan, R., Kumar, R., Rajesh, R.: IoT based smart home design for power and security management. Asian J. Appl. Sci. Technol. (AJAST) 1(2), 260–264 (2017) 16. Gupta, P., Chhabra, J.: IoT based smart home design using power and security management. In: International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH) (2016) 17. Alexandria, A.: Enabling communication between wireless sensor networks and the internet of things-ACOAP communication stacks. Int. J. Sci. Eng. 5, 6–7 (2014) 18. Dawood, M., Hossein, S., Fatemeh, N.: Design and implementation of a low-power active RFID for container tracking at 2.4 GHz frequency (2012) 19. Piyare, R.: Internet of Things, ubiquitous home control and monitoring system using android based smart phone 2(1) 5–11 (2013) 20. Egham. https://www.gart-ner.com, https://www.gart-ner.com/newsroom/id/3598917 21. Arpan Pal, B.P.: IOT technical challenges and solutions artech house (2017) 22. Wentao Shang, Y.Y.: Challenges in IoT networking via TCP/IP architecture. NDN Technical Report NDN (2016) 23. Raj, P., Raman, A.C.: The Internet of Things: enabling technologies, plat-forms, and use cases (2017) 24. Alliance, L.: What is the LoRaWAN™ specification? LoRa Alliance (2018). https://lora-all iance.org/about-lorawan 25. Corporate social responsibility (2018). https://www.cisco.com/c/en/us/about/csr.html 26. Packettracernetwork. What’s new in Cisco Packet Tracer 7.0 (2018). http://www.packettracer network.com/features/packettracer-7-new-features.html 27. Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing 10, 0163–6804 (2010) 28. Alfarsi, G.M.S., Omar, K.A.M., Alsinani, M.J.: A rule-based system for advising undergraduate students. J. Theor. Appl. Inf. Technol. 95(11) (2017) 29. Jetendra, J.: Performance enhancement and IoT based monitoring for smart home. In: International Conference on Information Networking (ICOIN), pp. PP 468–473 (2017) 30. Marian, C.E.: Smart concept anew idea in the internet of things. In: IEEE Conference Application (2015)

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31. Abdi, A.: Designing smart campus using internet of things. Int. J. Comput. Sci. Trends Technol. (IJCST) 6, 109–116 (2018) 32. Cata, M., et al.: Designing smart campus using internet of things 6(3) (2015) 33. Ang, C.S., Farzin, D., Chaiwut, N., Pruet, P.: Exploring the Internet of “Educational”. IEEE (2015)

ADAPT- Automated Defence TrAining PlaTform in a Cyber Range Muhammad Mudassar Yamin1(B) , Ankur Shukla2 , Mohib Ullah1 , and Basel Katt1 1

Norwegian University of Science and Technology, Gjøvik, Innlandet, Norway {muhammad.m.yamin,mohib.ullah,basel.katt}@ntnu.no 2 Department of Risk, Safety and Security, Institute for Energy Technology, 1777 Halden, Norway [email protected] Abstract. With the passage of time, many cyber security training programs are being developed. These programs teach skills ranging from ethical hacking to different cyber defence operations. Teaching or training such skills is a complex undertaking and requires complex platforms and tools, like cyber ranges. This is especially true for training and teaching defenders. For example, teaching realistic cyber defence requires building a vulnerable infrastructure instrumented and monitored with complex and sophisticated software. Due to ever-increasing cyber attacks, teaching such cyber defence operations are in high demand. Most of the current research activities within cyber ranges and cyber security training focus on (1) the generation of a general purpose vulnerable infrastructure and (2) the automatic assessment of skill and the generation of appropriate feedback in cyber security exercises. While providing training platforms for general purpose blue-team training is important, it is not enough. There is a need to adapt the training platforms to the evolving skills and competencies required to address the new challenges posed by the evolving cyber threat landscape. On the other hand, there is no specific focus in the current research on SoC (Security Operations Center) training. To tackle the aforementioned challenges, we developed an open source training platform focusing on SoC training, which is adaptable to cope with the new and evolving skills and knowledge requirements. We used our platform in a case study in a university setting. Keywords: Security Operations Defense

1

· Cyber Range · Exercises · Cyber

Introduction

Cyberspace has evolved from the playground of pranksters, script kiddies and opportunistic criminals to a highly contested war theatre of state-sponsored eleM. Yamin—Research idea, system design, writing the original paper draft. A. Shukla—Paper review and editing. M. Ullah—Paper review and editing. B. Katt—Research work supervision, paper review and editing. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 184–203, 2023. https://doi.org/10.1007/978-3-031-31153-6_17

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ments and criminal enterprises in over a decade. This evolution resulted in an increased number of cyberattacks on the technology on which modern society relies. These attacks are mainly targeting different countries’ critical infrastructure for sabotage and ransomware purposes. According to a recent blog post by Google project zero, the number of identified zero-day exploits are doubled in 2021 compared to 2020 [3]. This is a very clear indication that this evolution isn’t slowing down, and more major cyber-attacks are expected in the coming years. This situation is very alarming, and urgent steps are needed to be taken to tackle the increasing number of cyberattacks. Researchers around the world are working on detection mechanisms for such attacks. Whenever these kinds of attacks happen, they follow certain steps that can be represented in different attack models like Lockheed Martin Cyber Kill Chain [13] and MITRE ATT&CK1 . In general, such attacks involve a phase of scanning and probing the organization’s infrastructure for the identification of vulnerabilities, use of known or zero-day exploits to gain initial access, performing actions to have persistent access to the infrastructure and performing actions to achieve certain objectives. In each phase of an attack, the attacker leaves behind traces. These traces contain different activity and event logs which can be used to detect the attacker’s actions and prevent them. However, to train people for detecting such event logs and activities, we need platforms on which cyber attacks can be executed in a safe and secure manner. Such platforms can be cyber ranges as they provide a safe and secure environment to train for cyber security skills [23]. In a cyber range, the digital infrastructure of an organization can be replicated on which complex and novel cyber attacks can be launched. The digital infrastructure can be instrumented with security monitoring tools like SIEM (Security information and event management), which can be used by the blue team or the cyber defenders to monitor the digital infrastructure and detect various kinds of attacks. The SIEM provides a central view of all the activities within the organization’s infrastructure and contains a lot of information that is not easy to understand for an untrained individual. Therefore, there is a need for such platform to provide necessary hands-on experience on SIEM solutions. Moreover, The solution should also contain the capability of launching complex and realistic attacks to create meaningful events for the cyber defenders to detect. Current cyber ranges are focusing on general purpose vulnerable infrastructure generation [7,11,16,22] for conducting exercises and assessment [18] of cyber security skills. The required skills and knowledge to be trained are evolving and changing with the evolving threat landscape. It is the responsibility of the scenario designer to adapt the training scenario to the new skills and knowledge requirements [20] We believe that there is a need for a systematic adaptation of the training scenario to the changing skills and knowledge requirements. In this work, we focused on SoC (Security Operations Center) training that has not gained enough attention in the literature [19]. We develop a platform that provide cyber defenders, or blue team members, hands-on experience on 1

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a SIEM solution. We developed a model to connect the skills and knowledge requirements with different attack profiles that are involved in a cyber security scenario. In particular, we use in our work recent skills and knowledge framework from ENISA (European Network and Information Security Agency) to teach the necessary skills for cyber defence. We map different knowledge needs defined by ENIS’s framework to (1) specific profiles of different attackers defined by MITRE ATT&CK [2] and (2) SOC activities defined by Common Criteria2 . We evaluated our solution based upon qualitative and quantitative metrics in term for full-filling the knowledge needs and system deployment efficiency. The rest of the paper is organized as follows; first, we will discuss the research background and related work, and then we will continue with the research methodology which we followed in conducting this work. Continuing that, we will share the insights of the proposed system; following that, we will discuss the platform evaluation, and finally, we will conclude the article with the conclusion and future work.

2

Research Background and Related Work

ENISA is currently working on a cyber-security skills framework3 , in which they defined 12 professional profiles that are involved in an organization cyber security. One of the profiles in this framework is the Cyber Incident Responder profile. The mission of this profile, as per ENISA, is “Analyses, evaluates and mitigates the impact of cybersecurity incidents. Monitors and assesses systems’ cybersecurity state. According to the organisation’s Incident Response Plan, restores systems’ and processes’ functionalities to an operational state”. This is achieved thorough skills related to technical, functional and operational aspects of cybersecurity incident handling and response, operating systems, servers, clouds and relevant infrastructures. And as per ENISA these skills are developed based upon the following knowledge requirements: 1. 2. 3. 4. 5. 6. 7. 8.

2 3

Knowledge of cybersecurity incident handling methodologies Knowledge of cybersecurity incident handling practices and tools Knowledge of incident handling communication cycle Knowledge of operating systems internals, networking protocols and services Knowledge of cybersecurity attacks tactics and techniques Knowledge of cyber threats and vulnerabilities Knowledge of legal framework related to cybersecurity and data protection Knowledge of the operation of Secure Operation Centres (SOCs) and Computer Security Incident Response Teams (CSIRTs) https://secureframe.com/hub/soc-2/common-criteria. https://www.enisa.europa.eu/topics/cybersecurity-education/european-cybersecur ity-skills-framework/ecsf-profiles-v-0-5-draft-release.pdf.

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ENISA also highlighted the the competence level required for each of the developed cyber security profile. In cases of Cyber Incident Responder it is on average 3/5 level of competence compared to a software developer which is on average 1/5 level4 . This indicates that there is a considerable amount of training and experience required to be a Cyber Incident Responder, which can be provided in a secure and safe environment of a cyber range. This research is a continuation of our work related to cyber ranges. We are developing a platform for modelling and executing cyber security exercise scenarios efficiently and realistically. In our previous works, we developed a DSL (Domain Specif Language) that help us to orchestrate different kinds of operational scenarios such as simple CTF (Capture the flag), attack/defence, and red versus blue exercises. We are using model-driven engineering to model the cyber security exercise scenarios in a domain-specific language. We developed an orchestrator to automatically deploy the scenario based on the scenario model and execute part of cyber security exercises like dry-run and execution to make it efficient [22]. For realism in the exercises, we developed agents that can emulate the role of attackers and defenders in the exercise scenario to add necessary friction for the exercise participants when trying to achieve their objectives. Our proposed system is getting matured now and is now being used for conducting different national and international exercises. Based on the feedback of the exercise participants and the requirements given by different exercise organizers, we are adding new capabilities to our developed orchestrator for conducting realistic incident response exercises. These capabilities will enable us to deliver cyber defence operations training to a wider audience. In 2015 researchers presented I-tee [10], an open-source platform for developing cyber defence exercises. The researcher created the capabilities of automating VM provisioning using templates for creating the exercise infrastructure, and they used Puppet for this purpose. They created a bot for launching specified attacks on the infrastructure for generating the necessary traffic for the cyber security exercise. These attacks include denial of service, ShellShock, heart-bleed, SQL injection, misconfigurations etc. They developed a scoring bot that uses the IP addresses of attack bots to allocate scores. When a student blocks an attack bot IP address, the student receives the score. They also included noise generation to make the exercise environment a bit realistic. Their proposed solution was useful for conducting cyber defence exercises, but it requires a lot of manual labour and configuration in terms of generating Bash and Python Scripts for creating the demo labs and configuring the monitoring environment manually. Moreover, the tool only provide concrete attacks and vulnerabilities which were not mapped systematically with concrete knowledge needs. Recent works from academic researchers [8,15] are focusing on cyber defence training. However, most of the exercise infrastructure created in those works is manually developed, which is quite error prone and labour-intensive. However, automation for cyber security exercise infrastructure is being carried out [16,22]. 4

https://ecfusertool.itprofessionalism.org/.

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However, there is little focus on specific cyber defence operations [19]. This indicates the need for such platforms to be developed for making them widely available. In comparison, there are commercial solutions which are providing the capability to train cyber defences operations. One of the very similar works we get inspiration from was the Splunk attack range [4]. It uses a mixture of open source and commercial software to create a small lab infrastructure similar to real production environments for testing different kinds of attacks using the atomic red team or CALDERA [2]. It uses Ansible and Terraform to generate the required infrastructure and deployment configuration containing Windows domain controller, Windows servers, Linux server and different kinds of sensors. It collects different kinds of logs for providing situational awareness to the defender. They also didn’t specify the knowledge needs for SoC operation for Cyber Incident Responder training. There is an other commercial tool known as the blue team training tool kit [1]. It includes a client-server architecture that allows the simulation of malware infection and targeted attacks with specific Command and Control communication channel, which is used to train blue team professionals. It should be noted that the infrastructure orchestration is missing in this tool and the exercise designer have to configure the relevant software for launching the exercise manually. It uses a command-line interface similar to Metasploit and is more centred on user training than the exercise infrastructure orchestration. This can be useful for conducting realistic exercises, however it is not linked with systematically defined knowledge needs required for Cyber Incident Responder. From the above-identified work, it can be found that there are multiple open source and commercial tools being developed for training cyber defence operations. However, they have their own limitations. The main limitation is that they didn’t systematically map the knowledge needs for SOC operators with the exercise design. Therefor research efforts are needed to develop methods and platforms that can systematically map and incorporate new knowledge requirements coming from an evolving cyber security threat landscape in the exercise scenario design.

3

Research Methodology

We used applied experimentation [9] as the research methodology for conducting this work. In applied experimentation, theoretical knowledge is validated in experimental results against defined benchmarks. We have long experience in developing and conducting cyber security exercises in cyber ranges [22–24]. From our previous studies, we identified that there was a serious need for a platform that can be used for training cyber defence operations based upon defined knowledge needs. We have theorized that a system that can provide security training based upon concretely defined profiles of adversaries can be useful for fulfilling different knowledge needs. This can be validated in experimentation comprising cyber security exercises that map the knowledge needs with adversary profiles. To achieve this, we have to develop a system that holistically integrates the

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knowledge requirements in the cyber security exercise scenario design process. We employed literature reviews and experimental observations to identify the current knowledge needs and skills. As an example of a skills and knowledge requirements, we take into account the cyber-security skills profile from ENISA knowledge framework related to SoC operations, i.e., Cyber Incident Responder. We decided to incorporate this profile’s knowledge needs in the scenario design process of our cyber range platform. As a tentative design, we proposed a system to fulfill those knowledge needs, which is the focus of this work. We used the proposed system in cyber security exercises for its evaluation.

4

Proposed System

Providing cyber security knowledge and skills isn’t an easy task. It involves creating a training environment in which individuals can practice cyber security skills. Such a training environment is created mostly by the subjective experience of subject matter experts. Whenever a training activity is being organized, the activity organizer provides a list of knowledge and skills that the training activity participant should learn by participating in the activity. The exercise scenario designer then creates a virtual environment based on those needs and their own subjective experience. If the same requirements are given to another subject matter expert, then it is likely that a different training environment will be created for fulfilling the same knowledge needs. This process is not systematic and can have variable results for providing the same knowledge needs. Therefore we are proposing a general model for profile-based training in which the knowledge needs are mapped with the kind of adversary profiles that a cyber security operator will tackle in real-life situations. 4.1

Profile-Based Training Design

In our general model presented in Fig. 1, first, we take knowledge needs that any external entity can provide for training their workforce. Based upon the requirement, a training activity can be organized. The training activity can have multiple types of cyber security exercises in which people can participate to fulfil their knowledge needs. In the cyber security exercises, there will be artefacts like exploitation logs or vulnerabilities that can be manipulated or generated by adversary actions. A computational agent or a human participant can generate these adversaries, whose actions and behaviour can be modelled in a concrete systemic profile. By utilizing such a model, a scenario designer has the capability to design the training scenario based upon the given knowledge needs. It makes designing cyber security exercises more systematic in fulfilling the relevant knowledge needs. In our previous work, we have developed a DSL [22] that can be used to model cyber security exercise scenarios. These scenarios contain attackers and defenders in a realistic environment. Our DSL can be used to specify the behaviour of these attackers and defenders. However, this behaviour specification is based upon

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Fig. 1. General model for profile based training

the subjective experience of the scenario designer. In a realistic cyber security exercise environment, the scenario designer interpretation of knowledge needs can be variable, but the knowledge needs are constant and must be fulfilled using a systematic process. 4.2

System Design

The knowledge needs highlighted by ENISA’s cyber security skill framework can be addressed by organizing different cyber security exercises. There are multiple types of cyber security exercises like operation-based, discussion-based and full-scale exercises, which we have discussed thoroughly before [22]. These exercises addresses different types of knowledge needs for the development of different types of skills and competencies. Such exercises are helpful in gaining the necessary technical and management skills for performing cyber security operations [21]. On a scale highlighted by United States Homeland Security Exercise and Evaluation Program in organizing cyber security exercises5 , discussion-based exercises were the easiest and full-scale exercises were the most difficult after operation-based exercises in terms of technical requirements. Operation-based exercise infrastructure is required to conduct full-scale exercises; therefore we focused on operation-based exercises. This kind of exercise addresses different knowledge needs required for Cyber Security Incident Responder, which are highlighted in the Fig. 2: As a part of the scenario design of such exercises adversary actions should be modeled and designed in a manner to fulfill the knowledge needs that can be used to train specific skills and develop cyber defenders’ competencies. This can be achieved by mapping the knowledge needs with SoC activities; and correlating it with specific event logs and traces generated by a cyber attacker in a secure and safe environment of a cyber range. To achieve this we mapped knowledge needs defined by ENISA cyber security skill framework profile of Cyber Incident Responder with attack profiles. We used predefined attack profile by MITRE CALDERA [2] to integrate attacker actions 5

https://www.hsdl.org/?view&did=467739.

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Fig. 2. Knowledge needs addressed by ADAPT

and profiles for generating the logs that are required for building Cyber Incident Responder competencies. These logs can be Application, System, Security that can be displayed on a SIEM solution and Management related logs that need crisis management tools. The SIEM solutions-related logs can be used for conducting operation-based cyber security exercises, while management level events and logs can be used for conducting discussion based exercises. We mapped these logs with SoC activities based upon common criteria6 and this mapping is presented in Table 1. In this work we are focusing on operation based exercises, therefore, we will not consider the knowledge needs where management level events and logs are involved. Different attacker profiles execute attacker actions. The attacker profiles are customizable based upon the training needs. Some default profiles are already defined by MITRE CALDERA, some of which are Discovery which act as an attacker that is performing system and user discovery and generates relevant application system and security logs. Similarly, another profile is Ransack which discovers hosts and steals information from sensitive files. This profile involves data exfiltration, and the management-level event is associated with it, which can limit access to the compromised system. Therefore, it generates hybrid events for operational and discussion-based exercises, which are needed to be considered while designing the exercise to fulfil the knowledge needs. Continuing that we mapped the concrete knowledge needs with different attacker profiles. Multiple attack profiles are needed to build the relevant competencies for fulfilling the knowledge needs, a sample of such mapping is presented in Table 2. In the mapping it can be seen that the knowledge need KN1 can be fulfilled just by one profile while other knowledge needs require additional profiles like KN2, KN3 with additional attacker actions.

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M. M. Yamin et al. Table 1. Mapping SoC activities with attack profiles SoC activities

Log types

Attack actions

Attack profiles

Control environment

Application, System, Security

Systems and users discovery

Discovery

Communication and Information

Application, System, Security, Management

Discovering host details and stealing sensitive files

Ransack

Risk Assessment

Application, System, Security, Management

Enumerate system processes

Enumerator

Monitoring Controls

Application, System, Security

Monitor the active user and Super Spy navigate through their digital belongings

Control Activities

Application, System, Security

An adversary to run terminal Terminal operations

Logical and Physical Access Controls

Management

An adversary to steal sensitive files

Advanced Thief

System Operations

Application, System, Security

Profile to check proper platform configuration

Check

Change Management

Application, System, Security

Lateral movement via SMB copy and execution via creation of Windows service “sandsvc”

Service Creation Lateral Movement

Risk Mitigation

Application, System, Security

This adversary consists of several abilities to bypass UAC on an updated Windows 10 system

You Shall (Not) Bypass

4.3

System Implementation

In the implementation, we integrated the knowledge needs with the scenario design process for cyber security exercises. Executing cyber security exercises is not a simple process. It involves multiple elements that need to be present for its successful execution. First and foremost, there is a need to be a scenario design which should be implemented based on the knowledge needs. Second, the scenario design should be orchestrated in a realistic infrastructure on which the exercise will be executed. When the infrastructure is orchestrated, the scenario can be executed, and the relevant scenario design can be evaluated based on the knowledge needs which the scenario fulfilled. This process is presented in Fig. 3.

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Table 2. Mapping knowledge requirements with attack profiles Knowledge requirements

SoC activities

Attack profiles

KN1

Control environment

Discovery

KN2

Control environment, Risk Assessment

Discovery, Enumerator

KN3

Control environment, Risk Assessment, Communication and Information

Discovery, Enumerator, Ransack

KN4

Control environment, Risk Assessment, Control Activities

Discovery, Enumerator, Terminal

KN5

Control environment, Risk Assessment, Logical and Physical Access Controls

Discovery, Enumerator, Advanced Thief

KN5

Control environment, Risk Assessment, System Operations

Discovery, Enumerator, Check

KN7

Control environment, Risk Assessment, Monitoring Controls, Change Management

Discovery, Enumerator, Super Spy, Service Creation Lateral Movement

KN8

Control environment, Risk Assessment, Risk Mitigation

Discovery, Enumerator, You Shall (Not) Bypass

Fig. 3. Profile based exercise scenario design process

Keeping in view the knowledge needs, we developed a system for scenario design, infrastructure generation and execution. The scenario is designed with our developed DSL in which the knowledge needs are integrated with different types of vulnerabilities that can be exploited to generate relevant logs. The system suggests and maps relevant vulnerabilities and threats that are needed to be fulfilled and also suggests the network topology on which such exercise

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can be executed. After that, concrete artefacts in terms of network topology and vulnerabilities and attack profiles are deployed in a realistic manner. We used OpenStack to deploy the necessary infrastructure for the exercise. We used our developed scenario description language to model the network topology of an organization that is going to be used in the exercise. A sample of the scenario description language used for generating the infrastructure is presented below: Listing 1. Scenario description language example

scenario: name: i c e l a n d description: i c e l a n d e x e r c i e s s t a r t : 2022 −03 −16 end: 2 0 2 2 − 0 3 − 3 1 infrastructure: - public network: kali: 6 server: 2 vulnerable server: 3 vulnerabiltities: - sqli - xss - rce - ftp brute force - buffer overflow - mz network: server: 2 vulnerable server: 2 vulnerabiltities: - sqli - xss - rce - ftp brute force - buffer overflow - internal network: server: 3 vulnerable server: 2 vulnerabiltities: - sqli - xss - rce - ftp brute force - buffer overflow - siem network: true # - Attacker_network : true

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Listing 1 represents a simple scenario that is modelled in the domain-specific language. The scenario has a name, description, start date, end date and infrastructure. The scenario was used to conduct an exercise in Iceland; therefore, we called it Iceland. The scenario description can provide details about the scenario; however, for saving space in the paper, we just added basic info about the scenario. The start date and end date are simple dates that represent the time in which the scenario infrastructure is active. The infrastructure can define different networks like a public network, militarized zone and an internal network. Each network contains different vulnerable, non-vulnerable and attacker machines that can be defined in it. For example, in the public network, Kali represents attacker machines, and the value six indicates how many machines are needed to be there for the attackers. Similarly, server indicates the number of non-vulnerable servers that are needed to be present in the exercise infrastructure; they can be randomly selected windows or Linux machines. And finally, the vulnerable server indicates the number of vulnerable machines that are needed to be present, followed by a list of vulnerabilities that can be injected into the vulnerable servers randomly. Two additional elements are integrated into the DSL, which are the siem network and the attacker network. The siem network value can be set to true, which indicates that all the digital infrastructure is connected to a central SIEM server and is forwarding the event logs to that server. The attacker network indicates the option of including CALDERA as an attack agent. When the option is set to true, the command and control infrastructure of CALDERA is automatically set up with the exercise infrastructure. In this specific exercise scenario sample, we didn’t need CALDERA capabilities. Therefore, it was commented out. However, it was used in another example, which is explained in the coming sections. That developed DSL also supports security groups in OpenStack that act as a firewall to configure different network properties. However, to speed up the process of generating the necessary IT infrastructure, its properties are set to optional. If an experienced scenario designer wants to create specific firewall rules, then they can be specified in the DSL; else, the orchestrator will automatically configure the firewall rules based upon specific ports, which are defined by default in the networking architecture to connect different networks. Additionally, a scenario designer can also precisely specify the types of operating systems and vulnerabilities that are needed to be present. However, for the aforementioned issue of speeding up the process, such properties are also assigned default values that are randomly selected when a specific number is given, as indicated in the example above. Listing 1 shows that the new designer only needs to mention the type of scenario and the number of machines that are needed to be there, and everything will be done automatically. For SIEM integration, we discussed multiple solutions with the SoC team at NTNU. The SoC team is responsible for managing the security of around 40,000 individuals learning and working at NTNU. Additionally, we took inputs from the SoC team of Taltech responsible for securing Taltech digital infrastructure. Based on their suggestions, we decided to use Wazuh SIEM [5]. It is a free and

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open-source SIEM solution that can be deployed in a variety of manners, like a single server installation or a multi-cluster deployment. It uses agents needed to be installed on the IT infrastructure for fetching system-level logs. Deploying the SIEM and integrating it with the digital infrastructure isn’t a simple task. We have to create the deployment timeline for the infrastructure, what is needed to be deployed first, and how things will be integrated. Considering this, the orchestrator first deployed the SIEM server in a separate network. Then it deployed the relevant digital infrastructure. When the deployment process is undergoing, our orchestrator takes the IP address of the SIEM server and uses it in the cloud-init function of OpenStack to automatically insert the Wazuh agent in the digital Infrastructure. When the deployment is finished, all the digital infrastructure is ended up integrated with the SIEM solution. The digital infrastructure can contain different virtual machines based upon scenario requirements like Windows-based OS or Linux OS. A sample integration of SIEM with deployed infrastructure is presented in Fig. 4:

Fig. 4. Automated SIEM integration with digital infrastructure

For the attack generator, we used CALDERA, an official MITRE attack project. It combines the tactics, tools, and techniques identified in MITRE attack framework and provides the capability to execute them automatically. It is a standalone installation on a server and has agents that are needed to be integrated with the digital infrastructure. The agents are specific to the operating system on the digital infrastructure and are integrated with cloud-init function of OpenStack. CALDERA provides the capability to launch automatic attacks based upon different profiles and different severity and stealth level to train cyber defence operation staff in a realistic manner. A sample execution of CALDERA attack is presented in the Fig. 5.

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Fig. 5. CALDERA sample execution and generation of event logs in SIEM server Table 3. Sample attack profile with different tactics and techniques Ordering Name

Tactic

Technique

1

Create staging directory

collection

Data Staged: Local Data Staging

2

Find files

collection

3

Stage sensitive files collection

4

Compress staged directory

Data from Local System Data Staged: Local Data Staging

exfiltration Archive Collected Data: Archive via Utility

With CALDERA, we can define the attacker profile to perform a certain action in order to generate realistic attack events and logs. These profiles can be mapped with a certain training and learning objective to teach about a specific skill set and fulfil the knowledge needs. Table 3 presents the attacker profile action whose goal is to exfiltrate data from the exercise network. Different tactics and techniques were defined in the profile, and the defender’s goal was to identify the attacker’s actions and stop them. Similarly other attackers profiles can be defined in similar manner using specific techniques to teach specif cyber defence skills. When the deployment process starts, the DSL instance is parsed, and the required number of resources for deploying the specified infrastructure in the DSL instance is analyzed. The resource analysis consists of analyzing the number of machines needed to be deployed and the amount of available CPUs, RAM, and storage present in the cloud infrastructure. If the required resources are available, then the orchestrator starts deploying in the scenario infrastructure deployment. There are additional options for deploying CALDERA and Wazuh SIEM based on the given requirements. When the infrastructure is deployed, the infrastructure is then configured automatically to add agents for SIEM and CALDERA. After that, the vulnerabilities are injected into the infrastructure. The vulnerabilities were looked up in a library, and if they exist, they will be injected using different operating system automation techniques. To check the vulnerabilities are injected properly, there is an additional Kali machine present in the infrastructure from where it performs scans. These scans contain scans

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Fig. 6. Infrastructure deployment process

from Nmap and Metasploit to verify the specific vulnerable ports are open, and the injected vulnerabilities are vulnerable. The scans have a threshold of five. If the results of successful vulnerability injection weren’t returned in five scans, then the orchestrator will try to reinject the vulnerability and continue until all the vulnerabilities are injected. And it will end the infrastructure orchestration process. The infrastructure deployment process is presented in Fig. 6.

5

System Evaluation

We used the developed system in two cyber security exercises. The first exercise was conducted at NTNU, and it was used to demonstrate the capabilities of the proposed platform. NTNU has a weekly cyber security competition in which university students from different levels participate. We used the system for an incident response exercise in which 11 people participated. The exercise was divided into two parts; in the first part, a detailed lecture about the cyber security exercise infrastructure and the infrastructure monitoring tool was given to the exercise participants. In the second part, the exercise participants were tasked with identifying specific computationally generated incidents by different attack profiles on SIEM. The tasks comprised different easy to medium cyber security incidents, such as identifying a particular activity during a particular period of time or identifying malicious activity within the exercise infrastructure. A list of tasks during the exercise is presented given in Appendix A: The whole exercise lasted for four hours in which the exercise participants had difficulties, in the beginning, understanding the SIEM solution. However, with the passage of time, they were able to identify some of the events that were given in the task. We received positive feedback from the exercise participant

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Fig. 7. Incident response exercises scenario digital infrastructure Table 4. Knowledge needs full-filled by ADAPT Number Knowledge Need

Full filled

1

Knowledge of cybersecurity incident handling methodologies

✓

2

Knowledge of cybersecurity incident handling practices and tools

✓

3

Knowledge of incident handling communication cycle

✗

4

Knowledge of operating systems internals, networking protocols and services

✓

5

Knowledge of cybersecurity attacks tactics and techniques



6

Knowledge of cyber threats and vulnerabilities

✓

7

Knowledge of legal framework related to cybersecurity and data protection

✗

8

Knowledge of the operation of Secure Operation Centres (SOCs) and Computer Security Incident Response Teams (CSIRTs)

✓

as it was a new learning experience for them. A detailed network schema of the indent response exercise is presented in the Fig. 7:

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Fig. 8. Scaled up exercises scenario digital infrastructure

The second exercise was conducted at Reykjav´ık University in Iceland. Around 44 bachelor level students participated in a two-week-long exercise. We used the same exercise digital infrastructure that we used in the previous exercise. However, we computationally generated nine replicas of it and assigned a group of five individuals a separate infrastructure each. The exercise was also divided into two parts; in the first part, each group attacked the assigned infrastructure to them for the first week. In the second part, the networks were shuffled among the groups, and they were tasked to identify the attacks from the other groups using the SIEM solution. The exercise also resulted in positive results as it provided the opportunity for the students to play with realistic systems. In term of setting up exercise infrastructure comprising of 154 virtual machines was deployed in 5o minutes. Out of which 7 min were taken to deploy the bare metal infrastructure, 13 min were taken to configure SIEM and 30 min were taken to inject the vulnerabilities. A detailed network schema of the exercise is presented in the Fig. 8: These exercises full-filled 6 out of 8 knowledge needs highlighted in ENISA cyber security skill framework Cyber Indent Responder Profile which are highlighted in Table 4:

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Conclusion and Future Work

In this work, we presented a platform to train cyber defence operation skills in a realistic manner. The proposed system uses open source technologies, making it very useful for training a wide audience with limited resources. The system provides the capabilities of setting up a cyber range with complex security events and information monitoring software that can provide a high level of situational awareness in case of a cyber incident. Moreover, we integrated open-source attack generator software in the system that can be used to launch automated attacks on the digital infrastructure. This helps to train people realistically involved in cyber defence operations. We used this system in multiple cyber security exercises and identified that it full fills 6 out of 8 knowledge needs defined by ENISA for Cyber Incident Responders. In future we are working on integrating low-level operational events from the SIEM to the CIM (Crisis Information Management) system. This will help us to create a high level of situational awareness while running a full-scale exercise. This will enable us to conduct exercises that involve operational, tactical and strategic layer of the organizations and train other profiles defined by ENISA cyber security skill framework. Finally, we will be using the developed system in different national and multinational exercises to gather as much data as possible for future research activities. Additionally we will focus on traffic generation in the cyber range environment to make exercises execution more realistic. We will utilise some of the recent advances in web robots technology [6,14,17] to achieve this objective. Acknowledgment. This work is part of a project ASCERT (AI-Based Scenario Management for Cyber Range Training) [12] in which we are developing an AI based solution for modeling and analysing attack defense scenarios in cyber ranges. We would like to acknowledge the valuable support from Md Mujahid Islam Peal, Espen Torseth and Lars Erik of the Norwegian Cyber Range engineering team. Additionally, we would also like to thank the comments of the SoC team of NTNU and Taltech for providing their valuable insights into SIEM solutions.

A

Sample Exercise Questions

1. Can you identify which tactic is implemented on which machine between 11:20 to 11:35 on 3/9/2022 2. Can you identify what happened between 11:40 and 12:00 on 3/9/2022 3. Can you identify a malicious user on a system who was created on 3/14/2022 4. Can you identify which privilege did the malicious user attained on 3/14/2022 5. Can you identify an abnormal action on server-612? 6. Can you identify BITS ADMIN Download via CMD on a system? 7. Can you identify where the short cut to cmd ‘t1547.009.lnk’ was created 8. Can you identify what happened in 172.21.219.56? 9. Can you identify where the user butter was added? 10. Can you identify what happened in 172.16.245.79?

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References 1. Blue team training toolkit. https://www.encripto.no. Accessed 21 Apr 2022 2. Caldera - a scalable, automated adversary emulation platform. https://caldera. mitre.org/. Accessed 21 Apr 2022 3. Project zero: The more you know, the more you know you don’t know. https:// tinyurl.com/3a3pbe75. Accessed 21 Apr 2022 4. Splunk attack range. https://github.com/splunk/attack range. Accessed 19 Apr 2022 5. Wazuh · the open source security platform. https://wazuh.com/. Accessed 21 Apr 2022 6. Brown, K., Doran, D.: Realistic traffic generation for web robots. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 178–185. IEEE (2017) 7. Costa, A.D., Kuusij¨ arvi, J.: Programmatic description language for cyber range topology creation. In: 2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 403–412. IEEE (2022) 8. DeCusatis, C., Bavaro, J., Cannistraci, T., Griffin, B., Jenkins, J., Ronan, M.: Redblue team exercises for cybersecurity training during a pandemic. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1055–1060. IEEE (2021) 9. Edgar, T., Manz, D.: Research Methods for Cyber Security. Syngress (2017) 10. Ernits, M., Tammek¨ and, J., Maennel, O.: i-tee: a fully automated cyber defense competition for students. ACM SIGCOMM Comput. Commun. Rev. 45(4), 113– 114 (2015) 11. Gustafsson, T., Almroth, J.: Cyber range automation overview with a case study of CRATE. In: Asplund, M., Nadjm-Tehrani, S. (eds.) NordSec 2020. LNCS, vol. 12556, pp. 192–209. Springer, Cham (2021). https://doi.org/10.1007/978-3-03070852-8 12 12. Hannay, J.E., Stolpe, A., Yamin, M.M.: Toward AI-based scenario management for cyber range training. In: Stephanidis, C., et al. (eds.) HCII 2021. LNCS, vol. 13095, pp. 423–436. Springer, Cham (2021). https://doi.org/10.1007/978-3-03090963-5 32 13. Hutchins, E.M., Cloppert, M.J., Amin, R.M., et al.: Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Lead. Issues Inf. Warfare Secur. Res. 1(1), 80 (2011) 14. Jagat, R.R., Sisodia, D.S., Singh, P.: Semi-supervised self-training approach for web robots activity detection in weblog. In: Suma, V., Fernando, X., Du, K.-L., Wang, H. (eds.) Evolutionary Computing and Mobile Sustainable Networks. LNDECT, vol. 116, pp. 911–924. Springer, Singapore (2022). https://doi.org/10.1007/978981-16-9605-3 64 15. Pattanayak, A., Steiner, S., de Leon, D.C.: Hands-on educational labs for cyber defense competition training. J. Colloq. Inf. Syst. Secur. Educ. 9, 8 (2022) 16. Russo, E., Costa, G., Armando, A.: Building next generation cyber ranges with crack. Comput. Secur. 95, 101837 (2020) 17. Sisodia, D.S., Borkar, R., Shrawgi, H.: Performance evaluation of large data clustering techniques on web robot session data. In: Tanveer, M., Pachori, R.B. (eds.) Machine Intelligence and Signal Analysis. AISC, vol. 748, pp. 545–553. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0923-6 47

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ˇ abensk` ˇ 18. Sv´ y, V., Vykopal, J., Celeda, P., Tk´ aˇcik, K., Popoviˇc, D.: Student assessment in cybersecurity training automated by pattern mining and clustering. Educ. Inf. Technol. 1–32 (2022) 19. Vielberth, M., Glas, M., Dietz, M., Karagiannis, S., Magkos, E., Pernul, G.: A digital twin-based cyber range for SOC analysts. In: Barker, K., Ghazinour, K. (eds.) DBSec 2021. LNCS, vol. 12840, pp. 293–311. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-81242-3 17 20. Yamin, M.M., Katt, B.: Inefficiencies in cyber-security exercises life-cycle: a position paper. In: AAAI Fall Symposium: ALEC, pp. 41–43 (2018) 21. Yamin, M.M., Katt, B.: Cyber security skill set analysis for common curricula development. In: Proceedings of the 14th International Conference on Availability, Reliability and Security, pp. 1–8 (2019) 22. Yamin, M.M., Katt, B.: Modeling and executing cyber security exercise scenarios in cyber ranges. Comput. Secur. 116, 102635 (2022) 23. Yamin, M.M., Katt, B., Gkioulos, V.: Cyber ranges and security testbeds: scenarios, functions, tools and architecture. Comput. Secur. 88, 101636 (2020) 24. Yamin, M.M., Katt, B., Nowostawski, M.: Serious games as a tool to model attack and defense scenarios for cyber-security exercises. Comput. Secur. 110, 102450 (2021)

An Improved Recommender System for Dealing with Data Sparsity Using Autoencoders and Neural Collaborative Filtering R. Devipreetha(B) and Anbazhagan Mahadevan Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham, Coimbatore, India [email protected], m [email protected]

Abstract. With the abundance of implicit feedback, some researchers have sought to develop Recommender Systems (RS) that are entirely dependent on implicit feedback. Implicit feedback records users’ preferences by keeping track of their behaviours, such as which products they visit, where they click, which items they purchase, or how long they spend on a web page. To elicit explicit input from the users, they are prompted by the system to rate products. When compared to explicit feedback, implicit feedback cannot accurately represent user preferences. As a result, while using implicit input for RS is more difficult, it is also more practical. Collaborative Filtering (CF) that uses traditional approaches, like Matrix Factorization (MF), considers preferences of the user where the user and item latent vectors are combined linearly. These have limited learning capabilities and are plagued by data sparsity and the cold-start problem. In an effort to address these issues, several researchers have proposed integrating a deep neural network with standard CF techniques. But the research on these techniques still remain in a sparse condition. This paper proposes an improved RS for dealing with data sparsity using Autoencoders (AE) and Neural Collaborative Filtering (NCF). AEs are integrated with NCF in the proposed system, which requires inferring user and item attributes from additional data in order to anticipate user rating. Keywords: Recommendation Neural Collaborative filtering

1

· Collaborative · Auto encoders ·

Introduction

RS [18], also known as a recommendation system [19], is a type of information filtering system that attempts to forecast a user’s “rating” or “preference” for an item. Many online companies, including e-commerce, online news, and social networking sites, have adopted RS in the age of information explosion, and they have a broad variety of applications in many industries. Web behemoths like c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 204–218, 2023. https://doi.org/10.1007/978-3-031-31153-6_18

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amazon, for example, utilize them to propose certain things to their most likely buyers. On-demand and streaming platforms like Netflix and Spotify recommend multimedia content to consumers based on their preferences. Apple and Google also utilize them to promote apps to their consumers based on what they’ve already downloaded. There are considerable economic concerns surrounding RS, as they have the potential to greatly increase the turnover of organizations that utilize them. Personalizing service recommendations, in fact, enhances the likelihood of a purchase by a significant amount. CF is a major strategy used in customised RS. CF aims to illustrate the link between users and objects using previously collected user-item data, such as items-ratings. When users and objects are projected onto a shared latent space, one of the most frequent CF algorithms, Matrix factorization (MF), is used to represent either a person or an item using a latent feature vector. After that, the latent vectors’ inner product is utilized to simulate the interaction of the user with the object. RS, in fact maintain an ultimate goal, which is to create items list that closely match the user’s tastes. On the other side, CF has two drawbacks. User preferences are not adequately represented as RS [14] mainly depend on implicit input. The performance of RS [15] is hampered as a result of this. CF has another problem: it uses linear models to interpret user-item interactions, which limits the usefulness of suggestions. Users and items with high relevance were decomposed into user and item latent vectors using MF. MF is a dimension reduction technique that invariably eliminates user-item interactions. As a consequence, typical CF techniques fail to enhance the accuracy of their recommendations. CF [16] systems have been proposed by researchers in a variety of ways. Several academics have looked at the usage of Deep Neural Networks(DNN) in RS [15], and have developed CF which is entirely based on neural networks. DNNs have been shown to train in a range of applications, including computer vision [14] and voice recognition. In RS, Salakautdinov et al. [16] used Restrictedd Boltzmannn Machines (RBMs) and constructed two-layer RBMs to mimic explicit user-provided item ratings. Ouyang et al. [5] used an AE to investigate high-level user-item relationships and developed AE with three-layers to represent users’ explicit judgments on objects. The AE makes the assumption that user ratings are corrupt, and the recommendation procedure entails reconstructing the original user ratings. By minimising reconstruction error, it discovers latent user-item relationships. He et al. [1] established a paradigm for implicit feedback recommendations called NCF. Similar to MF, the authors employed neural networks to discover the latent vectors of users and items. When it comes to understanding the non-linear interactions between consumers and goods and MF, the NCF model is a good fit. To avoid ambiguity, we refer to NCF as the framework described in [1] and to NCF as CF using Neural Networks (NN). All of these research sought to increase the effectiveness of RS via the use of more powerful neural networks to simulate user-item connections. Sajad Ahmadian et al. [17] used additional side resources such as trust and stage informations to improve the recommendation accuracy by implementing it with autoencoder.

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Jiajia Jiang et al. [18] proposed a model for high dimensional sparse matrix by using fast deep autoencoders. It also includes filling up of high dimensional matrix thereby improving its ability of representation learning. While the bulk of study has focused on user ratings, rating data on their own cannot sufficiently reveal user-item correlations. Furthermore, the lack of user evaluations has a negative impact on the majorities of CF-based techniques’ success. Some academics have used CF to combine users and item-level data and then built hybrid RS to improve performance. The AE, for example, is a popular NN that learns useful structures quickly and can thus extract representative characteristics from incoming input. In order to improve the effectiveness of CF systems, NN are employed by several authors to extract user and object attributes from auxiliary data. Collaborative Deep Learning (CDL) [17], proposed by Wang et al., is a strategy for dissecting a rating matrix and learning latent components in a single model in which an AE learns item characteristics that serve as item latent vectors for MF-CDL. While NN are used to improve CF performance in various ways, the heart of CF remains MF. The authors of the aforementioned research aimed to enhance recommendation effectiveness by adding side information through NN or by constructing unique CF models which are based on NN. However, no effort to date has combined the two objectives using neural networks. In the proposed paper, we propose an improved RS for dealing with data sparsity using AE and NCF. AEs [14] are integrated with NCF [1] in the proposed system, which requires inferring user and item attributes from additional data in order to anticipate user rating.

2

The Proposed Method

This research presents an improved RS for dealing with data sparsity using AE and NCF module. User and item-side data are used to construct an enhanced RS and boost the speed. Consumer preferences may be predicted using an algorithm that combines AE with NCF, a technique that uses auxiliary data to infer user and item properties. Real-world dataset experiments show that the system outperforms current techniques. The proposed system is implemented for filling up the sparse matrix in RS. Here different models are implemented and their metrics are evaluated with corresponding plots. Four models implemented are MF, Generalized Matrix Factorization (GMF), Multi-Layer Perceptron (MLP) and Neural Matrix Factorization (Neu-MF). Implementation of these models are done using Keras with TensorFlow as back end. The optimization is done using Adam optimizer and the loss function used is binary cross entropy function. This function measures the error of prediction. Out of these four models, AE with Neu-MF gives the maximum performance.

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207

Recomendation System Frameworks

Consider users and items are denoted by u and i respectively, and the binary rating matrix of the users is given in Eq. 1: |U |∗|I|

R = rui

(1)

whose element rui indicates whether the user u rates item i with rui equals 1 indicating that user u has a rating record for item i and rui equals 0 indicating that user u does not have a rating record for item i. The purpose of implicit feedback recommendation is to generate a list of items that reflects the user’s preference. Secondary information such as the user age, gender, employment, and item narrative and genre is easily accessible. As previously said, subsidiary data may aid RS [13] in improving their performance. By lowering the errors caused by reconstructing between the output and the original user and item features, AE are utilized to learn the user and item features for a user information matrix X and an item information matrix Y . The learned characteristics are stored in the intermediate hidden-layer vector. Unlike standard CF, the suggested system uses NCF to investigate user-item correlations, demonstrating a more strong learning potential. The proposed framework is depicted schematically in Fig. 1. This is composed of two modules: (1) feature extraction and ID embedding; and (2) NCF. In this study, two AEs are employed which can learn the features of users and items, respectively, for feature extraction and id embedding. The AE is fed with user (item) input- X(Y ) in order to extract user (item) latent features. The user (item) ID is generated using sparse one-hot encoding and then embedded in a dense vector.

Fig. 1. The Architecture

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Fig. 2. WorkFlow Diagram

The workflow diagram showcases the working of the proposed model. The model is initiated by loading the dataset. It uses two autoencoders, where one is used to extract user features and the other one to extract item features. The extracted user features are embedded with userid to obtain user latent vectors while extracted item features are embedded with item id to obtain item latent vectors. The user latent vectors and item latent vectors are fed into a neural collaborative filtering module which in turn fills up the sparse matrix. 2.2

Autoencoder Based Feature Extraction

The AE is a neural network that is trained to learn an identity function and output is generated which is close to the input. Auto-encoders are frequently used to discover features in a dataset. An Auto-encoder is typically a three-layer neural network. A basic AE is composed of three layers: an input layer, a hidden layer, and an output layer. The input and hidden layers combine to form an encoder. The hidden and output layers combine to form a decoder as shown in Fig. 2. There is a code between these two.

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Fig. 3. Autoencoder

The encoder’s function is to compress the input data into a different latent space. The decoder receives compressed input, represented by code, in the network. Decoder performs the opposite operation and reconstructs the original information by traversing from the latent to the original information space. 2.3

Embedding

Typically, in RS, embedding-based models are used. However, let us define what an embedding is. The dataset’s triples are actually used to construct a large matrix termed the user-item interaction matrix. The matrix is filled with user and object interaction ratings, with each row representing a person and each column representing an object. The matrix dimensions are M × N. As each user has only interacted with a small subset of the total number of items, this matrix is extremely sparse. The users and objects are represented as one-hot encoded vectors in most datasets, so it’s even sparser at 99 percent. The input dimension is too large and too sparse to send such a vector into a model in an efficient manner. As a result, a dimensionality reduction algorithm converts these enormous and sparse one-hot encoded vectors of users and items to embedding vectors in a shared latent space. Typically, the embedding space is small in dimension and contains latent structures. It converts a discrete set to a vector space, converting the embedding vectors to real numbers. When large and sparse dataset are used, this is significantly more computationally efficient. The embedding layer’s outputs are also referred to as latent vectors, whereas the latent factors are its elements. The embedding layer is actually a simple perceptron with a single layer and no activation function, and its weights are optimised. In RS, the concatenation of a user embedding and an item embedding is fed into a model, which outputs a single score indicating the user and item’s similarity.

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Matrix Factorization

[12] MF is almost a straight forward model. By computing the dot product of their embeddings, this approach calculates the interaction score between a user and an item. Two embedding layers are used to reduce the size of the model’s input vector. The predictive factors in this model are identical to the product of the two embeddings element by element. The element-by-element multiplication has the same dimension as the embeddings themselves. As a result, the output dimension of embeddings must equal the number of predictive components precisely. Here an initial value of eight is taken as default. Flattening the embeddings to fit the input of the subsequent layers. The flattened embeddings are then transmitted to a dot layer that calculates the score. 2.5

Generalized Matrix Factorization

By weighting the dot product, the Generalized Matrix Factorization (GMF) model may improve slightly on the prior one, with the weights learned from the data via optimization. Another enhancement is to apply a non-linear activation function to the result. This permits the model to be more sophisticated and non-linear, with the result that it should perform better than simple MF. Here Sigmoid function is the activation function. 2.6

Multilayer Perceptron

The multilayer perceptron is a highly prevalent type of neural network in Deep Learning. It is the first type of neural network and is composed of numerous stacked layers of neurons with activation functions following each layer. As with the previous two models, we feed recommender systems the concatenation of a user’s embeddings and an item’s embeddings as input. The output consists of a single unit that combines the results of all preceding layers and applies a final activation function to provide the score for the interaction between 0 and 1. The network is trained by propagating data samples across the network. Weights are initialised randomly prior to training using a conventional normal distribution. The output score is then compared to the goal score using the loss function we’re attempting to minimise. Finally, the weights are adjusted to match the data by error back-propagation via the network. All of these stages comprise an epoch, and the number of epochs is a critical hyperparameter that may be empirically determined by evaluating learning curves. The learning curves associate the epoch number with the losses (training and testing losses) and other problem-specific parameters. We may check these curves to see whether the model has been fitted appropriately and to avoid overfitting. 2.7

Neural Matrix Factorization

This model is a mixture of the two preceding models (GMF and MLP), allowing them to reinforce one another and so more accurately reflect the complicated

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user-item interaction function. This model takes use of the GMF’s linearity and the non-linearity of activation functions in neural networks. To provide the fused model additional flexibility, we enable GMF and MLP to train distinct embeddings and then integrate the two models by concatenating their final hidden layer.

3

Dataset

The dataset used for the proposed system is movielens-1M dataset. Movie Lens is a web-based RS that suggests films for users to watch based on their film tastes via CF of member film ratings and reviews. We analysed the version with only one million ratings and chosen users who have rated at least 20 films. Originally an explicit feedback dataset, since users are encouraged to assess the films with their consent, we converted it to an implicit feedback dataset by converting the interaction scores to binary values. Each seen entry is assigned a value of one, whereas undetected entries are assigned a value of zero. This was done to simplify things initially and to replicate the conditions described in the study [1].

Fig. 4. Dataset Description

Figure 3 summarises some statistics from the Movie-lens dataset. It can be seen that the dataset is quite sparse. This is logical, given that each user has interacted with a small subset of the enormous number of things. This is a property of RS, which is why the embedding layers are used to compress data. If you’re shopping on the e-commerce site amazon, for example, you should keep in mind that the number of things you’re interested in is rather little in comparison to the breadth of amazon’s catalogue. The dataset is split into two: one containing the training set and another containing the test set. The first file contains a lengthy list of triples in the format (u, i, y), where u denotes a user, i denotes an object, and y denotes the interaction’s score. As previously stated, y belongs to the range [1–5] for the dataset Movie-lens due to the explicit feedback, however we will translate into 1. The first step is to create a list of all triples(Numpy array). The training file only contains positive interactions, which indicates that each interaction has been seen and assigned a score of one. As we cannot fit models if the dataset contains just one class, we must obtain negative interactions with a score of 0. To do this, we determine that for each interaction, we will randomly

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choose N objects with which the user has not engaged; this generates a large number of negative interactions in the train set, increasing the dataset. N is a parameter that may be adjusted later on during model evaluation; it is referred to as the sample ratio. We take care that no overlap between positive and negative interactions occurs, as this would cause the model to break down. The test file comprises a single lengthy list for each user; this list contains the positive things that were chosen for the test set, as well as 99 randomly picked items with which he has not interacted.

4 4.1

Results and Discussions Metrics for Evaluation

To assess the performance of item suggestion, we employ the commonly used leave-one-out assessment. We used the most recent positive contact with each user as the test set and kept the rest data for training. The positive item is the name given to this beneficial encounter. Because ranking all things for each user would be prohibitively time intensive, we randomly choose 99 items that have not been interacted with by the user, creating a test set of 100 items to score for each user. We rank the 100 things by predicting their scores with the model we’re evaluating, and we keep track of the positive item’s position in the ranklist. The rank-list is a list of things arranged according to the scores predicted by the model we are evaluating. Two measures are used to evaluate the success of a ranked list: the Hit Ratio (HR) and the Normalized Discounted Cumulative Gain (NDCG). HR as shown in Eq. 2 determines if the positive item is in the top-K of the list, where K is a value that will be tuned later.  1, if r ∈ [1:K] HR(r) = (2) 0, otherwise Unlike HR, NDCG as shown in Eq. 3 computes a score between 0 and 1 based on the rank of the positive item. NDCG returns a score proportional to the rank, whereas HR returns only a binary number. To make the NDCG decrease as the rank decreases, use a log in the denominator, like in the following formula, where r is the rank of the positive item (r ≥ 1). The NDCG is one if r equals one, which is the ideal scenario, and very low if r is large, which is the worst case. This is an excellent statistic for evaluating a rank-list.  log(2)/log(1 + r), if r ∈ [1:K] N DCG(r) = (3) 0, otherwise Then these metrics are averaged for each user in the dataset.

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Experiment 1: Training of Models and Comparison of Performance

The first experiment is to train and compare the four models on the dataset. We will train them across ten epochs, which will allow the model parameters to converge without requiring hours upon hours of experimentation. To ensure comparable outcomes, we train them with the same number of predictive variables, which is the default value of eight. With this many epochs, training takes around 6 min on Movie Lens, which is adequate. In Fig. 4, we notice that MF significantly outperforms all other models across all measures and for both datasets. This second study [2] asserts that when properly parametrized, MF beats all other models. So, in the future, we should take a closer look at how MF and its parameters are trained. As predicted, MLP outperforms GMF, which outperforms MF in all visuals. This demonstrates the utility of employing learned similarity with more sophisticated models rather than a linear model such as MF or GMF. As we can see, NeuMF outperforms all other models in terms of the NDCG measure, but it outperforms all other models in terms of Hit Ratio, which is quite peculiar. The results are considerably different from those presented in the previous article. In terms of training loss curves, we may deduce that the MF takes significantly longer to converge than any other model, owing to its substantially larger training loss. To finish on this model, we should integrate separate pretraining for the GMF and MLP components in the Neu-MF. In any case, the NDCG more accurately represents a RS’s success since it takes the rank of the positive item into consideration.

Fig. 5. Evaluation of NCF methods over epochs on MovieLens (factors = 8)

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Experiment 2: Influence of the Sampling Ratio in the Dataset

We alter the value N and see the effect on performance in this experiment in Fig. 5. N is the number of negative interactions in the dataset that we sample for each positive interaction in the original dataset. We must bear in mind that the dataset originally had only positive interactions, as we deal with implicit feedback datasets. Therefore, we must balance the dataset by choosing numerous negative items for each positive interaction, with a sample size equal to N. We conduct experiments with N values ranging from one to ten. Because the author of the study [1] selected the default value of N = 4, the dataset size is multiplied by 5. N is sometimes referred to as the sampling ratio, since it is the quotient of the number of negative interactions in a dataset divided by the number of positive interactions. This time, we run the experiment on four models with sixteen predictive variables. The findings of this experiment indicate that performance appears to improve marginally as the sampling ratio is increased. However, because this tendency is not very strong and the performance advantage is minimal, we may infer that adopting a sampling ratio of four as the default value is a reasonable decision; it is not an extreme figure. Additionally, we must keep in mind that rising N increases the dataset size by N, making it extremely time consuming to train models for large values of N. On these graphs, we can see that Neu-MF beats all other models on both metrics and datasets, which is consistent with the previous paper’s result.

Fig. 6. Performance of NCF methods w.r.t the number of negative samples per positive interaction on MovieLens (factors = 16)

4.4

Experiment 3: The Number of Predictive Factors

Predictive factors are the model’s last hidden layer; we started with a default value of 8 and maintained it throughout the study. Now is the time to experiment with this parameter’s values. Inductively, the model’s capacity is determined by the quantity of predictive elements. However, including an excessive number of variables may result in model overfitting and a lack of generalizability as depicted in Fig. 6.

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Fig. 7. Performance of NCF methods w.r.t the number of predictive factors on MovieLens

The linear models MF and GMF appear to benefit greatly from the inclusion of many parameters, although MLP and Neu-MF appear to be unaffected. Both 4.5

Experiment 4: The Factor K in the Evaluation

Analysis of Top-K suggested lists with a rating position of 1 to 10 is the focus of this investigation. Recall that HR provides a binary value of 1 or 0 depending on the positive item’s rank, and this is the case with Hit Ratio (HR). NDCG better gauges the performance by returning a value that is dependent on the rank of this positive item. NDCG yields 0 if the affirmative item does not rank in the top-K recommendations.

Fig. 8. Evaluation of Top-K item recommendation where K ranges from 1 to 10 on MovieLens (factors = 8)

For both HR and NDCG, the metrics rise with K as shown in Fig. 7, for the MovieLens dataset and all models, as expected. Taking a tiny K seems pointless because positive things need to be towards the very top of the list in order to be intriguing. It is virtually often the case that the positive item is not ranked first in a rank list, hence both metrics are zero if K = 1. In fact, even if the most beneficial aspects of a model are not included at the top, even in the top five or

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ten, the suggestions may be relevant. The higher the factor K, the greater the likelihood that a positive item will be included in the Top-K. It appears that K = 10 is a good choice for all of the tests conducted by the authors of the publication [1]. Simply because a recommendation that comes in at 30th place in the ranking list has no bearing on whether K should be raised or not. 4.6

Experiment 5: Architecture of the Neural Network in MLP

Experiments like this one can illustrate how network architecture affects performance, such as the number of levels and units per layer. It is like a tower arrangement, in which the bottom layer is broadest and subsequent layers have a lower number of neurons, continues to be used throughout the project. Because we employ powers of 2, the number of predictive parameters has a direct impact on the number of neurons per layer. Starting with the predictive variables, we may create the network layer by layer by increasing the number of neurons in each layer by doubling the number of neurons in the previous layer. Models with varying quantities of predictive elements and varied unit architectures will be generated in large numbers. 8–16–32–64 are used as predicting variables in this study It is known as MLP-n if the hidden layers are n in number. If the MLP-4 contains four hidden layers, then it is an MLP-4. Figure 8 describes the findings. There are five hidden layers (from zero to four) and four predicting elements in each dataset, hence the experiment consists of training 20 models for each dataset. Each model is trained in just three epochs (Figs. 9 and 10).

Fig. 9. NDCG@10 of MLP with different structures on MovieLens

4.7

Comparison with Existing Methods

We have compared different existing models MF, GMF, MLP with our model NeuMF that uses autoencoders for feature extraction. It is found that NeuMF performs better than other models in all the experiments.

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Fig. 10. Comparing different models

5

Conclusion and Future Enhancements

We present a unique recommender framework, which, in this study uses implicit feedback and auxiliary information about the user and item to efficiently learn user and item attributes. Several experiments are conducted with comparison of CF, NCF, Neu-MF and MLP. We have found that NCF improves CF’s learning capacity and includes user and item additional information to increase user preference prediction performance. The results appear to indicate that the more sophisticated models, such as the MLP and Neu-MF, produce the highest performance. The proposed system is not limited to auxiliary textual information. This can be further extended to include various sorts of Auxiliary information. In future research, we will attempt to include more auxiliary information like user reviews which may be used as supplementary information to improve the effectiveness of the recommendation system.

References 1. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182, April 2017 2. Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: Fourteenth ACM Conference on Recommender Systems, pp. 240–248, September 2020 3. Liu, X., Wang, Z.: CFDA: collaborative filtering with dual autoencoder for recommender system. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE, July 2022 4. Liu, Y., Wang, S., Khan, M.S., He, J.: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min. Anal. 1(3), 211–221 (2018) 5. Ferreira, D., Silva, S., Abelha, A., Machado, J.: Recommendation system using autoencoders. Appl. Sci. 10(16), 5510 (2020)

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6. Kiran, R., Kumar, P., Bhasker, B.: DNNRec: a novel deep learning based hybrid recommender system. Expert Syst. Appl. 144, 113054 (2020) 7. Chen, W., Cai, F., Chen, H., Rijke, M.D.: Joint neural collaborative filtering for recommender systems. ACM Trans. Inf. Syst. (TOIS) 37(4), 1–30 (2019) 8. Du, X., He, X., Yuan, F., Tang, J., Qin, Z., Chua, T.S.: Modeling embedding dimension correlations via convolutional neural collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 37(4), 1–22 (2019) 9. Zhang, Y., Liu, Z., Sang, C.: Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation. Appl. Soft Comput. 106, 107345 (2021) 10. Krishnan, A., Sharma, A., Sankar, A., Sundaram, H.: An adversarial approach to improve long-tail performance in neural collaborative filtering. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1491–1494, October 2018 11. Chen, H., Qian, F., Chen, J., Zhao, S., Zhang, Y.: Attribute-based Neural Collaborative Filtering. Expert Syst. Appl. 185, 115539 (2021) 12. F´evotte, C., Idier., J.: Algorithms for nonnegative matrix factorization with the β-divergence. Neural Comput. 23(9), 2421–2456 (2011) 13. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) 14. Vismayaa, V., Pooja, K.R., Alekhya, A., Malavika, C.N., Nair, B.B., Kumar, P.N.: Classifier based stock trading recommender systems for Indian stocks: an empirical evaluation. Comput. Econ. 55(3), 901–923 (2020) 15. Sivaramakrishnan, A., Krishnamachari, M., Balasubramanian, V.: Recommending customizable products: a multiple choice knapsack solution. In: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, pp. 1–10, July 2015 16. Mahadevan, A., Arock, M.: Integrated topic modeling and sentiment analysis: a review rating prediction approach for recommender systems. Turk. J. Electr. Eng. Comput. Sci. 28(1), 107–123 (2020) 17. Mahadevan, A., Arock, M.: Credible user-review incorporated collaborative filtering for video recommendation system. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS), pp. 375–379. IEEE, December 2017 18. Ahmadian, S., Ahmadian, M., Jalili, M.: A deep learning based trust-and tag-aware recommender system. Neurocomputing 488, 557–571 (2022) 19. Jiang, J., Li, W., Dong, A., Gou, Q., Luo, X.: A fast deep autoencoder for highdimensional and sparse matrices in recommender systems. Neurocomputing 412, 381–391 (2020)

Neural Network Based Algorithm to Estimate the Axial Capacity of Corroded RC Columns Yogesh Kumar1 , Harish Chandra Arora2,3 , Aman Kumar2,3(B) , Krishna Kumar4 , and Hardeep Singh Rai1 1 Guru Nanak Dev Engineering College (GNDEC), Ludhiana 141006, India 2 Structural Engineering Department, CSIR—Central Building Research Institute,

Roorkee 247667, India [email protected] 3 Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India 4 Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India

Abstract. Columns are the important structural elements to transfer the superstructures load to the foundation. Deterioration and degradation of the structural elements is the critical issue facing by the entire world. Corrosion is one of the most common cause responsible for the deterioration. To maintain structural safety as well as serviceability, it is important to estimate the residual capacity of the deteriorated reinforced concrete columns. Available analytical models are unable to give the desired results, as these models were based on various assumptions and developed with a limited dataset only. These challenging issues are addressed by artificial intelligence based on machine learning techniques. In this work, the neural network-based model is developed to estimate the residual capacity of the corroded RC columns. The proposed model has good accuracy with an R2 value (the coefficient of determination) of 0.9981 and a mean absolute percentage error of 3.84%. The other performance indices such as mean absolute error, Nash-Sutcliffe effecience index, and a20-index have 10.97 kN, 0.99, and 0.96 values, respectively. The proposed model can be utilized by structural designers and researchers to estimate the residual life of the corroded RC columns. Keywords: Column · Reinforced Concrete · Corrosion · Machine Learning · Artificial Neural Network

1 Introduction Buildings are great assets to any country. In developed and developing nations, the structures that were constructed in the late 1970s are getting deteriorated due to various problems and corrosion is one of them. The corrosion can be chloride-induced, or carbonation based, it is mainly depending on the location of the structure, the material used at the time of construction, etc. In reinforced concrete (RC) buildings the columns play a vital role to transfer the superstructure loads to the foundation. But the RC columns’ © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 219–230, 2023. https://doi.org/10.1007/978-3-031-31153-6_19

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capability for bearing loads gets affected due to corrosion-like factors. And this process affects the overall safety and serviceability of the structures. Corrosion can be formed due to weather conditions, biological substances, harmful gases, chemicals, etc. To prevent corrosion, concrete form a passive film (pH > 13) on the embedded steel [1]. However, with the continuous attack of chloride and carbonation which is present in de-icing salt form, the passive film gets breakdown and initiates corrosion in steel. From past studies, it is accepted that corrosion in reinforcing steel reduces the axial capacity of the RC columns; therefore, corroded columns have a shorter service life than uncorroded columns. The design consideration of the RC columns is also affected by corrosion. Due to corrosion, the volume of steel is expanded which causes cracking and spalling of concrete cover. The bond between concrete and reinforcement gets reduced and it also affects the serviceability and ultimate load-carrying capacity of the structural members [2]. In literature, various methods are available and investigated by various researchers to forecast how well deteriorated RC columns will perform axially. However, these investigations were mainly concentrated on the axial capacity of the RC columns with an amount of corrosion level. Tapan and Aboutaha 2008 et al. [3] carried out a test on RC bridge columns and found deteriorated concrete columns with corroded longitudinal bars on the compression side. Depending on the level of corrosion, a column’s strength declines by 7–17%. Lee et al. [4] tested six rectangular columns with varying amounts of corrosion under cyclic loads and discovered that the degradation in structural behaviour was predominantly brought on by the loss of the confining effect, which was mainly carried on by the spalling of concrete cover and the drop in the mechanical characteristics of the corroded rebar. Revanthy et al. [5] performed a test on three RC columns with three different rates of corrosion. Axial capacity was reduced by 3% for 10% of corrosiondamaged columns. The columns show a 12% reduction in axial strength, which has 25% of corrosion. Rodriguez et al. [6], Xia et al. [7], and Altoubat et al. [8] experimented on corroded RC columns under compression load. The outcomes of the experiment demonstrate that the load-bearing capability of the column is inversely proportional to the level of corrosion. For calculating the axial strength of the corroded RC columns only limited analytical models are available in the literature. As these analytical models are based on various assumptions and developed using a limited dataset, restrict the use of these models. The limitations of analytical models can be adhered by the latest techniques like artificial intelligence [9]. The use of machine learning (ML) in civil engineering as well as other domains is at an all-time high, as evidenced by several research [10–12]. A few of the studies that use ML techniques are summarized: Fu et al. [13, 14] investigated the axial compressive performance of square and circular concrete-filled steel tubular (CFST) stub columns with artificial notches and observed that the mechanical properties of the latter were much worse than that of the former. Lee et al. [15] and Kumar et al. [16] used an ANN-based model to predict compressive concrete strength at 28 days. The value of regression coefficients is 0.9 means that the prediction result can fit for 90% of actual results. Du et al. [17] and Le et al. [18] also predict the axial load capacity of rectangular concrete-filled steel tube columns using ML techniques. The database utilized for the training and development of

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the ANN models contains 880 datasets that were split into three groups. In particular, 587 of 880 (66.70%) were designated as the training database, while 147 (16.70%) and 146 (16.60%) database were used as testing database and validation database, respectively. The coefficient of determination, a20-index, RMSE, and MAPE for the dataset was 0.9956, 0.9252, 154.6635, and 0.0754, respectively. The findings from the literature suggest that ANN is capable of making highly accurate predictions about the evaluation of axial strength. This paper is structured as follows: Section 2 discusses the importance of the research. Section 3 contains a methodology and discussed a collection of data from the literature, preparation and processing of the data, and performance criteria to validate the results of the proposed model. The development of the ANN model and its history is described in Sect. 4 to estimate the axial capacity of the corroded RC Columns. Section 5 of this study presents the conclusions and findings. The final Sect. 7 of this article included a brief discussion of the finding of the study.

2 Research Significance RC columns are an important structural member of any RC building. Corrosion has an impact on the total load-carrying capacity of the RC columns. To increase the safety of the buildings, it is crucial to assess the remaining life of the corroded columns. Many analytical models are available to find the axial strength of columns, but corrosion-based models are very less. Because of the assumptions and limited dataset, the analytical models were unsuccessful. As per the author’s knowledge, there is no computationally based model available in the literature to forecast the strength of the corroded RC columns. This work gives the first computational-based model to estimate the residual life of the corroded RC columns.

3 Methodology 3.1 Data Bank In this study, the database is collected from the published literature. The model has been developed using 204 experimental datasets. This dataset has a small number of outliers, and after removing them, the final dataset only has 153 samples [5, 19–29]. The input parameters such as the number of reinforcement bars (n), the diameter of the main reinforcing bar (d m ), diameter of stirrups (d s ), corrosion level (η), yield strength of steel (f y ), cross-sectional area (C AS ), concrete cover (c), and concrete compressive strength (f’c ) are the main influencing factor that affects the axial capacity of the corroded RC columns. The degree of corrosion in the selected dataset lies between 0 to 30%. The range of axial strength for different design parameters is from 45.03 to 3400 kN. Table 1 shows the statistical parameters of the collected database.

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Y. Kumar et al. Table 1. Statistical characteristics of the collected database.

Parameter

Symbol

Units

Reinforcement

n

-

dm

mm

ds

mm

8.32

Sv

mm

115.72

η

%

fy

MPa

416.71

CAS

mm2

68348.10

c

mm

26.24

MPa

30.82 622.14

Concrete

f Pu

c

-

kN

Mean

Max

Min

4.58

8

4

0.99

14.86

22

3.1

3.63

16

4

2.20

300

6

80.73

30

0

7.61

525

323.3

148000

5685.2

30

12.5

4.09

63.24

6.86

11.14

3400

45.03

714.13

5.29

Std

65.67 35601.56

3.2 Preparation of Dataset The processing of the data is a crucial stage in machine learning models. Since the input data comes in a variety of units, it is crucial to make the data unit-free and simple for machine learning models to analyse. Therefore, the normalization method is used to normalize the data in the range of −1 to 1 using Eq. 1 [30].   d − dmin −1 (1) Onormalized = 2 × dmax − dmin where, Onormalized is referred to as normalized output, d is the value of the input to be normalized, dmin and dmax are the minimum and maximum values of the input dataset, respectively. After normalisation, the dataset was partitioned for ANN training, testing, and validations. 70% of specimen used in the training set which is about to 107 samples. The remaining 30% is equally divided into validation and testing 23 samples each. Figure 1 shows the approach taken to accomplish the objective of this study. 3.3 Performance Criteria The effectiveness of the proposed model is assessed using some distinct statistical factors such as mean absolute error, root mean square error, coefficient of determination, mean absolute percentage error, Nash-Sutcliffe index, and a20-index. The error values are represented by the terms MAE, MAPE, and RMSE; if the value of these terms closes to zero, the performance of the developed model is considered as good. The linear correlation between input and output is evaluated by the value R2 . Therefore, R2 – value, NS, and a20-index close to 1 show better results of the fitting. The mathematical equations of these performances such as; R2 , MAE, MAPE, RMSE, NS, and a20-index is already available in the previous studies [31–33].

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Collection of Data Filtration of Data Normalization of Data

01 Splitting of Data Training (70%) Validation (15%) Testing (15%)

Results & Discussion

Conclusion

Performance Criteria R2, MAE, RMSE, NS, a20-index

Model ML-Based Model (ANN)

Fig. 1. Methodology of the present work.

4 Artificial Neural Network Warren McCulloch and Walter Pitts initially created an Artificial Neural Network (ANN) in 1943 [34]. The authors used threshold logic-based methods to develop ML approach for neural networks. The main purpose of an ANN is to transfer data or information from one source to another and make a relationship between them, this is like the biological nervous system (human brain) [35]. Currently, the applications of ANN are used in various fields. Many sectors like economics, study, research, diagnosis of hepatitis, and others are using ANN for accurate prediction and performance. A good advantage of ANN application is that it can make the nonlinear relationship between complex data and make models have higher accuracy with robust performance. The basic components of an ANN are nodes or neurons which are present in three different layers input, output, and hidden layer. ANN creates a nonlinear relationship between neurons of different layers via connection links. Each connection link has different weights and biases. To reduce error, backpropagation and gradient descent are frequently used methods [36]. The basic architecture of the ANN model is shown in Fig. 2. 4.1 Development of ANN Model To develop an ANN model for predicting the axial strength, the input parameters are already defined in Sect. 3. In this study, 9 input parameters are considered in the input layer with one hidden layer and one output layer. The dataset was randomly divided into three parts for training, testing, and validation, with respective values of 70%, 15%, and 15%. To train the ANN model Levenberg–Marquardt (LM) backpropagation algorithm was used with a single hidden layer. The hidden layer’s neurons were programmed to range from 3 to 15, as shown in Fig. 3. The neuron with the lowest MSE and highest R-value is number 10 and the same has been selected as the final neuron. Every single neuron of the hidden layer is corresponded to the upper layer and received input from them to calculate the weight value (wi ). After that activation function is applied to get output in the upcoming layer. All the value of

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Fig. 2. Architecture of ANN with 9-10-1.

Fig. 3. R2 and MSE of the ANN model.

the input is multiplied by the weight value after that biases (bi ) are added to get the result as shown in Eq. 2. Pu =

N

where, Pi is the normalized input values.

i=1

wi Pi + bi

(2)

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5 Results and Discussion ANN trains data in three different groups, training, validation, and testing. Figure 4 represents the accuracy of ANN prediction models with training, testing, and validation dataset. In the training dataset almost all the values lie in the range of +30% to −30%. Only two samples are out of this range therefore, the value of R2 is 0.9978. In Fig. 4b the validation set has the R2 -value is 0.9971. In Fig. 4c, the testing dataset has all values in the range of +20 to −20%, showing higher accuracy. In the training dataset the values of RMSE, MAE, MAPE, NS, and a20-index are 33.29 kN, 11.15 kN, and 4.17%, 0.99, and 0.9626, respectively. In the testing dataset the values of RMSE, MAE, MAPE, NS, and a20-index are 13.69 kN, 6.76 kN, and 1.53%, 0.99, and 1, respectively. The validation dataset has the values of performance indices are 35.72 kN, 14.39 kN, 4.65%, 0.99, and 0.91, respectively, as shown in Table 2. Figure 5 illustrates the relation between prediction and experimental value with respect to errors. In Fig. 5a, the light orange line shows the experimental values, and the predicted value is represented by the dotted green line. The dark red line shows a deviation in the error.

Fig. 4. Scatter plot (a) Training, (b) Validation, (c) Testing, and (d) All dataset.

In Fig. 5b, the validation plot shows the error line has the deflection at 10 and 14 number samples. In the testing plot (Fig. 5c) error line is almost straight, therefore, it represents very less errors.

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Y. Kumar et al. Table 2. Results of ANN model.

Model

R2

RMSE (kN)

MAE (kN)

MAPE (%)

NS

a20-index

ANN-Training

0.9978

33.2928

11.1470

4.1795

0.9978

0.9626

ANN-Validation

0.9971

35.7244

14.3892

4.6549

0.9967

0.9130

ANN-Testing

0.9998

13.6922

6.7632

1.5334

0.9997

1.0000

ANN-All

0.9981

31.5468

10.9754

3.8532

0.9981

0.9608

(a)

(b)

(c)

(d)

Fig. 5. Experimental and predicted values with errors (a) Training (b) Validation (c) Testing and (d) All dataset.

Figure 6 shows the effect of input parameters on the axial capacity of the corroded RC columns. Based on ANN predictions, the diameter of the longitudinal bar (d m ) has a 13.93% effect on the axial capacity. Similarly, the effect on other inputs parameters such as number of longitudinal bar (n), cross-sectional area (C AS ), spacing of stirrups (S v ), concrete cover (c), yield strength (f y ), concrete compressive strength (f c ), corrosion (η) and diameter of stirrups is (d s ) are 13.16%, 12.85%, 11.83%, 11.58%, 9.99%, 9.65%, 9.42%, and 7.58%, respectively. The formulation of the prediction model is expressed in Eq. 3. Pu = 0.03873K1 −0.2842K 2 + 0.0916K3 − 1.0589K4 − 0.3479K5 + 1.1194K6 + 0.8129K7 − 0.5589K8 − 0.5877K9 + 0.4246K10 − 0.51986

(3)

The values of K 1 , K 2 ….K 10 are given in Eq. 4.

Neural Network Based Algorithm

⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝

⎡⎛ ⎞ K1 0.128 ⎢⎜ ⎟ K2 ⎟ ⎢⎜ −1.512 ⎢⎜ ⎟ ⎢⎜ −4.666 K3 ⎟ ⎢⎜ ⎟ ⎢⎜ K4 ⎟ ⎢⎜ 1.221 ⎟ ⎢⎜ ⎟ ⎢⎜ K5 ⎟ ⎟ = tansig ⎢⎜ 4.650 ⎢⎜ ⎟ K6 ⎟ ⎢⎜ −1.083 ⎢⎜ ⎟ ⎢⎜ 0.642 K7 ⎟ ⎢⎜ ⎟ ⎢⎜ ⎟ K8 ⎟ ⎢⎜ −2.969 ⎢⎜ ⎟ ⎣⎝ −0.802 K9 ⎠ K10 1.079

0.893 1.746 1.213 −0.742 1.477 4.340 −1.576 −2.322 0.493 −1.716

⎞ ⎛ 2.166 −2.152 0.192 0.080 −2.832 −4.524 0.688 CAS 0.837 0.875 3.438 −2.895 0.563 1.703 0.118 ⎟ ⎟ ⎜ ⎟ ⎜ c ⎜ 0.009 −1.156 −3.236 4.687 0.506 0.271 −2.12 ⎟ ⎟ ⎜ f ⎜ −1.477 0.424 −0.370 1.231 −0.141 2.271 1.5292 ⎟ ⎟ ⎜ c ⎟ ⎜ fy ⎜ −1.466 0.440 3.738 −0.581 −0.601 −0.800 −1.670 ⎟ ⎟X ⎜ n ⎟ ⎜ 1.708 1.445 0.245 −4.292 1.177 2.602 1.484 ⎟ ⎜ ⎟ ⎜ dm ⎜ 0.379 3.214 0.314 0.697 −0.959 1.137 0.849 ⎟ ⎟ ⎜ ⎟ ⎜ ds 1.413 0.353 −2.152 −1.081 1.835 −0.317 2.521 ⎟ ⎜ ⎟ ⎝ sv 1.499 0.691 −1.959 −2.651 −1.071 1.029 1.001 ⎠ η 1.80 −1.986 −3.049 −1.785 −0.631 −0.651 −0.924

⎞⎤ 0.650 ⎜ 2.012 ⎟⎥ ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ ⎟ ⎜ 0.002 ⎟⎥ ⎟⎥ ⎟ ⎜ ⎟ ⎜ −0.691 ⎟⎥ ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ ⎟ ⎜ 0.463 ⎟⎥ ⎟⎥ ⎟+⎜ ⎟ ⎜ −1.118 ⎟⎥ ⎟⎥ ⎟ ⎜ ⎟ ⎜ ⎟⎥ ⎟ ⎜ −1.201 ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ 4.408 ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟⎥ ⎠ ⎜ ⎝ −1.950 ⎠⎦ ⎞

227

⎛

(4)

−1.469

Fig. 6. Influence of input parameters on axial capacity of corroded RC columns.

6 Conclusion In this study, the ANN algorithm is performed to find the axial strength of the corroded RC columns with 153 specimens that were collected from the literature. The developed model can predict the axial strength of RC columns with an R2 -value of 0.9981. The RMSE, MAE, and MAPE values of the developed ANN model are 31.5467, 10.975, and 3.835 respectively. Other performance parameters like NS and a20-index are also considered to check the performance of the ANN model. The value of these parameters is so close to 1 which shows the accuracy of the model. According to the sensitivity analysis, the diameter of reinforcing bars has a great influence on the axial capacity of the columns. The developed can be effectively utilized to forecast the axial capacity of the corroded RC columns. In this work, only one machine learning method (ANN) has been used to determine the axial strength of columns the application of other machine learning algorithms can enhance the accuracy of the developed model.

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ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers Sarvanshdeep Singh Sahota1 , Harish Chandra Arora2,3 , Aman Kumar2,3(B) , Krishna Kumar4 , and Hardeep Singh Rai1 1 Guru Nanak Dev Engineering College (GNDEC), Ludhiana 141006, India 2 Structural Engineering Department, CSIR-Central Building Research Institute,

Roorkee 247667, India [email protected] 3 Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India 4 Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India

Abstract. Concrete is one of the most commonly used construction material on the earth after water. The compressive strength of concrete is an important parameter and is considered in all structural designs. Production of the cement is directly proportional to carbon emissions. The cement content in the concrete can be partially replaced with waste materials like steel fibers, silica fumes, etc. Calculating compressive strength in a laboratory takes huge amount of time, manpower, cost and produces a large amount of wastage. Apart from the constituents of concrete, the compressive strength also depends on various factors such as temperature, mixing, types of aggregate, and quality of the water. The analytical models failed to deal with difficult problems. Artificial intelligence has enough capabilities to deal with such kind of complex problems. In this work, an artificial neural network (ANN) based model has been developed to predict the compressive strength of steel fiber and silica fumes-based concrete. The R-value of the developed model is 0.9948 and the mean absolute percentage error is 5.47%. The mean absolute error and root mean square error of the proposed model is 1.73 MPa and 6.89 MPa, respectively. The developed model is easy to use and reliable to estimate the compressive strength of concrete incorporating silica fumes and steel fibers. Keywords: Compressive Strength · ANN · Silica fume · Steel fiber

1 Introduction Humans have built quite the construction spectacles in the last century. Concrete has been a construction material for the majority of these structures because of its exceptional mechanical properties. This strength is given by the bond between cement and water © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 231–244, 2023. https://doi.org/10.1007/978-3-031-31153-6_20

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paste with the aggregate present in concrete as hydration occurs in the concrete mix and causes it to harden and gain the strength required. Concrete has its uses but it is also harmful to the environment, as during hydration while the formation of concrete a lot of CO2 gas is produced [1]. Concrete is the major constituent of any structure and it is not possible to entirely replace it. So, to reduce the CO2 emissions, researchers used waste materials such as; silica fume, fly ash, blast furnace slag, etc. Replacing a certain amount of cement with silica fume helps us achieve a sustainable mix design as it reduces the carbon footprint during cement production. Steel fibers can be paired with silica fume to achieve better flexural strength as well. Silica fume (SiO2 ) and Steel fibers when used simultaneously can significantly increase the performance of concrete as silica fume is a cementitious material and it helps cement in establishing a strong bond with the steel fibers and reinforcements used in the member due to its fine particulate size between 0.1 to 0.3 µm [2]. Numerous articles on the integration of SiO2 and steel fibers on concrete properties had been published. Wang et al. [3] examined the combined effects of SiO2 and steel fiber on the fracture behavior of recycled aggregate concrete (RAC) mix exposed to a higher temperature. Nili et al. [4] studied the mechanical properties of concrete reinforced with fiber steel paired with silica fume at different water-cement and aspect ratios with 3 volume fractions (0%, 0.5%, 1%) of reinforcing material. Xie et al. [5] inspected the compressive and flexural strength of RAC and normal concrete incorporated with silica fume and steel fiber. Nili et al. [6] observed the early- and later-stage compressive strength of concrete mix at two different water-cement ratios (0.36 and 0.46) paired with 8% silica fume by weight of cement and had 3 different steel fiber contents 0%, 0.5%, 1% by volume of concrete. Machine learning (ML) has been used in the various field of Civil Engineering as well as other domains like medicine, education, agriculture, etc. [7–9]. The supervised-based ML technique such as; artificial neural networks (ANN) has gotten exercised in many civil engineering hitches to estimate and predict the various output parameters with few errors and higher performance. Cemalgil et al. [10] predicted the durability performance of concrete modified with Silica fume and Steel fibers, experimental study was performed to envisage the functioning metrics throughout the service period of concrete while integrating silica fume and steel fiber by single and hybrid generalized extreme learning machine (GRELM) methods. Abrasion resistance and freezing & thawing were the outputs of the algorithm. Binary and ternary methods were obtained by the algorithms particle swarm optimization (PSO) and grey wolf optimization (GWO). The following models were put forward: single use of GRELM, binary use of GRELM-PSO, GRELMGWO, and GRELM integrated with hybridized PSO and GWO. GRELM-PSO-GWO was a significantly accurate model compared to others in this paper. Khan et al. [11] applied ML approaches like gradient boosting, random forest, and XGBoost in this paper to estimate the compressive strength of SFRC having a certain amount of silica fume, and also SHAP (SHapley Addictive exPlanations) analysis was integrated with ML algorithms. Random forest ML approach gave the highest value of the coefficient of correlation (R2 ), i.e., 0.96 being considerably higher than the other models and SHAP analysis showed that cement content is directly proportional to the compressive strength of SFRC.

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Former research papers have focused on this configuration of waste material and very few of those use ML to predict mechanical properties of concrete but the results of the computational models in previous papers uses different methods like GRELM, PSO, GWO, etc. This paper uses a dataset of over 200 specimens to generate a computational model using ANN which is a very reliable and accurate method for predictions based on mathematical algorithms. The model is this paper achieves a coefficient of correlation of 0.9948 with minimal errors hence giving an efficient and accurate model. The objective of this study is to generate a computational model using ANN to predict the compressive strength of concrete incorporating silica fume and steel fibers which are waste materials and see what effect creating a sustainable concrete with this configuration of waste material has on concrete mix and which constituent effects the concrete mix the most. After studying various research papers on this topic, the trend of optimum quantities of silica fume and steel fibers in concrete is 10–20% and 0.2–0.4%, respectively. ANN can be effectively utilized to calculate the compressive strength of steel fiber and silica fume-based concrete. This paper has been arranged as follows: Sect. 2 consists of the research significance of this paper. Section 3 describes the methodology adopted to achieve the objective of this study with the collection of data, preparation of data, and performance indices used to check the reliability of the model. Section 4 describes the summary of ANN and the development of the ANN model. The results and discussion is explained in Sect. 5. The conclusion of this study is summarized in Sect. 6.

2 Research Significance Cement during its production produced a very substantial carbon footprint that has been playing a big role in global warming. So, to reduce this trail of carbon emissions, silica fume has been used to reduce carbon emissions and create more sustainable concrete by replacing a certain amount of cement from the concrete. Silica fume helps to achieve high compressive strength and by using steel fibers we can achieve higher flexural strength as well. So, silica fume and steel fiber are used together with their respective optimum quantities to achieve the highest possible strengths. This research has been conducted to prepare an effective computational model for compressive strength using ANN. This model has achieved a Pearson coefficient of correlation of 0.995 at its 11th neuron with acceptable errors.

3 Methodology 3.1 Data Bank For this research, a dataset of 218 samples was collected from the literature. Few outliers have been removed from the data bank to achieve the accuracy of the model. The final selected dataset contains only 202 samples [4, 20–35]. The basic statistical feature of the collected database such as minimum, maximum, standard deviation and mean values are shown in Table 1. Based on the literature survey, the parameters that have a significant

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f ’c

TSF SP AR vf w/c FA CA Vf

SF

C

influence on compressive strength (fc ) of the concrete are: cement (C), silica fume (SF), steel fiber content (V f ), coarse aggregate (CA), fine aggregate (FA), water-cement ratio (w/c), aspect ratio of steel fiber (ARvf ), superplasticizers (SP), type of steel fiber (T SF ) and the same has been adopted in this research. The value of the compressive strength lies in the range of 27 to 190.8 MPa at 28 days strength. Figure 1 is a graphical representation of the correlation coefficient between the inputs and the output. It is observed from Fig. 1, the highest correlation coefficient is between fc and SF i.e., 0.54, and the lowest value of correlation coefficient of −0.8 can be observed between FA and CA.

C

SF

Vf

CA

FA

w/c

AR vf

SP

TSF

f’c

Fig. 1. Correlation among inputs and output parameters.

Table 1. Statistical parameters Parameter Concrete

Silica Fume

Abbreviations

Unit

Average

Min.

Max.

Std. Dev.

C

kg/m3

495.54

315

1200

205.93

CA

kg/m3

715.12

250

1170

318.13

FA

kg/m3

878.36

582

1655.1

189.29

w/c

-

0.40

0.18

0.55

0.09

SP

kg/m3

5.34

0

35.8

7.85

SF

%

6.29

0

25

5.67

Steel Fiber

Vf

%

0.67

0

2

Aspect Ratio

ARvf

-

60.46

0

120

Type of steel fiber

TSF

-

1.54

1

6

Compressive Strength

fc

MPa

61.11

27

190.8

0.48 23.12 1.15 25.66

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Fig. 2. Methodology diagram.

Figure 2 represents the methodology followed for this research. Firstly data was collected from previous literature which was then filtered and normalized using MATLAB, split into 3 phases training, validation, and testing which were used to create a ML (Machine learning)-based ANN (Artificial neural network) model using Levenberg Marquardt algorithm, performance of the computational model was check with performance parameters R (corelation coefficient), NS (Nash-sutcliffe coefficient), a20-index(), MAE (Mean absolute error), MAPE (Mean absolute percentage error), RMSE (Root mean square error). At last results and conclusion were drawn from the observations. 3.2 Preparation of Data Normalization of data means bringing the dataset to a certain scale so; that machinelearning algorithms easily process it. The dataset in this work is normalized between − 1 to +1 scale. Equation 1 has been used to normalize the data [12].   y − ymin −1 (1) xnorm = 2 ymax − ymin where, x norm is the normalized input, y is the value to be normalized, ymin and ymax is the minimum and maximum value of in the whole dataset. The methodology of the present work is shown in Fig. 2. To analyze the effectiveness of the ANN model a few statistical analysis parameters are used; correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe coefficient (NS), root mean square error (RMSE), a20-index. If R, NS, a20-index reaches 1 and RMSE, MAE, MAPE reaches 0, that gives us a high-performance ANN model. The equation of these performance indices is already available in previous studies [13–15].

4 ANN ANN is an imitation of the biological neuron residing in most living beings. The function of a biological neuron is to identify and recognize any patterns in the input they receive and perform complex functions of linear and non-linear nature accordingly.

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Fig. 3. ANN architecture.

ANN replicates the same thing though not as effective as a biological neuron yet but still can perform various tasks with some technical restrictions. ANN has been used for tasks like diagnosis, image recognition, machine translation, etc. ANN generally consists of at least three layers, input layer, hidden layer, and output layer. Input layers send the data to the neurons which interpret the data using a pre-defined function and can give us the output. An ANN can be trained using various datasets as it learns setting up the synaptic weights of the information based on the ANN’s internal architecture [16]. ANN can use a supervised learning technique called backpropagation; this allows the ANN model to alter its output by taking the error into account. This allows the algorithm to adjust its synaptic weights proportionally to the effect they had on error. Due to backpropagation, the ANN model learns how to reduce the error as it flags the inputs causing higher errors during retraining giving an overall accurate result [17]. ANN adapts as it learns from the various phases it has for any dataset. These phases include training, validation, testing, and all. It uses a certain amount of data to first train its algorithm which allows it to understand what’s the proportionality of its synaptic weights then it splits the remaining data for the testing and validation phase. This allows the ANN algorithm to apply the activation function efficiently yielding a high-performance model. Figure 3 shows the architecture of the ANN model used in this paper. It has 9 input parameters, 11 neurons in the hidden layer, and compressive strength as an output. 4.1 Development of ANN Model This ANN had 3 layers namely; input, hidden, and output. As shown in Fig. 4, the neuron in the single hidden layer was vary from two to twenty. Levenberg-Marquardt algorithm was used to predict the compressive of concrete, due to its fast processing and accuracy. Based on the performance indices (R and MSE) the final selected neuron is the 11th neuron. The 11th neuron is ranked 1st for having the highest Pearson’s correlation coefficient and least MSE. After the selection of the best neuron synaptic weights and biases were calculated between the input and hidden layer and the transfer function was

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applied to the normalized input using Eq. 4, and the obtained values of Pi were used in Eq. 2 to calculate the final value of the compressive strength [18, 19]. Tansig activation function has been used in the algorithm because of its non-linearity which makes it so that it can adapt to the variations easily which are inevitable in this research and create trends from the varying dataset to give an accurate result. There are other activation functions as well like sigmoid function which despite being non-linear and similar to tansig. is a bit inferior because of its range i.e. (0,1) while tansig has a range of (−1, 1). ReLU (Rectified linear unit) is another activation function which is widely used in ANN algorithms and is not used here because it doesn’t offer the same non-linearity as tansig function. fc = purelin(WHO Pi + BHO )

(2)

Purelin = f (x) = x

(3)

Pi = tansig(WIH Qi,norm + BIH )

(4)

tansig =

2 −1 1 + e−2n

(5)

where Qi, norm are the normalized inputs, W IH is the synaptic weights among the input layer and hidden layer, BIH is the bias among the input layer and hidden layer. W HO and BHO are the weights and bias between output to hidden layer.

Fig. 4. (a) R and (b) MAE values of neurons at various phases.

5 Results and Discussion Figure 5 illustrates the scatter plot between experimental and predicted values of compressive strength. The data has been divided into 3 phases: training, validation, and

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Predicted f'c (MPa)

200 180

-10%

160

-20%

140 120 100 80 60 40 20 0 0

20

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Experimental f'c (MPa) 180

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(a)

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(b)

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90

-20%

80 70 60 50 40 30 20 10 0 0

10 20 30 40 50 60 70 80 90 100 110

Experimental f'c (MPa)

Predicted f'c (MPa)

(c)

+20% +10%

200 180

-10%

160

-20%

140 120 100 80 60 40 20 0 0

20

40

60

80 100 120 140 160 180 200

Experimental f'c (MPa)

(d)

Fig. 5. Scatter plot and line graph with error comparison for (a) training, (b) validation (c) testing, and (d) all

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testing. Four graphs have been plotted for training, validation, testing, and a combined graph of the whole dataset. Figure 5(a) illustrates the scatter plot and line graph between experimental and predicted values for the training phase. Most of the data lie between +20% to −10% correlation lines and the line graph in the same figure shows the experimental and predicted values are mostly coincident with a few noticeable deviations in the dataset represented as spikes in the error line. Figure 5(b) illustrates the same graph for the validation phase. In the scatter plot majority of the values lie between the +10% to −10% correlation line and the line graph shows noticeable deviation at a few datasets indicated on the error line. Figure 5(c) illustrates the same graphs for the testing phase. The scatter plot here again shows that the majority of the values lie between +10% to − 10% correlation lines and the line graph shows noticeable deviations at various datasets again indicated by the spikes on the error line. Figure 5(d) illustrates the same graphs for the whole dataset. The graphs here show all the results in training, validation, and testing for an overall review (Table 2). Table 2. Performance of ANN model Model

R

MAE

MAPE

NS

RMSE

a20-index

ANN-Training

0.9950

1.7629

3.3084

0.9897

2.6567

0.9929

ANN-Validation

0.9973

1.2946

2.1834

0.9940

2.0568

1

ANN-Testing

0.9886

2.0317

4.0238

0.9758

3.1464

0.9667

ANN-All

0.9948

1.7333

5.4667

0.9892

6.8949

0.9901

Figure 6 illustrates the percentage of the database corresponding to the error it poses. The error for training lies between the range of −20 to +10 shown in Fig. 6(a), the error for validation lies between −10 to +5 shown in Fig. 6(b), error for testing lies between −15 to +5 shown in Fig. 6(c), and error for the whole dataset lies between −20 to + 10 shown in Fig. 6(d). Over 80% of the whole dataset borders around an error value of 0 which can be seen in Fig. 6(d) implying that this ANN model has trivial errors and is highly accurate for predicting fc .

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100

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20

40 20 20

14 60

12 10

40

8 6 4

20

Cumulative Percent

Counts

60

80

16

Counts

80 40

Cumulative Percent

18

2 0

0 -20

-10

0

0

0 -10

10

-5

0

Error

Error

(a)

(b)

5

100

100

60 5

40 20

0 -15

80 60 60 40

20

-5

0

20

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40

5

Cumulative Percent

80

Counts

Counts

10

Cumulative Percent

80

0 -10

0

Error

Error

(c)

(d)

10

Fig. 6. Bar graph for percentage of data with error range (a) training, (b) validation (c) testing, and (d) all dataset

Figure 7 gives the percentage of the influence held by each of the inputs on fc . Following are the influence percentages of all the input parameters. SP, C, SF, FA, w/c, T SF , V f , CA, and ARvf have 16.60%, 13.74%, 13.02%, 11.26%, 10.54%, 10.17%, 10.16%, 9.56%, and 4.94%, respectively, effect on the compressive strength of concrete.

6 Formulation The proposed formulation to calculate the compressive strength of silica fume and steel fibre-based concrete is given in Eqs. 6 and 7. A matrix has been generated using Eq. 4. After solving the matrix, we get the values of S1 , S2 , S3, …, S11 which are used in Eq. 2 to get values of fc (Compressive strength) as shown in Eq. 7. ⎡

S1 ⎢ ⎢ S2 ⎢ ⎢ S3 ⎢ ⎢ ⎢ S4 ⎢ ⎢ ⎢ S5 ⎢ ⎢ S ⎢ 6 ⎢ ⎢ S7 ⎢ ⎢ ⎢ S8 ⎢ ⎢ S ⎢ 9 ⎢ ⎣ S10 S11

⎤

⎡⎛ −0.8534 ⎥ ⎢⎜ ⎥ ⎢⎜ 0.8601 ⎥ ⎢⎜ ⎥ ⎢⎜ 1.2815 ⎥ ⎢⎜ ⎥ ⎢⎜ ⎥ ⎢⎜ −0.04947 ⎥ ⎢⎜ ⎥ ⎢⎜ ⎥ ⎢⎜ −0.8968 ⎥ ⎢⎜ ⎥ = Tansig ⎢⎜ −1.4914 ⎥ ⎢⎜ ⎥ ⎢⎜ ⎥ ⎢⎜ −0.9613 ⎥ ⎢⎜ ⎥ ⎢⎜ ⎥ ⎢⎜ −0.7026 ⎥ ⎢⎜ ⎥ ⎢⎜ −0.8745 ⎥ ⎢⎜ ⎥ ⎢⎜ ⎦ ⎣⎝ −1.2498 −0.8529

⎞ 0.0011 0.3961 0.8125 ⎟ ⎛ C 0.1509 −0.6334 1.8150 −0.5251 −0.3329 −1.5519 −0.0073 −2.8118 ⎟ ⎟ ⎜ SF 0.1333 −1.0648 0.5342 0.1462 0.2878 −0.1426 0.5541 −0.5376 ⎟ ⎟ ⎜ ⎟ ⎜ ⎜ V −0.9915 1.5570 −0.6482 1.9032 0.8674 0.0731 1.0208 0.6704 ⎟ f ⎟ ⎜ ⎟ ⎜ CA 0.5323 0.8892 −1.7701 −1.3409 −0.6176 0.4382 −1.5992 0.2179 ⎟ ⎜ ⎟ ⎜ ⎜ × ⎜ −1.1810 −0.5552 −0.3451 −0.2497 0.1813 0.0614 2.2032 0.5593 ⎟ FA ⎟ ⎜ ⎟ ⎜ 1.2873 0.3338 0.4574 −2.2083 0.3075 0.1048 0.7006 −0.3803 ⎟ w/c ⎟ ⎜ ⎟ ⎜ 0.6831 0.3613 −0.3899 −0.2107 0.4330 0.3884 3.1077 0.9223 ⎟ ⎜ ARvf ⎟ ⎜ ⎜ ⎟ ⎝ SP −0.6943 0.2854 0.5196 −0.2228 1.4146 −0.4230 0.9439 0.1663 ⎟ ⎟ 1.1647 −0.3410 −0.5358 0.7833 −1.0883 0.5778 −1.7887 −0.8941 ⎠ TSF −0.9051 −0.6476 −0.1354 −0.6889 1.9476

2.2270

0.9170 −0.5496 0.7179

0.3516

0.3252 −0.5409 −0.2587

⎞⎤ 1.6793 ⎜ ⎟⎥ ⎜ −0.2389 ⎟⎥ ⎜ ⎟⎥ ⎟ ⎜ ⎥ ⎟ ⎜ −0.8894 ⎟ ⎟⎥ ⎟ ⎜ ⎥ ⎟ ⎜ 2.3057 ⎟ ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ 0.2152 ⎟ ⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ ⎥ ⎟ + ⎜ −0.0919 ⎟ ⎟⎥ ⎟ ⎜ ⎟ ⎟ ⎜ −0.5708 ⎟⎥ ⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ ⎟⎥ ⎟ ⎜ −0.6695 ⎟ ⎥ ⎟ ⎟⎥ ⎟ ⎜ ⎟⎥ ⎠ ⎜ ⎜ −0.9904 ⎟⎥ ⎟⎥ ⎜ ⎝ −2.6984 ⎠⎦ ⎛

⎞

−2.0419

(6)

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Fig. 7. Influence of input parameters on the output

fc = −1.3375S1 + 0.3033S2 − 1.2820S3 + 0.6494S4 − 0.8054S5 − 0.5531S6 + 0.3781S7 + 0.2136S8 − 0.7718S9 − 0.4646S10 − 0.6850S11 − 0.48399

(7)

7 Conclusion In this research 202 samples were collected from previous literature to develop an ANN model based on a supervised-ML algorithm that can predict fc . Based on the performance criteria, R, NS, and a20-index values of the ANN model are 0.9948, 0.9892, and 0.9901, respectively for the whole dataset. All of these values border around one indicating a peak performance model. The MAE, MAPE, and RMSE values of the developed are 1.733 MPa, 5.4667%, and 6.8949 MPa, respectively. Based on the sensitivity analysis it is found that the most influencing parameter that affects the compressive strength of concrete is superplasticizer and after that cement has an impact of 13.74%. Further, the accuracy of this model can be enhanced by using nature-inspired algorithms and also enriching the database. The future scope of this research comprises of applications like analysing quantities of constituents of concrete mix, predicting precise values of compressive strength digitally saving material and labour cost, and stabilizing increasing production of various waste material by incorporating them into concrete mix.

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Indian Sign Language Digit Translation Using CNN with Swish Activation Function Seema Sabharwal(B)

and Priti Singla

Department of Computer Science and Engineering, Baba Mastnath University, Rohtak, Haryana, India [email protected]

Abstract. With the change in technology at such a fast pace, every piece of information is available on the Internet. But certain sections of society like deaf-mute persons are still struggling for their communication rights. They use countenance, kinesics, and gestures to communicate with each other. Further, due to the paucity of linguistically annotated and documented material on Sign Language, research on grammatical and phonetic aspects of the language is limited. Despite the wellestablished purposes of Sign Language, the use of technology to facilitate deaf persons is under-explored in research studies. Consequently, the design of an efficient and automatic Indian Sign Language Translation System (ISLTS) is essential. This paper proposes a deep learning-based seven-layered two-dimensional Convolution Neural Network (2D-CNN) for efficient translation of Indian sign language digits using the swish activation function. The proposed framework uses max pooling, batch normalization, dropout regularization, and Adam optimizer. An open-access numeric customized dataset of India Sign language of approximately 12 K images has been utilized. Our model achieves the highest validation accuracy of 99.55% and average validation accuracy of 99.22%. The results show that the swish activation function outperforms traditional ReLU and Leaky ReLU activation functions. Keywords: CNN · Indian Sign Language · Translation · Deep Learning · Swish

1 Introduction The syntax and semantics of Sign language vary from one area to another as it is evolved naturally over the years. More than 120 forms of sign language are used for communication among deaf-mute persons across the world [1]. The sign language of India is called Indian Sign Language (ISL) and consists of alphabets, numbers, words, and sentences of the English language. Apart from ISL, there are other which is far less than the English language [2]. The dactylology concept came into existence as a result of the vast lexical gap between ISL and the English language. Dactylology is also known as the Fingerspelling approach. In dactylology, every alphabet and digit is portrayed via predefined hand movements. Sign language processing involves sign language recognition, sign language translation, and sign language generation. Much research has already been done on translating sign language to speech and text sign language. There is a requirement for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 245–253, 2023. https://doi.org/10.1007/978-3-031-31153-6_21

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an efficient sign language translation system to help deprived persons. Automatic Sign language translation is a very complex task involving various research fields such as machine learning, computer vision, image processing [3], natural language processing [4], and pattern recognition. Currently, sensor/glove/contact/direct-based and computer vision/image-based techniques are used for the sign language translation system [5]. The former approach uses gloves with sensors to translate the gestures generated by the user whereas the latter one emphasizes translating images or videos of sign language. The conventional sign language translation framework focuses mainly on the alphabet level translation, as very less work has been done on the numeric translation of sign language gestures. This research article proposes an Indian sign language translation model based on a popular vision-based deep learning framework convolution neural network. Our proposed model is a seven-layer 2D-CNN with batch normalization, dropout regularization, max pooling, swish activation function, and Adam optimizer. The contribution of this paper is listed below. • This paper proposes the deep learning-based CNN model for the translation of static ISL digits into text. • The role of the activation function in the translation model has been explored in the proposed Indian sign language translation model by performing a comparative study on three popular activation functions. The swish activation function performed better than the traditional ReLU and leaky ReLU activation functions. A customized numeric dataset [6, 7] of 12 K images is utilized for this model. This research article is organized as follows: Sect. 2 presents the literature review. Section 3 discusses the design and architecture of the proposed sign language translation model. Section 4 explains the result and discussion followed by the conclusion and future scope in Sect. 5.

2 Related Works Sign language recognition and translation are the popular research domains in computer vision and several cutting-edge technologies have been put forth in these fields. [8] proposed a static alphabet-level American sign language recognition system using Inception v3 for 24 alphabets. The system achieved the greatest validation accuracy of 98% and average validation accuracy of 90%. [9] proposed word-level translations of ISL gestures using Long short-term memory (LSTM) and gated recurrent unit (GRU) with an accuracy of 97%. The model translates 11 commonly used words of ISL on a self-made dataset having 1100 video samples of 28fps. In the category of sensor-based, [10] developed a machine learning-based American sign language recognition model for 24 alphabets using a magnetic positioning system. SVM algorithm was used on openaccess image dataset with Media pipe library for image pre-processing. A deep CNN model [11] was developed to recognize single-handed gestures of American sign language. The proposed model achieved 99.67% recognition accuracy for 24 alphabets of ASL. [12] developed CNN-based interface DeepASLR for the American sign language recognition system with an accuracy of 99.38% and a loss of 0.0250. The proposed model

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uses 3 convolution layers, and a ReLU activation function to classify 26 alphabets. Three datasets have been used for the analysis of the proposed model. Martin and Espejo [13] developed a Spanish alphabet recognition system. A self-made dataset of 8300 colour images for 30 letters has been used for the training and testing of the system. It has been concluded that CNN gives better results than RNN. On comparative analysis of CNN and SVM for the classification of Tanzania sign language, [14] found that the accuracy of CNN was better than SVM. Yirtici and Yurtkan [15] used regional CNN as an object detector to recognize character-level Turkish sign language. The model attained 99.7% average precision on extracted images from video files. [16] used diffGrad optimizer with stochastic pooling in the architecture of CNN to recognize the alphabets of Indian sign language. [17] used hybrid SIFT and CNN to classify 50 gestures of ISL. Hybrid SIFT used adaptive thresholding and the Gaussian blur technique in the image preprocessing phase. It has been observed that CNN has been a popular choice of researchers for image classification in static sign language translation due to its optimal performance. But, tuning the parameters of CNN is a very challenging task, as each attribute plays a very important role in the overall system’s performance.

3 Methodology This section discusses the dataset used, the proposed architecture, and the experimental setup. 3.1 Dataset The dataset plays an important role in the classification of sign language gestures. A customized dataset of 10 classes (0–9 digits) has been utilized by combining datasets on Kaggle [6, 7]. The dataset consists of 1200 approx. images corresponding to each class. A total of approx. 12 K ISL images of size 70 × 70 have been used to train and test the proposed model. Figure 1 depicts a snippet of the dataset. 3.2 Preprocessing With the help of hold-out validation, the dataset is partitioned in the ratio of 80:20 for training and testing. Six data augmentation (rotation, width shift, height shift, horizontal flip, scaling, translation) operations were applied to the training set. The dataset consists of coloured images, these images are converted into grayscale images and adaptive thresholding has been applied to the input images to have better feature extraction. Further, the images are resized to 70 × 70 to have better feature extraction and fed to CNN for further processing. 3.3 Proposed CNN Architecture Feed-forward neural networks identifying spatial patterns among the data are called Convolution neural networks [16]. Convolution, pooling, dropout, batch normalization, flatten and softmax layers are stacked multiple times to construct the proposed 2D-CNN framework. The summary of our proposed model is depicted with the help of Fig. 2.

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Fig. 1. Dataset

Convolution Layer: Five convolution layers of two dimensions are used in the architecture of the proposed model to extract spatial features. The first convolution layer uses a stride function with value 1, 32 filters, and 3 × 3 kernel size. The output of the proposed model is a dense layer with 10 nodes. The summary of each layer of 2D-CNN, filters/number of nodes has been shown with the help of Fig. 2. Activation Function: It is the responsibility of the activation function to trigger the neuron’s properties to address nonlinear issues [18]. Rectified linear unit (ReLU) and leaky rectified linear unit (leaky ReLU) are the most commonly used activation functions in sign language translation models. Swish is a nonlinear activation function that can be represented using Eq. (1) [19]. swish(x) = x.σ(β.x)

(1)

where σ is the sigmoid activation function and can be expressed using Eq. (2), and β is a trainable parameter defined with Eq. (3). σ (x) = x. exp(x) − 1

β=

(2)

⎧ ⎨

0, f (x) = x/2 1, sigmoid function ⎩ ∞, behaves like ReLU

(3)

The zero value of β becomes a scaled linear function as shown in Eq. (3), while the swish function behaves similarly to a sigmoid weighted linear unit on 1 value of β. If the value of the trainable parameter is infinity, the function behaves similarly to ReLU [19]. The output value of the swish function ranges (−∞,∞). The value of the learnable parameter is set to 1 in our model. The swish activation function overcomes the dying ReLU and vanishing gradient problem.

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Fig. 2. Summary of Proposed Model

Max Pooling: To minimize the computation and spatial dimension, two-dimensional max pooling is applied. It chooses the maximum value out of the selected kernel after processing. Dropout: The dropout layer is used for regularization to minimize co-adaptations of neurons [20]. Adam optimizer has been used in our model due to its ability to converge faster. The categorical cross-entropy loss function is used to evaluate the loss by comparing the target and anticipated output values.

3.4 Experimental Setup TensorFlow backend with open-source library Keras is used to implement the proposed model on Google Colab pro. A categorical cross-entropy loss function is used and the learning rate is initialized to 0.001. The proposed model was implemented on an Intel Core i7 NVIDIA CPU with 16 GB RAM and windows 11. The maximum number of epochs was set to 50 and the best training and validation accuracy were selected for evaluation.

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4 Results and Discussion Thresholding is an important part of image preprocessing where binary images are generated from grayscale images [21]. Adaptive thresholding has been used in preprocessing phase of our proposed model. The output image of digit 0 after adaptive thresholding, grayscale conversion and size reduction has been shown in Fig. 3. In the same way, these operations have been applied to the entire dataset.

Fig. 3. Thresholded image of digit 0 after preprocessing phase

4.1 Performance The performance of the proposed model for the translation of Indian sign language is presented in Table 1 in terms of training and validation accuracy and training and validation loss for 50 epochs. Our proposed model attains 99.55% validation accuracy and 0.017% validation loss. It achieves maximum training accuracy of 100% and on the 50th epoch, average validation accuracy of 99.22% for 5 runs (if maximum accuracy is considered for every run) and it attains a training accuracy of 99.77%. The model reported a testing accuracy of 99.20%. Table 1. Performance of the proposed model Activation function

Epochs

Training Accuracy

Validation Accuracy

Testing accuracy

Validation loss

Swish

50

99.77

99.55

99.2

0.017

Figure 4 depicts the accuracy and loss curve of the proposed sign language transition model using the swish function. Figure 4(a) shows a comparison between training and validation accuracy while Fig. 4(b) represents the training and validation loss curve of the first run with 50 epochs.

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(a)

(b) Fig. 4. (a): Accuracy curve of the proposed model. (b): Loss curve of the proposed model

4.2 Comparison with Other Models The performance of our model is compared with other existing models in Table 1. A hybrid framework FiST_CNN, a combination of Fast Accelerated Segment Test (FAST), Scale Invariant Feature Transformation (SIFT), and CNN, was developed to classify 34 (24 alphabets and 10 digits) gestures of ISL [22]. The proposed system achieved 97.89% accuracy on the ISL dataset of one-handed static gestures. [23] developed a model for the classification of Indian sign language gestures with different backgrounds. The model compared various activation functions (ReLU, Leaky ReLU), optimizers (Adam, SGDM, RMSProp), and various datasets. It has been concluded that Adam with ReLU works better on ISL simple, mixed, and complex background datasets with an accuracy of 99.1%. [24] proposed a dynamic alpha-numeric Indian sign language translation framework using CNN. Canny edge detection segmentation is used for segmentation. CNN with ReLU activation is used to achieve an accuracy of 99%. The performance of our proposed framework outperforms all other models as shown in Table 2.

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Reference

Year

Method

Language

Accuracy (%)

[22]

2022

CNN (Leaky ReLU), SIFT, FAST

Indian SL

97.89

[23]

2021

CNN (ReLU)

Indian SL

99.10

[24]

2022

CNN (ReLU)

Indian SL

99

CNN (Swish)

Indian SL

99.22

Proposed

5 Conclusion and Future Scope This paper proposes an Indian sign language translation model for digits using the deep learning framework 2D-CNN. The model comprises of convolution layer, max pooling, batch normalization, dropout, swish activation function, softmax at the outer layer, and Adam optimizer. A customized digit dataset of 10 classes (0–9) has been employed for the evaluation of our proposed translation framework. Our model achieves maximum validation accuracy of 99.55% and average validation accuracy of 99.22%. It has been concluded that the swish function performs better than traditional ReLU and leaky ReLU activation functions. Future works will include alphanumeric translation of sign language gestures. Further, the effect of increasing or decreasing the number of layers in existing translation mechanisms can be studied along with the inclusion of other datasets to evaluate the performance of the existing model.

References 1. Shivashankara, S., Srinath, S.: American sign language recognition system: an optimal approach. IJIGSP 10, 18–30 (2018) 2. ISLRTC. History | Indian sign language research and training center (ISLRTC), Government of India. Indian Sign Language Research and Training Center (ISLRTC) (2022). http://islrtc. nic.in/history-0 3. Varun Chand, H., Karthikeyan, J.: CNN based driver drowsiness detection system using emotion analysis. IASC 31, 717–728 (2022) 4. Sitender, Bawa, S.: Sanskrit to universal networking language EnConverter system based on deep learning and context-free grammar. Multimed. Syst. 28, 2105–2121 (2022). https://doi. org/10.1007/s00530-020-00692-3 5. Sharma, S., Singh, S.: Recognition of Indian sign language (ISL) using deep learning model. Wireless Pers. Commun. 123, 671–692 (2021) 6. PRATHUM ARIKERI. Indian sign language (ISL). Kaggle https://www.kaggle.com/datasets/ prathumarikeri/indian-sign-language-isl 7. PRATHUM ARIKERI. American sign language (ASL) dataset. Kaggle https://www.kaggle. com/datasets/prathumarikeri/american-sign-language-09az 8. Das, A., Gawde, S., Suratwala, K., Kalbande, D.: Sign language recognition using deep learning on custom processed static gesture images. In: 2018 International Conference on Smart City and Emerging Technology (ICSCET), pp. 1–6. IEEE (2018). https://doi.org/10. 1109/ICSCET.2018.8537248

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9. Kothadiya, D., et al.: Deepsign: sign language detection and recognition using deep learning. Electronics 11, 1780 (2022) 10. Rinalduzzi, M., et al.: Gesture recognition of sign language alphabet using a magnetic positioning system. Appl. Sci. 11, 5594 (2021) 11. Mannan, A., et al.: Hypertuned deep convolutional neural network for sign language recognition. Comput. Intell. Neurosci. 2022, 1–10 (2022) 12. Kasapba¸si, A., Elbushra, A.E.A., Al-Hardanee, O., Yilmaz, A.: DeepASLR: a CNN based human computer interface for American sign language recognition for hearing-impaired individuals. Comput. Methods Programs Biomed. Update 2, 100048 (2022) 13. Martinez-Martin, E., Morillas-Espejo, F.: Deep learning techniques for spanish sign language interpretation. Comput. Intell. Neurosci. 2021, 1–10 (2021) 14. Myagila, K., Kilavo, H.: A comparative study on performance of SVM and CNN in Tanzania sign language translation using image recognition. Appl. Artif. Intell. 36, 2005297 (2022) 15. Yirtici, T., Yurtkan, K.: Regional-CNN-based enhanced Turkish sign language recognition. SIViP 16, 1305–1311 (2021). https://doi.org/10.1007/s11760-021-02082-2 16. Nandi, U., Ghorai, A., Singh, M.M., Changdar, C., Bhakta, S., Kumar Pal, R.: Indian sign language alphabet recognition system using CNN with diffGrad optimizer and stochastic pooling. Multimed. Tools Appl. 82, 9627–9648 (2021). https://doi.org/10.1007/s11042-02111595-4 17. Dudhal, A., Mathkar, H., Jain, A., Kadam, O., Shirole, M.: Hybrid SIFT feature extraction approach for Indian sign language recognition system based on CNN. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds.) ISMAC 2018. LNCVB, vol. 30, pp. 727–738. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00665-5_72 18. Wang, Y., Li, Y., Song, Y., Rong, X.: The influence of the activation function in a convolution neural network model of facial expression recognition. Appl. Sci. 10, 1897 (2020) 19. Adu, K., Yu, Y., Cai, J., Asare, I., Quahin, J.: The influence of the activation function in a capsule network for brain tumor type classification. Int. J. Imaging Syst. Technol. 32, 123–143 (2022) 20. Rajarajeswari, S., Renji, N.M., Kumari, P., Keshavamurthy, M., Kruthika, K.: Real-time translation of Indian sign language to assist the hearing and speech impaired. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds.) Innovations in Computational Intelligence and Computer Vision. AISC, vol. 1424, pp. 303–322. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-0475-2_28 21. Kumar, N.: Thresholding in salient object detection: a survey. Multimed. Tools Appl. 77(15), 19139–19170 (2017). https://doi.org/10.1007/s11042-017-5329-y 22. Tyagi, A., Bansal, S.: Hybrid FiST_CNN approach for feature extraction for vision-based Indian sign language recognition. IAJIT 19(3), 403–411 (2022) 23. Dhiman, R., Joshi, G., Rama Krishna, C.: A deep learning approach for Indian sign language gesture classification with different backgrounds. J. Phys. Conf. Ser. 1950, 012020 (2021) 24. Katoch, S., Singh, V., Tiwary, U.S.: Indian sign language recognition system using SURF with SVM and CNN. Array 14, 100141 (2022)

Prognosis of Viral Transmission in Naturally Ventilated Office Rooms Using ML Nishant Raj Kapoor1,2(B)

, Ashok Kumar1,2 , and Anuj Kumar1,2

1 CSIR-Central Building Research Institute, Roorkee 247667, India

[email protected] 2 Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Abstract. More than 6.5 million people have already died as a result of the coronavirus pandemic, and there have been almost 0.6 billion cases of infection documented. Numerous organisations foresee the fourth wave, even though many countries are presently dealing with the severe consequences of the previous waves and the long-COVID repercussions. For all stakeholders, it is essential to forecast the rate of COVID-19 transmission likelihood inside enclosed environments. Based on the relevance of machine learning (ML) techniques in estimating the COVID-19 transmission probability, we collected the data through real-time measurements of eleven input parameters namely indoor temperature, indoor relative humidity, area of opening, number of occupants, area per person, volume per person, CO2 concentration, air quality index, outer wind speed, outdoor temperature, and outdoor humidity. The R-Event value was predicted using these inputs. Current literature is lagging behind the current pace of research in ML techniques which can be an effective and sustainable option for increasing public health and safety. In this work as a novel contribution, the prediction of new COVID-19 instances inside a workplace is done using MLtechniques and the parameters have been connected using novel methods. The performances of the six techniques are contrasted with one another using traditional statistical indicators to determine the merits of the suggested algorithms. This work will nudge the readers to use AI techniques in the prognosis of COVID-19 cases. Keywords: Indoor Air Quality · CO2 · Artificial Intelligence · Machine Learning Algorithms · Public Health · COVID-19

1 Introduction As a result of the COVID-19 pandemic, every country experiences significant worldwide losses (including human mortality, economic losses, losses to physical and mental health, losses to education and culture, and losses to employment) [1–5]. It is crucial to understand how ventilation systems and building typology may alter the likelihood of enhanced airborne transmission inside the building since COVID-19 spreads primarily through respiratory aerosols in closed structures [6, 7]. While it could be tempting to implement “one-size-fits-all” social isolation or occupancy restrictions, the truth is that indoor air quality varies significantly depending on the climate, type of structure, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 254–266, 2023. https://doi.org/10.1007/978-3-031-31153-6_22

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and usage pattern [8, 9]. Therefore, it is impossible to consider a particular ventilation method to be universal. However, cutting-edge artificial intelligence (AI) models for different ventilation strategies to prognoses the disease transmission inside the building may produce useful guidance for decision-making. In order to maintain the economy, viral transmission issues have been given more consideration while establishing facilities like offices, schools, hotels, commercial buildings, hospitals, and other buildings [10–12]. The majority of super-spreading incidents, in which numerous people become infected, take place in confined spaces like workplaces, restaurants, schools, homes, building complexes, hospitals, assembly halls, etc. People spend the majority of their time indoors, and the state of their indoor spaces directly affects how they feel [13, 14]. SC-2 has been used to abbreviate SARS-CoV-2 throughout this work. It has been established that SC-2 transmission through stagnant contaminated air is the major contributing factor to the significant rise in COVID-19 cases in indoor settings which is due to insufficient ventilation. Researchers, decisionmakers, building-related research institutes, and HVAC associations and societies from all over the world have released updated guidelines on the management and operation of ventilation systems during pandemics in response to the realization of the significance of ventilation systems in preventing the spread of SC-2 [10, 11, 15]. Several nations, including the United States, Canada, Japan, India, China, and the European Union, have released recommendations to combat COVID-19. The majority of the recommendations [16–25] emphasized the benefits of boosting fresh air and regularly ventilating a closed space. Several guidelines recommended temperature and relative humidity thresholds, as well as natural ventilation, CO2 as a proxy indicator, door/window opening, and other parameters, for the successful prevention of the SC-2 spread in buildings. Additionally, anthropogenic respiratory activities have an effect on the transmission rate because they alter the number, velocity, size, and settling distance of virus particles as well as the length and frequency of activity occurrence. The next section will throw light on some existing literature available on airborne transmissions and highlight the relevance of this study.

2 Existing Literature Wang et al. [26] published a review summary on the airborne transmission of respiratory viruses. Rudnick and Milton [27] evaluate the risk of viral transmission through indoor air based on the carbon dioxide level. They develop a model that calculates how much of the air being inhaled has already been expelled by someone within the building by using the CO2 concentration as a measure for exhaled breath exposure. Exhaled CO2 was used as a proxy indication for identifying the presence of SC-2 in a numerical study by Peng and Jimenez [28].Bazant and Bush [29] advised limiting the amount of time spent in a shared space with an infected person. Afterward, a mathematical model based on real-time CO2 monitoring was developed by Bazant et al. [30] to predict the spread rate of airborne SC-2. Mecenas et al. [31] analysed the literature on the relationship among humidity, temperature, and COVID-19 transmission and led to a conclusion that since these factors may affect the viral transmission, they should be included in future studies. Research to forecast AI-based event-specific airborne virus transmission

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in a naturally ventilated office area was just published by Kapoor et al. [32]. By using Artificial Neural Networks (ANN) and curve-fitting techniques, the authors of the [32] study linked IEQ parameters (CO2 levels, temperature, and humidity) with occupancy, occupant behaviour of door-window opening, outdoor pollution level, air-conditioning with operable fan, and event specific viral transmission probability. An effective door and window opening strategy based on spatiotemporal fluctuation and occupant behavioural patterns aids in efficiently reducing indoor CO2 concentrations, which in turn influences the possibility of SC-2 transmission in indoor environments [33]. AQI is also an essential indicator since it indicates the amount of pollutants. The most critical components of AQI are PM10 and PM2.5. Several researchers [34] propose that micro level particulates influence the transmission rate of SC-2. Tupper et al. [35] introduced the concept of “event R” in relation to SC-2. It is sometimes referred to as an “R-Event.“ Tupper et al. described R-Event as “event-R is the predicted number of new infections attributable to the presence of a single infected individual at an event.” REHVA added R-Event next to the infection probability in their improved version (Version 2.1) of the airborne COVID-19 prediction tool since it provides more information on viral transmission based on occupancy [36]. The REHVA computing technique is utilised to determine the R-Event value in the research environment under consideration, which is then recorded together with other real-time variables to construct a link between indoor CO2 and R-Event in an office context. An unknown single contaminated person (among four static persons) in the office setting is considered an infectious source to estimate the risk of airborne transmission (infecting through aerosols and droplets only). Using indoor CO2 levels, this study seeks to aid readers in exactly estimating the R-Event value in an office context. To arrive at prediction results, in addition to CO2 concentration, indoor and outdoor environmental factors, occupancy, and occupant data have been used. In this work, three supervised ML-based approaches were used, together with their optimised algorithms, to establish the R-Event value as a proxy for the potential of SC-2 spreading. The following are the study’s impact, necessity, and originality. COVID-19 is mostly disseminated by indoor airborne transmission, which happens when a susceptible individual inhales virus-laden aerosol droplets exhaled by an infected individual. This is scientifically proven by researchers worldwide. In 2021 [10], the CSIR, India produced a safety guideline to avoid airborne transmission in buildings by appropriately operating ventilation systems, which was recently modified by the CSIR in 2022 [11] to support public health standards. Carbon dioxide (CO2 ) and particulates (AQI) were included in safety recommendations because they can be easily monitored in most indoor settings and have been connected to viral transmission. As the pandemic approaches its fourth/fifth wave, our plan provides the groundwork for enhanced public health by increasing natural ventilation in buildings with reduced economic consequences. It will aid in the prediction of viral transmissions in advance, allowing management to effectively administer reopened workplaces. It will also help those people who have home offices built in residential complexes due to the rising work from home culture. Our findings highlight the need of assessing viral transmission risk in office settings. The following are the key novel contributions of this work to the current scientific

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literature: (i) Identifying crucial parameters (based on literature) and gathering realtime data on eleven input parameters for the office room. (ii) Six machine learning (ML) methods were modelled, including three base methods “Support Vector Machine” (SVM), “Ensemble Learning” (EL), and “Gaussian Process Regression” (GPR). Rest three methods are the optimised ML algorithms namely, Optimized SVM, EL, and GPR for estimating the R-Event value within the office room. (iii) Comparing the six models using conventional and contemporary statistical parameters. Following an introduction in Sect. 1, the study article is broken into five more parts; Section 2 deals with existing literature, Sect. 3 with data collection, normalization, and performance assessment to measure the accuracy of the ML models. Section 4 describes all ML algorithms. Section 5 discusses the findings and their implications, and Sect. 6 concludes this study.

3 Channelization of Data The computation was performed in a MATLAB R2021a environment. This article examines occupancy, indoor and outdoor climatic conditions, and CO2 content attributed to human breathing in order to anticipate the R-Event within the office room to predict indoor airborne virus transmission. The prediction models were developed utilising 638 data sets, with input variables such as indoor temperature (TIn ), indoor relative humidity (RHIn ), area of opening (AO ), number of occupants (O), area per person (AP ), volume per person (VP ), CO2 concentration (CO2 ), air quality index (AQI), outer wind speed (WS ), outdoor temperature (TOut ), outdoor humidity (RHOut ), and one output variable, R-Event. Because people are the key carbon dioxide generators, the concentration inside the room has a considerable influence on viral transmission owing to occupancy. The spread of viruses within any structure is influenced by the surrounding environment as well. 3.1 Collection of Data In the year 2022, data for this research was recorded in an office room at the CSIRCentral Building Research Institute in Roorkee, India. The measurements were taken between February and March. The coordinates of the investigated site are NL 29°51 54 and EL 77°54 10 . The office space is 24 m2 in size and has a ceiling height of 3.5 m. The office room has two entrances, one ventilation, and one large window (with three partitions). Figure 1 depicts a 3D model of the office room. Because of the Hawthorne effect, subjects were not briefed on the study aims during data collection in order to avoid interfering with their normal behaviour (talking, working, activity level, free mobility, etc.). A recent study by Kapoor et al. [32] contains the detail of subjective and objective information related to this research. The statistical analyses of the acquired input and output data are shown in Table 1. 3.2 Criteria for Evaluation To assess the accuracy of the ML models and propose an appropriate model for estimating the R-Event, the performance of each individual model must be compared using

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Fig. 1. 3D representation of the tested office room.

Table 1. Outcome of the collected data statistical analysis. Parameters

Unit

Symbol

Min.

Indoor Temperature

°C

TIn

21.7

Mean 25.0497

Max. 28.6

Std. 1.4947

Kurtosis 2.2800

Skewness

Type

0.2420

Inputs

Indoor RH

%

RHIn

24.7

42.3632

58.7

5.6256

4.1108

−0.3198

Area of Opening

m2

AO

1.5

2.9680

3.9

0.8083

1.5057

−0.1541

Occupants

nos

O

2

3.0094

5

0.8003

2.1961

0.2406

Area per person

m2 /person

AP

4.8

8.5850

12

2.3697

1.7804

0.4468

Volume per person

m3 /person

VP

16.8

30.0473

42

8.2939

1.7804

0.4468

CO2 Level Inside

ppm

CO2

327

514.7335

964

116.5317

4.0832

1.0456

Air Quality Index

–

AQI

85

144.4530

194

24.2872

3.1434

−0.3272

Outer Wind Speed

km/h

WS

2.7

11.7770

23.8

5.2473

2.3937

0.3443

Outdoor Temperature

°C

Tout

13

23.6991

31

4.0235

3.1174

−0.1954

Outdoor RH

%

RHOut

13

28.9091

48

7.7925

2.7992

−0.0159

R-Event

–

R-Event

0.04

0.24

0.0472

2.4088

0.5565

0.1062

Output

specified criteria. Commonly used performance metrics such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe (NS) efficiency index are employed for this purpose and are specified in Eqs. 1 to 4 [37, 38].     Mv − M × Pv − P (1) R=   2  2  Mv − M × Pv − P

Prognosis of Viral Transmission in Naturally Ventilated Office Rooms

1  |Mv − Pv | N v=1  N 2 v=1 (Mv − Pv ) RMSE = N N (Mv − Pv )2 NS = 1 −  v=1 2 N v=1 Mv − Pv

259

N

MAE =

(2)

(3)

(4)

where, N is the sample size in the dataset, measured values and predicted values are termed as Mv and Pv respectively andthe mean of the predicted values is denoted by Pv . 3.3 Data Normalization Data normalization is the process of making the data unitless so that ML algorithms may readily understand it. In this investigation, data in the range of 0 to 0.8 were standardised using Eq. 5 [39].   yI ,i − yI ,min ∗  Y = 0.8 ×  (5) yI ,max − yI ,min where, yI , is the measured value of Ith input (I = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) in the ith database (i = 1, ……., 638). yI,min and yI,max are the minimum and maximum values in the Ith input respectively.

4 Machine Learning (ML) Algorithms In this study, ML algorithms are used to anticipate the R-Event in the office setting and provide the most precise prediction model. Support vector machines (SVM), ensemble learning (EL), and Gaussian processes regression (GPR) are used to forecast the R-Event value. Additionally, the optimization of these algorithms are used, that is “Optimized SVM”, “Optimized EL”, and “Optimized GPR”. The predictions of each model were examined and contrasted in order to determine which was the most accurate. 4.1 Support Vector Machine (SVM) As tools for classification and forecasting, SVMs are becoming more and more common. SVMs use separation theorems and hyperplanes to constrain the real risk by the empirical risk in order to identify the optimum decision rule. Since SVMs don’t rely on any asymptotic concerns, they may be used on small data sets. They are particularly beneficial for data including several dimensions [40]. Vapnik’s theory and a particular type of statistical ML serve as the foundation for this categorization. The optimal machine maintains a balance between future dataset generalization and training dataset consistency. Additionally, SVMs enable us to avoid the performance degradation brought on by

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high-dimensional problems. These essential characteristics make this a strong contender for chaotic, scatter, and high-dimensional data collected for analysis. SVM incorporates theories that restrict the real risk in terms of the empirical risk in order to quantify error via asymptotic convergence to normality [41]. As a result, reliable error predictions may be made even with tiny sample sizes and without making any distributional assumptions. 4.2 Ensemble Learning (EL) EL algorithms have shown outstanding generalisation capacity in a variety of academic and scientific tasks encompassing a wide range of industries and areas. Ensemble techniques train a variety of machine learning algorithms to obtain a solution. ELs are based on human behaviour, with the notion that every problem can be handled by assembling and implementing the advice of several experts [42]. Given the numerous competing viewpoints, a decision is made. When compared to single classifiers, ELs yield superior results. There are two basic strategies for constructing fundamental models in the EL algorithm. Bagging and boosting are two of these strategies. Boosting approaches are algorithms that create basic ML models successively. The boosting approach mainly relies on one of the “basis models”, and training is guided by the “former basis model”. Bagging approaches train the “base models” in parallel, each of which is completely independent [43]. As a consequence of different techniques, the boosting strategy significantly improves a model’s prediction variance and bias, but the bagging method just increases a model’s stability by lowering variance. In terms of prediction accuracy, several studies have demonstrated that the boosting strategy surpasses the bagging technique. 4.3 Gaussian Process Regression (GPR) GPR is a non-parametric Bayesian regression technique used in machine learning. GPR provides a variety of advantages, including the capacity to handle tiny datasets and provide forecast uncertainty evaluations. Instead than estimating the probability distribution of parameters of a specific function, GPR computes the probability distribution of all admissible functions that match the data. A priority is required to analyse the training dataset and generate the estimated posterior distribution on the points of interest. [44] provides further GPR-related information. Several studies on GPR for predicting various aspects are available in the literature. Because of their predictability and inbuilt assessments of uncertainty, GPR models have been frequently employed in machine learning [45].

5 Results and Discussion 5.1 Application of ML Algorithms For the analysis phase of ML algorithms, the data was separated into two proportions. To minimise overfitting, the splitting ratio is set to 8:2, with 80% of the data (510 samples) utilised for training and the remaining 20% (128 samples) used for testing.

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5.2 Results of Machine Learning Models Six ML approaches were utilised to anticipate the viral transmission likelihood of SARSCoV-2 inside the workplace (defined as R-Event). The actual findings were compared to the anticipated R-Event, and performance indicators were utilised to estimate the inaccuracies. In the EL model, the ensemble boosted tree model, as opposed to the ensemble bagged tree model, displays the improved fitted model. The prediction accuracy of the EL model is 98.92%, with RMSE and MAE values of 0.007791 and 0.0040866, respectively. The cubic SVM is best-fitted with RMSE and MAE values of 0.006935 and 0.0035211, respectively, the SVM model has a prediction accuracy of 99.61%. The GPR model accurately predicts the R-Event throughout the training stage, however, there is a little deviation during the testing stage. The GPR model predicts with a 99.95% accuracy, with RMSE and MAE values of 0.001871 and 0.0002813 for the full dataset. 5.3 Discussion Tables 2 and 3 present a comparison of the R, MAE, RMSE, and NS of various techniques. All other techniques were surpassed by the Optimized GPR technique. GPR (Matern5/2) has a comparable potential in R-Event prediction, but owing to larger errors, it is the second most accurate prediction method after Optimized GPR. Optimized EL is the thirdbest prediction method, outperforming Optimized SVM, Cubic SVM, and Ensemble Boosted Tree. The Ensemble Boosted Tree is the least accurate algorithm identified during this investigation. The R of the Optimized GPR model is the highest of any model, with a value of 0.9994 across all datasets. Tables 2 and 3 indicate that the Optimized GPR model has the lowest MAE of all the techniques tested. According to Tables 2 and 3, the worst estimation came from the Ensemble Boosted Tree model, with a correlation coefficient of 0.9892, while the R2 is 0.9785, the MSE is 0.0000607, the RMSE is 0.007791, the MAE is 0.0040866, the NS is 0.9726857, and the a-20 index value is 0.976489. GPR (Matern 5/2), Optimized EL, Optimized SVM, and cubic SVM were the intermediate models studied between Optimized GPR and Ensemble Boosted Tree, with corresponding correlation coefficients of 0.9992, 0.9987, 0.9985, and 0.9961. Table 2. Comparison of six models based on statistical parameters (R and MAE). S.No. Method

R

MAE

Training Testing All

Training

Testing

All

1

Optimized GPR

1

0.9971

0.9994 0.0000382 0.0005494 0.0001408

2

GPR 1 (Matern5/2GPR)

0.9963

0.9992 0.0000559 0.0011794 0.0002813

3

Optimized EL

0.9993

0.9966

0.9987 0.0003612 0.0008276 0.0004548

4

Optimized SVM

0.9992

0.9955

0.9985 0.0007413 0.0018583 0.0009654

5

SVM (Cubic)

0.9968

0.9937

0.9961 0.0034132 0.0039513 0.0035211

6

EL (Boosted Tree)

0.9926

0.9765

0.9892 0.0038764 0.0049240 0.0040866

262

N. R. Kapoor et al. Table 3. Comparison of six models based on statistical parameters (RMSE and NS).

S.No. Method

RMSE

NS

Training

Testing

All

Training

Testing

All

1

Optimized GPR

0

0.003674 0.001643 0.9999927 0.9941274 0.9987739

2

GPR 0 (Matern5/2GPR)

0.004171 0.001871 0.9999945 0.9924446 0.9984256

3

Optimized EL

4

Optimized SVM 0.001817 0.004528 0.002608 0.9984908 0.9910725 0.9969503

5

SVM (Cubic)

0.006348 0.008899 0.006935 0.9933679 0.9873135 0.9921146

6

EL (Boosted Tree)

0.006885 0.010649 0.007791 0.9784322 0.9507035 0.9726857

0.001703 0.003987 0.002345 0.9986629 0.9930858 0.9975049

6 Conclusion The purpose of this study is to look at the different ML-based prediction models for predicting SARS-CoV-2 infection risk based on indoor CO2 concentration and the other 10 inputs. Various techniques are used within an office area to forecast the R-Event value as an infection probability proxy. During this investigation, algorithms such as SVM, GPR, EL, Optimized SVM, Optimized GPR, and Optimized EL were employed. Table 4 presents all the abbreviated terms used in this study. Overall, 638 datasets were gathered from a real-time dynamic office environment for this investigation. The REvent was predicted using both indoor and outdoor environmental factors, as well as occupancy parameters (TIn , RHIn , AO , O, AP , VP , CO2 , AQI, WS , TOut , and RHOut ). To select the best model, comparisons among models were done on the basis of traditional and sophisticated statistical performance indicators such as “R”, “RMSE”, “MAE”, and “NS”. The following are the study’s findings: (a) The R-Event value for the office environment was predicted using both traditional and optimized machine learning approaches. In terms of errors, the Optimized GPR model performed the best, with a lower discrepancy between actual and predicted values. (b) The Optimized GPR model had training and testing correlation values (R) of 1 and 0.9971, respectively, whereas the Ensemble Boosted Tree had training and testing correlation coefficients (R) of 0.9926 and 0.9765, respectively. The results demonstrate that the Optimized GPR model predicts the R-Event with more accuracy and reliability, whereas the other models have the lowest accuracy and reliability for the same prediction. (c) The precision of the ML models are shown in the following order (higher to lower) based on performance criteria: (1) Optimized GPR, (2) Matern 5/2 GPR, (3) Optimized EL, (4) Optimized SVM, (5) Cubic SVM, and (6) Ensemble Boosted Tree. Because of the nonlinear and complicated character of the airborne SARS-CoV2 virus and the difficulty in estimating viral transmission aspects using conventional epidemic models, artificial intelligence technologies have been used to forecast its spread and the probable number of infected people inside the closed structure. The ML approaches handled the intricate non-linear correlations between the eleven input

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parameters and R-Event successfully. However, the correctness of these approaches has not been tested due to the unexpected behavior of human-virus interaction. However, research is being conducted in this regard. The novelty of this work is the method to connect several parameters related to airborne transmission and that too without any requirement of costly and time-consuming experimental labour, ML prognosis is used to quickly predict the infection likelihood inside built environments. More crucially, the ML-based estimation tools enable quick assessment of essential parameters, resulting in a cost-effective and trustworthy solution.The future scope of this work contains different types of ventilation scenarios like air-conditioned office rooms, mixedmode ventilated office rooms and models based on advanced computational techniques. Researchers around the world can consider other built settings with several other variables (environmental/structural/pathogen-related/subjective/objective) by considering this study as a base study as well. Table 4. Abbreviated terms used throughout the study. Abbreviation

Terminology

Abbreviation

Terminology

AQI

Air Quality Index

IEQ

Indoor Environmental Quality

AI

Artificial Intelligence

ML

Machine Learning

ANN

Artificial Neural Network

MAE

Mean Absolute Error

R

Correlation Coefficient

NS

Nash-Sutcliffe Efficiency Index

COVID-19

Coronavirus Disease

REHVA

CSIR

Council of Scientific and Industrial Research

Representatives of European Heating and Ventilation Associations

EL

Ensemble Learning

RMSE

Root Mean Square Error

GPR

Gaussian Process Regression

SC-2

SARS-CoV-2

HVAC

Heating, Ventilation & Air-Conditioning

SVM

Support Vector Machine

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Impact of Organization Justice on Organizational Citizenship Behavior and Employee Retention Bhawna Chahar(B) Department of Business Administration, Manipal University Jaipur, Jaipur-Ajmer Express Highway, Dehmi Kalan, Near GVK Toll Plaza, Jaipur 303007, Rajasthan, India [email protected]

Abstract. Organizational justice (OJ) is considered as one of the important components influencing organisational citizenship behaviours (OCB) and assisting management in increasing employee retention (ER) in the organisation. The present study, which is based on the social exchange theory, aims to assess the impact of organisational justice on organisational citizenship behaviours and employee retention in firms. Questionnaire was used to collect the data from 491 employees associated with service sector organizations from Uttarakhand province and Delhi national capital region (NCR) of India. PLS-SEM is used to test hypothesized relationships between variables. It was found that five important components of Organizational justice namely, distributive justice, procedural justice, interactional justice, interpersonal justice, and temporal justice seems to have a significant positive influence on organizational citizenship behavior. Study revealed that OCB of employee impacts the employee retention. The outcome indicates OCB mediates the relationship between OJ and ER. This study may be found worth and useful for academics and those interested in the subject. Analysis of the results, suggestions for further study, and theoretical and managerial contributions are also worth discussing. Keywords: Organizational Justice (OJ) · Organizational Citizenship Behavior (OCB) · Employee Retention (ER)

1 Introduction Organizations are the most essential parts of civilization and are important for human society. Human resources are an organization’s greatest strategic asset. Responsible, devoted, bold, and wise workers are considered one of the most basic aspects of the new approach to management and organizational behavior, and behaviors such as helping other employees are very significant. Kan and Katz (1987) defined organizational citizenship behavior as extra-role behaviors. Firms in the hospitality sector struggle with a lot of complexity and ambiguity when it comes to employee retention (ER) (Park et al. 2019). Employee retention depends © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 267–286, 2023. https://doi.org/10.1007/978-3-031-31153-6_23

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heavily on organizational justice and citizenship behavior (Covella et al. 2017). It moves followers by its appeal to cultural and moral principles and concepts (Burns 1978). Motivating employees to quickly adapt to the changing conditions has emerged as a critical challenge for businesses. Most businesses utilize procedures, regulations, and processes to manage employee behavior. But prescribed behavior of its personnel is unable to successfully deal with fast organizational changes. Organizational justice is viewed as a synthesis of different aspects - Distributive justice, procedural justice, and interactional justice are main facets (Adams (1965), Leventhal (1980), and Bies and Moag (1986), Donglong, Taejun, Julie and Sanghun 2019; Jameel and Ahmad 2019). Employee perceptions of organisational justice are strongly related to OCBs because they reflect trust and encouragement that employees need. Organizational Citizenship Behaviour is a subset of out-of-role behaviour that may be used to define individual employee behaviour (Organ 1990; Moorman, Niehoff and Organ 1993; Jameel and Ahmad 2020a). Organizational justice causes anxiety in management operations such as hiring, performance evaluation, incentive schemes, conflict resolution, and downsizing (Cropanzano, Bowen, and Gilliland (2007). Organizational justice (OJ) is described as a moral status that encompasses both distributive and procedural justice (Jameel, Mahmood, and Jwmaa 2020). OCBs and organisational justice have been intensively investigated in the context of higher education (Donglong et al. 2019; Bauwens et al. 2019; Dong and Phuong 2018; Awang and Ahmad 2015). Based on the self-determination theory, Galletta, Maura, and Portoghese, Igor (2012) examined the organizational citizenship behavior of healthcare professionals (SDT). In a survey of 554 nurses associated with Italian hospitals, it was found that organizational citizenship behavior is activated among employees who are self-motivated and learning oriented. In the health sector, just a few studies have been undertaken (Farid, Iqbal, Jawahar, et al. 2019; Arbabisarjou, Hajipour, and Sadeghian 2014). The hospitality and tourism sector seeks to direct its human resources toward the achievement of its goals for meeting current societal requirements and maintaining the nation’s economic stability in addition to sustainable development. However, the sector is having a difficult time reducing personnel attrition and turnover. Thus, the goal of this research is to look at organisational justice features and how they affect organisational citizenship behaviour and employee retention. The present research tries to determine effect of Organisational justice dimension on OCBs among employee working with diferent hospitality and travel organisations in Uttarakhand state of India.

2 Organizational Justice and Employee Retention Organizations that treat their employees well tend to be more effective. Retaining competent staff is one of the most critical difficulties that firms face. Employees are shifting to another firm in quest of a better compensation package and perks (Suresh and Krishnaraj 2015). Retention of effective staff is a critical component of any corporate strategy. Employee retention is a big challenge for businesses. Organizational sustainability and performance are contingent on decreasing turnover and increasing retention. Employee retention refers to a company’s capacity to keep its staff (Fernandez and Worasuwan 2017). A voluntary endeavor by an employer to establish a suitable atmosphere that engages personnel for the long term is referred to as retention (De Sousa

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and colleagues 2018). Imran, Rabia, and Allil, Kamaal (2016) investigated the role of organisational justice principles in employee retention. All three elements of organizational justice were shown to have a positive influence on employee retention. Many previous researchers found that firms utilise a variety of approaches, such as guaranteeing organizational justice to employees through remuneration, employee engagement programmes, socialization, training, and development activities. Employees prefer to work for a firm that they feel suitably rewards their efforts. Organizational justice has a significant impact on employee turnover intentions. There has been little empirical study on the effect of organisational justice on employee retention. Organizations that are successful in ensuring that their employees are paid evenly and fairly can create a happy staff that performs better. By conducting a study in the Indian context, researchers are seeking to answer this demand (Abu Elanain 2009; Al-Zu’bi 2010; Sarnecki 2015; Karatepe and Shahriari 2012). Employees will be more likely to stay with their businesses if they are treated decently. Organizations that are successful in ensuring that their employees are paid evenly and fairly can create a happy staff that performs better. Organizational justice has a substantial influence on the retention of talented individuals (ASRM, Al-Zu’bi 2010; Bakhshi et al. 2009; Kumar 2014; ChitsazIsfahani and Boustani 2014). Academics disagree on the precise number of elements that make up organizational justice. Organizational justice has three facets, according to some academics. Others have described justice as having four aspects (Bies and Moag 1986; Cole et al. 2010; Gupta and Kumar 2012; Duffy et al. 2013). These arguments lead to following hypothesis: H1: Organizational justice has a positive influence on employee retention in the organization

3 Organizational Justice and Organisational Citizenship Behavior Organizational citizenship behaviors (OCBs) remain critical to organizational sustainability and play a fundamental role in businesses. Individuals strongly appreciate OCBs, which are behaviors that occur outside of the conventional job description. Greater trust and dedication, increased work performance, and more helpful civic activities are all advantages. Organizational justice has been identified as a critical factor influencing perceived organizational support and organizational citizenship activities. Several studies have found a substantial link between OJ and OCB (Kittikunchotiwut, Ploychompoo 2017, Parivash and Shabnam Bidarian 2012). A crucial component for the success and long-term sustainability of a company, OCB is an employee behavior resulting in “maintenance and growth of the social and psychological environment which promotes work performance” (Organ 1997, p. 91; Takeuchi et al. 2015). Using organizational citizenship behavior (OCB) as a mediator, Singh, S.K., and Singh, A.P. (2019) investigated the relationship between organizational justice (OJ) and job satisfaction (JS) and found that OJ significantly and positively enhances psychological empowerment. The study suggests that businesses appreciate OCB for enhancing JS and advancing ER. Employees who are treated well typically distribute their OCB to their company or other people but withhold it when they are not. Employees’ loyalty to the company and performance will both improve if they have a favorable impression of justice. According to the study,

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boosting employee knowledge and comprehension of their roles might improve retention. (Ayman Abu-Rumman, Lubna Khalid Qalaja, Asaad Hameed Al-Ali and A. Milena Ratajczak-Mrozek 2019) Organizational justice and organizational citizenship behavior have a good association. This argument takes the lead to the subsequent hypothesis. H2: Organizational Justice has a significant influence on the Organizational Citizenship Behavior of the employee in the organization.

4 Organisational Citizenship Behavior and Employee Retention Employees exhibit civic behavior (CB) when they believe their company supports and treats them fairly. OCB refers to discretionary individual behavior not immediately or officially acknowledged by the formal incentives system. Top management regards OCBs as desirable results because they improve organizational effectiveness. The use of OCB in the study of employee retention is a relatively new research focus. According to Lavelle (2010), exhibiting OCB requires personal motives that go beyond the desire to contribute something in exchange for being treated properly. Dr. Ella Mittal and Navneet Kaur (2018) explored the influence of corporate citizenship on employee retention in the banking business. 132 banking employees were chosen for the study, and it showed a positive relationship between OCB and ER Practices. To prevent this loss, several retention tactics are utilized, including employee connections, realistic job previews, recognition, training and development, leadership talents, and culture. Several studies have found a beneficial relationship between OCB and employee retention policies in organizations (Allen 2011). Every company spends considerable time and resources on the hiring process to secure these people as employees; as a result, losing those employees would be disastrous for the business. These reasons lead to the following hypothesis: H3: Organisational citizenship behavior has a significant influence on employee retention in the organization.

5 Organizational Justice, Organizational Citizenship Behaviour and Employee Retention: Mediation Analysis Organizational citizenship behaviour (OCB) is critical to a company’s success. OCB has successfully and efficiently managed companies limited resources, available fund, enhanced service and product quality, expanded/diversified resources, earned the trust of stakeholders and the general public, and has great personnel recruitment and retention (Podsakkoff, Mackenzie, Paine, and Bachrach 2000). Organ (1993) argued that increasing organizational citizenship behavior will improve organizational performance. In particular, OCB contributes to the development of a positive team environment, an improved capacity for the organization to respond to external changes, and the building of organizational social capital, all of which promote worker productivity and organizational performance. Although the majority of them are theoretical, several investigations of this kind have been carried out over time. There are currently few empirical studies. As a result, it was believed that OCB might moderate the link between employee retention and the antecedents (distributive justice, procedural justice, interactional justice, and temporal justice). This argument leads to following hypotheses:

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H4: Organizational citizenship behavior of employee in the organization mediates the relationship between organizational justice and employee retention.

6 Research Methodology The present study employs a descriptive research approach. Secondary data were gathered from various published sources such as books, periodicals, journals, and online sites. A survey was used to acquire primary data. Primary data was acquired from nonexecutive personnel connected with various service sector firms in Uttarakhand state and Delhi NCR, India. A well-structured questionnaire was developed that addressed various aspects of organisational justice, organisational citizenship behaviour as well as employee retention. Present research solely looks at hospital personnel. Employees were requested to participate in the survey by email, and potential respondents were asked to fill out a Google forms questionnaire. The construct for the study was identified by the researcher based on previous research work of Miller B K, Konopaske R, Byrne Z S (2012), Jen Ling Gan Faculty of Management, Halimah M. Yusof (2018), Senobari, M. (2008), K. Salehi and H. Zeinabadi (2011), S. Tourani, O. Khosravizadeh, A. Omrani, M. Sokhanvar, E. Kakemam, and Najafi (2016), and D. W. Organ (1988). Researchers revised certain questionnaire items for content validity and to focus on specific facts. So, a formal questionnaire was created, and an online survey was conducted. The first section of questionnaire denotes the demographic characteristics of the respondents. The second section consists of measurement variable related to various constructs of organizational justice, organizational citizenship behavior, and employee retention. Respondents were advised to rate each item on a five-point Likert scale (1 – strongly disagree to 5 – strongly agree). An original questionnaire was examined and verified from academic and topic specialists to assure the content validity of the survey instrument. The online poll was published between January and February 2022 by publishing the URL to referral groups and social networking platforms (Facebook, Researchgate, LinkedIn, and email addresses obtained through referrals). There were 500 answers in all. Following the removal of incomplete and disingenuous questionnaires (9), 491 valid surveys questionnaires were used to examine organizational justice and its influence on employee retention. During the editing process, certain outliers in the data were discovered and replaced with the nearest mean. Cronbach’s alpha was 0.974 for the combined scale, showing adequacy of measurement scale reliability. All data obtained was carefully edited, coded, tabulated, and analyzed using SPSS version 22.0 and Smart PLS version 3.0 to test the hypothesis and accomplish study objectives. The demographic characteristics of respondents are shown in Table 1.

7 Results The Table 1 reflects the demographic profile of respondents. Sample consists of 273 (55.6%) from Uttarakhand and 218 (44.4%) from Delhi NCR region. The sample is composed of middle-aged respondents, with 230 (46.8%) falling between the ages of 26 and 35. Another 82 (16.7%) responders are between the ages upto 25 years. 87 (18.7%) respondents are in the age of 36–45 years. 51(10.4%) of respondents are between the

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B. Chahar Table 1. Demographic Characteristics of Respondents (N = 491)

Description

Characteristics

Number of respondents

Percentage

State

Uttarakhand Delhi NCR

273 218

55.6 44.4

Age

Upto 25 years 26–35 years 36–45 years 46–55 years Above 55 years

82 230 87 51 41

16.7 46.8 17.7 10.4 8.4

Gender Categories

Male Female

323 168

65.8 34.2

Marital Status

Married Unmarried

270 221

55.0 45.0

Level of Education

Metric & below Under graduate Graduate Post Graduate Professional Qualification

23 117 182 121 48

4.7 23.8 37.1 24.6 9.8

Income Level

Below Rs.15000 pm (USD 200) Rs 15001 to Rs 25000 pm (USD 201 to USD 325) Rs. 25001 to Rs 40000 pm (USD 326 to USD 500) Rs. 40001 to Rs 60000 pm (USD 501 to USD 750) Rs. 60001 to Rs 150000 pm (USD 751 to USD 1875) Above Rs 150000 pm (USD 1876)

54 76 228 100 24 9

11.0 15.5 46.4 20.4 4.9 1.8

Period of association

0–5 years 6–10 years 11–15 years 16–20 years More than 20 years

243 136 89 11 12

49.5 27.7 18.1 2.2 2.4

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ages of 46 and 55, while 41 (8.4%) are above the age of 55. Gender wise classified respondents indicated that 323 (65.8%) are male and remaining 168 (34.2%) are female. The sample includes 270 (55.0%) married and 221 (45%) are female. Looking at the educational profile of the sample, it is observed that the sample is a combination of good education level respondents as 182 (37.1%) graduate, and 121 (24.6%) are post graduate. 48 (9.8%) respondents are having professional Qualifications. Looking at the Family income profile of respondents it is observed that 54 (11.0%) respondents indicated their income up to Rs 15000 PM (USD 200). Another 76 (15.5%) respondents indicated their monthly income from Rs 15001 to Rs 25000 PM (USD 201 to USD 325). 228 (46.4%) respondents indicated their monthly income from Rs 25001 to Rs 40000 PM. 100 (20.4%) indicated that they are having monthly income from Rs 40001–Rs 60000 PM (USD 326 to USD 500). 24 (4.9%) Rs 60001–Rs 150000 PM (USD 501 to USD 1875) and the remaining 9 (1.8%) Respondents indicated their monthly income was above Rs 150000 pm (USD 1876). It is observed that 243 (49.5%) respondents are associated with their present organization from 0–5 years. 136 (27.7%) respondents are associated from 6–10 years. 89 (18.1%) of respondent indicated their association from 10–15 years, 11 (2.2%) respondents indicated their association from 16–20 Years and remaining 12 (2.4%) respondents indicated their association for more than 20 Years. It is observed that Workload is the biggest reason for an employee leaving the profession. An effort was made to know from the employee about the nature of the workload in the organization. The study revealed that 93 (18.9%) indicated that their Workload is very much and tasks are not finished at the appropriate time. Another 187 (38.1%) employees indicated that Workload is very much but due to the participative environment employees can complete their tasks. 152 (31.0%) indicated that workload is evenly distributed and employees can complete their tasks on time. 59 (12.0%) indicated that Workload is less as compared to other companies (Table 2). Table 2. Perceived Nature of Work Load Sl. No Nature of Work Load

No of respondent s Percenta ge

A

The workload is very much, and tasks are not finished at the appropriate time

93

B

The workload is very much but due to participative 187 environment employees can complete their task

38.1

C

Work Load is evenly distributed and employees can 152 complete their tasks on time

31.0

D

The workload is less as compared to other companies

59

12.0

Total

491

100.0

18.9

One Way ANOVA was carried out to test whether different components of Organizational Justice, OCB and Employee Retention differs significantly across the employee working in different States. Test statistics as presented in Table 3 indicates that calculated

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value of F for Distributive Justice, Procedural Justice, Interpersonal Justice, and Interactional justice is 75.574, 32.961, 86.445 and 59.441 Which is greater than table value 3.84 (v1 = 1, v2 = 489, p ≤ 0.05) hence test statistics do not support the null hypothesis indicating that effect of this construct differs significantly across the employee of different states. However, Temporal Justice, Organizational Citizenship Behaviour, and Employee Retention do not differ significantly across the states. Table 3. One Way ANOVA of Different components of organizational Justice, OCB and Employee Retention across the employee working in different States Different Components

Sum of Squares

df

Mean Square

F

Sig

Distributive Justice

Between Groups

83.583

1

83.583

75.574

.000

Within Groups

540.818

489

1.106

Total

624.400

490

Between Groups

32.588

1

32.588

32.961

.000

Within Groups

483.460

489

.989

Total

516.048

490

Between Groups

71.287

1

71.287

86.445

.000

Within Groups

403.252

489

.825

Total

474.539

490

Between Groups

52.450

1

52.450

59.441

.000

Within Groups

431.495

489

.882

Total

483.945

490

Between Groups

.576

1

.576

.276

.600

Within Groups

1022.122

489

2.090 .485

.487

1.329

.250

Procedural Justice

Interpersonal Justice

Interactional justice

Temporal Justice:

Total

1022.699

490

Organizational Citizenship Behaviour

Between Groups

.903

1

.903

Within Groups

910.843

489

1.863

Total

911.745

490

Employee Retention

Between Groups

1.684

1

1.684

Within Groups

619.505

489

1.267

Total

621.188

490

Descriptive statistics of all measurement variables connected to organizational justice, organizational citizenship behaviour, and employee retention concept were developed to encapsulate a specific data set, that can be a depiction of complete population or a sample of a population. Table 4 measures’ standard deviation and variance show that, among the five organizational justice components, interactional justice has the highest mean score of 3.3031 with SD = .99380 and σ = .988. it is followed by Interpersonal Justice with mean = 3.2984, SD = .98410 and variance (σ) = .968, Temporal Justice

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with mean = 3.2797, SD = 1.44469 and Variance (σ) = 2.087, Distributive Justice with mean = 3.2383 and SD = 1.12884 and Variance (σ) = 1.274 and Procedural Justice with mean = 3.2369, SD = 1.02624 and variance (σ) = 1.053. The Employee Retention has scored mean = 2.9957, SD = 1.12594, and σ = 1.268. Mediating variable organisational citizenship behaviour has scored mean = 3.1658, SD = 1.36408 and Variance (σ) = 1.861. Table 4. Organisational Justice, Organisational Citizenship Behaviour and Employee Retention: A Descriptive Analysis (N = 491) Mean

Std. Deviation Variance

Distributive Justice

3.2383 1.12884

1.274

Organizational decision on resource allocation to facilitate employees for smooth working is fair

3.1711 1.53970

2.371

I have a proper and fair work schedule in the organization 3.0631 1.31634

1.733

My pay and reward packages are fair and compatible with the organization

3.2607 1.36025

1.850

Theallocatedjobresponsibilityandworkloadinthe organization are fair

3.4420 1.37551

1.892

I feel that my reward is a reflection of my effort put into work

3.2546 1.31334

1.725

Procedural Justice

3.2369 1.02624

1.053

We have a standard performance evaluation method in my organization

3.4012 1.26488

1.600

We have a standard reward and punishment system in the 3.1548 1.32652 organization

1.760

Employees in my organization have established channels 3.3462 1.29610 via which they can question choices they believe are incorrect

1.680

We have got well-documented policies and procedures for every management function in the organization

3.1894 1.20321

1.448

Employees are involved in the decision-making process that will affect them

3.0672 1.28157

1.642

The organization procedure of allocating resources is based on fundamental moral and ethical value

3.2627 1.14563

1.312

Interpersonal Justice

3.2984 .98410

.968

Management Treat employees in the organization with respect

3.1731 1.23246

1.519

Supervisor Refrain from improper remarks or comments

3.3462 1.26905

1.610 (continued)

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B. Chahar Table 4. (continued) Mean

Std. Deviation Variance

There is a culture of Treating employees in a polite manner

3.3360 1.14780

1.317

I can maintain my dignity in the organization

3.3381 1.16047

1.347

Interactional Justice

3.3031 .99380

.988

There is a formal and informal communication system in 3.4460 1.17397 the organization

1.378

Management in this organization is honest, open, and frank in communication about work concern

3.2342 1.16239

1.351

Management in this organization explains the process and workflow to its subordinates thoroughly

3.3401 1.24064

1.539

Employees are allowed to discuss and redress their grievances in the organization

3.3299 1.18369

1.401

Management is willing to share relevant information with 3.1650 1.45380 employees

2.114

Temporal Justice

3.2797 1.44469

2.087

I used to spend time with my spouse and children

3.3259 1.48039

2.192

I get time to enjoy myself with friends, gym, have hobbies, sleep

3.1079 1.60819

2.586

I can adjust my time with professional time

3.4053 1.41879

2.013

Organisational Citizenship Behaviour

3.1658 1.36408

1.861

I am completely involved in acting out of concern for the 2.9389 1.77865 well-being of other people

3.164

I always focus on enhancing my efficiency as well as group efficiency beyond the formal requirement

3.2974 1.56921

2.462

I avoid spending time humming and give more time to organizational endeavor

3.1527 1.57113

2.468

Instead of putting the problem in the organization, I give more constructive and appropriate information

3.2220 1.33003

1.769

I participate voluntarily in different committee services and attend functions

3.2179 1.25168

1.567

Employee Retention

2.9957 1.12594

1.268

I would like to remain with this organization for a longer 3.2179 1.25168 time

1.567

There is no reason for me to leave this organization

2.7923 1.42199

2.022

I think this place is the right place for me to work

3.0102 1.56162

2.439 (continued)

Impact of Organization Justice on Organizational Citizenship Behavior

277

Table 4. (continued) Mean

Std. Deviation Variance

I have no plan to leave this organization in the coming future

3.0631 1.46039

2.133

The work environment of this organization motivates me to remain for a longer period

2.9185 1.46018

2.132

I will probably look for a new profession in the next year 3.1039 1.53842

2.367

I feel at ease working with my coworkers

2.9409 1.23659

1.529

I have a clear understanding of the career path in this organization

3.1079 1.38305

1.913

My career path and promotion plans are satisfactory to me 2.9959 1.34619

1.812

Valid N (listwise)

Measurement Model Evaluation The measurement model investigates the link between latent variables and their measurements. In PLS-SEM route modelling, the estimated model is concerned with latent variables (Hair et al. 2014). The latent structures in this model are made up of many reflecting experiences. Cronbach’s alpha, composite reliability, convergent validity, and AVE tests were used to assess model fitness (Hair et al. 2017). Cronbach’s reliability assessments for all groupings were much higher than the acceptable level of 0.6 and approaching best edge of 0.7 (See Table 5). Figure 1 demonstrates components in each construct had loading factors greater than 0.5, indicating that no element was removed from the model. All constructs have a CR larger than 0.70, as shown in Table 5, and the AVE values range from 0.644 (Interactional justice) to 0.924 (Temporal Justice). By comparing the square root of each AVE in the inclining with the relationship coefficients (off-diagonal) for each construct in the relevant rows and columns, Fornel and Larcker (1981) were employed to evaluate discriminant validity. Table 6 shows no inconsistency, and generally speaking, this model’s support for the components’ discriminant validity may be said to be legitimate.

8 Structural Model and Hypotheses Testing The structural model and its fitness were tested using “PLS-SEM. To avoid factor multicollinearity, VIFs should be less than 5.0, R2 should be within suitable cutoff points, and standardized coefficients should be truly significant (Johnston et al. 2018; Hair et al. 2019). Collinearity exists when there is a significant correlation between at least two indicator constructs, which allows one construct to be reliably predicted by another indicator variable. The value of VIF (Variance Inflation Factor) determines collinearity, with VIF regarded as free of collinearity concerns. All VIFs were more than 1.0, with the most significant VIF of 4.725 being inside the acceptable range (See Table 5). It demonstrated that multicollinearity was not an issue. According to R2 calculations, the remaining structural model pieces accounted for 76.9 percent of employee retention and

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B. Chahar

Fig. 1. Components in each construct

Table 5. Reliability Statistics Cronbach’s Alpha

rho_A

Composite Reliability

Average Variance Extracted (AVE)

VIF

Distributive Justice

0.874

0.900

0.910

0.673

2.877

Employee Retention

0.925

0.936

0.938

0.629

N/A

Interactional justice

0.837

0.846

0.891

0.673

4.358

Interpersonal Justice

0.860

0.862

0.900

0.644

3.643

Organisational Citizenship Behaviour

0.945

0.953

0.958

0.821

N/A

Procedural Justice

0.900

0.914

0.923

0.669

2.334

Temporal Justice

0.959

0.967

0.973

0.924

1.056

Impact of Organization Justice on Organizational Citizenship Behavior

279

Table 6. Discriminant Validity Distributive Justice

Employee Retention

Interactional justice

Interpersonal Justice

Organisational Citizenship Behaviour

Procedural Justice

Distributive Justice

0.836

Employee Retention

0.832

0.835

Interactional justice

0.770

0.849

0.785

Interpersonal Justice

0.711

0.810

0.941

0.839

Organisational Citizenship Behaviour

0.786

0.799

0.818

0.820

0.915

Procedural Justice

0.836

0.786

0.725

0.699

0.701

0.838

Temporal Justice

0.366

0.283

0.290

0.313

0.335

0.195

Temporal Justice

0.965

Table 7. R Square Q-Square and SRMR R Square

R Square Adjusted

Q-Square

SRMR

Employee Retention

0.624

0.623

0.527

0.128

Organisational Citizenship Behaviour

0.309

0.308

0.568

Organizational Justice

1.000

1.000

0.432

68.9 percent of organizational citizenship behavior. At the 0.01 level, all of the standardized path coefficients were significant. All things considered, these requirements validated the structural model’s fitness (Table 7). PLS-SEM technique provides model connections (path coefficients) between constructs that represent the expected links between the constructs. Table 8 shows path coefficients, t-values, and p-values for each component of organization justice such as Distributive Justice -> Organizational Justice (β = 0.284, t = 29.08, p = 0.000), Interactional justice -> Organizational Justice (β = 0.228, t = 30.922, p = .000), Interpersonal Justice -> Organizational Justice (β = 0.275, t = 31.199, p = 0.000), Procedural Justice -> Organizational Justice (β = 0.318, t = 37.545, p = 0.000), Temporal Justice -> Organizational Justice (β = 0.125, t = 8.475, p = 0.000) was found significant and indicated all the construct of organizational justice have positive effect in organizational justice system in the organization. Looking at the structural relationship, between the construct, it is observed that organizational justice has a positive influence on employee retention (β = 0.200, t = 5.933, p = 0.000) and hence supporting the research hypothesis no 1 and it is concluded

280

B. Chahar

that the organizational justice system in the organization has a positive effect on employee retention. The second hypothesis was proposed as organizational justice has a positive and significant influence on the organizational citizenship behavior of employees. Test statistics confirm that the indirect effect of organizational justice on organizational citizenship behavior (β = 0.556, t = 17.5, p = 0.000, p < 0.05) is significant and hence supports research hypothesis no 2 and concluded that organizational justice has a significant influence on organizational citizenship behavior of employees. The third hypothesis was proposed as organizational citizenship behavior has a positive effect on employee retention in the organization. Test statistics confirm that the effect of OCB on employee retention (β = 0.662, t = 24.057, p = 0.000) is significant and supports the research hypothesis no 3. Table 8. Mean, STDEV, T-Values, P-Values Original Sample (O)

Sample Mean (M)

Standard Deviation (STDEV)

TStatistics (|O/STDEV|)

P Values

Distributive Justice -> 0.284 Organisational Justice

0.284

0.010

29.008

0.000

Interactional justice -> 0.228 Organisational Justice

0.228

0.007

30.922

0.000

Interpersonal Justice -> Organisational Justice

0.275

0.275

0.009

31.199

0.000

Organisational Citizenship Behaviour -> Employee Retention

0.662

0.662

0.028

24.057

0.000

Organisational Justice -> Employee Retention

0.200

0.200

0.034

5.933

0.000

OrganisationalJustice -> Organisational Citizenship Behaviour

0.556

0.557

0.032

17.500

0.000

Procedural Justice -> Organisational Justice

0.318

0.318

0.012

27.554

0.000

Temporal Justice -> Organisational Justice

0.125

0.124

0.015

8.475

0.000

Mediation Analysis A mediation analysis is used to measure the causal relationship between an antecedent variable, a mediating variable, and a dependent variable. Mediation analysis could be

Impact of Organization Justice on Organizational Citizenship Behavior

281

most beneficial in randomised therapeutic and preventative programmes to answer causeand-effect issues. Because organizational citizenship behaviour (OCB) mediates the link between organisational justice and employee retention, a study hypothesis is employed to assess the mediation impact of OCB. The direct and indirect impacts were evaluated using the “Smart-PLS bootstrapping” method. Organizational justice was found to have a strong direct impact on employee retention in the first phase of the analysis (Table 9). The second stem looked at the indirect impact of a mediating factor (organisational citizenship behaviour) on the relationship between organizational justice and employee retention. As suggested by Hypotheses 4, primary purpose of research was to examine the mediating role of OCB in the association between organizational justice and employee retention. Mediator test results are summarized in Table 9, which demonstrates that the indirect association suggested in H4 is valid. The analysis shows that OCB notably mediates the relationship between organizational justice and employee retention (β = 0.662 t = 24.057 p = 0.000)). It is discovered that incorporating OCB reduces the variation from 0. 0.034 to 0.023from the direct influence of organizational justice on employee retention via OCB. As a result, OCB mediates association between OJ and employee retention, hence supporting hypothesis 4. Discussion The finding of study suggests all identified components of the organizational justice system like distributive, procedural and integrative, interpersonal, and temporal justice contribute significantly to making organizational justice more effective in the Indian context. These findings are consistent with previous research (Imran, Rabia and Allil, Kamaal 2016; Nithyajothi Govindaraju 2019; Kaurl, Mohindru, and Pankaj 2013; AlZu’bi 2010; Bakhshi et al. 2009). Organizations spend their efforts in discovering the elements that might aid in employee retention. Organizational justice is seen as a crucial element that contributes to staff retention. If employees believe that their company treats them fairly and equally based on their achievements, their loyalty towards organization grows and they want to stay (Al-Zu’bi 2010). Thus, organizational fairness is seen to be a substantial predictor of employees’ retention intentions inside a business. (Bakhshi et al. 2009). Finding revealed that most of the components of organizational justice differs significantly across the employee associated with different states. Another significant finding of the study indicates that organizational justice has a good impact on developing strong citizenship behavior among employees, which influences employees to stay with the firm for a longer length of time. This is also consistent with Kittikunchotiwut and Ploychompoo’s (2017) earlier study findings. According to Zhou, L., Wang, M., Chen, G., and Shi, J. (2012), organizational justice systems have a considerable beneficial influence on perceived organizational support. Perceived organizational support has a strong favorable influence on organizational citizenship behaviors, according to the current research findings, which imply that OCB mediates the link between OJ and employee retention. The outcome of this research work is also in support of previous research work of Saoula, Oussama and Johari, and Husna (2016). Md. H Asibur Rahman, Dewan Niamul Karim (2022) whose work outcomes provide necessary guidelines for the organizations regarding how they might improve citizenship behavior by ensuring justice and engagement in the workplace.

282

B. Chahar Table 9. Mediation Analysis: Path coefficient and Confidence Interval Original Sample Variance T P Bias Sample Mean Statistics Values (O) (M) (|O/STD EV|)

2.5% 97.5%

Direct Effect Organisational Justice -> Employee Retention

0.200

0.200

0.034

5.933

0.000

−0.003 0.133 0.301

Organisational Justice -> Organisational Citizenship Behaviour

0.556

0.557

0.032

17.500

0.000

0.006

0.145 0.280

Organisational Citizenship Behaviour -> Employee Retention

0.662

0.662

0.028

24.057

0.000

0.001

0.210 0.427

0.368

0.369

0.023

16.229

0.000

0.568

0.568

0.032

17.834

0.000

Indirect effect Organisational Justice -> Organisational Citizenship Behaviour -> Employee Retention Total Effect Organizational Justice -> Employee Retention

9 Theoretical Implication This research is one of the few studies that look at organizational justice as a predictor of ER, the current study provides a theoretical contribution to the field of organisational justice. The goal of this research is to get a better knowledge of the connections between three elements of organisational justice: distributive justice, procedural justice, interactive justice, perceived organisational support, and organisational citizenship actions. According to organizational justice, this study focuses on its significance in the context of justice, making new theoretical contributions by extending the organizational behaviour and psychology literature by bringing management and organizational behaviour sectors

Impact of Organization Justice on Organizational Citizenship Behavior

283

together. Furthermore, one theory, social exchange theory, is introduced to describe the model’s interactions between variables. This study broadens the scope of organizational justice measurement by altering the scale to represent the justice context. Similarly, more research is needed to collect data from varied groups of samples and/or comparable populations or other business sectors to increase the degree of trustworthiness and widen the research contributions.

10 Managerial Implication This research assists executives and managers in making decisions, and practitioners will learn how to apply organizational citizenship practices. Organizational survival considerations indicate that perceived organisational support and organisational fairness promote organisational citizenship activities. They must recognize organizational behaviour and social exchange theory in order to offer distributive justice, procedural justice, interactive justice, perceived organizational support, and organizational citizenship behaviours. Furthermore, social exchange theory can expand organizational citizenship behaviours, which are behaviours at work which go beyond the conditions of the job; they contribute to the firm’s social and psychological environment and aid in task performance. Furthermore, managers and policymakers in Oman may use current study findings to foster views of organizational justice through the fairness of rules, processes, and compensation systems to retain their talented workforce. This study has certain shortcomings as it solely looks at the connection between organizational justice and ER. It did not look at additional factors or the interrelationships between justice aspects.

11 Conclusion This research’s approach provides an important theoretical foundation for investigating organizational fairness and employee retention. Organizational justice may successfully increase organizational citizenship behavior while also assisting in the achievement of employee retention goals. This research studies and comprehends the actual position of the employee in India, focusing on the link between organizational justice and employee retention.

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Adopting Metaverse as a Pedagogy in Problem-Based Learning Riya Baby1 , Amala Siby2

, Jerush John Joseph2(B)

, and Prabha Zacharias3

1 School of Sciences, CHRIST (Deemed to be University), Bangalore, India

[email protected] 2 School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University),

Bangalore, India {amala.siby,jerush.john}@christuniversity.in 3 School of Humanities and Social Sciences, CHRIST (Deemed to be University), Bangalore, India [email protected]

Abstract. Pedagogical practices vary from time to time based on the requirement of various academic disciplines. Course instructors are constantly searching for inclusive and innovative pedagogies to enhance learning experiences. The introduction of Metaverse can be observed as an opportunity to enable the course instructors to combine virtual reality with augmented reality to enable immersive learning. The scope of immersive learning experience with Metaverse attracted many major universities in the world to try Metaverse as a pedagogy in fields such as management studies, medical education, and architecture. Adopting Metaverse as a pedagogy for problem-based learning enables the course instructors to create an active learning space that tackles the physical barriers of traditional pedagogical practices of case-based learning facilitating collaborative learning. Metaverse, as an established virtual learning platform, is provided by Meta Inc., providing the company a monopoly over the VR-based pedagogy. Entry of other tech firms into similar or collaborative ventures would open up a wide array of virtual realitybased platforms, eliminating the monopoly and subsequent dependency on a singular platform. The findings of the study indicate that, currently, the engagements on Metaverse are limited to tier 1 educational institutions worldwide due to the initial investment requirements. The wide adoption of the Metaverse platform in future depends on the ability of the platform providers to bridge the digital gap and facilitate curricula development. Keywords: Metaverse · Problem-Based Learning · Pedagogy · Virtual Learning

1 Introduction The emergent behaviours in the virtual world points toward an explosion of avenues in the areas of innovation, creativity and engagement in web-based media platforms for knowledge creation. This is made possible predominantly due to the advancements in computational power, an increased access to high-speed internet connectivity, and the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 287–295, 2023. https://doi.org/10.1007/978-3-031-31153-6_24

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allure of the graphical 3D reproductions possible on any easily accessible home-based personal computer device. The development and demand of new software products into the market has made advanced digital content available and accessible for common people. Although there have been advances, many still consider virtual worlds a fad among young gamers or a technology that is not yet ready for serious use by professionals or academics. In this article, the authors explore the effects of the Metaverse’s development and attempt to assess the possible applications of the Metaverse in higher education through case studies from institutions that have already embraced Metaverse as a pedagogy tool for problem-based learning. The article also discusses the unique role of higher educational spaces in facilitating the students prepare for the impending rise of virtual worlds. Virtual reality, or the idea of connecting with others in a computerized, digital settings, has been around for decades. It is often observed that the 1982 release of the cult classic science fiction movie Tron directed by Steven Lisberger is a milestone in making “virtual reality” a trending idea to work with in the early 1990s. In films like Lawnmower Man, people join digital landscapes with the aid of external gadgets they wear on their bodies, such as goggles for vision, specialized gloves, etc. Neal Stephenson initially coined the term Metaverse in 1992 in his literary work – Snow Crash [1]. It was envisioned that these gadgets could be naturally worn, which would enable the users to interact with both an immersive virtual reality and an enhanced version of the actual world simultaneously. Virtual worlds represent an intersection of several technology trends that, if realized, would put us on the verge of a more profound change that could significantly alter how we interact with and perceive both our physical and virtual worlds on an everyday level in a new ecosystem of digital tools, spaces, and networks that exist online. While acknowledging the critique of living in an ‘Age of Distractions’, the authors explore the possibility of Metaverse having genuine implications for higher education because of the changing ways in which we can access, interact with, and create information. Increasingly we are aware of the transformations in the patterns with which we access, interact with, and connect with one another. The paper explores the possibility of adopting Metaverse as a pedagogic tool in higher education, making it an accessible tool from the digital age which has immense potential in unlocking the imaginative realms of the human mind through a virtual medium.

2 Literature Review Makransky et al. [2] studied how well immersive virtual reality (VR) works as a training tool for laboratories. Their findings show that in order to appropriately evaluate the educational effectiveness of VR learning environments, behavioral measures of transfer in realistic contexts may be required. Wang et al. [3] studied how the Metaverse can support and improve e-learning. They observed that even though the software platforms reduce immersion and fail to replicate the classroom experience, there is a possibility of transforming the traditional physical classroom into a virtual-physical cyberspace as new social networks of learners and educators stay connected at an unprecedented scale.

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2.1 Characteristics and Scope of Metaverse The term ‘Metaverse’ was coined to effectuate the fictional characters that appear as avatars and other virtual characters to impersonate their interactions with the users in a daily situational context. The influential characteristics of metaverse has the potential to be appropriated as an effective tool in an educational environment [4]. Among these characteristics include substantiality, endurance and interactivity where users across the world can come together in a virtual learning platform. The feature of interactivity can open a whole world of collaborative synergy in the scenario of education by introducing a level of autonomy in accessing all the available resources [5]. The outlook to metaverse as a teaching pedagogy in problem-based learning is equally beneficial to educational institutions and the industry. The requirement for an educated workplace can be accomplished through a well-trained workforce through metaverse as a training pedagogy for management scenarios which can pave the way to the adoption of new management practices and leadership models. Among other things, these systems help analyze human behavior and provide an opportunity to have a simulated interface, enabling the users to engage with multiple real-life scenarios while they are getting a higher education. Such initiatives can creatively bridge the academia-industry gap if carefully utilized [6]. Higher education institutions can provide a platform in the metaverse to build boundary-less classrooms where there is an absence of the physical constraints commonly attributed to a traditional classroom environment. This flexible classroom mechanism provides communication facilities in a total digital space with the click of a button. In the recent studies, the focus of metaverse got shifted to artificial intelligence technologies and collaborative learning through hybrid learning models. These learning tools are used to enhance teaching pedagogies, thereby making learning more engaging to the students. It has also been observed that to ensure the successful implementation of these systems, there is a requirement for adequate tools to trace the actual development in performance of those learners to whom such pedagogy has been implemented. To accomplish this purpose there have been techniques such as eye tracking to assess how the learners proceed in their learning by way of measuring their processing of various visuals and texts in the process of reading [7].

3 Metaverse and Its Role in Education The term “metaverse” was first used by science fiction author Neal Stephenson in his 1992 book “Snow Crash.” Since then, the idea has been investigated in both video games and films like “Avatar,” “Ready Player One,” and the virtual reality experience “Second Life.” The term “metaverse” can refer to a networked system of 3D virtual environments. Through a virtual reality headset, people can enter these worlds and move about them by utilizing their voice or eye movements. Additionally, it is accessible via augmented reality (AR) headsets, phones, consoles, and connected devices. The term “metaverse” is still in flux since its use is changing, much like the term’s “internet” or “the cloud” when they were still in their infancy [8]. The way we connect with technology is typically described in terms of:

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• Virtual Reality (VR) is an entirely virtual immersive experience of an “alternative” world created solely by technology/computers. • Augmented Reality (AR) is a real-world setting upon which technologically enhanced/augmented components like noises, or visual pictures are superimposed. • Mixed reality (MR) combines the physical and digital worlds. Consider Snapchat filters as an illustration. A metaverse-style virtual environment has a lot of promise to offer a platform for interaction between students, instructors, and staff in a setting that can be completely adjustable to meet varied demands [9]. The Metaverse can assist in developing virtual worlds that could help teachers connect with pupils regardless of geographical boundaries when the present educational system is criticized for being cut off from the actual world. Educators can develop a more immersive learning environment due to the Metaverse. Many researchers are working on how individuals use the Metaverse to go to school and learn, using the available VR features, such as playing games and going to virtual concerts [10]. 3.1 Metaverse Pioneers in Higher Education Due to the epidemic, the typical collegiate road trip was transformed into an online experience that included videos and Zoom sessions. Virtual tours are expanding into the Metaverse, giving students a chance to virtually experience campus life without needing to use their legs. Some universities and colleges are already making headway into this new technological world, from individual classes to establishing a whole “metacampus” and future “metaversities” [11]. The transition from physical classrooms to institutions was already underway, but COVID-19’s digital transformation expedited it [12]. But it’s a new stride into the Metaverse, and these are just a few of the several institutions and universities that are doing so (Table 1):

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Table 1. Initiatives to establish immersive learning Platforms by the Universities across the world (Compiled by the Authors)

The decommissioned Ford Nuclear Reactor is being recreated by the University of Michigan in a secure XR environment. Medical students at the Queen Mary University of London recently attended the first lecture in the Metaverse, considered such a way that it is in the Metaverse in the UK.

Within their brand-new Advanced Research Centre, the University of Glasgow is working on a dedicated XR space that has already attracted £1.5 million in funding.

The E-Learning Centre of the University of Bahrain has opened the BBK Lab for Virtual and Augmented Reality. Tsinghua University maintains a "Metaverse Cultural Laboratory to study market trends."

By 2023, the Kenya-KAIST campus will host the Korea Advanced Institute of Science and Technology virtual campus.

4 Benefits of Metaverse Metaverse has many advantages in this world, especially after the Covid 19 Pandemic. It can significantly alter where and how individuals’ study. Metaverse is entirely immersive, which might enable more in-depth experience learning and increase interaction, for instance, in a biology course explaining the anatomy lesson or a recreated moment in time in a history course [13]. The opportunity for students to commit mistakes and benefit from them in a realistic simulation without risk is much more interesting than simply learning simple concepts. Learning is more effective when it is active, and gamification of learning, which has several advantages, could be advanced in the Metaverse. In addition, the Metaverse offers the opportunity to cross geographic gaps, bringing classes of students and lecturers together regardless of where they are in reality. With many locations for programs and conferences, it also provides more significant opportunities for academic and student collaboration among universities. This might positively impact sustainability and cut down on institutions’ carbon footprint [14] (Fig. 1). Metaverse creates a unique learning experience in learning spaces where collaborations happen in virtual spaces such as Minecraft, Roblox etc. Such platforms help in bridging the gap between real and virtual life by making interactions more natural. With

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the spread of online education, there comes an inclusivity in education also. Many virtual platforms have created a larger availability of knowledge sources which can further be extended with the help of metaverse and thus making remote learning more accessible to students across the globe [15]. A hands-on experience given to students on a metaverse platform not only enhances the hybrid learning experience, but also provides an effective measure to visually demonstrate the topics in most effective ways. This further can lead to increased student engagement and satisfaction in students [16] (Fig. 2).

Fig. 1. CAVE facility at the University of California, San Diego established for studying Human Perceptions in the Workplace.

5 Metaverse as an Effective Pedagogy in Higher Education Metaverse has some characteristics that align well with certain learning goals and processes in higher education because they enable learners to engage in certain tasks. Using metaverse we can increase the interest and motivate students to learn in a better way. Inceoglu et al. [8] discussed the historical development of the Metaverse in education. The strengths and weaknesses of the use of Metaverse in the field of education are emphasized in this paper. Also, the opportunities, problems, and threats encountered using Metaverse are examined. This paper notes that the Metaverse environment can add a new dimension to educational technologies and appropriate strategies using Metaverse in the academic field. We can start determining its widespread effect until the infrastructure of Metaverse matures. The disruptions created by online education can be best handled by the implementation of metaverse technologies in all levels of higher education. Ranging from sciences

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Fig. 2. A class at University of Tokyo, Metaverse School of Engineering

to arts to management studies, metaverse can be applied in all spectrum of educational settings. When medical education fields could be diversified into metaverse platforms, it not only enhances the learning experiences, but also eliminates a whole lot of unethical practices that are associated with medical education. In management studies, metaverse can be utilized to globalize the teaching pedagogy to have live experience of real-world business problems in a simulated business environment [17].

6 Challenges The ethics of how technology can be used and digital connections, in general, are linked to many potential problems [18]. These problems can be classified majorly as under: • Private social media corporations like Meta (previously Facebook), which operate the VR platform, have the authority to exclude particular topics from discussion or decide what gets taught to the students. • With cell phones, it is already challenging for teachers and students to manage distractions in the classroom. So, it isn’t easy to perform in light of recent AR and VR advancements. • Communication relies on nonverbal clues, and many lectures focus on discussion and debate. Therefore, our ability to communicate with one another is compromised without nonverbal cues. • The university or college experience is more than just attending lectures. Many say it’s about the sense of community, but it isn’t easy to be successfully simulated in a virtual reality environment. • This new technology creates a social-economic learning barrier, a new “digital gap,” where some people can “afford” an on-campus learning environment but only have access to online or Metaverse learning.

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• While some kids may find the technology very helpful, there is also the accessibility issue that needs to be taken into account. • Access for students who might require special accommodations or who have visual or auditory disabilities could be impeded rather than assisted. • Furthermore, nobody is yet aware of the ideal graphical XR visual style for learning. • Additionally, it will require a group of knowledgeable instructors to train students in using cutting-edge XR techniques and technologies, as well as upgrades to internet connections’ speed and bandwidth (like 5G and Multi-access Edge Computing). • Some people are also interested in learning how this area will be governed. • Cryptocurrencies are used to pay tuition in the higher education sector, and this presents opportunities for money laundering or the nefarious use of technology. • Difficult to maintain the confidentiality of data. • There may also be worries about how long-term usage of this technology and XR learning could result in more addictive behaviors.

7 Conclusion The stakeholders in higher education who are interested in the development of Metaverse as a pedagogical platform will be curious to observe how the campus might evolve in this world. The potential advantages and disadvantages of this new technology on a social or psychological level are still being studied for AR and VR. As it scales and spreads, a lot more research will be required. Higher education will likely have to make various decisions, not the least of which will decide which technology vendor to use and invest in [19]. It is now up to researchers, academics, and programmers to contribute to creating this new virtual environment and discuss some of the consequences of using the Metaverse for education. However, we may anticipate that VR and AR will develop further as technology and disrupt the current methods of instruction, employment, and daily living.

References 1. Dwivedi, Y.K., et al.: Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 66, 102542 (2022). https://doi.org/10.1016/j.ijinfo-mgt.2022.102542 2. Makransky, G., Borre-Gude, S., Mayer, R.E.: Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. J. Comput. Assist. Learn. 35(6), 691–707 (2019) 3. Wang, X., Wang, J., Wu, C., Xu, S., Ma, W.: Engineering brain: metaverse for future engineering. AI Civil Eng. 1(1), 1–18 (2022) 4. Díaz, J., Saldaña, C., Avila, C.: Virtual world as a resource for hybrid education. Int. J. Emerg. Technol. Learn. (iJET) 15(15), 94–109 (2020) 5. Farjami, S., Taguchi, R., Nakahira, K.T., Nunez Rattia, R., Fukumura, Y., Kanematsu, H.: Multilingual problem based learning in metaverse. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011. LNCS (LNAI), vol. 6883, pp. 499–509. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23854-3_53 6. Hirsh-Pasek, K., et al.: A whole new world: education meets the metaverse policy (2022)

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7. Hwang, G.J., Chien, S.Y.: Definition, roles, and potential research issues of the metaverse in education: an artificial intelligence perspective. Comput. Educ.: Artif. Intell. 3, 100082 (2022) 8. Inceoglu, M.M., Ciloglugil, B.: Use of metaverse in education. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) International Conference on Computational Science and Its Applications, pp. 171–184 (2022). Springer, Cham. https://doi.org/10.1007/ 978-3-031-10536-4_12 9. Kanematsu, H., Fukumura, Y., Barry, D.M., Sohn, S.Y., Taguchi, R.: Multilingual discussion in metaverse among students from the USA, Korea and Japan. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS (LNAI), vol. 6279, pp. 200–209. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15384-6_22 10. Kye, B., Han, N., Kim, E., Park, Y., Jo, S.: Educational applications of metaverse: possibilities and limitations. J. Educ. Eval. Health Prof. 18 (2021) 11. Lombardi, J., Lombardi, M.: Opening the metaverse. In: Bainbridge, W. (eds.) Online Worlds: Convergence of the Real and the Virtual. Springer, London, pp. 111–122 (2010). https://doi. org/10.1007/978-1-84882-825-4_9 12. Collins, C.: Looking to the future: higher education in the Metaverse. Educause Rev. 43(5), 51–63 (2008) 13. Novak, K.: Introducing the metaverse, again! TechTrends 66, 1–3 (2022) 14. Newport, C.: Deep work: Rules for Focused Success in a Distracted World. Hachette, London (2016) 15. Queiroz, A.C.M., Nascimento, A.M., Tori, R., da Silva Leme, M.: Using HMD-based immersive virtual environments in primary/K-12 education. In: Beck, D., Allison, C., Morgado, L., Pirker, J., Peña-Rios, A., Ogle, T., Richter, J., Gütl, C. (eds.) iLRN 2018. CCIS, vol. 840, pp. 160–173. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93596-6_11 16. Tlili, A., et al.: Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis. Smart Learn. Environ. 9(1), 1–31 (2022) 17. Cruz-Lara, S., Osswald, T., Guinaud, J., Bellalem, N., Bellalem, L., Camal, J.-P.: A chat interface using standards for communication and e-learning in virtual worlds. In: Filipe, J., Cordeiro, J. (eds.) ICEIS 2010. LNBIP, vol. 73, pp. 541–554. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19802-1_37 18. Wang, Y., Lee, L.H., Braud, T., Hui, P.: Re-shaping post-COVID-19 teaching and learning: a blueprint of virtual-physical blended classrooms in the metaverse era (2022). arXiv preprint arXiv:2203.09228 19. Xi, N., Chen, J., Gama, F., Riar, M., Hamari, J.: The challenges of entering the metaverse: An experiment on the effect of extended reality on workload. Inf. Syst. Front. 25, 1–22 (2022)

Design of Fuzzy Based Controller for Temperature Process in Fire Tube Boiler K. Srinivasan, V. Rukkumani, T. Anitha(B) , and V. Radhika Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India [email protected]

Abstract. Modeling of high-pressure boiler is a tedious method in process industries. In this paper High pressure boiler inlet temperature is taken as the input data and High-pressure boiler outlet temperature is taken as the output for the boiler process. The input and output temperature are measured manually. The mathematical model of the boiler process is recognized in the research laboratory circumstances using MATLAB simulations using system identification procedure. This identified model is used for implementing controllers like conventional and fuzzy based controller in the simulation environment. Here the fuzzy controller in the MATLAB is implemented for the obtained transfer function of the Boiler process. In future further intelligent and adaptive controllers can be incorporated for this fire tube boiler process along with the attention of additional nonlinearities which would be more cooperative in controlling of fire tube boilers. Keywords: Process control · Fuzzy logic control · Proportional Integral control · Proportional Integral Derivative control · MATLAB

1 Introduction Thermal power plant generates more than 80% of the total electricity produced in the world. Steam plays a vital role for generating machine-driven energy. A thermal power plant converts energy storing fossil fuels. The location of thermal power stations is much suitable near coal mines or coal supply fields. The pressure ranges from 10 kg/cm2 to super critical pressure and temperature varies from 250 to 650 ◦ C. Power plant is situated near tuticorin and spread over 160 hectares. It has total installed capacity of 1050 MW computing 5 units of 210 MW each. The station was initiated in three stages. First phase comprises of unit I&II of 210 MW each at a entire price of 178crores. Second phase comprises of 210 unit III at a cost of 89crores. Third phase comprises of units IV&V of 210 MW each of total cost of 804crores.The sections are coal based. This power plant comprises of 4 key components, coal and ash component, air and flu gas component, water and steam component, condensing water component.

2 Process Description Power plant consists of boiler, evaporator, economizer, air preheater, condenser, and turbine. A boiler or steam generator is a structure utilized for generation of steam by © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 296–305, 2023. https://doi.org/10.1007/978-3-031-31153-6_25

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the application of hotness to water. Even though the explanations are slightly stretchy, it can also be said that steam generators from past ages were usually named as tanks and operated at low to medium pressure but, for operating at higher pressure beyond this, then it is better to say as steam generators. A device used to vaporize part or all of the solvent from a solution. Evaporators are most commonly used in the chemical industry. The table salt which we use every day is made by boiling a saturated brine inside an evaporator. The salt precipitates as a solid in suspension of the brine. This slurry is propelled endlessly to a sieve, from which the solids are recovered and the fluid portion is returned back for added evaporation. Evaporators also find its application in food industry, generally as a method of dropping volume to permit easier storing and shipment. Evaporators are employed for generating potable water from sea water or other polluted resources. An air preheater (APH) is a universal term used to define any structure which is intended to heat air prior to another process (like incineration in boilers) main source of improving the thermal efficacy of any process. They can swap a restorative thermal system or to substitute a steam loop. The use of the air predetermine heater is to improve the hotness from the boiler tank and it rises the heat effectiveness of the boiler by plummeting the beneficial hotness vanishing in the vent gas. Economizers are motorized structures proposed to diminish energy consumption, or to execute additional beneficial job such as preheating a fluid. This economizer is utilized for other purposes as well. The usage of Boilers in power plant with respect to heating, ventilating and air-conditioning (HVAC) are also conversed in this research article. Economizer can also be called as heat exchanger which exchanges heat for preheating feedwater. A steam turbine in Fig. 1 is used to extract power from compressed steam by making it to do motorized labor on a rotating output shaft, which also involves in power generation. Cooling towers are heat elimination devices which are used to transmit unused heat to the troposphere. Cooling towers may either use the evaporation of water to remove working hotness and condense the working fluid close to the wet-bulb air temperature.

Fig. 1. 210 MW Power Turbine in thermal power plant located in Tamilnadu, India

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Fig. 2. Cooling tower

3 Fire Tube Boiler The boilers in powerplant in fire tube boilers with an older design and structure. This was widespread during the 18th century and was primarily utilized for steam locomotives. Working of fire tube boiler is much simpler like construction. Fuel is combusted inside the furnace chamber and hot gases are liberated. The fire tubes are wrapped up within the primary container of the boiler which is filled with water. As these hot gases pass complete these pipes, the gas is passed through conduction to the water which surrounds the fire tubes inside. Due to this activity, steam is created from the water in the fire tube boiler. This steam is utilized for generation of power through running the turbine in the powerplant. The cooled water in Fig. 2 is fed back to the boiler again complete the inlet water bay. Both vapor and water are kept in the similar container, which is pretty tough to yield very difficult vapor from them. Universally extreme volume of this kind of boiler. In the case of fire tube boiler, the boiler vessel is under heavy pressure, and if there is any sort of explosions then there will be a possibility of dangerous accident due to this burst. Conferring to the position of the furnace it can be classified into 2 kinds of fire tube boilers and are provided with exterior and interior furnaces. Here primarily 3 kinds of external furnace fire tube boilers and they are given as mentioned below. 1) Horizontal return tubular fire tube boiler 2) Short fire box fire tube boiler 3) Compact fire tube boiler. The internal furnace firetube boilers are also classified into two types. They are a) Horizontal tubular fire tube boiler. b) Vertical tubular fire tube boiler.

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Horizontal return fire tube boiler is appropriate for lower rating control plants. The kind of boiler in constructed with one giant steam drum which is horizontally oriented upon supportive assemblies. Enormous number of fire tubes are aligned horizontally inside this giant steam drum. The tubes are submerged into the drum filled with water.

Fig. 3. Diagram of Fire tube boiler

The coal burnt in furnace liberate heat and hot gases moves in those fire tubes and transmission heat to the water surrounding the tubes and reach the boiler drum. These gases are finally release through the burn box. The burning smokes in these tubes immersed in water transfers the hotness to the liquid via the cylinder walls. This transferred thermal energy generates steam bubbles which raise up the water surface. This steam increase raises up the pressure within the boiler drum which later increases the temperature and pressure of the steam released out for performing work. The fire tube boiler in Fig. 3 regulates its own pressure in this manner which is known as selfpressure-controlled boiler.

4 Fuzzy Logic Controller Fuzzy logic is a controller with decision making ability from its knowledge base given in Fig. 4. It consists of mainly three maneuvers in it. They are a) Fuzzification b) Knowledge base c) Defuzzification

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Fig. 4. Block diagram of fuzzy logic

4.1 Data Base The membership functions written for the error, variation in error and variation in controller output for the above-mentioned fire tube boiler is given in Figs. 5, 6, 7.

Fig. 5. Fuzzy Logic Input Error Mf

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Fig. 6. Fuzzy Logic Input Derivative Error Mf

Fig. 7. Fuzzy Logic Output Mf

4.2 Rule Base The rule base is used to characterize the controlling of areas and controlling the policies of setting the language variable by means of controlling rules. The rule base having the rules in Fig. 8 to control the linguistic variables and they are going to describe the operation which is about to performed by the linguistic variable. The rules that are structured for the above boiler control is shown below in Fig. 9 [1].

5 Fuzzy Logic Tool Box In this paper the output was shown with simulation using MATLAB. The Fuzzy logic toolbox in MATLAB has membership function block, fuzzy logic controller block and fuzzy logic rule viewer block [8]. Differential sigmoidal, the membership function dsigmf relys on 4 constrictions, a1, c1, a2, and c2, which is the alteration amongst these 2 sigmoidal equations. f1(x; a1, c1) − f2(x; a2, c2)

(1)

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Fig. 8. View Of Rules

Fig. 9. Surface View Of Rules

6 Results and Discussion The boiler water inlet temperature is taken as the effort data and boiler outlet temperature is occupied as the output data for the boiler process. Data collection is done manually and the graphs are plotted by means of MATLAB and are shown underneath. Figures 10 and 11 gives the input and output plot. System identification procedure is used to generate the mathematical model of the fire tube boiler from the acquired data of the fire tube boiler. Box Jenkins model of system identification is used for mathematical modeling.

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Fig. 10. Input plot

The Box Jenkins procedure is chosen because of its superior characteristics to handle noise and disturbance in the system. The Box-Jenkins model is beneficial when conflicts enter late in the development. The identified transfer function for the boiler process using Box Jenkins model is given by, Gp (s) =

0.001283s2 − 0.0001267s + 5.887e − 006 s3 + 0.03154s2 + 0.00303s + 8.754e − 006

(2)

Fig. 11. Output plot

This obtained mathematical model is one step ahead predicted model. The fitness percentage of above-mentioned mathematical model with respect to estimation is 96.4%

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and with respect to validation of model is 98.23%. The PID controller is tuned using Ziegler Nichols method of tuning. The PID values designed for the process are Kp = 2.54, Ki = 0.0089, Kd = −226.9. The response curve shown in Fig. 12 are the PI, PID, and Fuzzy controller actions for the boiler process (Table 1).

Fig. 12. Comparison of three controller outputs

Table 1. Comparison of Three Controller Output Parameters S.No

Parameter

PI

PID

Fuzzy

1

Rise time (s)

219

182

176

2

Settling time (s)

1672

1596

1537

3

Overshoot (%)

1.087

12.5

-

4

Peak overshoot

0.92

0.08

-

7 Conclusion The fuzzy logic controller output response shows a significant improvement in the controlling action for control of the process in a steady state. Thus, it achieves the set point with a smooth control action for the corresponding temperature control for the boiler process. For this simulation fuzzy control proves better for the boiler temperature control. In upcoming period of time additional intelligent and adaptive controls can be employed so that the non-linearities can be considered and eliminated. In future real time implementation of these controllers can be done and verified.

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Acknowledgment. We are immensely thankful to our principal and management for their support throughout the research process.

References 1. Raghul, R., Mohaideen Shahidh, S.H., Luca Nelson, S., Singh, A.B.: Model identification & fuzzy controller implementation for evaporator process in sugar industry. In: International Conference on Electrical Engineering and Computer Science, pp. 27–31 (2012) 2. Gaikwad, A.J., et al.: Effect of loop configuration on steam drum level control for a multiple drum interconnected loops pressure tube type boiling water reactor. IEEE Trans. Nucl. Sci. 56(6), 3712–3725 (2009) 3. Qiliang, G.Y., Jianchun, X., Ping, W.: Water level control of boiler drum using one IEC611313-based DCS. In: Proceedings of the 26th Chinese Control Conference, pp. 252–255, 26–31 July 2007 4. Nanhua, Y., Wentong, M., Ming, S.: Application of adaptive grey predictor based algorithm to boiler drum level control. Energy Convers. Manag. 2999–3007 (2006) 5. Zappe, R.W.: Valve Selection Handbook. Jaico, New York (1995) 6. https://la.mathworks.com/help/fuzzy/simulation.html 7. http://paginapessoal.utfpr.edu.br/ajmello/disciplinas/controle-inteligente/MATLAB%20-% 20FUZZY%20Toolbox%202014a.pdf/view 8. Shome, A., Ashok, S.D.: Fuzzy Logic Approach for Boiler Temperature & Water Level Control 9. Srinivas, P., Durga Prasada Rao, P., Vijaya Lakshmi, K.: Modelling and simulation of complex control systems using labview. Int. J. Control Theory Comput. Model. 2(4), 1–19 (2012) 10. SSørensen, K., Karstensen, C.M., Condra, T., Houbak, N.: Modelling and simulating fire tube boiler performance (2003) 11. Water Measurement Manual, U.S. Govt. Printing Office, 1984 12. Shome, A., Ashok, S.D.: Fuzzy Logic Approach for Boiler Temperature & Water Level Control, Vol. 3, No.6, pp.1–6 (2012) 13. Anie Selva Jothi, A., Ajith Singh, B., Jeyapadmini, J., Sheebarani, S.: Fuzzified control of deaerator system in power plant & comparative analysis with PID control scheme. J. Adv. Res. Dyn. Control Syst. 11(1), 923–931 (2019)

Accelerating the Prediction of Anti-cancer Peptides Using Integrated Feature Descriptors and XGBoost Deepak Singh(B) , Pulkit Garg, and Anurag Shukla Department of Computer Science and Engineering, National Institute of Technology, Raipur, G.E. Road, Raipur 492001, Chhattisgarh, India [email protected]

Abstract. Bringing the cancer therapies and cures is a long and challenging road; the researchers have overcome many hurdles to get this far and realize that despite the advancement, human cancer is still the most feared disease and according to various health reports almost one in every deaths is due to cancer. Customary therapy strategies center around killing malignant growth cells by utilizing anticancer medications or radiation treatment, however the expense of these techniques is very high, and likewise there are incidental effects. With less toxicity and fewer side effects of anticancer peptides (ACP), a great approval rate of this techniques has been enjoyed in the cancer treatment. The technique expects to distinguish whether a protein has a place with anticancer peptide. Notwithstanding, exploratory assurance of the anticancer peptide remains tedious and relentless. Subsequently, creating and working on a quick and successful method for foreseeing whether a protein is anticancer peptide would be exceptionally fundamental. In this paper, we propose the binary classification of anti-cancer peptides is performed using the XGBoost classification technique. We employed an ACP benchmark database in this investigation. The protein’s amino acid sequence was decoded utilizing a variety of feature descriptor approaches that includes amino acid sequences, physicochemical properties, evolutionary information, and discrete cosine transform (DCT). As a result, we had four feature descriptors and predictions were made by XGBoost classifier. Fivefold cross-approval is utilized to survey the classifiers’ presentation, and the discoveries were contrasted with five cutting edge techniques. The proposed approach has a promising accuracy of 88% to 93% in identifying anti-cancer peptides. Keywords: Anti-cancer peptide · Feature descriptors · Feature integration · XGBoost Classifier

1 Introduction Cancer has a throbbing impact on humankind since last two centuries. Cancer can be defined as uncontrollable, constant proliferation of cells. The cell division does not stop, and a tumour is formed. Early detection and treatment with radiation therapy, surgery, and chemotherapeutic drugs such as alkylating agents or corticosteroids can help to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 306–316, 2023. https://doi.org/10.1007/978-3-031-31153-6_26

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reduce the risk of cancer [1]. The patients are however traumatized by the negative side effects. Ongoing research is being conducted to develop cancer treatments that are more target specific and have fewer side effects. Surgery, radiation therapy and chemotherapy are common approaches for the treatment of cancer, which also hinges on various characteristics like the stage of the illness, location, and the condition of the patient. These approaches are costly, despite advancements, and can also show harmful effects on normal cells [2]. ACPs can interfere with the components of cancer cell membranes, and it is possible to eliminate cancer cells leaving normal cells unharmed [3]. Anticancer peptides do not affect the physiological functions of the body, giving cancer treatment a new path [4]. Albeit the creation cycle of ACPs has detriments, ACPs are more secure than engineered medications and show more noteworthy adequacy, selectivity, and particularity. Treatment with ACPs has the potential to be very effective. These are short peptides (typically 5 to 50 amino acids in length) that have high explicitness and growth entrance, as well as being simple to synthesize and modify. In addition to having low production costs, they also have high specificity and tumor penetration. They are gotten from protein successions, and it is by all accounts very expensive, tedious, and pointlessly convoluted to involve them in a high-throughput way to group, ACPs from a protein sequence. It is therefore important to establish sequence-based prediction model to quickly classify target ACP candidates from amino acid sequences prior to their synthesis. The advancement in computational models [5–9] using a wide variety of machine and deep learning techniques and features descriptors has been successfully applied to classify possible ACP candidates. Tyagi et al. proposed a model for the identification of ACPs that used the amino acid composition and binary profiles as input to the support vector machine (SVM) [10]. Shortly thereafter, Hajisharifi et al. proposed a model to do ACP prediction, which was based on the pseudo amino acid composition of Chou and the local alignment kernel-based method, respectively [11]. Both approaches have shown very promising results and have thus played an important role in stimulating the growth of this field. The composition of the feature set is a key factor for the performance of the prediction process. The optimal feature set should capture the major and subtle trends of the series to separate the real positive from the negative. Progresses in trial innovation have empowered to recognize whether a protein has a place with anticancer peptide. Be that as it may, exploratory assurance of the anticancer peptide remains tedious and arduous. Consequently, creating and working on a quick and successful method for anticipating whether a protein is anticancer peptide would be extremely essential. The established techniques have shown that the most widely used features for ACP predictions are compositional, physicochemical and primary qualities, sequence order, and terminal residue patterns [12]. The integration of the various types of features into a classifier and a predictive model could further improve the predictive performance [13]. More effectively, the knowledge incorporated in these functional descriptors is needed to achieve optimum usage. Moreover, several of the function descriptors in use have been predicted only by sequence of information such as AAC [14]. The intrinsic highlights of ACPs can’t be considered to precisely separation genuine ACPs against non-ACPs.

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The use of diverse and consistent features in learning helps to increase the output accuracy. Multiple classifiers are trained on smaller feature subsets in multiple training, thereby dramatically reducing the complexity of the trained model and reducing the variance error [15]. These multi qualified classifiers then predict in an ensemble form, reducing the bias error [16]. The ensemble model reduces the overall error of the multi feature ensemble learning by reducing both variance sections and partial variation [17]. The main purpose of the paper is to use separate sets of features to implement anticancer peptide classification by feature fusion learning. Features derived from multiple feature descriptors techniques is designated as independent feature. An ensemble model is paired optimally using feature fusion technique. Each feature descriptors along with the paired boosting ensemble model forms learning model and a prediction is made on the test dataset in an ensemble way. The degree, size and dimensions of the dataset are the factors that influences the presentation of different classification algorithms. We have conducted this analysis separately for nine base line classifiers. Notably, the number of different features integrated by the proposed model achieve considerably better performance than already known ensemble model, demonstrating the efficacy of the suggested fusion feature method. As a result, the paper’s contribution may be summarized as follows. • Supposedly, this is the first attempt on ACPs prediction by means of an feature fusion with boosting based XGBoost model that increases the predictability of ACP. • Not at all like a few current ACP expectation strategies, the proposed framework exploits matching six component descriptor with XGBoost troupe model that investigate different parts of preparing information through the joining of construction, grouping, physicochemical and change of the incorporated elements. • Exhaustive investigations on benchmark peptide dataset and correlations with four cutting edge strategies represent the practicality of the proposed framework. The paper contents are as follows. In Sect. 2, a brief review of anticancer peptide prediction done by various researchers are included. Section 3 presents the methodology of the proposed model carrying description on feature descriptor and the approach of constructing a complete predictive model. In Sect. 4, experimental setup and result discussion is summarized. At long last, Segment 6 finishes up the entire paper and focuses to a few examination subjects from here on out.

2 Related Work Different computational methodologies have been built lately utilizing the objective communication setting highlights. Several computational intelligence-based methods for accelerating the anti-cancer peptide prediction have been introduced in recent years. Initially, Tyagi et al. proposed, amino acid sequence-based features to encode peptides. This includes di-peptide composition and amino acid composition (AAC) features [10]. This was the first supervised learning-based tool to predict anti-cancer peptides named as Anti-CP. To gain high performance in the identification of ACP and non-ACP, Support Vector Machine (SVM) was employed as a classifier. Vijayakumar et al. claims that

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ACP and non-ACP cannot be sufficiently characterized by the usage of sequence based features [18]. To solve the ACP prediction problem, they proposed an integrated feature encoding system which integrates compositional information, distributional and centroid aspects of amino acid characters present in peptide sequence. Their integrated features enhance the predictive capability and to prove its superiority they further compare their model performance with AAC-based feature descriptors. In a different analysis, Hajisharifi et al. enhances a SVM based predictor, that uses the pseudo-acid composition of Chou’s amino, which takes account of both local similarity and sequence information on the residues to enhance prediction of ACPs [11]. More recently, Chen et al. have discussed the association between longer-range residues with sequence-based effects. They used optimal g-gap dipeptide components for feature descriptors and named model as iACP [19]. The model proposed by them proves to be the best predictor among the rest of the predictors. To predict anticancer peptides, the amalgam composition of the AAC, reduced amino acid composition (RAAC), and average chemical shifts (ACACS) have been chosen. The three features were fused and paired with SVM and scores 93.61% of the overall prediction result. Akbar et al. proposed a predictor model named as iACP-GAEnsC which takes the advantage of genetic algorithm for the formation of ensemble model on the derived features from multiple feature descriptor compositions [20]. ACPred has recently been developed using a variety of peptide features using conventional machine learning techniques including SVM and random Forest [3]. The overall accuracy of ACPred in ACP recognition was found to be 95.61%. A deep learning-based model named as ACP-DL uses an effective amino acid binary profile feature and a sparse k-mer matrix of the RAAC to recognize ACP and non-ACPs. The proposed model utilize recurrent neural network for the classification of anti-cancer peptides [21]. Although the current approach has a particular advantage in the identification of anticancer, the accuracy of the predictive model needs improvement with effective features [22].

3 Methodology 3.1 Feature Extraction Various kinds of properties can be used to depict the essentiality of peptides in different ways. To integrate effective features for predictive analysis, in the first place, we extracted six features in total from sequence based and physico-chemical based categories. (a) Pseudo Amino Acid Composition (PseAA): The peptide is converted into real values of length (20 + λ) where λ signifies the maximum distance between the two amino acids, the first 20 features describe the composition, and the rest values mimic the sequence order (λ = 15). (b) Amino Acid Composition (AAC): The amino acid composition of a protein is defined as the proportion of amino acids present in the sequence. (c) Bi-grams or 2-g: It determines the frequency of occurrence of a few amino acids in a peptide. When encoding an amino acid sequence, the bi-gram algorithm disregards the order of the amino acids. Additionally, it is referred to as 2-g.

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(d) Quasi-Residue Couple: Encodings consider the protein’s physicochemical (hydrophobicity) properties as well as the sequence order. Currently, order m ≤ 3 is considered. (e) Substitution matrix representation (SMR): The database of amino acid indexes includes a preconfigured replacement matrix that replaces the amino acid with its matrix value. (f) BLOMAP: Each amino acid is represented numerically by a five-dimensional real number derived from the BLOMAP physicochemical attribute. 3.2 Detailed Methodology In proposed model, multiple feature descriptors are applied to extract different features of a dataset in order to generate diverse set of features. The trained model predicts the class label of the test sample of each generated feature. Finally, for the purpose of predicting the class labels for the test sample, the XGBoost classifier is used. It is well-known that XGBoost makes use of latent and superfluous features extracted from the diverse set of feature descriptor techniques. If the learning algorithms are not properly mapped to the extracted features, the performance of XGBoost will suffer. Feature integration combines all the features obtained via the feature descriptor strategies mentioned in the Sect. 3.1, due to which the dimensionality of the data increases, even though only a small number of samples were available. It is possible that the classification model will be over fitted as a result. Using optimal parameter values in XGBoost, we were able to overcome this challenge while also improving generalization. Feature descriptor generates the features that are used to train the base model. Using the boosting method, these numerous trained, homogeneous models created several independent predictions about the unlabelled test case, and the final prediction is a collection of these predictions.

Fig. 1. Flow Diagram of the Proposed Model

The architecture of a model considers a feature set of a specific feature extraction method for the analysis. Thus, we have two different types of feature descriptor (extraction) process, i.e., sequence-based and physicochemical-based, to train XGBoost model

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and make a set of predictions for the test data set. The grid search approach was used to pick the optimum set of parameters for SVM kernels. Figure 1 clearly set out this technique with the help of data flow process for the formation of proposed model.

4 Experimental Setup and Result Discussions The adequacy of the proposed calculation is shown on a few certifiable enemy of disease peptide datasets. For a fair exhibition examination, six famous enemy of disease peptide datasets are used in nine benchmark characterization calculations. 4.1 Experimental Setup In the experiments, we have utilized publicly available benchmark dataset to ensure the reliability of the optimal multi view ensemble model. These datasets were retrieved from the various work and their characteristics are recorded in Table 1. All the datasets are balanced, and they are specifically designed to investigate machine learning algorithms. Six datasets are used in total for the experimental study, which will be referred as dataset with their number of ACPs: ACP82, ACP250, ACP138, ACP150, ACP861 and ACP970. Table 1. Dataset characteristics S. No

Name

Positive Class (ACP)

Negative Class (Non-ACP)

1

ACP82

82

82

2

ACP250

250

250

3

ACP138

138

136

4

ACP150

150

150

5

ACP861

861

861

6

ACP970

970

970

Proposed optimal multi view ensemble learning models can be evaluated by comparing the observed performances with baseline classifiers that comprise both simple and ensemble learners. These methods are Nearest Neighbors (NN), Back Propagation Neural Network (BPNN), Support Vector Machine with Linear kernel (LSVM), Gaussian (RBF SVM), Decision Tree (DT), Naïve Bayes, AdaBoost and Quadratic Discriminant Analysis (QDA) classifiers. In a large part, the algorithms performances are determined by the parameters of the technique. Table 2 summarizes the parameter values used in the experiment. For the purpose of evaluating both individual and proposed model, these parameters under consideration are initialized with the values indicated in table. We use fivefold cross-validation to estimate each experiment’s performance measures. All learning models are developed in MATLAB and tested on a system equipped with an Intel Xeon processor running at 3.60 GHz and 32 GB of RAM.

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Table 2. Parameter values of the learning models and optimization techniques used in the experiment Method

Parameters

Linear SVM

Box constraint (C) = 0.25

Decision Tree

Max_depth = 5

RBF-SVM

Coding = ‘one vs one’, Box constraint (C) = 1, Kernel Scale (gamma) = 2

Random Forest

Max_depth = 5, Max_features = 1, N_estimators = 10

4.2 Result Discussion For the purpose of examining the performance of the proposed model, we conducted an experimental study in which we utilized two factors that are commonly used in anticancer peptide prediction. The first factor is the amalgamation of diverse views, which is accomplished through the use of nine feature descriptor techniques. Secondly, determining the effectiveness of multi view learning over the state-of-the-art models. The formation of optimal view-kernel from the pool of large pairs introduces the search space problem which needs to be solved in reasonable amount of time. We therefore divided the result discussion into two subsections, each of which focused on one of the factors. The accuracy, specificity, sensitivity, F-measure, area under the receiver operating characteristic curve (AUC) and Mathew’s correlation (MCC) of the peptide classification are the six performance criteria that are evaluated, which is defined as follows:

Accuracy =

Tp Tp + Tn Sensitivity = Tp + Tn + Fp + Fn Tp + Fn

Specificity =

2Tp Tn F − Measure = Tn + Fp 2Tp + Fp + Fn

Tp × Tn − Fp × Fn MCC =       Tp + Fp + Tp + Fn + Tn + Fp + (Tn + Fn ) Here Tp denotes the correctly classified ACPs, and Tn denotes the number of correctly predicted non-ACPs. The incorrect prediction of ACPs and non-ACPs are denoted by Fp and Fn . To begin, we evaluated our model ability to predict anticancer peptides by running it on six benchmark datasets. Table 3 reports the 5-fold cross-validation results of the proposed model. The average accuracy of 92.56% with ±1.14 standard deviation on ACP dataset. Average sensitivity of the ACP prediction scores 93.98% with ±0.92 standard deviation. The average specificity obtained on benchmark dataset is 91.79% ± 2.09 standard deviation. The mean precision of the proposed model is 96.28% with ±2.37 standard deviation, and the Matthews correlation coefficient (MCC) obtained is 84.02% with

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±2.66 deviation. The proposed model showed an exceptional capability in ACP250 dataset. Table 3. Five-fold cross validation performance of proposed model S. No

Data

Accuracy

Sensitivity

Specificity

Precision

MCC

1

ACP82

0.9130

0.9318

0.9111

0.9535

0.8136

2

ACP250

0.9412

0.9535

0.9535

0.9535

0.8735

3

ACP138

0.9118

0.9302

0.9302

0.9302

0.8102

4

ACP150

0.9275

0.9451

0.8958

1.0000

0.8506

5

ACP861

0.9275

0.9438

0.9130

0.9767

0.8459

6

ACP970

0.9325

0.9342

0.9038

0.9628

0.8476

Additionally, in Fig. 2, we plotted the receiver operating characteristic (ROC) curves for various datasets. The six ROC plots for the ACP82, ACP250, ACP138, ACP150, ACP861, and ACP970 datasets are shown in the Fig. 2(a), 2(b), 2(c), 2(d), 2(e) and 2(f). Clearly, the AUC of the ACP250 benchmark dataset (93.80%) is higher than the AUC of the entire benchmark dataset. These figures suggest that the proposed model is capable of building a comprehensive model for the purpose of identifying ACPs. The proposed model resulted in a more differentiated distribution of ACPs and non-ACPs in the feature space, which resulted in improved performance. Consequently, it can be concluded that the classifiers employed in the construction of proposed learning models are highly correlated with the predictive performance obtained because of the ensemble

(a) ACP82

(b) ACP250

(c) ACP138

Fig. 2. ROC plot and AUC value of the proposed ensemble model on ACP dataset.

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model. This is most likely since the quality of ensemble formation is highly dependent on the choice of optimal feature fusion. Table 4. Comparison of the proposed model with baseline classifier on AUC Classifiers

ACP82

ACP250

ACP138

ACP150

ACP861

ACP970

Nearest Neighbors

0.8921

0.8667

0.8542

0.7945

0.8949

0.8679

Linear SVM

0.8223

0.8333

0.8471

0.7894

0.8224

0.8344

RBF SVM

0.7665

0.7333

0.7664

0.7635

0.7682

0.7352

Gaussian Process

0.7665

0.7333

0.7764

0.7525

0.7714

0.7339

Decision Tree

0.8863

0.9032

0.8757

0.8115

0.8866

0.9076

Neural Net

0.7821

0.8667

0.8571

0.8775

0.7848

0.8697

AdaBoost

0.8827

0.8933

0.9028

0.9066

0.8840

0.8973

Naive Bayes

0.8987

0.8979

0.9142

0.9083

0.9004

0.9004

QDA

0.8613

0.8796

0.8857

0.8268

0.8647

0.8819

XGBoost

0.9242

0.9368

0.9274

0.9206

0.9267

0.9278

To demonstrate the proposed model effectiveness, we evaluated the model performance against several well-known supervised learning algorithms. Table 4 compares the proposed model performance to nine conventional supevised models. The prediction results in Table 4 were calculated by averaging the AUC across 10 runs of 5-folds cross validation. The results from the aforementioned methods demonstrate the proposed optimal model superiority. As can be seen in the table, the proposed model achieved the highest AUC in all benchmark datasets when compared to the other conventional models. The proposed model performed commendably on the ACP250 dataset, with an accuracy of 93.68%, a sensitivity, specificity, and precision of 95.35%. The proposed model achieves an MCC of 87.35% and an AUC of 0.938. The proposed model increased accuracy by more than 3.36%, specificity by more than 3.82%, and MCC by more than 2.97%, respectively. Clearly, the multiview model demonstrates its strength, and our model is applicable to the identification and prediction of anticancer peptides. The results of the comparison experiment affirmed our hypothesis.

5 Conclusion In this paper, a feature fusion-based ensemble learning model to predict the anticancer peptide is proposed. The construction of a proposed model requires the integration of features generated from two feature. XGBoost technique is used to form an ensemble model for anticancer peptide identification. In general, the exploratory capability is enriched through feature-model pair selection. Six benchmark datasets were used to conduct the systematic experiments. According to the experimental results, proposed model outperforms other commonly used machine learning models in terms of classification performance measure. Taking the comparison study findings into account, we

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can conclude that the proposed model generates a solution that achieves the optimal trade-off between fused features and classifier performance. Diversification achieved through integrated features has a cumulative effect on target peptide identification and can be regarded as the primary reason for superiority. in the future work, we will evaluate the proposed optimal multi view ensemble model to realize transfer learning to identify ACPs in the situations where training data is inadequate.

References 1. Lopez-Rincon, A., Tonda, A., Elati, M., Schwander, O., Piwowarski, B., Gallinari, P.: Evolutionary optimization of convolutional neural networks for cancer miRNA biomarkers classification. Appl. Soft Comput. 65, 91–100 (2018). https://doi.org/10.1016/J.ASOC.2017. 12.036 2. Piao, Y., Piao, M., Ryu, K.H.: Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles. Comput. Biol. Med. 80, 39–44 (2017). https:// doi.org/10.1016/J.COMPBIOMED.2016.11.008 3. Schaduangrat, N., Nantasenamat, C., Prachayasittikul, V., Shoombuatong, W.: ACPred: a computational tool for the prediction and analysis of anticancer peptides. Molecules 24, 1973 (2019). https://doi.org/10.3390/molecules24101973 4. Xu, L., Liang, G., Wang, L., Liao, C.: A novel hybrid sequence-based model for identifying anticancer peptides. Genes 9, 158 (2018). https://doi.org/10.3390/genes9030158 5. Basheer, S., Bhatia, S., Sakri, S.B.: Computational modeling of dementia prediction using deep neural network: analysis on OASIS dataset. IEEE Access 9, 42449–42462 (2021). https:// doi.org/10.1109/ACCESS.2021.3066213 6. Dev, K., Khowaja, S.A., Bist, A.S., Saini, V., Bhatia, S.: Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks. Neural Comput. Appl. 9, 1–16 (2021). https://doi.org/10.1007/s00521-020-05641-9 7. Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., Hassanien, A.E.: Sentiment analysis of COVID-19 tweets by Deep Learning Classifiers—a study to show how popularity is affecting accuracy in social media. Appl. Soft Comput. 97, 106754 (2020). https://doi.org/ 10.1016/j.asoc.2020.106754 8. Arif, M., et al.: StackACPred: prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach. Chemom. Intell. Lab. Syst. 220, 104458 (2022). https://doi.org/10.1016/J.CHEMOLAB.2021.104458 9. Zhang, S., Li, X.: Pep-CNN: an improved convolutional neural network for predicting therapeutic peptides. Chemom. Intell. Lab. Syst. 221, 104490 (2022). https://doi.org/10.1016/J. CHEMOLAB.2022.104490 10. Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., Raghava, G.P.S.: In silico models for designing and discovering novel anticancer peptides. Sci. Rep. 3, 1–8 (2013). https://doi.org/10.1038/srep02984 11. Hajisharifi, Z., Piryaiee, M., Beigi, M.M., Behbahani, M., Mohabatkar, H.: Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J. Theor. Biol. 341, 34–40 (2014). https://doi.org/10.1016/j.jtbi.2013. 08.037 12. Rao, B., Zhang, L., Zhang, G.: ACP-GCN: the identification of anticancer peptides based on graph convolution networks. IEEE Access 8, 176005–176011 (2020). https://doi.org/10. 1109/ACCESS.2020.3023800 13. Jin, Y., Okabe, T., Sendhoff, B.: Neural network regularization and ensembling using multi-objective evolutionary algorithms. In: Proc. 2004 Congr. Evol. Comput. (IEEE Cat. No.04TH8753), vol. 1, pp. 1–8 (2004). https://doi.org/10.1109/CEC.2004.1330830

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Intelligent Door Locking System Fathima Ismail, Jennifer Julien Felix, and N. Sabiyath Fatima(B) Department of Computer Science and Engineering, B.S.A. Crescent Institute of Science and Technology, Chennai, India [email protected]

Abstract. In this current world, in a rush to keep up the pace with everyone around, one may forget about the little things that are of huge importance, just like confirming whether the main door of one’s home is locked or not. The purpose of the paper is to create an intelligent door locking system using Arduino IDE (Integrated Development Environment) and to develop a software which remotely controls the door, which not only improves security but also its accessibility. This paper is aimed to develop a software which is minimalistic and simple and at the same time it doesn’t require a user manual to operate. The developed software would work on any device while being connected to any local network. This system is designed for the very purpose that anyone should be able to use, including the differently abled/old people. This paper can be very useful in places like hospitals and hotels to remove the hassle of maintaining the keys. In hospitals, patients will have remote access to the door which will enable them to control the door without physical moving. In hotels, the customers can control the door with their mobiles, provided they have the login credentials and permission to access a particular room. The developed application can be used to control the status of the lock from anywhere around and the user-friendly GUI (Graphical User Interface) of the application makes it easy for anyone to use it without any complexity. The resultant software is better than the others based on the metrics like layers of security level, information disclosure, confidentiality and number of attempts taken to open the lock and outshines all other available devices in the market. Keywords: Door lock system · Arduino · Accessibility

1 Introduction This paper serves as an excellent device that helps to maintain security in workplaces and industries, and aids the differently abled and old people. Just by keying in a password, one can unlock the door. This paper serves its purpose with maximum security and also with an affordable cost. One major thing that most locking technologies do not concentrate on is for the use of differently abled people. Every person must be able to access these new technologies as they emerge to create more awareness of the technologies among people. This paper concentrates for the use of everyone by proposing its very simple and intuitive UI (User Interface) – A keypad for entering the password and a UI for the interaction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 317–328, 2023. https://doi.org/10.1007/978-3-031-31153-6_27

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2 Related Works There are many door locking systems available in the market but none of them are designed especially for the differently-abled/old people. The first smart lock to be introduced in the market was Kwikset’s Kevo. One key aspect that makes it one of the easiest smart locks to use is its Touch-to-unlock feature. The Kevo app can be used to assign a digital e-key to anyone with a compatible smartphone in a matter of a few taps. The Kevo Plus connects Kevo to the web server, thereby giving the owner access to its variety of features from anywhere at any time. August Connect, the August smart locks can be easily installed on the inside while keeping the outside of the door untouched. These locks have self-lock and self-unlock technology because of which it can unlock/lock itself when the owner approaches/leaves, respectively. If the owner doesn’t want the door to function automatically, then he can manually take over the control of the door using the August mobile application. Samsung smart door locking system provides security for office premises, houses and industries. The only thing that is required to control the status of the lock is a 6 to 14 digit pin which will be known to the user and that pin can be used only by the owner or a registered user to control the status of the lock. The security that comes with this system ranges from medium to high protection. S. Nath et al. [1] suggests a door unlocking system with real time control based on Arduino. The system has utilized a central database of a central server. For the ability of controlling, the system requires and utilizes Arduino IDE paired with Processing Development Environment (PDE). The system’s automatic synchronization via secure web page allows monitoring of activities in real-time such as entry-exit of users. Sedhumadhavan S et al. [2] proposes a system that involves using a wireless technology which allows the user to use devices such as their tablet or phone for sending a signal which gives them the ability to unlock and lock doors while they are within or outside the place as long they are in Wi-fi range. Areed et al. [3] makes a website that would allow users to control room door locks to be able to do functions such as locking and unlocking the door locks, the website was created for the purpose of prototyping the current system to legitimize and justify its use case and functionality, how it could be useful for the users by making life easier. Siddharth et al. [4] describes a home automation and security approach in their paper where an Arduino board will be integrated with the sensors along with a wireless module through which the status of household appliances would be sent to a cloud platform. The mobile device of the user and the system will be on the same wireless network which enables the sensors to either enable or disable the sensors that are under the user’s control.

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Hashem Alnabhi et al. [5] proposes alerting individuals in the vicinity by utilizing an alarm system, send SMS (Short Message Service) to registered responsible user using a GSM (Global System for Mobile communication) module, a web camera is utilized to record attempts by someone to break the lock. After fingerprint detection, a password keypad is used for the purpose of enhanced security. Five seconds after the moment it opens, the door gets closed. R Anirudh et al. [6] suggests a system that has primary components such as the Arduino uno, Bluetooth HC-05, and Servo motor. The Arduino uno serves as the system’s microcontroller, controlling the whole function. The user can utilize Bluetooth to link the system to the application, allowing the message to be sent to the system. The system receives power through a USB connection or an external power supply, allowing it to operate efficiently and without interruption. Krishnapriya Sreenadh et al. [7] proposes a system that has a fingerprint sensor coupled with RFID (Radio-Frequency IDentification) Sensor and numeric keypad. To enter the passcode, a Numeric Keypad is used. Access is provided to the user to further lock if the password entered by them is valid. If not, the LCD (Liquid Crystal Display) 16*2 shall display a “wrong password” message. Ketan Rathod et al. [8] builds a door locking system which employs an Arduino and a HC-05 as a controller and communication link between the mobile app. The goal of this paper is to create a door security system using LDR (Light Dependent Resistors) and Ultrasonic sensors, as well as a servo motor coupled with a laser module. Erwan et al. [9] introduces an IoT (Internet of Things) Smart Door Lock, which is activated by a smartphone via Wi-Fi. The door lock can be activated by using an android smartphone within a certain range. Wi-Fi technology will be used to transmit data. Taslim et al. [10] proposes a smart door lock with IoT application that has been successfully created utilizing a Wi-Fi enabled microcontroller and via MQTT (MQ Telemetry Transport) protocol linking user apps and devices. The door lock can be remotely locked and unlocked. N. Krishnamoorthy et al. [11] proposes a method that is based on IoT technology along with the usage of mobile communication technology and conventional devices such as door locks to convey the status of the door, such as whether it is locked or unlocked. Everistus et al. [12] proposes a system that, for entering the pin, uses a 4x4 keypad unit paired alongside a LCD display unit whose function is to display information to the user. To unlock and lock the door, the servo motor acts as a switch accompanied by a microcontroller which takes appropriate action after interpreting the data received.

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3 Proposed Methodology This paper provides a very simple User Interface in which the password has to be keyed in order to unlock the door. This project specifically targets the old-aged people and people who are physically challenged. Intelligent door locking system (IDLS) is a system which controls Door Locks remotely. It uses an Arduino Microcontroller to control the door and the devices use serial communication to send commands to the Arduino Microcontroller. A software with a simple user interface is used by the user to login and control the lock. The workflow of the project is shown in Fig. 1.

Fig. 1. Workflow of the system

Components Required are - Arduino Uno, Jumper wires, Door Latch and Servo Motor. The Arduino Uno (Fig. 2) is a microcontroller board established on the ATmega328. It has 14 digital pins for Input/Output, 6 analog inputs, a 16 megahertz crystal oscillator, a USB (Universal Serial Bus) connection, an ICSP (In-Circuit Serial Programming) header, a power jack, and a button for resetting operations.

Fig. 2. Arduino Uno

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Jumper wires are electric cables frequently used to interconnect various electronic components. The jumper wires used in this project have male pins at either ends. Door latches are utilized in this paper for the purpose of unlocking and locking doors automated using Arduino microcontroller. Servo Motor is a rotary or linear actuator which can push or rotate an object with high precision and also controls its velocity and acceleration [1]. It has a motor coupled to a sensor to get position feedback and it usually requires a dedicated controller which is especially designed to work with servomotors. The software must have a simple user interface, the system must be secure, any user must not require manual to use the software are the user interface requirements. Pentium 4/AMD A6 CPU (Central Processing Unit) or above, 256MB of RAM (Random Access Memory, Keyboard and Mouse, LAN (Local Area Network) Card or WLan (Wireless LAN) Adapter [802.I I a/b/gn] are the hardware requirements. WINDOWS Operating System(XP or above), NetFramework 2.0 or Above are the software requirements. In the Functional Requirements, the Administrator will be given more power (set login credentials/ update) than other users that will use the credentials set by the administrator. It will be ensured that the information entered is valid to the set credentials. Non-Functional Requirements: Performance Requirements: It is necessary for this system to perform at all circumstances and all the time for perfect execution as otherwise might result in locking away what is behind the door indefinitely. Safety Requirements: The information may not be shared carelessly. In an unlikely event of the User losing the details, backup of this information or a copy of it may be stored by the Administrator. Security Requirements: The secured Lock system is protected over by a Username and a corresponding password. Depending upon the category of user, the access rights are decided.

4 Implementation

Fig. 3. System Architecture (left)

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Fig. 4. Sequence Diagram (right)

Figure 3 depicts the system architecture. The software on the client side lets the users login to the system. Once logged in, they are navigated to a windows form which enables the user to lock and unlock the door. There are two buttons “Lock” and “Unlock” which creates a Socket connection to the server and sends the Lock/Unlock request. The Server side waits for the connection from the user. Once the client is connected, it reads data from the input stream and converts it to String. The server passes the string value to the Microcontroller through the serial communication port. Then the client socket is Closed and the Server is ready for another connection. The microcontroller waits for the server’s request. After receiving the request, it either rotates the motor to 0° or 180° depending on the request. The motor Drives the Latch which controls the Door lock. Figure 4 represents the sequence diagram. In this, User is the person who uses the software. Controller is the software which sends signals to the Arduino microcontroller. Checker validates the login request. When the user gives his login credentials to the software, the checker will verify whether the user is authorized or not. If the user is authorized, the checker will validate the login session and the user can access the lock system. Arduino receives the serial input from the C# application. If the input is 1, then the motor rotates to 180° otherwise it rotates to 0° [platform used - Arduino IDE V1.8.1]. The C# windows form application is used as a GUI to control the lock [Platform used – visual studio 2017]. It asks for the username and password. If they match with the password stored in the memory, then it will give access to the user to control the lock. The user then has options to set the port name and baud rate to unlock and lock the door.

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Algorithm For IDLS: Step 1: Include the Servo library. Step 2: Attach servo to pin number 9. Step 3: Begin the serial communication. Step 4: Read the serial input Step 5: If the serial input is equal to ‘1’ then rotate the motor to 180 degrees. Otherwise, rotate the motor to 0 degrees. Flow diagram:

Fig. 5. Flow diagram

The above flowchart depicts the flow of the program (how the program code actually works) (Fig. 5).

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Pseudo code: Start include Servo.h Servo myservo; int password = 0; void setup() function Begin Serial.begin(9600); myservo.attach(9); End function void loop() function if (Serial.available() > 0) then password = Serial.read(); if (password == 1) then myservo.write(180); delay(500); End if Else myservo.write(0); delay(500); End if stop Figure 6 (below) is a snapshot of the hardware output that was obtained after implementing the code and successful proper circuitry of all the components. The device was working properly according to the given conditions and as per the user’s requirements.

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Fig. 6. Hardware output (left)

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Fig. 7. Login Screen (right)

Figure 7 (above) shows the Login Screen. The owner must enter the correct password and click on the Login button in order to access and check up on the status of the door lock. If by any chance the owner forgets the password, then the forgot password option can be used to reset the password. It would be difficult for an unauthorized user to decrypt the password and move on to the next step, even if the offender gets successful in his attempt, further security measures will not let the offender penetrate into the system further. Figure 8 (below) shows the Lock Control Screen. The user, after logging in, has options to set the port name and baud rate to unlock and lock the door. A message “Access granted” will be displayed in the message box below in the Lock Control Screen. Once the owner accomplishes his task of locking/unlocking the lock, he can logout of the application.

Fig. 8. Lock control screen (left)

Fig. 9. OTP Screen (right)

Figure 9 (above) shows the OTP (One-Time Password) Screen. For confirming whether a verified user is using the application or not, the OTP Screen will ask for

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the user’s registered mobile number. After entering the mobile number, click on the ‘send’ button. After clicking, an OTP will be sent to the registered mobile number. Enter that OTP in the OTP box and submit, after submitting, the user will be “Successfully Logged Out”.

5 Result and Analysis The following Table 1 shows the comparison among the different locking systems available in the market. The IDLS (Intelligent Door Locking System) has the upper hand in almost all the grounds of comparison. Table 1. Comparing Different Locking Systems

In the security and reliability graph given below (Fig. 10), there are two main parameters taken into consideration - layers of security levels and number of attempts. Here in this graph, the layers of security have been divided into three levels, namely - level1, level2, level3. Level1 of security indicates the process of getting the password from the user. Level2 of security indicates mobile registration. Level3 of security indicates getting the OTP (one time password) from the registered device. The IDLS has all the levels of security which helps this system to be more secure than the other systems. The number of attempts parameter indicates the number of times the user can try to enter the password before the system/device gets locked out. In the IDLS system, after trying to login for 2 consecutive times, the lock will get permanently locked, which makes the offender take a step backward, and the lock can only be opened when the owner verifies his identity. Hence, the IDLS system is better than the other available systems based on these two parameters. In the information disclosure and confidentiality graph shown above (Fig. 11), there are two main parameters taken into consideration - information disclosure and confidentiality. When there is a lot of information disclosure, then the security is highly compromised. Hence we need a system which has less information disclosure. The IDLS has more confidentiality because the information disclosure is hardly 5%. The August connect has 10% information disclosure.In Kwikset kevo, other applications can be used to get the information which is related to the lock. Samsung has the least amount of confidentiality because there are cases where the pin number was easily stolen by the hackers. So in the case of information disclosure, the IDLS system has the upper hand.

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Fig.10. Performance comparison Based on Security and Reliability (left)

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Fig. 11. Performance comparison Based on Information Disclosure and Confidentiality (right)

Confidentiality and information disclosure are inversely related. More the information is disclosed, less the confidentiality is. Hence, in confidentiality as well, the IDLS stands out more than the other available door locking systems. The IDLS (Intelligent door locking system) uses wired communication which enables the locking and unlocking of the door easier unlike Kwikset and August Locking system that uses wireless communication to control the status of the lock. Wireless locking systems are sometimes slow and vulnerable to hacking.

6 Conclusion and Future Work The proposed Intelligent Door Locking System works with a network, which means that it’s efficiency might decrease if there is some network issue or an outage. Therefore, adding up a bluetooth module will make the system even more efficient and reliable as it’s functioning, performance and features won’t get affected by some network outage or internet issues in that scenario. Inclusion of a bluetooth module will also make the system more secure, cost effective and reliable unlike other Wi-Fi enabled locks that are available in the market. The door locking system can simply be unlocked by the tap of the phone if a bluetooth module is added to the system. Furthermore, it can be improvised by adding a feature of NFC (Near Field Communication), and can be customized according to the customer. Also, the forgot password option can be developed further in which a registered mobile number will be asked for which an OTP will be generated. When this OTP is entered in the subsequent window, it will be redirected to the password reset page which could be a very useful add-on feature.

References 1. Nath, S., Banerjee, P., Biswas, R.N., Mitra, S.K., Naskar, M.K.: Arduino based door unlocking system with real time control. In: 2016 2nd International Conference on Contemporary

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F. Ismail et al. Computing and Informatics (IC3I), pp. 358–362 (2016). https://doi.org/10.1109/IC3I.2016. 7917989 Sedhumadhavan, S., Saraladevi, B.: Optimized locking and unlocking a System Using Arduino (2014) Areed, M.F.: A keyless entry system based on Arduino board with Wi-Fi technology. Measurement 139, 34–39 (2019) Wadhwani, S., et al.: Smart home automation and security system using Arduino and IOT. Int. Res. J. Eng. Technol. (IRJET) 5(2), 1357–1359 (2018) Alnabhi, H., et al.: Enhanced security methods of door locking based fingerprint. Int. J. Innov. Technol. Exploring Eng. 9(03), 1173–1178 (2020) Anirudh, R., Chandru, V., Harish, V.: Multilevel Security Biometric Authentication Locking System Using Arduino UNO (2021). https://doi.org/10.3233/APC210121 Sreenadh, K., Kulkarni, H., Pangave, V., Joshi, M.: Triple lock security system using Arduino. Int. J. Eng. Res. Technol. (IJERT) 09(02) (2020) Rathod, K., Vatti, R., Nandre, M., Yenare, S.: Smart door security using Arduino and Bluetooth application, pp. 2394–0697 (2017) Erwan, A., Alfian, M., Adenan, M.: Smart door lock. Int. J. Recent Technol. Appl. Sci. 3, 1–15 (2021). https://doi.org/10.36079/lamintang.ijortas-0301.194 Ahmad Taslim, H., Md Lazam, N.A., Mohd Yahya, N.A.: Development of smart home door lock system. In: Mat Jizat, J.A., et al. (eds.) iCITES 2020. AISC, vol. 1350, pp. 118–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70917-4_13 Krishnamoorthy, N., Asif, N.: IOT based smart door locks. 4, 151–154 (2018) Orji, E., Nduanya, U., Oleka, C.V.: Microcontroller based digital door lock security system using keypad. VIII, 92–97 (2019)

Automated Generation of Multi-tenant Database Systems Based upon System Design Diagrams Rebecca Camilleri(B) and Joseph G. Vella Department of Computer Information Systems, Faculty of Information and Communication Technology, University of Malta, Msida, Malta [email protected]

Abstract. Energetic technological advancements in the past decade, have led to an exponential increase in the amount of data being stored. Consequently, the need for effective Database Management Systems (DBMSs) which embody security considerations from the initial stages of database design is imperative in modern information systems development. This prerequisite is further accentuated by the recent transitioning from single tenant information systems to multi-tenant based on public cloud infrastructures. Within this context, this paper proposes a ComputerAided Software Engineering (CASE) tool capable of generating secure database related structures and processes. A comprehensive overview of a specific information system design is provided to the CASE tool by means of entity-relationship diagram, entity life history and dataflow diagram. Subsequently, crucial security controls targeted towards the protection of database objects are incorporated by means of discretionary and role-based access controls. Following the encoding of the system’s design data is adorned with the required security elements, the CASE tool performs an in-depth analysis that aids in the detection of potential security pitfalls. Provided that the database design enhanced with security elements is satisfactory, i.e., all checks conducted are successful, the CASE tool generates Structured Query Language (SQL) language constructs for well-known multi-tenant scenarios. Keywords: DBMS · Multi-tenancy · Data Security features · Code generation

1 Introduction Continuous expansion in the amount of data being stored by both individuals and organisations, requires the assurance that the current information system storage systems are secure and efficient in terms of both costs and performance levels. Moreover, with the current shift from single tenant to multi-tenant database systems, other concerns, especially those related to cross-tenant access need to be considered by the database administrator. Although the notion of multi-tenant database systems is attractive for numerous organisations particularly due to its advantages with respect to storage space and cost of ownership, database security is an ongoing concern [1]. Since survival of a company depends on the appropriate management and security of the data it has, data security is an important notion which must be considered throughout © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 329–340, 2023. https://doi.org/10.1007/978-3-031-31153-6_28

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all stages of the data lifecycle [2]. Security lapses, that can happen unintentionally or on purpose, do reduce a system’s overall performance and effectiveness. To ensure that only authorized personnel have access to data and that same data is used in accordance with their granted access rights, proper security access control procedures must be in place. Despite the indispensability of these data security requirements, the first phases of the software development lifecycle rarely include this requirement. As a non-functional requirement, security is frequently disregarded or treated separately at the end of the program lifecycle [3, 4]. This lacuna is alluded to by Zahra and Vella in [5] and diminished by their proposed framework capable of adorning security directives from the initial design stages. To further address this issue, this paper proposes a CASE tool which is capable of automatically generating secure database code following the specification of initial design diagrams. The tool makes it easier for system designers and database administrators to include security safeguards and access restrictions in the initial system designs, namely through artefacts described in entity relationship diagrams (ERM), entity life histories (ELH), and dataflow diagrams (DFD). The utilisation of such a tool guarantees that the database’s design is structured as necessary from the beginning, thus ensuring that an effective security regime is in place. The paper is organized as follows; the second section provides fundamental concepts related to this paper. The third section examines the methodology utilized in the development of the CASE tool, followed by a thorough description of the most relevant design and implementation details. The last section wraps up the study by identifying the most salient points in the paper together with possible further enhancements.

2 Literature Review 2.1 Database Design Diagrams The utilization of structured diagrams is instrumental in the illustration of logical and robust application systems. Collectively, diagrams such as ERMs, ELHs and DFDs are pivotal in providing a complete view of an application system. ERMs depicts all entities within an application whilst ELHs illustrates the states of all entity instances in terms of processes and transitions. The DFDs demonstrates the flow of information across entities, processes as well as data stores. The three diagrams are closely inter-related namely because data sources found in DFDs correspond to ERM entities whilst the events of the ELH diagrams latch to the DFD processes. Moreover, such diagrams do not only provide a graphical illustration of the entire system but are also necessary for the implementation of security procedures during the design process. In fact, Guili and Iglio [1] extend the ERM diagrams with role-based access controls to be able to construct a secure database design. Nonetheless, some aspects of the database system which are depicted in ELHs and DFDs, such as transition mechanisms and the utilization of processes, are not catered for in this research. Although tenants might be using the same information system, their data access requirements might differ. Thus, runtime configurations of security profiles based upon not only ERMs but also on ELHs and DFDs is indispensable [5].

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2.2 Multi-tenancy A primary objective of organization owning a computing infrastructure is that of exploiting resource utilization. This entails reducing the overall costs of maintaining a database system on one hand, whilst maximizing its utilization on the other hand. A multi-tenant the same database is accessed by different tenants, even though their system’s security requirements may differ from one entity to the other. There are three primary design patterns for implementing multi-tenancy – separate database, separate schema and same table implementations. With multi-tenancy, the cost of database ownership is expected to decrease, amongst other advantages. Separate database implementation offers the highest possible level of data separation. In this case, whilst the same software application is used amongst multiple tenants, the data of each tenant is stored in a separate database. Thus, through this approach, each tenant will only be provided with access to her database only, significantly reducing the possibility of security risks associated with inter-tenant breaches. However, the main drawback of this approach is the additional costs incurred by the database provider related to an increase in resources required and software maintenance. In a separate schema approach, all the tenants’ data is stored in a single database but located under different schemas. In this case, for each new tenant, a new schema with a set of the table is introduced in the database instance. The level of security risks related to inter-tenant breaches is moderate. Such an approach is appropriate for organizations that opt to strike a balance between customization, tackling security risks and resource utilization [2]. The major limitation of this approach is that customization can be complex and costly to perform. The remaining approach, i.e., same table implementation, is the least costly since all the multi-tenant data is stored in the same database and schema and oftentimes in the same table [3]. Hence, all the tenants can access the same table but only view the data related to their organization. In this case, identification tokens attributed to each row determine the data which can be viewed by each tenant. Of all three approaches, the same table approach is the least costly since the largest number of customers can make use of a single database and schema. However, this approach poses the highest level of security risk related to inter-tenant breaches as all the tenants’ data is collectively located in the same tables [2]. 2.3 Security Implementation in Database Design Security is crucial in any database system, and in a multi-tenant environment whereby multiple users would have access to the same repository, other issues must be addressed too. This further justifies why security artefacts need to be considered and designed from the very initial stages of a database design. Security attacks can destabilize the whole system and expose tenants’ confidential information [6]. A notorious security attack on a DBMS occurred in 2019 when in total more than 1.3 billion records of insurance holders’ data were leaked from the First American Financial Corporation. The published information included the clients’ social security numbers, tax information and their bank account numbers [7]. To tackle database security breaches Basharat et al. [8], established a semantic data model and security classification language to determine constraints from an EntityRelationship diagram. However, the primary limitation of Basharat’s work is that only

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the Mandatory Access Controls (MAC) mechanism is considered and thus, does not tackle all the security concerns in database systems (see Sect. 2.4 below). Nevertheless, it is vital to consider the concept of adopting database diagrams such as ERMs, upon which security can be immediately implemented. ERMs combined with ELH and DFD present a full overview of the whole system. Henceforth, if the correct security features are implemented on such diagrams, one may be confident that the system is being addressed in its entirety. Moreover, diagram construction is crucial. When it comes to the amount of detail present in the diagrams, the proper level of balance must be identified. Providing too much detail makes security too complex and difficult to manage whilst providing too little information tends to conceal essential requirements. 2.4 Access Control Mechanisms Adopting access control mechanisms is an essential means of enhancing security in a database system. There are four primary types of mechanisms, each of which addresses a subset of security threats. These include Discretionary Access Control (DAC), Mandatory Access Control (MAC), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC) [9]. In DAC, access rights are provided to recognized tenants. Once a tenant has been provided with a privilege on an object, she can then disseminate the privileges to other tenants. DACs are the most implemented since this type of access control mechanism provides flexibility. However, its primary drawback is its venerability to Trojan horse attacks [10, 11]. MAC is a type of access control mechanism which offers less flexibility compared to DAC. In MAC the security of resources is defined utilizing a security label. The security label includes the security level and security categories. Security level specifies the hierarchical classification of information whilst security category defines the category to which information belongs [11] Subsequently, access rights are then computed according to the model’s rules. RBAC is another type of access control which is quite similar to DAC in the sense that it assigns permissions and privileges to authorized users. The main difference between both types of access controls is that RBAC use group level permissions whilst DAC use object-based ones. ABAC is an innovative access control mechanism. In this access control mechanism, access rights are granted according to three primary attributes i.e. subject, resource and environmental attributes [9]. 2.5 Software Development of Multi-tenant Database Systems Both single-tenant or multi-tenant database systems are based on various scripts which form the foundations of every database system. For instance, there are scripts which form the basis of database instance and setup and other related to tenant management. The former entails database creations and changes to configuration files whilst the latter involves the creation of schemas, tables and processes (e.g. stored procedures). There also scripts which directly impact the database security. Such scripts include granting and revoking of access rights and privileges [8].

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Furthermore, after considering multi-tenant scenarios, additional scripts are required to avoid cross-tenant access. Additional scripts appertaining specifically to each tenant such as the fields to be encrypted or specific auditing parameters are included as deemed necessary. In a multi-tenant scenario, three separate versions of each SQL script are required i.e. one for each possible scenario. This is essential since in some cases the information which is required for the code to be executed differs [3].

3 CASE Tool Development The CASE tool proposed in this research work comprises five major stages. The first part oversees the analyses of the input design diagrams (ERM, ELH and DFD) which are depicting a comprehensive computer information system. Following the analyses, the design diagrams are stored and encoded in a data dictionary; the latter is accessible to SQL which facilitates target language constructs generation. The third component handles the incorporation of access control measures. Granting of privileges on database objects and roles is conducted via discretionary and role-based access controls in accordance with the ERM, ELH and DFD specifications. To assist this process, a Create, Read, Update, Delete (CRUD) matrix is constructed, ensuring that specific roles are only granted access to required objects. During the conduction of such components, several checks are executed both on the database design diagrams as well as on the CRUD matrix and roles to ensure that the security of the overall system is not undermined. Such checks include for instance the assurance that all database objects are reachable, and that roles can only access objects following a least privilege basis. Conclusively, the last stage is responsible for the generation of code (SQL DDL constructs) implementing the input IS adorned with security considerations. Different aspects of the generated code differ owing to the three different multi-tenant scenarios i.e., separate database, separate schema, and same table. Figure 1 illustrates a schematic representation of the process required for the implementation of the CASE tool:

Analsyis of design diagrams (i.e. ERM, ELH and DFD) Encoding of design diagrams as relational objects Incorporation of access control mechanisms (ACM) and creation of CRUD matrix Security design decisions per tenant based on the multi-tenant scenario required Automatic generation of SQL code Fig. 1. Schematic representation of process required for CASE tool implementation

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3.1 Reading of System Design Diagrams The initial part of tool is responsible for the reading and analysis of the three system design diagrams, which are a basis for adding security aspects annotations in the later phases. The three diagrams are encoded in JSON notation, with the notation following a set of Extended Backus-Naur Form (EBNF) rules and saved in their respective file (i.e., one each for ERM and DFD, and one ELH for each entity in the ERM). Encoding of an ERM The first part of the ERM specifications define the system’s entities whilst the second part focuses on their relationships. The entities’ definition representing either a strong or weak entity, has the entity’s name and corresponding properties and data types. The relationships are listed under one of five categories – strong-weak, weak-strong, weakweak, strong-strong, and ternary relationships. Each relationship encompasses the list of participating entities together with their participation in relationship and cardinality. Encoding of an ELH Each entity listed in the ERM, has a corresponding ELH, identifying the list of processes associated with it. All processes are listed in the expected order of execution and the definition of all ELHs comprises the process name and identification of whether the process is iterative identified by (‘*’) or selective, denoted by (‘o’). An iterative process is one which can be repeated indefinitely whilst a selective process indicates OR events. Figure 2 depicts an ELH representation for the ‘Transaction’ entity.

Fig. 2. ELH JSON representation of ‘Transaction’ entity

Encoding of an DFD The encoding of DFDs is more involved than the previously mentioned diagrams. This is attributable to the fact that DFDs are made up of four categories of information – external entities, data stores, processes, and dataflows. The first three categories are

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listed sequentially in JSON format. Each category is described solely by the names of the corresponding component. Successively, the latter category, i.e., dataflows, defines the interaction between each of the three previously mentioned components. It specifies the dataflow name, together with the name and type of both its inward and outward connecting entity. 3.2 Conversion of System Diagrams to a Data Dictionary To facilitate the process of generating SQL code, the three system diagrams are stored in relational tables that can implement the required data dictionary. This is done firstly, by importing each JSON file as a new tuple in a table. Subsequently, following the assurance that the JSON text is appropriately written, different tables are created for each of the three diagrams reflecting their components. Two tables catering for the ELH name and ELH processes, are created in relation to the ELH whilst four tables related to dataflows, datastores, external entities and processes are created for the DFD. Conclusively, several other tables such as entities, attributes, relationships are created to store ERM related data. Checks Conducted on System Design Diagrams Throughout this process of reading and decoding of system diagrams, several checks are conducted. These include checks done on each diagram individually, as well as those carried out on a combination of two or more. A positive outcome, following such checks, indicates that the system has its first layer of security. Individual checks include for instance the assurance that there are no entities which are not related to any relationship and that every process and datastore has at least one input and one output. Furthermore, it should be ensured that all entities can be reached by at least one external entity. Combination checks include, for example, ensuring that all entities specified in the ERM, and processes specified in the ELH are mentioned in the DFD. Furthermore, every entity listed in the ERM should have a corresponding ELH diagram depicting the stages/processes that particular entity goes through throughout its lifetime. 3.3 Incorporation of Access Control Mechanisms The Access Control Management component of the CASE tool embellishes the three system design diagrams outlined in the preceding sections with security elements. Since, role-based access restrictions are used in this architecture, the primary aim of this stage is to give roles access rights and permissions to objects, including functions. To identify the rights needed by each role, a thorough understanding of the duties performed by each role must be identified. A CRUD matrix is generated to assist in this regard. This matrix is primarily based on the DFD and ELH diagrams presented. A SELECT privilege on base tables is identified wherever data flows are pointing towards a process, whereas the INSERT, UPDATE, and DELETE privileges on the data store are represented by data flows exiting a process. The ELH is then used to determine which of the INSERT, UPDATE, or DELETE privilege is expected. Since processes are shown in an ELH sequentially, the first subtree of the ELH indicates an entity’s first state. Such cases would thus necessitate an

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INSERT privilege. The sub-tree in the middle, which depicts any alterations that might be made to the entity, follows the same line of reasoning. Hence an UPDATE privilege is associated with it. Whilst the functions located in the last sub-tree of the ELH, indicate a DELETE privilege since they are usually associated with entity’s termination. Following the creation of the CRUD Matrix, several checks are conducted, so that another layer of security is ensured. These include ensuring that every entity has at least one process associated with it which performs SELECT, INSERT, UPDATE or DELETE operations as well as ensuring that all entities and processes mentioned in the system design diagrams are included in the CRUD matrix. 3.4 Automated Generation of SQL Constructs The final and central part of the CASE tool is that of generating a comprehensive multitenant database system, based on the system diagrams read in and the incorporated access controls. The generated SQL scripts cater for the three types of multi-tenant scenarios and are grouped and explained hereunder. Database Creation The first script generated by the tool, irrespective of the database system being developed, is responsible for creating a database. The number of times such a script is utilized is determined by the multi-tenant scenario being implemented. Implementation of separate schema and same table scenarios, necessitate the execution of this script solely once at the start, prior to any tenant creation. In the separate database situation, however, this script is invoked whenever a new tenant is introduced. Schema Creation Following, generation of the CREATE DATABASE script, the CREATE SCHEMA script is produced. The invocation of such a script is dependent on the multi-tenant scenario being used. In the case of separate database and separate schemas scenarios, a new schema is created for every tenant whilst in the case of same table scenario, the tenant schema is only created once at the beginning. Other schemas such as the one containing information valuable to the software provider might also be created. Figure 3 shows the procedure for the data driven and automated creation of schemas. It expects the name of the schema and that of its owner as its input parameters. Creation of Entity’s Tables The CASE tool then generates the code required for tables creation, centered primarily upon the entities and relationships present in the input ERM diagram. The entities present in the ERM diagram are converted to CREATE TABLE SQL constructs and their corresponding attributes and primary keys are added. Subsequently, SQL constructs representing the relationships presented in the ERM diagram are included. The code included depend on the type of relationship indicated i.e. 1-1, 1-M or M-N. For 1-1 and 1-M relationships, the primary key of one of the tables is added in the other table, serving as its foreign key. For M-N relationships, a new table composed of the primary key of both tables and any relationship attributes is included. The respective primary key then serves as a foreign key to its corresponding table. The CREATE TABLE script

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Fig. 3. ‘Create Schema’ Function

varies in the case of the same table multi-tenant scenario. In this case, every table has an extra field that represents a tenant identifier. Process Creation Processes are generated based upon the information present in the DFD and ELH. Those used for the inputting of data accept a single parameter of type JSON as input, indicating the attribute names and data types. Subsequently, in the method’s body an INSERT statement, responsible for the inputting of the JSON parameter in the corresponding table is constructed. Processes used for update purpose, require the primary keys together with a JSON field as input. These primary keys are required so that the tuple which needs to be updated is identified whilst the JSON contains the updated data. Delete processes, accept solely the primary keys as the input parameter for identification purposes. The generated SQL code contains UPDATE or DELETE constructs in the processes’ body to match the required operation. Lastly, those processes which are not associated with any particular entity i.e., which are found in the DFD but not in the ELH such as for example ‘Generate report’ processes do not have any input parameter. A SELECT statement is generated inside the method’s body to retrieve the data required by the user and a SETOF table containing the required report details set as the return value. Figure 4 shows the generated code for a ‘startWork’ process. Role Creation Prior to the creation of roles, the CASE tool constructs a roles-processes matrix. Roles are associated with processes in accordance with external entities-processes relationships present in the DFD. The external entities with an inward arrow to processes indicate an ‘Execute’ permission on the process, whilst those with an outward one, suggest receipt of information from processes. Following the creation of the roles-processes table, several checks are conducted to ensure that the data inputted is plausible. These include for instance the assurance that every role is associated to at least once process and that every entity is reachable by at least one role. Provided that the roles-processes table is appropriately created, and all checks produce positive outcomes, then the roles can be created. The SQL constructs generated,

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Fig. 4. Generated code for the ‘startWork’ process

are based primarily on the external entities defined in the DFD for non-partitioned roles. Should any partitioned roles be required, the user can then indicate such partitions in a separate JSON file. During runtime, the tenant-specific requirements such as the user-role associations and the partitioned conditions can be set-up. Granting of Privileges to Roles on Entities and Functions The generation of code responsible for the granting of privileges on roles and functions, is primarily based upon the roles-processes matrix explained in the prior section and the generated CRUD Matrix explained in Sect. 3.3. Execute privileges on processes required by each role, as established in the roles-processes matrix are granted and SELECT, INSERT, UPDATE or DELETE permissions established in the CRUD matrix are generated. Revoking of Privilege to Roles on Entities and Functions Apart from the grant functions discussed in the preceding section, revocation of rights is also essential when constructing a holistic database system. REVOKE statements are required to ensure that users are not allowed to access any data which might potentially serve as an opportunity for security breaches. The revoke privileges functions which are automatically generated by the CASE tool developed in for this paper, include the revoking of privileges on database, schemas, tables, and functions as well as those on other less frequently used objects such as foreign data wrappers and sequences. Creation of Policies The CASE tool presented in this paper, utilizes row-level security to be able to control access to rows in tables. Within this context, policies are created to cater for different multi-tenant scenarios. Thus, firstly, the tool automatically generates policies to mirror the type of multi-tenant scenario being utilized. In the case of a ‘separate database’ scenario, no policies are associated with the database administrator since she can access the entire database. On the other hand, specific policies are created for each individual role such as ‘Manager’ or ‘Clerk’ so that such users are only allowed to access the views and user conditions appertaining to them. Contrarily, in a ‘separate schema’ scenario,

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the database administrator can only access roles and views which have a tenant identifier corresponding hers. The other roles in the database are only allowed to access tuples in the views table, having the same role name, username and tenant identifier matching their own. Lastly, in a ‘same table’ scenario, policies are generated so that the database administrator and specific roles are only allowed access to views and user tuples which have a corresponding tenant identifier. Moreover, policies are created for every partitioned role specified by the user. These policies enforce the principle of least privilege by allowing users to access only those tuples in tables which commensurate with particular conditions. An example of the automatically generated ‘create policy’ code for the ‘Employee’ table for the ‘Clerk’ role is depicted in Fig. 5 hereunder.

Fig. 5. Automatically generated code for the ‘Employee_Clerk’ policy creation

4 Conclusions This paper proposes a CASE tool that aids the creation of secure multi-tenant database systems for software developers. It helps database designers incorporate security features from the earliest stages of system development. Moreover, it enhances security by automatically generating the SQL code required for the creation of a holistic database system. The rigorous checks conducted, together with the data driven mechanisms help in reducing the human errors and omissions. A benefit of such a tool is that it may be reused, allowing the database administrator to make the necessary changes to the privileges or design diagrams in the event that a new requirement for the security regime of the system is discovered. One shortcoming of the tool is the fact that only role-based access control measures were considered and supported. Such controls do not address all issues which might

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be present in multi-tenant database systems, for instance Trojan Horses and the issue of cascade deletion. If such issues must be solved, MAC would be more appropriate [6]. Therefore, it would be advantageous if such a framework were to be expanded in the future to include MAC as well as other non-function needs directly connected to security, including privacy and encryption. To further enhance the effectiveness of such CASE tool, future work can be targeted towards the inclusion of policies to cater for runtime requirements based on tenant-based partitioning. Nonetheless, the development of such a CASE tool represents progress in the field of multi-tenant database security. It helps ensure that secure database systems are developed, a basis for software development, and utilized.

References 1. Luigi, G., Iglio, P.: Role templates for content-based access control. In: RBAC 1997: Proceedings of the Second ACM Workshop on Role-Based Access Control, pp. 153–159 (1997) 2. Amadin, F., Obienu, A.: Multi-tenancy approach: an emerging paradigm for database consolidation. Comput. Inf. Syst. Dev. Inform. Allied Res. J. 8(1) (2017) 3. Gulati, S.S., Gupta, S.: A framework for enhancing security and performance in multi-tenant applications. Int. J. Inf. Technol. Knowl. Manage. 5, 233–237 (2012) 4. Zahra R., Vella J.G., Cachia E.: Specification of requirements to ensure proper implementation of security policies in cloud-based multi-tenant systems. In: International Conference on Network Security and Cloud Computing, London (2019) 5. Zahra, R., Vella, J.G.: Incorporating security features in system design documents utilized for cloud-based databases. In: Garg, L., et al. (eds.) ISMS 2020. LNNS, vol. 303, pp. 46–57. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-86223-7_5 6. Matt, B.: Introduction to Computer Security. Pearson Education India (2006) 7. Irwin, L.: List of data breaches and cyber-attacks in May 2019 – 1.39 billion records leaked (2019). https://www.itgovernance.co.uk/blog/list-of-data-breaches-andcyber-attacks-in-may-2019-1-39-billion-records-leaked. Accessed 18 Oct 2022 8. Basharat, I., Azam, F., Muzaffar, A.W.: Database security and encryption: a survey study. Int. J. Comput. Appl. 47, 28–34 (2012) 9. Jin, J., Shen, M.: Analysis of security models based on multilevel security policy. In: 2012 International Conference on Management of e-Commerce and e-Government, pp. 95–97 (2012) 10. Hasani, S.M., Modiri, N.: Criteria specifications for the comparison and evaluation of access control models. Int. J. Comput. Netw. Inf. Secur. 5, 19 (2013) 11. Meghanathan, N.: Review of access control models for cloud computing. Comput. Sci. Inf. Sci. 3, 77–85 (2013)

Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis Blessing Guembe1

, Ambrose Azeta2

, Sanjay Misra3

, and Lalit Garg4(B)

1 Department of Computer and Information Sciences, Covenant University, Ota, Ogun, Nigeria 2 Department of Computer Science, Namibia University of Science and Technology, Windhoek,

Namibia [email protected] 3 Department of Computer Science and Communication, Ostfold University College, Halden, Norway 4 Faculty of Information and Communication Technology, University of Malta, Msida, Malta [email protected]

Abstract. Anomaly detection enables the identification of the nature of the outliers to determine if they are errors or new trends that need to be understood and learnt by the model for improved generalisation capability. Multivariate and univariate anomaly detection is used to find an unusual point or pattern in a given set. A substantial number of articles have focused on this research domain; however, they primarily reflect on the impacts and trends of multivariate and univariate time-series data research. However, there is still a lack of bibliometric reports exhibiting the exploration of an in-depth research pattern in multivariate and univariate anomaly detection approaches. This study addresses that gap by analysing the widespread multivariate and univariate anomaly detection activities conducted thus far. This study analysed the Scopus database by using bibliometric analysis in a pool of more than 1385 articles that were published between 2010 and 2022, of which 679 are journal articles, 609 are conference papers, 72 are conference reviews, 13 are book chapters, 8 are reviews, 2 are erratum, and 1 is a book and a short survey. The multivariate and univariate anomaly detection bibliometric analysis was developed using R, an open-source statistical tool, and bibliophily was used to analyse the results. The findings reveal the following: (1) multivariate and univariate anomaly detection in collaboration with machine learning can enhance intrusion detection systems; (2) Researchers are interested in combining multivariate and univariate techniques with machine learning and deep learning classification problems to distinguish normality from abnormality; (3) the most active country in this research domain are the United States, China, France, India, Italy, and Germany; (4) Norway, Sweden, and Taiwan published few articles, however, receive many citations; (5) the United States, China, France, and Italy are the countries that collaborate the most in publishing articles on multivariate and univariate anomaly detection approaches; (6) keyword analysis reveals that researchers are adopting multivariate and univariate approaches to detect anomalous patterns in big data and data mining application domain. Supervised, unsupervised and semisupervised machine learning algorithms, in collaboration with multivariate and univariate, play a significant role in classifying abnormality from normality.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 341–363, 2023. https://doi.org/10.1007/978-3-031-31153-6_29

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B. Guembe et al. Keywords: Anomaly Detection · Bibliometric Analysis · Machine Learning · Multivariate · Univariate

1 Introduction Multivariate and univariate anomaly detection is widely used in many essential scenarios, including smart grid production data created by many devices, medical diagnostics, financial fraud analysis, industries, and monitoring data provided by diverse sensors (Zhou et al. 2022). Anomalies in multivariate and univariate time series exhibit out-ofthe-ordinary data behaviour at a given time step or during a period. Anomaly detection is a technique for finding an unusual point or pattern in a given set. Many engineering and scientific fields work with the assumption that behaviours or processes exist in a manner that follows certain principles or broad rules. However, variation from the norm may occur in these processes, leading systems to produce abnormal results or be in abnormal states (Mehrotra et al. 2017). Anomaly detection enables the identification of the nature of the outliers to determine if they are errors or new trends that need to be understood and learnt by the model for improved generalisation capability. During an anomaly detection process, we can discover that: an anomaly may be due to an error or incorrectly measured or inputted data, an anomaly may not change the results but affect assumptions (in regression), and an anomaly may affect both assumptions and results as in when a new trend is being observed; and so on (Quesada 2017; Ramchandran and Sangaia 2018). Univariate anomalies are anomalies in single-dimensional data space and can be recognised as very distant values or extreme points in a dataset (Demestichas et al. 2021). It effectively identifies anomalies when there is only one variable, feature or attribute in the dataset or data stream. However, it cannot be performed optimally in a scenario with multiple attributes because it does not automatically capture the underlying relationship between the characteristic variables (Ding et al. 2019). While Multivariate anomalies are the type of anomalies where the values of the different variables of a data point, taken together as a single entity, appear anomalous or significantly different from other observations in distribution even if the values of the individual variables are not unusual (Demestichas et al. 2021). Anomaly detection is hampered by high dimensionality because as the number of features increases, so does the amount of data required to generalise effectively, resulting in data sparseness, in which data points are more dispersed and isolated. This data sparsity is caused by extraneous variables or a high noise level of multiple insignificant features, which obscure the underlying anomalies. This is commonly referred to as the curse of dimensionality (Thudumu et al. 2020). Multivariate anomaly detection can be achieved using techniques and analyses ranging from multivariate statistics, robust statistics, social network analysis and machine learning. Techniques include Hotelling’s T2 statistic, Average correlation, Principal Components Analysis, and Node Centrality (Vilenski et al. 2019). Various anomaly detection methods exist and are used for different purposes based on a wide range of backgrounds in literature, like signal processing, artificial intelligence,

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and system theory techniques. This sometimes makes selecting an anomaly detection method that best fits a given application difficult for a specialist in a given domain (for example, finance, environment monitoring, medical diagnosis and so on). Different literature has used some criteria to characterise or define anomaly detection algorithms, like the nature of the anomaly, the analysed data, the detection principles, and the application domain (Sebestyen et al. 2018; Mozaffari and Yilmaz 2019; Przekop 2020). However, these studies have shown the vital need for exploratory actions in this research domain. However, these studies paid little attention to bibliometrics research. A substantial number of articles have focused on this research domain; however, they primarily reflect on the impacts and trends of multivariate and univariate time-series data research. Bibliometrics is used in data science and the library domain to determine author engagements, publishing trends, and country connections. It involves gathering data about the growth and understanding of specialised expertise. This practice facilitates the improvement of an organisation’s data asset, which is vital for effectively managing data. The bibliometric analysis enables researchers to validate the importance of their publication, exploration, and research. Institutions can also evaluate publications and assess their quality and impact through bibliometric analysis. Scientists can also predict future research studies and the vital impact of research on certain domains, while analysts can appraise the growing corpus of knowledge (Garg et al. 2008, Jahangirian et al. 2011, Wu et al. 2015; Dehdarirad et al. 2015; Tomaselli et al. 2016, Firdaus et al. 2019, Scerri et al. 2014, Tomaselli et al. 2020, Chukwu et al. 2022). This study provides a detailed assessment of multivariate and univariate anomaly detection research techniques published in the Scopus database from 2010 to 2022, indicating the evolution of the multivariate and univariate anomaly detection research domain.

2 Research Methodology This study is divided into four stages: search, result, findings, and analysis. Figure 1 displays the methodology flow of this study. This study begins by searching the Scopus database for the keywords “multivariate anomaly detection” and “univariate anomaly detection”. This is to identify the multivariate and univariate anomaly detection research direction and the database interest. The Scopus database was chosen for this study because it contains both Scopus-indexed and ISI rank publications. After executing the search query, 1385 articles published between 2010 and 2022 were generated. The subsequent section of this study focuses on exploring the information. We concentrated on various components, such as reviewing the retrieved article types, subject domain, citations, authors, and countries. The multivariate and univariate anomaly detection bibliometric analysis was developed using R, an open-source statistical tool. We specifically installed and used the bibliometrix library (Aria and Cuccurullo 2017), which is available in the R desktop software package. This bibliometrix tool has been used in numerous studies. However, this is the first study to use this tool to perform bibliometric analysis in multivariate and univariate anomaly detection. The tool can provide various multivariate and univariate anomaly detection insights, which we visualised and investigated in subsequent sections. Figure 1 contains the methodology workflow.

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Fig. 1. Methodology Workflow

3 Bibliometric Analysis The bibliometric analysis is organised into four primary categories, each of which has several sub-categories. The primary categories are document types, subject areas, authors, and countries. The author’s keywords, the total number of citations, the country’s number of articles, publications, the total citations, and collaboration networks are among the sub-categories defined under the author’s category. The findings of this study are important because they generate bibliometric data that can be used to identify high-impact scientific output that contributes to the development of new multivariate and univariate anomaly detection knowledge. Table 1 presents data from the Scopus database, which includes 1385 articles published between 2010 and 2022. As can be seen, 1385 publications were retrieved, of which 679 are journal articles, 609 are conference papers, 72 are conference reviews, 13 are book chapters, 8 are reviews, 2 are erratum, and 1 is a book and a short survey. The next section provides a detailed description of the article’s sources and their distributions.

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3.1 Document by Type Figure 2 and Table 1 depict data for analysing various types of documents. It goes into detail about the outcomes based on placements. Journal articles, with a total of 679 documents, (48.7%), have the highest ranking among the others, followed by Conference papers, with a total of 609 documents (44.0%) and Conference reviews, with a total of 72 documents (5.6%).

Fig. 2. Document Type Table 1. Document Type Document Type

Number of Documents

Journal Articles

679

Conference Paper

609

Conference Review

72

Book Chapter

13

Review

8

Erratum

2

Book

1

Short Survey

1

Figure 2 depicts a pattern in which researchers in this domain preferred to publish journal articles and conference papers over a collection of papers published in book chapters or conference reviews. This is because these conference papers were submitted before the conference and were accessible to all participants. This situation allows readers to grasp the concept behind the articles and, if feasible, provide suggestions after the articles are presented. As a result, the authors can refine and amend their methodology or conceptual framework in response to important feedback. As a result, papers published before the conference are far more valuable since they allow users to read the research many times and comprehend it well. Researchers will also likely publish their revised conference papers in special issues and top journals.

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3.2 Distribution of Publications Figure 3 depicts the number of papers published between 2010 and 2022. These publications include journal articles, conference papers, conference reviews, book chapters, reviews, surveys, and notes. The number of publications has steadily increased throughout this period, though at a slower rate in 2022. This is because we are still in the second quarter of 2022 and expect additional articles in this research domain to be published before the end of 2022.

Fig. 3. Distribution of Publication Year

3.3 Most Relevant Sources Figure 4 and Table 2 show that the platform where many authors submitted their papers appears to be lecture notes. In addition to lecture notes, researchers submitted articles to the Proceedings of SPIE - The international society for optical engineering. Figure 3 also shows that in the future, IEEE Access will see an increase in the number of occurrences in multivariate and univariate anomaly detection publications. The following section, on the other hand, focuses on the topic that drew the researchers to use multivariate and univariate anomaly detection techniques.

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Fig. 4. Most Relevant Sources

Table 2. Most Relevant Source Sources

Number of Articles

Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

78

Proceedings of SPIE - The International Society for Optical Engineering 45 IEEE Access

38

Communications in Computer and Information Science

14

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

13

Advances in Intelligent Systems and Computing

12

Journal of Geochemical Exploration

11

ACM International Conference Proceeding Series

10

Applied Sciences (Switzerland)

8

Ceur Workshop Proceedings

8

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing - Proceedings

8

Journal of Physics: Conference Series

8 (continued)

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B. Guembe et al. Table 2. (continued)

Sources

Number of Articles

Sensors (Switzerland)

8

Lecture Notes in Electrical Engineering

7

Proceedings - IEEE International Conference on Data Mining ICDM

7

Proceedings of the Annual Conference of the Prognostics and Health Management Society PHM

7

Sensors

7

Computers And Security

6

Expert Systems with Applications

6

IEEE Transactions on Instrumentation and Measurement

6

3.4 Subject Area Figure 5 and Table 3 display the topic of multivariate and univariate anomaly detection research. Computer Science, as expected, garnered the most attention from researchers, with 878 involvements in the research domain. Engineering and Mathematics also garnered attention from researchers, with 617 and 323 involvements, respectively. This is because multivariate and univariate anomaly detection deals with many statistical and time-series approaches.

Fig. 5. Subject Area

3.5 Authors This section highlights the researchers who have been the most active in the domain. This section evaluated the author’s involvement in the research domain based on their number of publications, keywords, and total citations. Table 4 includes the authors whose published articles are ranked in the top 20. Sun, Y leads in publication with 19 papers,

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Table 3. Subject Area Subject Area

Number of Documents

Computer Science

878

Engineering

617

Mathematics

332

Physics and Astronomy

153

Decision Sciences

134

Material Sciences

119

Earth and Planetary Sciences

114

Medicine

84

Energy

53

Environmental Science

53

followed by Harrou, F and Camacho, J with 17 and 15 papers, respectively, as shown in Table 4 and Fig. 6. 3.6 Authors Keywords This section discusses the relationship between keywords and multivariate and univariate anomaly detection. The researchers used several keywords associated with multivariate and univariate anomaly detection in their study. This is vital because we need to analyse the research trends, identify research gaps, and identify the field of study interested in combining multivariate and univariate anomaly detection. Table 5 shows the overall number of the author’s keywords in the top 20 rankings. Anomaly detection leads the ranking, followed by multivariate time series, outlier detection, and time series. Outlier detection is also referred to as anomaly detection. Outlier detection is a technique for identifying an unusual point or pattern in a given set. Outlier detection is also considered an observation that significantly differs from other observations in a distribution. They are usually detected and removed as a part of the data cleansing process and fraud detection activities to improve the accuracy of a model. Aside from outlier detection, time series, deep learning, and machine learning are other areas in which researchers combine multivariate and univariate anomaly detection research. This is because time series data are essential for detecting anomalies. For instance, using ECG data to detect abnormal heartbeat pulses (8; 9): ECG data is often represented as a periodic time series. A non-conforming pattern in terms of periodicity or amplitude would constitute an abnormality in this scenario and could signal a health problem (Li and Boulanger 2020). In addition, machine learning and deep learning provide a potential solution for automatically discovering anomalies even when manual inspection of data, such as visual spikes detection, has become infeasible due to the sheer size of the problem (Assem et al. 2017).

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B. Guembe et al. Table 4. Top 20 Authors with the Highest Number of Documents

Author(s)

Number of Publications

Sun, Y

19

Harrou, F

17

Camacho, J

15

Chen, Y

7

Madakyaru, M

7

Susto, G.A

7

Zuo, R

7

Bauer, K.W

6

Corsini, G

6

Diani, M

6

Fujiwara, K

6

Li, Z

6

Pei, D

6

Ye, N

6

Denzler, J

5

Garcia-Teodoro, P

5

He, X

5

Jamdagni, A

5

Kano, M

5

Kåsen, I

5

Fig. 6. Top 20 Authors with the Highest Number of Documents

Anomaly detection techniques are often classified into three types based on the type of input, including supervised, unsupervised, and semisupervised anomaly detection. As

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Table 5. Top 20 Author’s Keywords Keywords

Frequency

anomaly detection

471

multivariate time series

91

outlier detection

69

time series

63

deep learning

62

machine learning

62

unsupervised learning

31

principal component analysis

30

fault detection

27

multivariate analysis

23

data mining

21

Multivariate

19

Clustering

17

hyperspectral imagery

17

intrusion detection

17

Lstm

17

mahalanobis distance

17

Outliers

17

time series analysis

16

autoencoder

14

a result, the essence of a supervised anomaly detection problem is a machine-learning classification problem that attempts to distinguish normality from abnormality. On the other hand, unsupervised anomaly detection does not obtain access to the actual labels of the given dataset. It detects anomalies by recognising shared patterns among data instances and observing outliers. Furthermore, Semi-supervised anomaly detection takes both normal and abnormal information and determines the idea of normality or abnormality for the anomaly detection (Sebestyen et al. 2018; Al Mamun and Valimaki 2018; Shaukat et al. 2021). In contrast to rule-based anomaly detection, which is limited to manually programmed algorithms, the machine learning and deep learning approaches benefit from automatically learning and the detection of hidden patterns based on labelled data or deviations from normality across the entire dataset that best suit the purpose of data adaptation and conceptual drifts (Assem et al. 2017; Goldstein and Uchida 2016; Liu et al. 2016; Zemankova 2019). Figure 7, on the other hand, depicts the word TreeMap of keywords that researchers were interested in for multivariate and univariate anomaly detection. Figure 5 demonstrates that the multivariate and univariate anomaly detection common keywords are

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multivariate time series, outlier detection, time series, deep learning, machine learning, and fault detection. Principal component analysis, unsupervised learning, intrusion detection, multivariate analysis, LSTM, big data, and data mining are some more noteworthy keywords. This demonstrates that researchers are working hard to integrate these domains with multivariate and univariate anomaly detection. This evidence supports that multivariate and univariate anomaly detection can find anomalies in big data and intrusion detection systems. Furthermore, researchers are establishing more trust in multivariate and univariate anomaly detection from a data mining standpoint to discover malicious patterns during data mining activities.

Fig. 7. Word TreeMap

Figure 8 depicts a topic dendrogram of keywords with multivariate and univariate anomaly detection in addition to word TreeMap. Figure 8 depicts a subject dendrogram illustrating the hierarchical link between keywords from hierarchical clustering. It is used to assign objects to clusters by measuring the height of the various objects connected in branches. Initially, the researchers are represented as female, male, human and human, and the publications are represented as articles in the dendrogram. Other than female and male articles, human and human, the keywords in multivariate and univariate anomaly detection publications are the remaining objects. The first in Fig. 8 strand focuses on time series data connected to univariate time series, deep learning, multivariate time series, supervised anomaly detection, and longterm short memory to autoencoders, and the second strand focus on the algorithms and the composition of various approaches associated with multivariate analysis and outlier detection. The second strand defines different areas of interest and connection. For instance, data mining, regression analysis approaches, feature extraction, signal detection, etc. However, in detecting anomalous patterns effectively, most branches are related to machine learning techniques such as clustering algorithms, support vector machines, learning algorithms and learning systems. Figure 9 illustrates a word cloud of keywords from the research publications on multivariate and univariate anomaly detection. Figure 9 highlights the common keywords in multivariate and univariate anomaly detection, such as anomaly detection, multivariate

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Fig. 8. Topic Dendrogram

time series, outlier detection, time series, and deep learning. Aside from these general keywords, some of the most frequent keywords are machine learning, unsupervised learning, principal component analysis, and fault detection. In addition, the word cloud highlighted cybersecurity domains such as intrusion detection. Security practitioners use multivariate and univariate anomaly detection approaches in collaboration with machine learning techniques to detect anomalies in intrusion detection systems.

Fig. 9. Wordcloud

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3.7 Citation Output Table 6 and Fig. 10 list the top 20 ranking sources with the highest number of citations. The top five sources were Technometrics, IEEE Access, Neurocomputing, J Geochem Explor and Journal of the American Statistical Association. Table 6 also reveals more journal sources in the top 20 ranking papers. The top-ranking sources are Q1 journals with 89%, 90%, 93%, 88% and 95% percentile, respectively. This indicates that high-impact journals attract more researchers to publish their multivariate and univariate anomaly detection research. Table 6. Top Ranking Sources with the Highest Number of Citations Sources

Articles

Technometrics

307

IEEE Access

259

Neurocomputing

207

J Geochem Explor

161

Journal of the American Statistical Association

142

Sensors

131

IEEE Trans Geosci Remote Sens

128

Neural Comput

124

ACM Comput Surv

116

J Mach Learn Res

115

NA

108

Neural Computation

107

Expert Syst Appl

104

Journal of Geochemical Exploration

102

BIOMETRIKA

99

IEEE Trans Knowl Data Eng

89

J AM Stat Assoc

86

Nature

86

Science

83

IEEE Transactions on Visualization and Computer Graphics

81

3.8 Country The countries active in multivariate and univariate anomaly detection research are examined in this section. It highlights the author’s country, which was mentioned in the publications. It contains the total number of research articles, citations, and collaboration

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Fig. 10. Top Ranking Sources with the Highest Number of Citations

network for the country. The following sub-section begins with the total number of articles based on country production. 3.9 Country of Articles Figure 11 depicts the countries active in multivariate and univariate anomaly detection research. Figure 11 and Table 7 show that the United States ranks first with 337 total articles, followed by China (269), France (78), the United Kingdom (73), and Germany (67). It is worth mentioning that these five countries are located on three different continents. The United States represents North America, China represents Asia, France, the United Kingdom, and Germany represent Europe. China is Asia’s highest-ranking country, while France is the highest-ranking in Europe. Figure 11 also illustrates that each continent has a country that is a frontrunner in multivariate and univariate anomaly detection research. Figure 11 also shows that blue is more prevalent in certain European countries. This demonstrates that European countries (France, the United Kingdom, Germany, Italy, and Spain) used multivariate and univariate anomaly detection techniques in their research papers at a higher rate than other continents. This evidence indicates that the number of multivariate and univariate anomaly detection researchers in Europe is growing, and more publications are likely shortly. North and South America, in addition to Europe, is a significant player in the multivariate and univariate anomaly detection research domain, as evidenced by the prominent

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blue colours and the total number of articles. The United States and Canada are major players in the research domain. Figure 11 illustrates that, compared to other countries in North and South America, the United States and Canada have identified the potential of multivariate and univariate anomaly detection and have contributed their ideas to publications. Table 7. Top Ranking Sources with the Highest Number of Citations Countries

Number of Publications

United States

337

China

269

France

78

United Kingdom

73

Germany

67

India

64

Italy

64

Canada

51

Spain

47

Australia

46

Japan

44

South Korea

42

Saudi Arabia

31

Turkey

23

Austria

21

Belgium

20

Poland

20

Netherlands

19

Portugal

19

Norway

18

3.10 Country of Publication This section discusses multivariate and univariate anomaly detection articles for each country in terms of single and multiple publications based on corresponding authors. It also intends to examine the collaboration network that is taking place in various countries when it comes to publishing multivariate and univariate anomaly detection articles. Table 8 demonstrates that all the top 20 countries collaborated with others, and Table 8 indicates that the five most influential countries are the United States, China, France, India, Italy, and Germany.

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Fig. 11. Total Number of Articles Based on Countries

Table 8. Top 20 Ranking Corresponding Authors Country Country

Articles

Freq

SCP

MCP

MCP_Ratio

USA

259

0.217282

226

33

0.1274

CHINA

245

0.205537

194

51

0.2082

FRANCE

58

0.048658

46

12

0.2069

INDIA

50

0.041946

41

9

ITALY

49

0.041107

38

11

0.18 0.2245

GERMANY

45

0.037752

34

11

0.2444

UNITED KINGDOM

38

0.031879

27

11

0.2895

JAPAN

37

0.03104

33

4

0.1081

KOREA

36

0.030201

35

1

0.0278

SPAIN

30

0.025168

23

7

0.2333

AUSTRALIA

29

0.024329

22

7

0.2414

CANADA

29

0.024329

19

10

0.3448

SAUDI ARABIA

19

0.01594

7

12

0.6316

TURKEY

19

0.01594

14

5

0.2632

IRAN

16

0.013423

13

3

0.1875

PORTUGAL

14

0.011745

12

2

0.1429

NORWAY

13

0.010906

11

2

0.1538

POLAND

13

0.010906

11

2

0.1538

ISRAEL

12

0.010067

10

2

0.1667

BELGIUM

11

0.009228

5

6

0.5455

Table 8 demonstrates that the United States ranked first in both MCP and SCP, implying that the United States actively published in both SCP and MCP. China and France were second and third, respectively. However, from the standpoint of the MCP,

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many countries are less interested in publishing with other countries. Korea, Norway, Poland, Israel, and Portugal have 1, 2, 2, 2 and 2 MCP, respectively. They were more interested in publishing in the SCP range since the SCP publications were more than 2. It is worth noting that the highest values in the MCP were lower than the maximum values in the SCP, which were 33 and 226, respectively. This trend demonstrates that most countries collaborate and publish multivariate and univariate anomaly detection research in a single country rather than across several countries. Two countries, however, have chosen to collaborate with other researchers. Saudi Arabia and Belgium had the lowest number of articles published in SCP, with 7 and 5, respectively. Nonetheless, Saudi Arabia and Belgium expressed interest in participating and collaborating with other countries, having published 12 and 6 publications in the MCP region. This finding shows that Saudi Arabia and Belgium are more willing to engage with other countries on multivariate and univariate anomaly detection publications. 3.11 Country Collaboration Map Figure 12 depicts country collaboration worldwide, with blue representing the presence of collaboration. The blue colour denotes a greater frequency of collaboration with other countries. Among the countries that actively collaborate are the United States, China, India, France, and Italy. The map demonstrates that the United States contributes the most by including almost all active countries in publishing multivariate and univariate anomaly detection research, followed by China and a few European countries. It implies that international collaborations can increase the number of publications compared to single-country publications.

Fig. 12. Country Collaboration Map

3.12 Total Country Citations This section covers the multivariate and univariate anomaly detection publications in the top 20 global rankings that received citations from other researchers. Table 9 shows the overall number of article citations and the average number, and it arranges the results

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based on the total number of citations, from the highest to the least. The United States is ranked first, followed by China, Australia, India, and Japan. Table 9 shows that certain countries had low total citations but high average article citation scores. They include Norway (159–12.231), Sweden (190–38), and Taiwan (161–16.1). This evidence suggests that, despite publishing a limited number of publications, these three countries received substantial global citations for each publication. It also demonstrates that these countries published multivariate and univariate anomaly detection articles with high research quality rather than quantity. Table 9. Country Total Citations Country

Total Citations

Average Article Citations

USA

5927

22.884

CHINA

2050

8.367

AUSTRALIA

1463

50.448

INDIA

1189

23.78

JAPAN

743

20.081

SPAIN

669

22.3

FRANCE

605

10.431

CANADA

520

17.931

ITALY

459

9.367

GERMANY

382

8.489

KOREA

336

9.333

SAUDI ARABIA

319

16.789

UNITED KINGDOM

297

7.816

SINGAPORE

260

37.143

IRAN

234

14.625

BELGIUM

221

20.091

SWEDEN

190

38

TURKEY

176

9.263

TAIWAN

161

16.1

NORWAY

159

12.231

3.13 Network This section contains a bibliometric analysis of multivariate and univariate anomaly detection as a network. It comprises a country collaboration network and a network of keyword co-occurrences. This network was built to monitor the movement of the various nodes linked to one another.

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3.14 Country Collaboration Network Figure 13 depicts networking circles of collaboration to identify countries actively engaging with one another. Collaboration is a network that shows how researchers are connected to the network as a result of their co-authorships (Glänzel and Schubert 2004). The total number of articles is represented by the coloured circle noted in each node of the network, as seen in Fig. 13. The results show that the United States collaborates the most in the publication of multivariate and univariate anomaly detection articles, followed by China, India, France, and Italy. Figure 13 also highlighted several lines connected to the bottom of the collaboration network. This demonstrates that, compared to the upper portion, the countries in the bottom part were actively collaborating. Most of the countries in the bottom part were from North America, Europe, and Asian continents, such as the United States, Poland, Norway, China, Saudi Arabia, and the United Kingdom. This signifies that countries in North America, Europe, and Asia preferred to collaborate with others while publishing articles on multivariate and univariate anomaly detection research activities rather than publishing only in their own countries.

Fig. 13. Country Collaboration Network

4 Conclusion In this paper, we have employed bibliometric analysis to examine trends in multivariate and univariate anomaly detection research between 2010 and 2022. Throughout the process, we have explored four primary categories: document kinds, subject areas, authors, and countries. These domains have aided in highlighting global trends in multivariate and univariate anomaly detection research. The findings reveal that researchers in the United States are the most active contributors to the multivariate and univariate anomaly detection research domain. The United States ranks first with 337 total articles, followed by China (269), France (78), the United Kingdom (73), and Germany (67). Computer

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Science, as expected, garnered the most attention from researchers, with 878 involvements in the research domain. Engineering and Mathematics also garnered attention from researchers, with 617 and 323 involvements, respectively. Anomaly detection leads the ranking of authors’ keywords, followed by multivariate time series, outlier detection, and time series. Aside from anomaly detection and outlier detection, time series, deep learning, and machine learning are other areas in which researchers combine multivariate and univariate anomaly detection research. This is because time series data, machine learning and deep learning are very effective in detecting anomalies. In addition, the word cloud highlighted cybersecurity domains such as intrusion detection. This implies that cybersecurity researchers are currently integrating multivariate and univariate anomaly detection approaches in machine learning techniques to detect anomalies in intrusion detection systems. These findings suggest that training time series data with machine learning and deep learning models in combination with multivariate and univariate techniques is now regarded as the most effective approach to detecting anomalous patterns. Further research in multivariate and univariate anomaly detection will focus on implementing interactive machine learning with multivariate and univariate techniques to detect anomalous loan patterns in the financial industry. As part of further studies, the researchers recommend applying the research results obtained in this current study in workflow mining (Assem et al. 2017) and elearning (Al Mamun and Valimaki 2018).

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A Multi Objective Hybrid Collision-Free Near-Optimal Dynamic Path Planner for Autonomous Robots in Dynamic Environments Neeraja Kadari1(B) and G. Narsimha2 1 JNTUH University College of Engineering, Hyderabad, Telangana, India

[email protected] 2 JNTUH University College of Engineering, Sulthanpur, Telangana, India

Abstract. Autonomous Mobile robots are finding increasing applications in industry and everyday life, so the path-planning of robot in dynamic environments has become an emerging research problem. Dynamic path planning has, therefore, received more attention. In this paper, a innovative path planning method, a MultiObjective-Hybrid Collision-free-Near-Optimal Dynamic-Path Planner (MOHCNODPP) is proposed and is an advancement of former research work “A MultiObjective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in known static environments. It is utilized for autonomous mobile robots and has the ability to avoid both stationary and moving objects in dynamic environments. Challenge is to get a nearly optimal result by expanding a lesser number of nodes compared to existing approaches, while taking into consideration the nonholonomic constraints of the robot, with the end goal of generating near-optimal drivable paths for the robot. The proposed method has two operating modes. In the first mode, referred to as Offline-path-plan mode, the global search technique, A Multi-Objective-Hybrid Collision-free Optimal-Path-Finder, MO-HC-O-PF is used to discover an initial optimal route in the environment containing stationary impediments. The second mode, Onlinepath-Plan mode, uses help from sensors to find stationary obstacles which are dynamically occurred in environment. In order to bypass collisions with these obstacles alternate path segment is found and final path is updated. In order to minimize collision with these obstacles and to preserve the sub path segment’s optimality, a new method, MOHC-NODPP is utilized. To simulate and evaluate the effectiveness of the suggested strategy, the standard maps were utilized. The results of the simulation illustrate the capability of the proposed method to recognize stationary and dynamically appeared barriers, as well as its capacity to discover a collision-free and nearly optimal path to a goal location in settings that are subject to dynamic change. The length of the path, the absence of collisions on the way, the amount of time it takes to execute, and the smoothness of the path are the performance metrics that are utilized to select the best path. Studies conducted using the new method reveal that, in comparison to the conventional procedures, there is an average reduction of 15% in the length of the path, a reduction of 20% in the essential time obligatory for its accomplishment, and a success rate of 95%.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 364–383, 2023. https://doi.org/10.1007/978-3-031-31153-6_30

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Keywords: Dynamic path planning · Offline-path-plan mode · Online-path-plan mode · Optimal path · Collision-free space · Autonomous Mobile robots

1 Introduction Path planning in dynamic environments has seen significant advancements in a variety of fields recently, including autonomous mobile robotics [1], unmanned aerial vehicles, autonomous wheelchairs and Humanoids, etc., In particular, mobile robots have found applications in a wide variety of fields thanks to their flexibility and portability. Such as the management of rescue operations during natural disasters, the discovery of other planets, the management of warehouses, and the automation of industries, etc [6]. The path-planning is finding a practical path to the desired location inside the workspace is the aim of path-planning for autonomously moving robots [2]. Simply said, a robot with the ability to move without human intervention is considered an autonomous mobile-robot [1]. Path-planning in dynamically changing environments can be executed in 2 operating modes reliant on information available, including offline-path planning, which takes known environmental data and online-path planning, which is based on sensor data such as obstacle size and location. Considerable works of literature have been reported on the subject of path planning. In order to discover a real-time, collisionfree, smooth, and drivable path, a path-planning-algorithm is required. Sampling based and Graph based algorithms are the widespread path-planning approaches. The classic samping e-based algorithms are the Probabilistic-Road-Map (PRM) [4] and the RapidlyExploring-Random-Trees (RRTs) [3, 7] and their variations RRT* [5, 7, 10]. Algorithms based on sampling frequently provide jerky and dynamically impossible pathways. Even while the bulk of these strategies increases speed to some extent, the resulting path is not ideal. A* [2] and its variants [11, 13–25] are typical Graph based path-planning algorithms, although-they are likely to produce paths that are not even and do not abide with the non-holonomic limitations of the robot. The Key Contributions In the beginning, it was mentioned that there is a need for additional study to be done on real-time dynamic path planning. As can be seen, the implementation of the pathplanning research trend that has emerged in recent years has been characterized by the utilization of a variety of distinct algorithms in order to produce improved results. This research presents a novel path planning method that is termed A Multi -Objective-Hybrid Collision-free Near-Optimal Dynamic-Path-Planner (MOHC-NODPP), and it is based on A*. The goal of this method is to quickly find a drivable path that is near-optimal in a dynamic environment while also taking into account non-holonomic boundaries of the robot. The contributions are: 1. The proposed method MOHC-NODPP is a revolutionary path planning technique. It discovers collision-free nearly optimal traversable paths in a dynamically changing environment with an environment’s hybrid representation. This algorithm performs similarly to A*.

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2. The approach has two modes. In a static barrier environment, offline-path-plan mode finds a near-optimal path. The second stage, Online-path-plan mode, modifies the optimal route online to avoid crashes with dynamically introduced stationary impediments. Path tracking optimizes the path without affecting the algorithm’s performance in real life. 3. It uses kinematic restrictions such as robot’s direction to provide the shortest, fastest, and smoothest drivable paths for dynamically changing real-time settings. 4. It can be utilized in simple as well as complex dynamic contexts. 95% of attempts are successful. 5. The results of our suggested method demonstrate significant improvements in dynamic situations in terms of execution time and path length in relation to RRT and RRT*. This section describes the structure of the paper. The research that is relevant to the route-planning methods is discussed in Sect. 2. In Sect. 3, we introduce the method and underlying algorithm for path-planning when there are both static and moving impediments in the way. In Sect. 4, we offer experimental results and discuss how the proposed approach performed. Section 5 provides a summary and recommendations for moving forward.

2 Related Work There have been many path-planning procedures offered during the previous few decades. Among the most important are graph-based methods like Dijkstra’s algorithm [12] and the A* [2] algorithm. The exploration area of the path-planning challenge is discretized into a diagram structure, and then graph search techniques are used to discover a viable path. Dijkstra’s algorithm is one of them; it is a breadth-first search strategy that can find the best route. The A* system uses a heuristic function to speed up the search time of the D* method. The graph-based technique is both exhaustive and optimal and it can find an optimum path only if one exists and will report an error otherwise. However, the enormous search area created by graph-based partitioning of the state-space means that these graph-based approaches do not fare well when applied to problems of a significant magnitude. Recent developments in the A* algorithm have been detailed in academic articles such as Improved-Analytic-Expansions in Hybrid A* Path-Planning [15, 29] and Dynamic-Algorithm for Path-Planning utilizing A* [14, 25, 26]. Their processing times are extremely lengthy. There are a plethora of A*-based methods offered as well [8, 11]. One more major category of path-planning algorithms is the sampling-based one. As an alternative of discretizing the environment, a tree and/or graph is constructed using a sampling-based path planner. When faced with a huge task, sampling-based path-planning methods perform better than graph-based procedures. The sampling-based system increases the possibility of finding a suitable path as the number of cases increases to infinity. There are a few major algorithms employed by sampling-based planners, the most notable being Rapidly-Exploring-Random-Tree (RRT) [3] and ProbabilisticRoadmaps (PRM) [4]. Though, it is a time inefficient procedure to create a map for the whole area for single examination.

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In contrast, the RRT is faster than the PRM since it is a single-query path planning algorithm that just needs to explore the search space by making a tree with start point as a root. Despite its speed at finding a starting point in a environment of higher dimensions, many limitations are there for RRT. Furthermore, because of the random behaviour of the route generation, the route determined by RRT might not be the ideal route at all [8]. It was believed that RRT* (Rapidly Exploring Random Tree Star) [5] was a major improvement over RRT. RRT* requires substantial time and memory investment to locate the best possible solution. As with RT, RRT* is impacted by the problem of huge variations in exploration times. Variants of RRT and RRT* are created later on [27, 28].

3 Proposed System A path planning research challenge could be stated as “Discovering a method that moves mobile robot autonomously from a specified start posture to the desired destination pose in a dynamically changing environment without colliding with any obstacles by obeying non-holonomic boundaries”. Problem Formulation: The fundamental goal of the path-planning problem is finding an ideal route for an autonomously moving robot to move from a given initial point to a desired point while avoiding both static and dynamic stationery obstacles. The purpose of a path-planner is to find the best possible route for a mobile robot that will allow it to navigate through its environment with as few collisions as possible. This is how the surrounding is portrayed: building a prototype of the mobile robot in 2-dimensional surroundings is the primary phase in discovering its routes. The workspace of the mobile robot is represented by a grid of square cells of equal size. Each square on the grid is either navigable (logic 0) or blocked (logic 1) depending on the presence or absence of an obstruction in that square. In this region, you’ll encounter both stationary static obstacles and moving ones. Optimization Criteria Multi-Objective optimization criteria are considered in this proposed path planning system. The primary one is the Cost-function C. It computes the travelling cost between the current-position to an adjacent position. This cost-C is summation of GC, cost from source-node to present-node, the predictable cost (HC) amid the destination and the present-node. By using Eq. (1) this will be calculated. Cost C = GC + HC + SOC + DOC

(1)

where SOCis the cost for orientation switching, and DOC is the cost to bypass dynamic stationery obstacles in online-path plan. The second Criterion is the Execution time: Time required to execute the proposed method for finding a path both Offline as well as online. To avert the exploration graph from growing bigger, continuous point is round off to a discrete point for every node.

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The total time required for finding a final near-optimal path is expressed using the below Eq. (2). Ttotal = Toffline + Tonline

(2)

where Ttotal is the total time required for the complete execution of the path planner, Toffline is the time essentially needed for offline line path planner i.e., MO-HC-O-PF, and Tonline denotes time needed to revise the preliminary optimal route in order to bypass dynamically occurred stationery impediments. The optima-path planning problem can be well-defined as: “Discovery of the minimum cost and minimum processing time required nearly optimal safe path amid the starting location and the destination location, provided the above criteria C, cost function and T, execution time, are minimized by obeying robot’s nonholonomic boundaries”. The Architecture of the System The Proposed system’s block diagram for path planning is shown in Fig. 1. There are two modes of operation for the system. When working in an environment where static obstacles are already known, the optimal path is initially determined by using Offlinepath-plan mode. The next step is to trace the route taken. The second stage, Onlinepath-plan mode, continuously refines this optimal path to avoid dynamically presented stationary obstacles. The primary elements of Fig. 1, are the proposed path planning system.Environment: Known static obstacles exist side by side with newly produced stationary obstacles in a dynamic setting. A dynamic setting is portrayed by means of a binary occupancy grid map. Vehicles and robots that are not homogeneous have kinematic restrictions, which can be thought of as the brakes. Smoothness of the path, length, and time to detect a route are all examples of optimization standards. Path Planning: A path-planning system’s core component in solving a path-planning problem is the path-planning algorithm. Two different strategies for determining the best routes to take are employed in the system being proposed here. To begin, we apply our previous research on offline route planning, “A Multi-Objective Collision-free OptimalPath-Finder (MO-HC-O-PF)” [9], to discover a fast first optimal-route in a setting with only static, known obstacles. The second is a web-based system for planning routes around moving barriers; it uses multi-objective optimal dynamic path planning (MOHC NODPP). 3.1 The Proposed Approach MOHC-NODPP’S Working-Principle A new method, MOHC-NODPP, is proposed to evade the drawbacks of the conventional A* policy. The robot’s kinematic nature is additionally encompassed to forecast the drive of the robot that is reliant on direction in the environment of incessant representation. Each continuous state in the projected process is denoted by the point (xr, yr, θ), where (xr, yr) and θ designate the robot’s position and direction. by this the planner choose the best successor that a robot with non-holonomic nature can trail. The search tree expands corresponding to the steering directions. That are left-max, left, right-max,

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Fig. 1. The architecture of the path planner that is proposed.

Fig. 2. MOHC-NODPP integrates kinematic restrictions by means of 5 steering angles

right, and no change. They produce a curve of a minimal-turning area, in agreement to the robot’s kinematic boundaries. Figure 2 depicts the MOHC-NODPP algorithm’s selection of states in response to these events. A Statistical Analysis is taken from Dolgov Dpublication’s [2010] [29]. 3.2 Multi-criteria Optimization This research optimizes the path using various objectives. Cost Function: It computes the travelling cost between the current-position to an adjacent position. It is summation of GC, cost amid beginning node and present node and HC, the predictable-cost amid the destination and the present point. By using the above cost function Eq. (1) this will be calculated. Execution Time: Time required to execute the proposed method for finding a path both Offline as well as online. To avert the exploration graph from growing bigger, continuous

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point is round off to a discrete point for every node. The total time required for finding a final near-optimal path is expressed using the above Eq. (2). Path Length: The whole path is denoted by the formula P = {P1 , P2 ,…, Pn }, where P1 , P2, …, Pn path = segments. The final path length is the sum of all path-segments that link the initial and final states. As seen in Fig. 3. This ultimate travel length is calculated using Eq. (2).

Fig. 3. Path is a sequence of path segments

Path Length =



Pi ,

(3)

where i = 1 to n. and Pi is ith path segment. At each state minimum-cost next-state is selected and minimum turning radius curves guarantee near the shortest length path. Smooth Path: This proposed method produces minimal turning radius curves because it chooses the next node at each step based on kinematic constraints. So, the ultimate path that was built is plane.

3.3 The Heuristic Function Less anticipated cost amid any point and the goal node on the map is the heuristic function. Less nodes need to be discovered. Therefore, the efficacy of the algorithm is strongly influenced by the choice of the heuristic function. To make quick judgments, we use the Euclidean distance. Heuristic values for each node are determined using Eq. (4).  (4) h = ((x2 − x1 )2 + (y2 − y1 )2 )

3.4 The Proposed System The suggested procedure completes path planning exercise in dynamically changing setting comprising stationary specified in advance and dynamically appeared obstacles. In total, there are two distinct modes inside the system. In the first phase, static impediments are considered, hence Offline-path-plan is employed to determine the optimal path. Next, the robot will proceed down the path it has just discovered. This method promises the path-segment’s optimality by continuously updating this optimal-path on the fly to prevent collisions with any dynamic obstacle. This procedure is depicted in Fig. 1.

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3.4.1 The Offline-Path Planning Our previous work has focused on the MO-HC-OPF algorithm [9], which is used in the off-line path plan to quickly produce a path that is optimal for the specified environment containing only inert obstacles by obeying to non-holonomic constraints. The MO-HCOPF algorithm uses two lists: open-list and closed-list. They preserve trail of the positions in process of searching, much like the traditional A*. Openlist includes states that are neighbors to those that were added throughout the search process (OL). The closed list contains all of the states that have been completely processed (CL). Here is a summary of the MO-HC-OPF procedure:

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3.4.2 The Online-Path Planning An optimal path will be generated by the offline-path plan process where there are only static obstacles present in the environment.During the process of online-path plan, an autonomous mobile robot is provided with an optimal path, which it then follows in order to get from its starting place to its final destination while navigating a dynamically changing environment.Using a scanning procedure with the help of the surrounding sensor and, the robot makes its way down the course. The robot includes sensors that enable it to cover a space in 360-degree perspectives. The following step-by-step description gives an overall idea of the proposed method: Step 1: Reads sensor data to identify any newly added stationary obstacles. Step 2: Sensor readings are recognized for all information regarding the driving, location of robot, and obstacles in environment. The distance requirements are met, and a dynamic obstacle enters the range of the robot’s sensor. This information will be utilized to calculate the possibility of a crash amid robot and obstacle. If no obstacle is in its way, robot keeps going in the same direction. Step 3: In the occurrence of a collision, the proposed approach uses a local search technique known as MOHC-NODPP for the part of the path that comprises a potential point of collision. It is expected that the afresh found path segent will be the shortest possible. Step 4: The reference path is altered provided the robot can go on the revised no-collision trail segment. Obstacle detection is the process by which the robot is made conscious of the presence of moving obstacles in immediate neighborhood of it. Collision_Detection Method: Using the proposed method, the robot can make progress toward the objective while keeping an eye out for fresh obstructions. The following procedures are included in this rollout. Step 1: The data collected by the sensor is stored. It then calculates the distance amid robot and obstruction by measuring how far the robot’s centre from the nearest surface of the impediment. Step 2: Based on the following, an obstacle’s presence is determined: if the sensor detects a new obstacle the collision_detection method is called. Step 3: There are three points along the robot’s sub-path that could be damaged in the event of a collision. The first point, Xcurrent (x1, y1), represents the robot’s current position; the second point, Xcollision (x, y); and the third point, Xnew nxt (x2, y2), represents its next destination (x2, y2).

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In case of a crash, the impediment will be located on this branch of the route. It is for this reason that we offer a new local-search algorithm to find a path-segment that bypasses obstacle. The suggested method is activated whenever the following conditions both are met. The first is when the range of the robot’s sensors is suddenly invaded by an unexpected obstacle. Second, when an accurate collision detection decision is made. Using the Offline-path plan’s minimum cost value as determined by Eq. (1), MOHCNODPP does a local search to find the next neighbor from its neighbors’ list, designated Open-list. Again, collisions with the newly-added neighboring point are checked for; if none exist, a path segment with the smallest possible turning radius is built. This process is repeated until the desired result is achieved. Path segments with the smallest possible turning radii are guaranteed to be the shortest route. The proposed one provides the nearly optimal sub-path. Following this fork in the path, the robot is to continue on its predetermined mission. The computational-cost of the planned local-search MOHCNODPP is unnecessary due to the fact that the Offline-path planning stage of the proposed system already figured the outlays of 5 neighbors conforming to every direction for every point on the offline optimum path. Therefore, no extra processing time is required. Because there are only 5 neighbour locations relative to 5 steering angles while looking forward, the time required is also relatively short. There is hence less need to engage in a search. So, the time performance of the suggested approach is unaffected by the improved local-search. The steps for the recommended route planning process MOHC-NODPP is summarized in the following Algorithm 2:

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4 Experimental Results The performance of the innovative Multi-Objective-Hybrid Collision-free-NearOptimal-Dynamic-Path Planner (MO HC-NODPP), which was proposed, was examined on MATLAB on with an G5 Intel core i5, processor Windows-10–64-bit, NIVIDIA, Sensor views of −45° to +45°, −90° to +90° and −180° to +180° were taken for experimentation. It was found that the stationery obstacles dynamically occurred were efficiently avoided in 95% of the experiments. All simulations were run for different start and goal positions on a diversity of complex maps. The performance is measured with lengths of the ultimate paths and processing time amid the mentioned begin and destination locations in various environments. The ultimate path length and the processing time were noted. 4.1 Case_study-1 An environment that is simple with stationary obstacles that are aware in advance and stationary obstacles dynamically occurred. The built path in both the modes offline-pathplan and online_path_plan modes exhibited by Fig. 4.

(a)

(b)

Fig. 4. (a) Generated Offline Path (b) Generated Online path

The outcomes of by means of the suggested approach MOHC-NODPP this Simple environment publicized in Table 1. The success rate is 98%. Table 1. Results for Simple Map Performance metric/Mode

Offline-path-plan

Online-path-plan

Average Path length in meters

18.7918

19.7527

Average Execution_time in secs

0.29964

1.206467

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4.2 Case-Study 2 A intricate situation is presented. A complex situation embraces stationery obstacles both aware well in advance and occurred dynamically. The outcomes this intricate setting in both stages offline-path plan and online-path is showing in Fig. 5(a) and (b). it is evading dynamic obstacles effectually in 95 % of the experiments. The outcomes are listed in Table 2.

(a)

(b)

Fig. 5. (a) Generated Offline Path (b) Generated Online path for Complex Map.

Table 2. Results for Complex Map Performance metric/mode

Offline path planning

Online path planning

Average Path length in meters

64.5589

66.77099

Average Execution_time in secs

0.457936

1.877474

4.3 Case-Study-3 It offerings a warehouse with package pickup situation encompasses stationery obstacles both familiar and arose dynamically. Figure 6(a) and (b), exhibits the outcomes of suggested method. The proposed method was avoided successfully in 93% of experiments. Table 3 is covering the results.

5 Performance Evaluation The suggested system MOHC-NODPP’s efficacy compared to works RRT and RRT* algorithms in aforementioned three case studies. There have been extensive tests of the suggested method MOHC-NODPP. Below is a breakdown of how each option performed. The lengths of ultimate paths achieved by the Proposed Method MOHC-NODPP was higher to that accomplished by present methods.

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(a)

377

(b)

Fig. 6. (a) Generated Offline Path (b) Generated Online path for Package Pickup scenario.

Table 3. Results of Package pickup in a Warehouse scenario Performance metric/mode

Offline path planning

Online path planning

Average Path length in meters

73.5541

77.7064

0.4951

1.3974

Average Execution_time in secs

Case-Study 1: Simple Map: There is a comparison in Table 4 below between the results of the recommended approach MOHC-NODPP and the outcomes of the commonly used approaches RRT and RRT*. It evaluates the effectiveness of the proposed system MOHC-NODPP in comparison to the systems RRT and RRT*, using the metrics of performance length of ultimate route and Execution-Time. These methods can be applied to any sort of map. Clearly, the numbers show that the recommended method MOHC-NODPP led to shortest path and less processing time. Table 4. Performance efficiency comparison in a Simple map Planner/performance metric

Path length Mean in meters Avg_Execution Time in secs

RRT

28.64834

RRT Star

32.05821

Proposed method MOHC-NODPP 19.7527

3.029133 3.68875 1.227062

Figure 7 exhibits evaluation of performance, the proposed strategy MOHC NODPP, RRT and RRT* across a range of iterations and path lengths. The blue line shows the calculated path lengths using the suggested method MOHC-NODPP. When applied to a basic dynamic map, its hundred iterations consistently yielded the shortest pathways, a significant improvement above previous methods. Using Fig. 8, we can see how the suggested technique MOHC-NODPP compares to the methods RRT and RRT* over the course of hundred iterations. The blue line

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Fig. 7. Comparison of Path Lengths in Simple map

represents the time essential to bring out the outcome for the proposed scheme MOHCNODPP. In 99 out of hundred iterations, it completed significantly faster than existing methods.

Fig. 8. Processing time assessment in Simple map

Case Study 2: Complex Environment: Table 5 below illustrates the results of a comparison between the suggested strategy MOHC-NODPP and the contemperary approaches RRT and RRT* in the Complex dynamic map. As the complexity of the map increases, the results show that the proposed technique performs effectively. As can be shown in Fig. 9 below, the suggested method MOHC-NODPP outperformed in contrast to the present methods after hundred experiments. As the blue line shows, the suggested method outperformed all contemporary approaches by producing the least length paths throughout all iterations. Figure 10 below illustrates the efficiency with which the execution time is used. The suggested method MOHC-NODPP is depicted as a line of blue coloured in nearly all hundred iterations, and it generates faster pathways. Case Study-3: Warehouse: The proposed method was evaluated in contrast to RRT and RRT* using the Warehouse example. Table 6 displays the results of a comparison

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Table 5. Performance efficiency comparison in a Complex map Planner/performance metric

Path length Mean in meters Avg_Execution Time in secs

RRT

104.5361982

3.877897

RRT Star

99.31087

3.989557

Proposed method MOHC-NODPP

66.77099

1.877474

Fig. 9. Comparison of Path Lengths in Complex

Fig. 10. Execution time estimation in Complex

of various indicators of performance. Looking at how the proposed method stacks up against other methods in terms of path length and runtime. For such a complex warehouse layout, the proposed method performed admirably. Using Fig. 11 below, we can see how the proposed method MOHC-NODPP fares against the current works in a sample size of hundred. Illustrating the efficiency of the suggested scheme in blue coloured, it is easy to see that MOHC-NODPP consistently generated shorter paths .

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Planner/performance metric

Path length Mean

Avg_Execution Time

RRT

167.3139

4.3049969

RRT Star

147.7796

4.420484

90.7587

2.545391

Proposed method MOMOHC-NODPP

Fig. 11. Comparison of Path Lengths in Warehouse

In Fig. 12 below, we can see how the suggested method MOHC-NODPP stacks up against the methods RRT and RRT* after hundred iterations of training. The proposed method consistently delivered the fastest routing for the Warehouse dynamic map when compared to other alternatives. The average path length was reduced by 15%, while the average execution time was reduced by 20%.

Fig. 12. Processing time evaluation in Warehouse

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6 Conclusion and Future Scope The research work proposes a new Multi-Objective-Hybrid-Collision-free Near-Optimal Dynamic-Path-Planner (MOHC-NODPP) for mobilerobots operating autonomously in dynamically changing, partially-known environments. There are two operational modes. In the first mode, Offline-path-plan mode, A Multi Objective-Hybrid Collision-free Optimal-Path-Finder (MO-HC-O-PF) finds the preliminary optimal path based on our previous research. The robot uses this path as a reference path and follows it. In the second mode, Online-path-plan mode, if a dynamic stationery obstacle is detected while the robot is following the path, it will avoid this obstacle using sensor readings and locate an alternate local path segment. This will be modified in the final sequence of optimal path segments. This method is quite similar to A* in many respects, and it also respects the kinematic constraints imposed by the robot. Thus, we can guarantee that driveable smooth pathways, which are necessary in practical situations, will be generated. 95% of the time, there is a better, crash free, optimal route that can be attained ultimate route length, processing time & smoothing of route are optimized by MOHC-NODPP. The results of the aforementioned studies demonstrate that the suggested method often reduces path length by 15% and execution time by 20% contrast to current techniques RRT and RRT*. As opposed to conventional methods, the recommended strategy has proven to be significantly more effective in practice, especially when dealing with complexities. MOHC-NODPP is a technique that can be used in circumstances where there are constantly moving obstacles. In the future, it could be employed in higher-dimensional scenarios involving multiple robots.

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How Does Background Music at Stores Impact Impulse Buying Behavior of Young Customers in Vietnam? Cuong Nguyen1(B) , Nguyen Le2 , and Chau Huynh2 1 Faculty of Commerce and Tourism, Industrial University of Ho Chi Minh City, Ho Chi Minh

City, Vietnam [email protected] 2 Faculty of Business Administration, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

Abstract. This research paper was conducted to identify and analyze the relationship between background music at stores and the impulse buying behaviour of young customers in Viet Nam. The research method employs Confirmatory Factor Analysis (CFA) and structural equation modelling (SEM) by using AMOS 20 software. The sample size includes 300 respondents in Viet Nam. In the proposed research model, “Musical genres”, “Volume in music”, “Familiarity with the music”, and “Rhythm in music” of background music at stores would positively impact to “emotional responses” of young customers, the “emotional responses” would impact positively to “mood changes”, and “mood changes” would impact positively to “impulse buying behaviour” of young customers. From this research, the authors also propose some managerial implications for organizations to increase the effectiveness of using background music at their stores to attract and trigger the impulse buying behaviour of young customers to increase their sales revenue. In addition, this research may reference other related research in the future. Keywords: impulse buying behavior · young customers · background music · emotional responses · mood changes

1 Introduction Currently, many stores or shops, restaurants or commercial centres use background music to attract customers; background music is like a stress reliever for customers. According to the report of McKinsey & Company (2016), in the year 2015, the Asian digital music market accounted for only $900 million, less than a third compared to the global music market; however, the Asian market had grown 8,8% from the year 2011–2015, which was significantly faster than the global market with the growth of 0,4% only. Asian market potential is high and needed to pay more attention from international investors, and the background music at the store was part of this market. Music can influence the emotions of the human; these emotions might be a pleasure (good, happy, pleased, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Garg et al. (Eds.): ISMS 2022, LNNS 671, pp. 384–399, 2023. https://doi.org/10.1007/978-3-031-31153-6_31

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joyful), arousal (stimulated, excited, alert or active), dominance (influential, in control, significant) (Andersson, Kristensson, Wästlund and Gustafsson, 2012; Hai, 2016; Zhang, Leng and Liu, 2020; Adelaar, Chang, Lancendorfer, Lee and Morimoto, 2003). These emotions, such as excellent, happy, pleased, stimulated, excited, alert, and active, might turn into a good mood (Robbins and Judge, 2018; Davis, 2009), and these emotions could be impacted by good background music at the store. Moreover, Rook (1987) proved that the buyers’ mood would impact their impulse buying behaviour; 85% of the respondents, who were in a good mood, such as a joyful and excited mood, would buy impulsively rather than in a bad mood. Bellenger and Korgaonkar (1980) also showed that impulse buying behaviour took part in 27%–62% of the total sales revenue of the big stores. Impulse buying behaviour happens when the urge or temptation to buy something increases to the level where without planning, customers would rush to buy even knowing this good is unnecessary (Choudhary, 2014). Therefore, with retailers, the sales revenue from impulse buying behaviour might become one of the most critical parts of their total sales revenue; this is why business administrators have been trying to implement many strategies to trigger customer impulse buying behaviour. Even though many researchers tried to understand the relationship between emotions, mood and the impulse buying behaviour of customers, few studies focused on factors such as background music that might impact emotions and mood and customer impulse buying behaviour. Age was one of the essential factors which could predict impulse buying behaviour. Young people or customers might face less risk when spending money, and therefore impulse buying might occur to a higher degree among 18 to 39 years old and to a lesser extent afterwards (Wood, 2005). Huynh and Nguyen (2022) investigated the relationship between music at the point of sales and the incredible shopping behaviours of young customers in Ho Chi Minh City but not entirely in Vietnam. Hence, the authors aim to investigate the impulse buying behaviour at stores of young customers in Viet Nam. The findings are expected to help marketers better understand how background music at stores stimulates impulse shopping among young Vietnamese.

2 Literature Review Koelsch (2010) showed that the emotions evoked by music could modulate human brain activity, and music could arouse powerful emotions and influence an individual’s mood reliably. Music was essential to society; its power evoked solid emotions and influenced people’s moods (Koelsch, 2014). Emotions were a centre of music enjoyment, with many emotional states consistently reported when listening to music (Vuilleumier and Trost, 2015). Music often evoked feelings of wonder, nostalgia, tenderness, or pleasure. Emotions when listening to music were aroused through a combination of activation in the emotional and motivational brain systems that gave their values to music. Loizou et al. (2014), music positively impacted the emotional state and psychological needs deep inside people. On the other hand, Arjmand et al. (2017) also showed that different motives, sharps, tempo, and volume would lead to different emotional responses from the human being; familiarity with background music was also another critical factor in making listeners evoke emotions (Pereira and et al., 2011). Taruffi et al. (2017) also demonstrated that humans recognized emotions expressed by music to a better level

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than by chance. Humans also showed the ability to recognize five basic human emotions (happiness, sadness, tenderness, fear, and anger) conveyed through movie soundtracks. Impulse buying behaviour refers to any buying behaviour the buyer makes without prior planning (Stern, 1962). Rook (1987) added emotional responses and impulse buying into the concept of impulse buying behaviour. Rook (1987) argued that impulse buy might occur when the customers experience a sudden feeling, which might bring the urge to buy something immediately, and this and impulse buying represented a complex emotional state, which could create certain contradictions in the mind of customers. Besides, Wood (2005) states that impulse buying behaviour was known to increase the enjoyment of customers’ lives; it was simply because they wanted to experience the feeling of shopping and release their psychological stress when they finished shopping for products they liked. Besides, Park et al. (2006) found that positive mood influences impulse buying behaviour. Simon (1991) showed that impulse buying was an unexpected behaviour, resulting when the consumer was exposed to a stimulus and decided to buy immediately. Impulse buying behaviour might be considered unplanned, immature buying (Thanh et al., 2016). Mohan et al. (2013) insist that many factors influence the impulse buying behaviour of consumers. Hawaldar and Pinto (2020) showed that consumer moods, and s2022tore environments, such as store layout or ambience, would influence impulse buying behaviour. Indrawati et al. () confirm that the hedonic motive had a strong positive impact on impulsive shopping tendency, whereas, in contrast, the utilitarian motive had a strong positive impact on shopping intentions. Therefore, the store environment, consumer mood, and emotional responses directly or indirectly influence consumer impulse buying behaviour. Davis (2009) stated that “a strong emotion of joy can prolong itself and create a positive mood over several days”. Robbins & Judge (2018) concluded that “the emotions can turn into the mood when you lose focus on the event or object that started the feeling”, which meant that when a person had good emotions, later that person could have a good mood. Besides, Norwood et al. (2019) confirm the presence of different patterns of brain activity in response to different emotional responses. Fiebig et al. (2020) also determined and analyzed the relationship between emotions, mood and attitudes. Their research came up with the model in sequences: emotions could turn into a mood, and mood could turn into attitudes. Recently, Maruani and Geoffroy (2022) confirmed that emotional reactivity refers to emotion response intensity and threshold. Mehrabian and Russell (1974) proposed the Stimuli, Organism and Response model, widely known as the S-O-R model. This model assumed that Stimuli (S) would produce psychological responses (Organism - O), and from that, psychological responses would lead to the behaviour of people (Response - R) with the environment. Robert and John (1982) showed that the buyer’s feeling of interest and excitement at the store was decisive to the customers’ purchase intention. In addition, Zhang, Leng, & Liu’s (2020) research also showed that the emotional responses evoked by environmental stimulation would impact mobile consumers’ impulsive purchase intention. Therefore, listening to a song or sound could directly affect customers’ moods by alternating tempos, volume levels, and rhythm; customers’ moods would impact impulse buying behaviour (Rook, 1987). Recently, Chiu and Cho (2022) confirmed that emotional responses are associated with impulse buying behaviour of fitness products.

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3 Hypotheses Development and Research Model 3.1 Hypotheses Developement 3.1.1 Musical Genres A musical genre is a musical composition category characterized by a particular music style, form, or content description (Aucouturier and Pachet, 2003). Song et al. (2016) showed that “music preference influenced perceived and induced emotion”, and people evoked different emotions with different musical genres. Zentes et al. (2017) showed that listeners would have different emotions and responses to musical genres. There was an emotional connection between specific musical genres, and stereotyped judgments about musical genres could cause listeners to have specific emotional responses (Susino and Schubert, 2019). Taruffi et al. (2017) demonstrated that humans recognized emotions evoked by music to a better extent than by chance. They also showed humans’ ability to recognize five basic emotions (happiness, sadness, tenderness, fear and anger) conveyed through the soundtrack. Zentner et al. (2008) also showed the same results as Taruffi et al. (2017) but added more emotional responses, such as transcendence and dysphoria. In conclusion, background music genres at stores might influence the emotional responses of young customers. Greenberg et al. (2022) also suggest that genres of background music at stores may influence the emotional responses of young customers in Viet Nam. The first hypothesis is stated as follows: H1: Musical genres positively influence the emotional responses of young customers in Viet Nam. 3.1.2 Volume in Music Volume in music was also a factor that might impact customers’ emotional responses. In music, volume was one of the characteristics of vocal music and had qualitative meaning. The research of Biswas and Lund (2018) showed that consumers would make different product choices with different volumes of background music. According to Arnett (1994), the higher volume of background music, young customers could have higher arousal, which would provide intense stimulation to seek sensation. The importance of loud music was demonstrated when organizations used very high-volume music at many events to attract young people (Welch and Fremaux, 2017). In conclusion, a high volume of background music at stores might be an advantage in attracting young customers because it might evoke emotional responses, such as arousal. Moreover, Stano et al. (2022) also conclude that the volume of background music at stores may influence the emotional responses of young customers in Viet Nam. The second hypothesis is stated as follows: H2: Volume in music positively influences the emotional responses of young customers in Viet Nam. 3.1.3 Familiarity with the Music Daltrozzo et al. (2010) confirm that familiarity with the musical melody could trigger emotional transmission or association with melodic semantics. Familiarity with the

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music was a conditional response, which arose from evaluating and relating to the situations that had occurred before; when a song was associated with some positive or negative events, people might subconsciously evoke happy or sad emotions (Juslin et al., 2008). According to Pereira et al. (2011), familiarity with the music seemed to be essential in making listeners evoke emotional responses. Van et al. (2013) used the EDA index, which was an objective index of emotional arousal, to show that when listeners listened to a familiar song, there would be a significant contribution in evoking emotional responses. As listening to familiar music, listeners would predict over time, based on knowledge of the songs, that the memory factor played an important role. Recently, Brown and Bidelman (2022) stated that When masked by familiar music, response latencies to speech were less susceptible to informational masking, suggesting concurrent neural tracking of speech was easier during music known to the listener. Chen et al. (2022) also conclude that musical familiarity can impact shopping. Hence, the third hypothesis is stated as follows: H3: Familiarity with the music positively influences the emotional responses of young customers in Viet Nam. 3.1.4 Rhythm in Music Rhythm in music is the length of the relationship between successive sounds, which follow each other in an organized manner to build a musical image (Huong, 1997). Rhythm in music is a movement of sound repeatedly measured or controlled over a specified period. Li and Ogihara (2003) used rhythm in music to classify the emotional responses that music brought. Lu et al. (2005) also used sound features such as rhythm, intensity and timbre to identify emotional expression. Rhythm in music can make human bodies dance and stir emotions in humans (Panksepp and Trevarthen, 2009). Panksepp and Trevarthen (2009) also showed that rhythm in music might convey emotions vividly in ways that could not be expressed in words and other art forms. Background music with a slow tempo or rhythm would make customers stay longer in the stores and feel more comfortable when waiting to order or preparing to close the store; playing music with a fast rhythm would make customers shop faster and leave on time for employees (Milliman, 1982). Recently, Jerath and Beveridge (2020) also concluded that rhythm in background music at stores might influence emotional responses. The fourth hypothesis is stated as follows: H4: Rhythm in music positively influences the emotional responses of young customers in Viet Nam. 3.1.5 Emotional Responses Emotional responses involve not only cognitive appraisal but also a physiological response, action tendency and regulation of humans (Juslin et al., 2010). Emotion is defined by American Psychological Association (APA) as “conscious mental reactions (such as anger or fear) subjectively experienced as strong feeling usually directed toward a specific object and typically accompanied by physiological and behavioural changes in the body.“ and the environmental stimulation would trigger emotional responses of

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human (Adelaar et al., 2003). Koelsch (2010) also showed that emotions evoked by music could modulate virtually all limbic and paralimbic brain structures, arousing powerful emotions and reliably influencing an individual’s mood. The emotional responses to music were triggered through “a combination of activation in emotional and motivational brain systems that confer its valence to music” (Vuilleumier and Trost, 2015). When listeners experience in response to music, emotional arousal appears to be a significant contributing factor to this experience (Van et al., 2013). According to Koelsch (2014), music is essential to society because it can evoke strong emotions and influence people’s moods. Chan et al. (2019) also conclude that emotional responses may influence mood changes. The fifth hypothesis is stated as follows: H5: Emotional responses positively influence mood changes of young customers in Viet Nam. 3.1.6 Mood Changes Coley and Burgess (2003) showed that using attractive store signage and public address announcements or advertisements might trigger impulse buying behaviour rather than based on perception or speculation. According to Rook (1987), consumers’ mood states would impact their impulse buying, and mood changes would stimulate consumer impurity. The post-purchase mood of consumers would positively encourage impulse buying behaviour, and impulse buying behaviour would not influence the post-purchase mood of consumers (Ozer and Gultekin, 2015). Consumers, who had a positive mood, would show more outstanding impulse buying than those who had a negative mood (Pornpitakpan et al., 2017). However, the research of Youn and Faber (2000) showed the opposite result: the unhappy mood of consumers also influenced their impulse buying behaviour. Parsal et al. (2021) also showed that the mood of the individual consumer plays an essential role in increasing impulsiveness, which leads to impulsive buying behaviour. Febrilia and Warokka (2021) also found out that impulse buying behaviour was based on two main factors: consumer traits and situational factors, and consumer mood was one of the essential factors in consumer traits. Iyer et al. (2019) also came up with the same results: marketing stimulation, positive mood, and negative mood would influence the impulse buying behaviour of consumers. Besides, Memon et al. (2019) also conclude that mood changes may influence the impulse buying behaviour of young customers in Viet Nam. The last hypothesis is stated as follows: H6: Mood changes positively influence the impulse buying behaviour of young customers in Viet Nam. 3.2 Proposed Research Model (See Fig. 1)

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Musical genres

H1+

Volume in music

H2+

Familiarity with the music Rhythm in

Emotional responses

H5+

H6+ Mood changes

Impulse buying behavior

H3+ H4+

4 Method music

Fig. 1. Proposed Research Model

3.3 Sample Description The authors would conduct the official survey with a limited sample of estimated 300 respondents, which was suitable for this research topic (the online survey method via Google Form would be applied in this research). After collecting and surveying the sample, the results were obtained with the data of 327 participants, of which 300 participants were valid, and 27 participants were invalid (including respondents who had never shopped at the stores with background music and respondents who were under 16 years old and over 25 years old). After excluding invalid respondents, the data of 300 valid respondents were used for further analysis. Over 60.5% were female, 53.4% were between 19–22 years old. As expected, most valid respondents were pupils or students, with 78.6%, 39.2% of the income being under 3 million VND per month, and the purchase frequency from 1 to 3 times was the most with 66.7%. The sample characteristics are provided in Table 1. 3.4 Data Collection Method This research was carried out in two phases: qualitative and quantitative. In the qualitative research, the authors would determine the scale and the questionnaire for the next step (quantitative research). The authors had inherited from previous studies and built a preliminary scale for 4 independent variables (genres of background music, volume in background music, familiarity with background music, and rhythm in background music), 2 mediating variables (emotional responses, mood changes) and dependent variable (impulse buying behaviour). In the quantitative research, the authors used the survey method by questionnaire. Data collection was done by online survey (send Google Form link via email or social network). This research also used convenience sampling; the estimated sample size was 300. The survey subjects were young customers between 16 and 25 who used to shop at stores with background music. The collected data would be cleaned and analyzed by using SPSS 20 software with the following methods: descriptive statistics, reliability testing by Cronbach’s Alpha, and exploratory factor analysis (EFA). After the above steps, the authors would continue to analyze confirmatory factor analysis (CFA) and structural equation modelling (SEM) using AMOS 20 software to test the authors’ hypotheses.

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Table 1. Respondent’s Characteristic

Gender Age

Occupation

Income

Purchase frequency

Frequency

Percentage

Female

182

60,7%

Male

118

39,3%

From 16 to 18 years old

85

28,3%

From 19 to 22 years old

165

55,0%

From 23 to 25 years old

50

16,7%

Student

233

77,7%

Officer

40

13,3%

Worker

15

5,0%

Others

12

4,0%

Under 5 million VND

119

39,7%

From 5 to under 10 million VND

94

31,3%

From 10 to under 15 million VND

48

16,0%

From 15 to under 20 million VND

28

9,3%

Above 20 million VND

11

3,7%

From 1 to 3 times

192

64,0%

From 4 to 6 times

75

25%

From 7 to 10 times

22

7,3%

Above 10 times

11

3,7%

Source: Author’s survey data (2022)

3.5 Research Instruments This research would adopt items with good internal consistency from reviewing the literature and modifying the items to the current situation. Four items measured musical genres, four items measured volume in music, four items measured familiarity with the music, four items measured rhythm in music, four items measured emotional responses, four items measured mood changes, and 2 and four items measured impulse buying behaviour of consumers. A five-point Likert scale measured observed variables: (1) Total disagree; (2) Disagree; (3) Neutral; (4) Agree; (5) Total agree. The five-point Likert scale helped more accurately assess and analyze the impact of background music at stores on changing consumer behaviour.

4 Results 4.1 Exploratory Factor Analysis All factors in the research model meet the reliability test requirements, which require Cronbach’s Alpha to be greater than 0.7 (Hair et al., 2012) (Table 2).

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Variables

Items

Cronbach’s Alpha

Musical genres

4

0.726

Volume in music

4

0.859

Familiarity with the music

4

0.702

Rhythm in music

4

0.679

Emotional responses

4

0.832

Mood changes

4

0.793

Impulse buying behaviour

4

0.890

Source: Author’s survey data(2022)

The first EFA test eliminated 02 items which are (TT1) and (AL3) because its factor loading exists in 2 factors, so it was rejected (1st test). Then the authors started to rerun the second EFA test. As a result, after eliminating the item (TT1) and (AL3), the variables “musical genres”, “volume in music”, “ familiarity with the music”, “rhythm in music”, “emotional responses”, “mood changes”, and “impulse buying behaviour” The KMO coefficient = 0.882, which satisfied the condition 0.5 ≤ KMO ≤ 1; Bartlett test results with sig = 0.000 (< 0.05); the Eigenvalues was 1.006 > 1 and the cumulative variance was 65.314%, above 50% (Table 3). Table 3. Exploratory factor analysis (EFA) Variables

Items

Component 1

Impulse buying behaviour

Emotional responses

HV2

0.833

HV1

0.775

HV3

0.759

HV4

0.727

2

CX3

0.754

CX4

0.715

CX2

0.641

3

4

5

6

7

(continued)

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Table 3. (continued) Variables

Items

Component 1

CX1 Musical genres

Volume in music

Familiarity with the music

Rhythm in music

Mood changes

2

3

4

5

6

7

0.599

TL2

0.747

TL4

0.681

TL3

0.666

TL1

0.656

AL2

0.869

AL1

0.860

Al4

0.604

QT1

0.751

QT4

0.738

QT2

0.738

QT3

0.636

ND4

0.770

ND3

0.754

ND2

0.529

ND1

0.484

TT3

0.797

TT4

0.767

TT2

0.646

KMO (Kaiser-Meyer-Olkin)

0,872

Sig

0,000

Eigenvalues

1,006

Cumulative variance (%)

65,314%

Source: Author’s survey data(2022)

4.2 Confirmatory Factor Analysis (CFA) The results showed that CMIN/DF = 1.757 (< 3); CFI = 0.932; TLI = 0.920; GFI = 0.891 and RMSEA = 0.050 (< 0.08). According to the research of Hu and Bentler (1999), Doll and Torkzadeh (1994), due to the limitation of sample size, the GFI value would be difficult to reach 0.9 or more because this index depended a lot on the number of scales, the number of items and the sample size. Therefore, a GFI value lower than 0.9 but above 0.891 was still acceptable (Fig. 2).

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Fig. 2. Confirmatory Factor Analysis. Source: Author’s survey data (2022)

4.3 Structural Equation Modelling The results showed that Chi-square = 1.941 with P = 0.000 < 0.05; CMIN/DF = 1.941 (< 3); CFI = 0.912 (≥ 0.8, accepted); TLI = 0.901; GFI = 0.877 and RMSEA = 0.056(< 0.08) (Fig. 3). According to the research of Hu and Bentler (1999) and Doll and Torkzadeh (1994), due to the limitation of sample size, the GFI value would be difficult to reach 0.9 or more because this index depended a lot on the number of scales, the number of items and the sample size. Therefore, a GFI value lower than 0.9 but above 0.8 was still acceptable (Table 4).

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Fig. 3. Structural equation modeling (Source: Author’s survey data (2022))

Table 4. Unstandardized Regression Coefficient Estimate

S.E

C.R

0,498

0,157

P

CX