Digital Technologies and Applications: Proceedings of ICDTA’22, Fez, Morocco, Volume 2 (Lecture Notes in Networks and Systems, 455) 303102446X, 9783031024467

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Lecture Notes in Networks and Systems 455

Saad Motahhir Badre Bossoufi   Editors

Digital Technologies and Applications Proceedings of ICDTA’22, Fez, Morocco, Volume 2

Lecture Notes in Networks and Systems Volume 455

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

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

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

Saad Motahhir Badre Bossoufi •

Editors

Digital Technologies and Applications Proceedings of ICDTA’22, Fez, Morocco, Volume 2

123

Editors Saad Motahhir Ecole Nationale des Sciences Appliquées Fez, Morocco

Badre Bossoufi Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez, Morocco

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-02446-7 ISBN 978-3-031-02447-4 (eBook) https://doi.org/10.1007/978-3-031-02447-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

We are honored to dedicate the proceedings of ICDTA’22 to all the participants and committees of ICDTA’22.

Foreword

It is with deep satisfaction that we write this Foreword to the proceedings of the ICDTA’22 organized by École Nationale des Sciences Appliquées and Faculté des sciences which belong to SMBA University, Fez, Morocco, on January 28 and 29, 2022. This conference was bringing together researchers, academics, and professionals from all over the world, experts in digital technologies and their applications. This conference particularly encouraged the interaction of research students and developing academics with the more established academic community in an informal setting to present and discuss new and current work. The papers contributed the most recent scientific knowledge known in the field of digital technologies and their applications. Their contributions helped to make the conference as outstanding as it has been. The organizing and technical program committees put much effort into ensuring the success of the day-to-day operation of the meeting. We hope that this book proceedings will further stimulate research in digital technologies such as artificial intelligence, Internet of things, embedded systems, network technology, information processing, and their applications, in several areas including hybrid vehicles, renewable energy, mechatronics, medicine, etc. We feel honored and privileged to serve the best recent developments to you through this exciting book proceedings. We thank all authors and participants for their contributions. S. Motahhir B. Bossoufi

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Preface

This volume contains the second section of the written versions of most of the contributions presented during the conference of ICDTA’22. The conference provided a setting for discussing recent developments in a wide variety of topics including artificial intelligence, Internet of things, embedded systems, network technology, information processing, and their applications, in several areas such as hybrid vehicles, renewable energy, mechatronics, and medicine. The conference has been a good opportunity for participants from various destinations to present and discuss topics in their respective research areas. ICDTA’22 conference tends to collect the latest research results and applications on digital technologies and their applications. It includes a selection of 169 papers from 350 papers submitted to the conference from universities and industries all over the world. This volume includes half of the accepted papers. All of the accepted papers were subjected to strict peer-reviewing by two–four expert referees. The papers have been selected for this volume because of their quality and their relevance to the conference. We would like to express our sincere appreciation to all authors for their contributions to this book. We would like to extend our thanks to all the referees for their constructive comments on all papers; especially, we would like to thank organizing committee for their hardworking. Finally, we would like to thank the Springer publications for producing this volume. S. Motahhir B. Bossoufi

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Acknowledgments

We request the pleasure of thanking you for taking part in the second edition of the International Conference on Digital Technologies and Applications ICDTA’22. We are very grateful for your support, so thank you everyone for bringing your expertise and experience around the conference and engaging in such fruitful, constructive, and open exchanges throughout the two days of the ICDTA’22 conference. We would like to extend our deepest thanks and gratitude to all the speakers for accepting to join us from different countries. Thank you for being such wonderful persons and speakers. Again, thanks for sharing your insight, knowledge, and experience. Of course, this event could not be that successful without the effort of the organizing and technical program committees. Therefore, Pr. Badre and I would like to express our sincere appreciation to all of you who generously helped us. We would like to especially thank all the participants for the confidence and trust you have placed in our conference. We hope we lived up to your highest expectations. Our humble acknowledgment would be incomplete without thanking our biggest source of support; therefore, our deepest gratitude goes to Prof. Redouane Mrabet the president of Sidi Mohammed Ben Abdellah University, and the same goes for Prof. Lahrach Abderrahim, the director of the National School of Applied Sciences; Prof. Belmlih Mohammed, the dean of the faculty of science; Prof. Mohammed Ouazzani Jamil, the vice president of UPF University; Prof. Abdelmajid Saka, the deputy director of the National School of Applied Sciences; and Professor Elhassouni Mohammed, the vice dean of faculty of sciences. Thank you all for your support and for being all-time all set up to host such scientific events. S. Motahhir B. Bossoufi

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Contents

Artificial Intelligence, Machine Learning and Data Analysis Decision Making Support for Quality 4.0 Using a Multi Agent System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samiha Mansouri, Latifa Ouzizi, Youssef Aoura, and Mohammed Douimi

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ISBRNM: Integrative Approach for Semantically Driven Blog Recommendation Using Novel Measures . . . . . . . . . . . . . . . . . . . . . . . . M. Anirudh and Gerard Deepak

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Arabic Broken Plural Model Based on the Broken Pattern . . . . . . . . . . Mariame Ouamer, Rachida Tajmout, and Karim Bouzoubaa

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Performance Improvement of DTC for Doubly Fed Induction Motor by Using Artificial Neuron Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . Said Mahfoud, Aziz Derouich, and Najib El Ouanjli

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Knowledge Management Models: Overview and Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Safaa Essalih, Mohamed Ramadany, and Driss Amegouz

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Automatic Story Generation: Case Study of English Children’s Story Generation Using GPT-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Zahra Fagroud, Mohamed Rachdi, and El Habib Ben Lahmar

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Prediction of Olive Cuttings Greenhouse Microclimate Under Mediterranean Climate Using Artificial Neural Networks . . . . . . . . . . . Sanae Chakir, Adil Bekraoui, El Moukhtar Zemmouri, Hassan Majdoubi, and Mhamed Mouqallid HSCRD: Hybridized Semantic Approach for Knowledge Centric Requirement Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rituraj Ojha and Gerard Deepak

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The Challenges and Opportunities for Developing the Use of Data and Artificial Intelligence (AI) in North Africa: Case of Morocco . . . . . . . . . Mohamed Oubibi, Yueliang Zhou, Ayoub Oubibi, Antony Fute, and Atif Saleem Sustainable Mobility Model for Fez City (Morocco) . . . . . . . . . . . . . . . . Mohammed Mouhcine Maaroufi, Laila Stour, and Ali Agoumi

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Sustainable Mobility Plan Indicators: Application to the Moroccan Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Mohammed Mouhcine Maaroufi, Laila Stour, and Ali Agoumi Demand Driven DRP vs DRP: An Empirical Study . . . . . . . . . . . . . . . . 114 Yassine Erraoui and Abdelkabir Charkaoui Effect of Questions Misspelling on Chatbot Performance: A Statistical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Rachid Karra and Abdelali Lasfar Contribution to the Economic Analysis of Numerical Data of Road Accidents in Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Abdelaziz Zerka and Fouad Jawab Study and Analysis of Accidents Database for the Years 2015–2016 . . . 145 Halima Drissi Touzani, Sanaa Bollouz, Sanaa Faquir, and Ali Yahyaouy The Behavioral Intention of Healthcare Professionals to Accept Remote Care Technologies: 20 Years of Scientific Production . . . . . . . . 153 Mohammed Rouidi, Abd Elmajid Elouadi, Amine Hamdoune, and Khadija Choujtani Leveraging the Automated Machine Learning for Arabic Opinion Mining: A Preliminary Study on AutoML Tools and Comparison to Human Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Moncef Garouani and Kasun Zaysa Day-Ahead Photovoltaic Power Production Forecasting Following Traditional and Hierarchical Approach . . . . . . . . . . . . . . . . . . . . . . . . . 172 Ali Agga, Ahmed Abbou, and Moussa Labbadi Short-Term PV Plant Power Production Forecasting and Hyperparameters Grid Search for LSTM and MLP Models . . . . . . . . . 181 Ali Agga, Ahmed Abbou, Moussa Labbadi, and Rachid Touileb Computational Analysis of Human Navigation in a VR Spatial Memory Locomotor Assessment Using Density-Based Clustering Algorithm of Applications with Noise DBSCAN . . . . . . . . . . . . . . . . . . . 190 Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Noureddine Rahmoun, Mohamed Zaoui, Alexander Castilla, Alain Berthoz, and Bernard Cohen

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Analysis of UNSW-NB15 Datasets Using Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Hakim Azeroual, Imane Daha Belghiti, and Naoual Berbiche OntoFusionCrop: An Ontology Centric Approach for Crop Recommendation Based on Bagging and Semantic Alignment . . . . . . . . 210 Aparna Chandramouli and Gerard Deepak Employee Attrition by Ensemble Approach . . . . . . . . . . . . . . . . . . . . . . 220 Arifa Shamim, Ramsha Khan, and Sadia Javed Internet of Things, Blockchain and Security and Network Technology Bigdata Applications in Healthcare: Security and Privacy Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Maida Ahtesham Ensure the Confidentiality of Documents Shared Within the Enterprise in the Cloud by Using a Cryptographic Delivery Method . . . 241 Hamza Touil, Nabil El Akkad, and Khalid Satori Survey IOT Systems Security Based on Machine Learning . . . . . . . . . . 251 El Mahdi Boumait, Ahmed Habbani, Mohammed Souidi, and Zaidan Didi Reduce the Energy Consumption of IOTs in the Medical Field . . . . . . . 259 Mohammed Moutaib, Tarik Ahajjam, Mohammed Fattah, Yousef Farhaoui, Badraddine Aghoutane, and Moulhime El Bekkali Query Processing in IoT Based on Spatial and Temporal Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Chaimae Kanzouai, Abderrahim Zannou, El Habib Nfaoui, and Abdelhak Boulaalam Toward an IoT-Based System to Ensure Product Identification of Moroccan Handicrafts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Youssef Aounzou, Fahd Kalloubi, and Abdelhak Boulaalam A New Dual Band Antenna with Improvement Performances for the Internet of Things Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Youssef Mouzouna, Hanane Nasraoui, Jamal El Aoufi, and Ahmed Mouhsen Target Classification Algorithm Based on Characteristics of UWB Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Dounia Daghouj, Mohammed Fattah, M. Abdellaoui, S. Mazer, Y. Balboul, and M. El Bekkali Forward Error Correction for Routing Protocols in WSN: A Comparative Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Ikram Daanoune and Abdennaceur Baghdad

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Packet Delay Budget-Based Scheduling Approach for 5G Time Division Duplex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Asmae Mamane, Mohammed Fattah, Mohammed El Ghazi, and Moulhime El Bekkali Mutual Coupling Reduction in Array Antenna Using a New EBG Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 Abdellah El Abdi, Moussa El Ayachi, and Mohammed Rahmoun Web-Based Techniques and E-learning Predicting Student Success in a Scholarship Program . . . . . . . . . . . . . . 333 Majjate Hajar, Jeghal Adil, Yahyaouy Ali, and Alaoui Zidani Khalid Software Architecture for Personalized Learning Systems: A Systematic Literature Review-Camera Ready Version . . . . . . . . . . . . 342 Maida Ahtesham, Ramsha Khan, and Ayesha Zulfiqar A Modeling Learner Approach for Detecting Learning Styles in Adaptive E Learning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Ibtissam Azzi, Loubna Laaouina, Adil Jeghal, Abdelhay Radouane, Ali Yahyaouy, and Hamid Tairi A Hybrid Recommender System for Pedagogical Resources . . . . . . . . . 361 Yassamina Mediani, Mohamed Gharzouli, and Chahrazed Mediani An Investigation of the Effect of Flipped-Jigsaw Learning Classroom on Primary Students’ Autonomy and Engagement in E-Learning Context and Their Perceptions of the Flipped-Jigsaw Learning Classroom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372 Vahid Norouzi Larsari, Flora Keysan, and Radka Wildova MRDFPD: Metadata Driven RDF Based Product Discovery Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Saloni Gordhan Rakholiya, Gerard Deepak, and A. Santhanavijayan OntoKIQE: An Ontology Infused Approach for Knowledge Integrated Query Expansion Using Semantic Intelligence . . . . . . . . . . . 394 Vignesh Mohanavelu, Gerard Deepak, and A. Santhanavijayan MetaBlog: A Metadata Driven Semantics Aware Approach for Blog Tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406 Harsh Shaw and Gerard Deepak An Extended Framework for Semantic Interoperability in PaaS and IaaS Multi-cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Karima Benhssayen and Ahmed Ettalbi

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Image and Information Processing Design of a Security System Based on Raspberry Pi with Motion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Zaidan Didi, Ikram El Azami, and El Mahdi Boumait Robust Face Recognition Under Advanced Occlusion Proposal of an Approach Based on Skin Detection and Eigenfaces . . . . . . . . . . . . . . . . 435 Faouzia Ennaama, Khalid Benhida, and Sara Ennaama Moroccan Carpets Classification Based on SVM Classifier and ORB Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 Hanae Moussaoui, Nabil El Akkad, and Mohamed Benslimane Interactive Large-Scale Graph Visualization and Analysis for Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456 Jamal Elhachmi and Youssef Cheikhani Toward a New Process for Candidate Key-Phrases Extraction . . . . . . . 466 Lahbib Ajallouda, Oumaima Hourrane, Ahmed Zellou, and El Habib Benlahmar Integrative KnowGen: Integrative Knowledge Base Generation for Criminology as a Domain of Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Gurunameh Singh Chhatwal and Gerard Deepak The Proposal of a Process Flow Model and Metamodel for the Feature Driven Development Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Soukaina Merzouk, Abdelaziz Marzak, and Nawal Sael Advanced Technologies in Energy and Electrical Engineering A Critical Review of DC/AC Converter Structures for GridConnected Photovoltaic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Sara Khalil, Naima Oumidou, Ali Elkhatiri, and Mohamed Cherkaoui Improved Performance of the Grid Side Power Conversion Chain by Adopting Multilevel Inverter Topologies with an Optimized LCL Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Wijdane El Maataoui, Soukaina El Daoudi, Loubna Lazrak, and Mustapha Mabrouki MPPT Algorithm of Piezoelectric Power Generation System Based on Nonlinear Extrapolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 Souad Touairi, Abdelkhalk Aboulouard, and Mustapha Mabrouki A Comparative Study Between Hydrogen and Battery Storage for Residential Applications in Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Hanane El Bakkali, Mostafa Derrhi, and Mustapha Ait Rami

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DC-DC Converters for Smart Houses with Multiple Input Single Output Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532 Said El Mouzouade, Karim El Khadiri, Zakia Lakhliai, Driss Chenouni, Mouhammed Ouazzani Jamil, Hassan Qjidaa, and Ahmed Tahiri Optimization Power Control for Rotor Side Converter of a DFIG Using PSO Evolutionary Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Elmostafa Chetouani, Youssef Errami, Abdellatif Obbadi, Smail Sahnoun, and Hamid Chojaa Renewable Energy Sources Integration in a Microgrid Control System: Overview and Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 Youssef Oubail, Mouaad Boulakhbar, Imad Aboudrar, Moulay Rachid Elmoutawakil Alaoui, and Lahoussine Elmahni A New Approach to Manage the Energy Flow in a Hybrid Renewable Energy System Based on Green Hydrogen . . . . . . . . . . . . . . . . . . . . . . . 562 Sanaa Boullouz, Sanaa Faquir, and Ali Yahyaouy The State-of-the-Art of Safe Fly Zone for a Power Transmission Lines Inspection UAV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Lamyae Fahmani, Siham Benhadou, and Hicham Medromi Grid Stability: High Penetration Levels of Renewable Energies . . . . . . . 579 Omar Boutfarjoute, Hamid Chekenbah, Yassir Maataoui, and Rafik Lasri IoT Technologies in Service of the Home Energy Efficiency and Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588 Mouhcine Arich, Abdelghani El Ougli, and Belkassem Tidhaf The Vortex Shedding Suppression and Heat Transfer Characteristics Around a Heated Square Cylinder by Using Three DownstreamDetached Partitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598 Youssef Admi, Mohammed Amine Moussaoui, and Ahmed Mezrhab Numerical Investigation of Phase Change Material (PCM) Cooling in Photovoltaic Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Abir Bria, Benyounes Raillani, Mourad Salhi, Dounia Chaatouf, Samir Amraqui, and Ahmed Mezrhab Modeling and Simulation of a Photovoltaic Panel by Using Proteus . . . 621 Halim Eddahbi, Loubna Benaaouinate, Mohamed Khafallah, and Aziz El Afia Hourly Solar Power Forecasting Using Optimized Extreme Learning Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 Ibtissame Mansoury, Dounia El Bourakadi, Ali Yahyaouy, and Jaouad Boumhidi

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Detection and Self-correction of Partial Shading Deficiency on Photovoltaic Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638 Tahri Imane, Chennani Mohammed, Belkhayat Driss, and Zidani Youssef Optimized Energy Output from a PV System Using a Modified Incremental Conductance Algorithm for Rapidly Changing Insolation . 649 Mohamed Ez-Zghari, Smail Chtita, Naoufal El Youssfi, Tarik Zarrouk, Karim El Khadiri, and Ahmed Tahiri Solar Radiation Time-Series Prediction Using Random Forest Algorithm-Based Feature Selection Approach . . . . . . . . . . . . . . . . . . . . 659 Gaizen Soufiane, Fadi Ouafia, and Abbou Ahmed Analysis of Wind Turbine Vibration Faults to Improve Predictive Maintenance Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Aziza Nana, Lalouli Abderrezzak, Kamilia Mounich, and Aicha Wahabi Integral Sliding Mode Control for DFIG Based Wind Energy Conversion System Using Ant Colony Optimization Algorithm . . . . . . . 675 Hasnae Elouatouat, Ahmed Essadki, Tamou Nasser, and Hamid Chojaa An Improved Supervised Fuzzy PI Collective Pitch Angle Control for Wind Turbine Load Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Seif Eddine Chehaidia, Hamid Kherfane, Hakima Cherif, Abdallah Abderrezak, Hamid Chojaa, Laila Kadi, Boubekeur Boukhezzar, and Mohammed Taoussi Maximum Power Generation and Pitch Angle Control of a PMSG-Based WECS Connected to the Grid . . . . . . . . . . . . . . . . . . 696 Adil El Kassoumi, Mohamed Lamhamdi, Azeddine Mouhsen, and Ahmed Mouhsen Design a Power Quality Analyzer Using an ARDUINO Card and Display Signals in the LABVIEW Environment . . . . . . . . . . . . . . . . . . . 706 Yassine Taleb, Azeddine Bouzbiba, and Ahmed Abbou IoT, Comparative Study Between the Use of Arduino Uno, Esp32, and Raspberry pi in Greenhouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718 Zaidan Didi and Ikram El Azami Numerical Resolution of the LWR Method for First Order Traffic Flow Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 Hamza El Ouenjli, Anas Chafi, and Salaheddine Kammouri Alami Transverse Controller Design for a Self-driving Car Model Based on Stanley’s Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737 Younesse El Hamidi and Mostafa Bouzi

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Contents

Road Safety Enhancement of Intelligent Transportation Systems: From Cellular LTE-V2X Toward 5G-V2X . . . . . . . . . . . . . . . . . . . . . . . 745 Adil Abou El Hassan, Imane Kerrakchou, Abdelmalek El Mehdi, and Mohammed Saber Mechatronic, Robotic and Control System Evaluation of the Dynamic Behavior of a Rotor Based on a Vibration Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757 Mohammed Bouaicha, Imad El Adraoui, Hassan Gziri, Nadia Machkour, and Mourad Zegrari Optimized PID Controller by Ant Colony Optimization of DTC for Doubly Fed Induction Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 Said Mahfoud, Aziz Derouich, Najib El Ouanjli, Abderrahman El Idrissi, and Mohammed El Mahfoud Speed Sensorless Fuzzy Direct Torque Control of Induction Motor Based MRAS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779 Najib El Ouanjli, Said Mahfoud, Aziz Derouich, Soukaina El Daoudi, and Mohammed El Mahfoud Control of a Four Degrees of Freedom Robot Using a Sine Cosine Algorithm for Joint Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 Inssaf Harrade, Achraf Daoui, Mohamed Kmich, Zakaria Chalh, and Mhamed Sayyouri Real-Time Implementation of Tuning PID Controller Based on Sine Cosine Algorithm for Micro-robotics System . . . . . . . . . . . . . . . . . . . . . 801 Ehab Seif Ghith, Farid Abdelaziz Tolba, and Sherif Ali Hammad Improved Performance of Wind Turbines Based on Doubly Fed Generator Using the IFOC-SVM Control . . . . . . . . . . . . . . . . . . . . . . . . 812 Houda El Alami, Badre Bossoufi, Mohammed El Mahfoud, Majout Btissam, Mourad Yessef, El-Houssine Bekkour, and Hassna Salime Robust Power Control for Wind Turbine System Based on PMSG Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 Nada Zine Laabidine, Badre Bossoufi, Chakib El Bakkali, Btissam Majout, Hassna Salime, Houda El Alami, Ikram Saady, and Younes El Mourabit Wind Turbine Research Works, Modeling, Designing and 3D Printing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 834 Badreddine Lahfaoui Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843

Artificial Intelligence, Machine Learning and Data Analysis

Decision Making Support for Quality 4.0 Using a Multi Agent System Samiha Mansouri(B) , Latifa Ouzizi, Youssef Aoura, and Mohammed Douimi CED-Recherche et Innovation pour les Sciences de l’ingénieur, Université Moulay Ismail_Ecole Nationale Supérieure d’arts et Métiers, Meknes, Morocco [email protected]

Abstract. Recently, the fourth industrial revolution comes with a very important advantage that solves the difficulties faced by the companies regarding customer satisfaction in terms of quality, as the demand is instantly changing; the production process needs to be more flexible and reactive to deliver the product in the defined time with the desired quality. Therefore, this paper focuses on the production and quality management and gives a brief introduction of the quality 4.0 and multi-agent system, and then we present our field of study related to modeling a multi-agent system of the new quality 4.0 where we identified three main agents: quality control, quality cost improvement and quality planning agent, using MaSE methodology in order to have an efficient decision making. Keywords: Quality 4.0 · Industry 4.0 · Customer satisfaction · Multi agent system · MaSE

1 Introduction Smart factory, Fourth industrial revolution, internet of thing, cyber physical system and big data are all refer to the same field, which is the industry 4.0 [1], it started in Germany in 2011, with highly advanced technology and opening the doors to new research opportunities, the industry 4.0 ensures: • The flexibility of the production system in case of design change as the diversity of product is constantly increasing. • The instant reactivity to customer demand with better quality. The industry 4.0 is also defined by “the ability to solve existing and future problems via an open infrastructure that allow solutions to be implemented at the speed of business while creating advantageous value” [2]. And to implement the industry 4.0, company’s needs to work on all its supply chain, by implementing an intelligent production process, advanced logistics, and highly developed quality management system called quality 4.0 [3], where the quality management includes interconnectivity, big data integration, and mainly concentrate on customer satisfaction, supplier monitoring, company’s performance, product and process compliance [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 3–11, 2022. https://doi.org/10.1007/978-3-031-02447-4_1

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Given that the quality 4.0 is a vast field that is vulnerable to high variations in its inputs influencing the overall company’s performance [5], Therefore in this paper we have chosen to work on a framework that presents the strong link between all the processes that have a direct impact on the quality management system, thus we are heading to use the multi agent system (MAS) [6] to set up an interactive community of intelligent agents, on which each one has a defined tasks to execute separately within a fluctuating environment, and then communicate with other agent in a purpose to ensure the global objective of customer satisfaction with minimum costs [7]. The present work is composed of three intelligent agents: quality control, quality cost improvement and quality planning, Part of the MASE methodology [8] advantages is that the three agents’ algorithms can run in the same time without issue, which present a real time decision tool support. The paper is organized as follow: the next section presents a brief introduction of our study key elements: quality 4.0, Cost of quality, processes included in the quality management system and multi agent system based on MaSE methodology. Then the second part present the methodology followed, where we define the different agents contributing in our case study using MaSE methodology, the last section concludes the paper and proposes some perspectives.

2 Background 2.1 Quality 4.0 The quality 4.0 refers to the quality management system in the era of industry 4.0 [9], and shares the same objectives which are the digitalization, automation, connectivity and analytics of the quality management system. This one has known a remarkable evolution as the industry 4.0 (the below figure shows the evolution of the quality management system over the years), starting from quality control focusing only on the product quality, to quality assurance that works on both product and process, and then comes the total quality management which ensures the product quality, the compliance of the production process and company performance at all levels, and recently we have the notion of quality 4.0, that includes interconnectivity, big data integration, and mainly concentrate on customer satisfaction, supplier monitoring, company performance, product and process compliance (Fig. 1). A survey is performed to study the implementation of the quality 4.0 (in March 2019), as a result only 21% of the European companies has implemented the quality 4.0 (see figure below), mainly in Germany, as German companies have been among the global leaders in developing and investing in Industry 4.0 as an overall strategy for improving operations [10]. To implement Quality 4.0, Companies need an organized methodology that incorporates organizing use cases to determine basic focuses by looking deep in the process to identify in which step of the process, the variation of the process is critical to quality and efficiency of the process. The critical item to quality needs special treatment, first we need to measure it using intelligent sensors, and compare it with limited values defined for this critical variable, for any difference that exceed the limit, the company should take necessary actions to get back to the nominal level. The continuous follow up of

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Fig. 1. The evolution of quality management system

all the critical to quality items, makes the production process more efficient and deliver good product to customer in time [11]. a. The cost of quality definition In previous decades, the companies were defining the cost relative to quality is the cost generated due to quality defects and poor quality, without considering the cost relative to special controls and checks. Nowadays, the industry considers the global cost of quality is the total of cost of poor quality and good cost spent to prevent process failure. The cost of poor quality is quantified through the cost defects and the cost of the 7 wastes in the manufacturing process: Defect correction, Waiting, Motion, Overproduction, Inventory, Transportation, Extra processing. The smart factory comes with interesting advantages to reduce the cost of quality and improve the company’s profit [11] by: – Reducing the total cost of operation – Increasing the cost for preventing the non-quality – Reduce the cost of internal/External defect by quick detection of defect and high reactivity of the process – Reduce the cost relative to checks and test as the production process is more intelligent. b. The Quality Trilogy The quality trilogy introduced by Dr Joseph M. Juran, is a time basis concept that shows the evolution of the cost of poor quality in three steps [12]: 1. Before quality improvement which identified in the below figure as the original zone of quality control.

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2. The second step is when implementing quality improvement in order to reduce the cost of poor quality. 3. The third step is the necessary controls in order to maintain the new level of quality.

Fig. 2. Juran trilogy

The Juran trilogy presents a new reflection of the quality management system, by identifying 3 main quality activities that are (Fig. 2): – The quality planning or quality by design, which is based on the lessons learned from previous quality improvement in other project. By improving the design of the product and production process in order to prevent failures and defects. Recently this step is part of the product quality planning demarches such us the Advanced Product Quality Planning (APQP) demarche used in the automotive industry in all the product life cycle. – The quality improvement can be based on many quality tools known in the industry to remedy on the high level of poor quality level, by making deep analysis of the issue and dress robust action plan to back to normal situation, still in case of continuous improvement process, the tool the most used the lean six sigma, which ensure a capable process to produce the very good quality constantly. – Quality Control is a continuous activity in the production process, where the company monitors the quality level using statistical methods, using sensors and real time data records and analysis.

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2.2 Multi Agent System The multi agent is an approach for the design of a system containing a society of intelligent agents [13]. Those agents are autonomous and have the possibility to communicate, cooperate and negotiate between each other, in order to achieve a better overall performance, following determined instructions that can be executed in parallel, and one of the characteristics of the multi agent system is that can be implemented in a dynamic environment efficiently and with higher reactivity to difficulties and flexibility [14]. • MaSE Methodology The problem of developing multi-agent systems has brought together many disciplines in order to create methodologies that facilitate the implementation of agents. Consequently, several methods have been proposed in the research field of Agent Engineering [15]. In our case, we decided to work with MaSE methodology as it easy to use and it’s based on UML diagrams, and do not require a specific platform. To build a multi agent system, MaSE methodology proposes a structure divided into 2 sections: analysis and design [8]: • In the analysis section, we need to define first the goals of our system, and then defines roles for each goal by referring to use cases to support approval of system objectives • In the design section, we define agent classes, architecture, the communication between them and finally the deployment diagram.

3 Methodology While companies facing difficulties in the coordination between the several processes related the quality and production management, we have chosen to model the production process and quality 4.0 using a multi agent system based on MaSE methodology, in order to benefit from the advantages of the multi agent system. It can modelize this structure based on artificial intelligence, by associating to each process of the quality management system an agent that have specific algorithm to execute and continuously communicate, negotiate and coordinate between each other with a global goal is to achieve the customer satisfaction, and ensure the flexibility and high reactivity of the production system. 3.1 Model of Multi Agent System of Quality Management Using MaSE Methodology • Goals definition Our system objective is mainly the improvement of the quality performance in the industry 4.0, therefore we defined 3 principle goals: Understand and monitor the critical factor to quality per process, reduce the cost of quality and include the lessons learned from previous projects into the quality planning, each goal has several sub-goals (Fig. 3).

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Fig. 3. System goals diagram

• Use case application 1. The system start first by identifying the critical factors to quality all along of the production process, the ones that have a direct impact on the product quality and productivity, and then identify the reaction loop for each factor by defining the measuring method of this variable (sensors…) and compare it with its defined limited value. In case of negative difference the system define necessary actions in order to correct the situation and get back to normal situation. 2. In parallel, the system monitor the cost of quality, by the identifying the major causes that increases the cost of poor quality and then dress necessary action for each type of waste. 3. Last the system update the quality planning based on lessons learned taken from the previous two steps in order to prevent the issue in next project phases. • Roles definition Based on our use cases we defined 3 main roles which are: – Quality control – Quality planning – Quality cost improvement The Fig. 4 shows the sequence diagram of our roles.

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Fig. 4. Sequence diagram

Next we assign for each role the relative goals/sub-goals as follow: 1. – – 2. – 3. – –

R1_Quality control role: Identify the critical factor to quality by process Define the reaction loop for each critical factor to quality R2_Quality planning role: Quality planning based on lessons learned. R3_Quality cost improvement role: Identify the causes of poor quality (7 wastes) Take corrective action per type of waste.

A role needs several capacities to achieve a goal, the figure below shows the different capacities need by role. • Agent definition – Quality control agent: it refers to the production quality controller who’s the main activity is to identify the critical steps of the process to quality, and define necessary follow up of it, using special data collection and treatment in order to react in a real time (Fig. 5). – Quality cost improvement agent: it represent the responsible of evaluating the company’s performance basing on the cost of quality level, and he work continuously to

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Fig. 5. Roles diagram.

identify the major wastes that cause most of the poor quality, then dress necessary action for each type of waste in the most efficient way. – Quality planning agent: it refers the project quality planning, where the main activity is to plan the quality activities in the current and next waves of the project, as part of its activity is to involve the lessons learned from previous two agents, and study the duplication and implementation of actions in order to prevent previous defect and risks.

4 Conclusion To conclude, the subject of this work was to study production and quality management system in the context of the industry 4.0, by well understanding the definition of the cost of quality as part of the main company’s performance, and identify methods how to reduce it, basing of the identification of the critical factory to the quality in the production process, and reduce the wastes (either defect, movement, inventory…), and ensure the close follow up of them. And next include the entire lesson learned into the project quality planning, in order to avoid this defects and risk in next waves of the project. For that we proposed in this paper a multi agent system that gives a decision making support based on the three main functions identified above (Quality control, Quality cost improvement and quality planning). Those agents are designed to work in parallel and cooperate with each other, in order to achieve the customer satisfaction.

References 1. Khamis, A.: Industry 4.0 and Smart Factory (2017). https://doi.org/10.13140/RG.2.2.36253. 00485 2. Büchi, G., Cugno, M., Castagnoli, R.: Smart factory performance and Industry 4.0. Technol. Forecast. Soc. Chang. 150, 119790 (2020). https://doi.org/10.1016/j.techfore.2019.119790

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3. Albers, A., Gladysz, B., Pinner, T., Butenko, V., Stürmlinger, T.: Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems. Procedia CIRP 52, 262–267 (2016). https://doi.org/10.1016/j.procir. 2016.07.067 4. Godina, R., Matias, J.C.O.: Quality control in the context of industry 4.0. In: Reis, J., Pinelas, S., Melão, N. (eds.) IJCIEOM 2018. SPMS, vol. 281, pp. 177–187. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14973-4_17 5. Assid, M., Gharbi, A., Hajji, A.: Production planning and control of unreliable hybrid manufacturing-remanufacturing systems with quality-based categorization of returns. J. Clean. Prod. 312, 127800 (2021). https://doi.org/10.1016/j.jclepro.2021.127800 6. Julian, V., Botti, V.: Multi-Agent Systems. Appl. Sci. 9(7), 1402 (2019). https://doi.org/10. 3390/app9071402 7. Pechoucek, M., Riha, A., Vokrinek, J., Marik, V., Prazma, V.: ExPlanTech: applying multiagent systems in production planning. Int. J. Prod. Res. 40(15), 3681–3692 (2002). https:// doi.org/10.1080/00207540210140086 8. Deloach, S.A., Wood, M.F., Sparkman, C.H.: Multiagent systems engineering. Int. J. Soft. Eng. Knowl. Eng. 11(03), 231–258 (2001). https://doi.org/10.1142/S0218194001000542 9. What Is Quality 4.0 in the Era of Industry 4.0.pdf 10. Küpper, et al.: Quality 4.0 Takes More than Technology.pdf 11. Quality 4.0: The Future of Quality? Juran, 15 June 2019 (2019). https://www.juran.com/blog/ quality-4-0-the-future-of-quality/. Accessed 13 Jan 2020 12. The Juran Trilogy: Quality Planning. Juran, 15 April 2019 (2019). https://www.juran.com/ blog/the-juran-trilogy-quality-planning/. Accessed 05 Apr 2021 13. Ballouki, I., Labbi, O., Douimi, M., Ouzizi, L.: A multi agent approach for simultaneous design of a product and its supply chain: suppliers’ selection example’. In: 2016 3rd International Conference on Logistics Operations Management (GOL), Fez, May 2016, pp. 1–7 (2016). https://doi.org/10.1109/GOL.2016.7731676 14. Leusin, M.E., Kück, M., Frazzon, E.M., Maldonado, M.U., Freitag, M.: Potential of a multiagent system approach for production control in smart factories. IFAC-PapersOnLine 51(11), 1459–1464 (2018). https://doi.org/10.1016/j.ifacol.2018.08.309 15. Selecting a methodology in Multi-Agent Systems.pdf

ISBRNM: Integrative Approach for Semantically Driven Blog Recommendation Using Novel Measures M. Anirudh1 and Gerard Deepak2(B) 1 Department of Computer Science and Engineering, SRM Institute of Science and Technology,

Ramapuram, Chennai, India 2 Department of Computer Science and Engineering, National Institute of Technology,

Tiruchirappalli, India [email protected]

Abstract. In the present-day time, Blog recommendation is of great importance as the Internet grows, individuals face immense amounts of information. Due to the rise of microblogs, it has become a vital tool to provide accurate and timely information. Thereby, in recent times of Semantic Web, blog recommendation is one of the crucial aspects to search and shortlist the user preferred relevant content which is of extreme value. This paper proposes an integrative approach for Semantically driven Blog recommendation using Latent Dirichlet Allocation for the Natural Language Processing, Gated Recurrent Units as a classifier and semantic similarities are computed using Normalized Pointwise Mutual Index, Normalized Google Distance and Theil index which is classified using the Moth Flame Optimization algorithm. The experiments have been conducted for the Blog Authorship corpus and the accuracy percentage of 95.85% is achieved. Keywords: Blog recommendation · Moth Flame Optimization · NGD · NPMI · Theil’s index

1 Introduction A blog is a website that consists of text entries that are usually short and informal. Posts are generally posted in reverse chronological order, and the most recent ones are usually first up on the web page. Blogs significantly impacted technology, entertainment, sports, and even politics. Microblogging is a type of blogging that allows people to discuss topics ranging from simple to thematic. Several celebrities use social platforms to post their latest developments and communicate with fans to improve their popularity [1]. Users can publish, repost, comment, and like micro-blog. They are concerned about realtime personalized micro-blog information [2]. Social platforms allow users to connect with their peers and improve their popularity. They also serve as marketing tools for businesses. There is an exponential increase in text data due to the recent advancements in smartphones, social media technology, social networking, the internet of things and other technologies. Therefore, a large of data is being generated, but a problem arises © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 12–21, 2022. https://doi.org/10.1007/978-3-031-02447-4_2

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since there is a challenge in the organization or management of data over the world wide web. So, the world wide web is transfiguring into a highly demanding web 3.0, yet the Semantic Web tries to accommodate the chaotic and increased contents of the web. Although the need for a semantic web 3.0 is a mid-way dream, making it entirely semantic might take one or two decades. However, there is a need for an approach that is semantically driven, semantically compliant, and able to handle high volumes of data being exploded on the web. As a result, a semantically infused machine learning paradigm is required to recommend the text content from the web, specifically the blogs, and provide relevant results. Motivation: There is a need for a blog recommendation strategy that is highly specialized semantically driven and able to manage vast amounts of data. The present data on the web is multiplying and finding microblogs is a difficult task. To facilitate the text data retrieval, a semantically driven with a powerful machine learning model is required than the existing ones. Blog recommendations should be improvised and knowledge-centric. User-driven microblog recommendation should not be the only priority but to include other factors such as relevance to the queries found and to add value to the user. As a result, a semantically deep learning technique is required. Contribution: An integrative approach for semantically driven blog recommendation has been proposed. Tag preprocessing is done using tokenization, lemmatization, stop word removal, named entity recognition and word sense disambiguation. Topic modelling is done using an NLP known as LDA and Relevant documents are yielded using the classifier called GRU combined with a heuristic algorithm called Moth Flame Optimization algorithm. Datasets are obtained from the Blog Authorship corpus. Semantic similarities are done using NPMI, NGD and Theil Index to diversify further and obtain relevant results where the data is further classified using Google’s Knowledge Graph API. Overall accuracy of 95.85%, nDCG of 0.95 and FDR of 0.06 have been achieved. Organization: The remaining paper is structured as follows. Section 2 elaborates the Related Works. Section 3 explains the Proposed System Architecture in detail. Section 4 consolidates the Implementation and the results of the proposed architecture. Finally, Sect. 5, depicts the conclusion.

2 Related Works Jiameng Gao et al. [1], have proposed a microblog recommendation system that is hybridized with heterogeneous features integrated with Deep Neural Network (DNN) that requires ratings of items before being fed into a DNN classifier. Dun Li et al. [2], had proposed a personalized recommendation system that uses a partial index feature to update and maintain the micro-blogs with an inference of its topic distribution and user interest vector. Kota Hema Naga Sita Ragini et al. [3], have put forth an improved hybrid collaborative filtering to recommend Microblogs that works based on user biased rating on items. Subramaniyaswamy et al. [4], developed a recommendation model for blogs using a topic-ontology based technique that incorporates static domain ontologies in

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Wikipedia categories and WordNet semantic relationships making it a semantic model. Shunxiang Zhang et al. [5], propose a topic recommendation system for microblogs that suggests the users-based user relationships in micro-blog that can be analyzed and saved to the user data graph. Donghyuk Shin et al. [6], created a Tumblr Blog Recommendation model integrated with a novel boosted inductive matrix completion method that is combined with deep learning to produce the recommendations. Yue He et al. [7], have proposed a system based on microblog recommendation using k-cores analysis technique to extract topics based on user’s preference and uses factor analysis to identify the index and uses RS and linear regression to regulate the parameters for matching the values of the tweet heat factor and user authority factor. Xingshan Zeng et al. [8], created a statistical model to recommend microblogs that are based on user interest and conversation that are tested on two Twitter datasets using a joint model of topics and discourse. Shunxiang Zhang et al. [9], have put forth a topic-based microblog recommendation system that is incorporated with knowledge flow and user selection that ultimately improves the retrieving speed of micro-blogs and the effectiveness in the topic recommendation. In [10–21] several models in support of the proposed literature have been depicted.

3 Proposed System Architecture Figure 1 depicts the system architecture of the Micro Blog recommendation system. The input to the approach is the user query which is subjected to pre-processing. After the user query is pre-processed, the terms are extracted and the input values of the user are subjected to pre-processing to extract the terms that are of user interest. The pre-processing involves tokenization, lemmatization, stop word removal, named entity recognition and word sense disambiguation. A wide space special character tokenizer has been incorporated. Wordnet lemmatizer is used for lemmatization and the terms that are extracted from the web usage data, as well as the input query, is subjected for topic modelling using Latent Dirichlet Allocation (LDA). LDA is a Natural Language Processing (NLP) based generative statistical model that permits sets of observations to be explained by unobserved groups that specify why some parts of the info are similar. for instance, if observations are words collected into documents, it posits that every document may be a mixture of a smaller number of topics which each word’s presence is due to one among the document’s topics. LDA is an example of a topic model and belongs to the machine learning field with an extensive perception of the Artificial Intelligence (AI) field. LDA assumes that documents are composed of phrases that help decide the topics and maps documents to a listing of subjects by way of assigning each phrase in the record to exceptional topics. The terms that are pre-processed from the input query, as well as the web usage data, is subjected to topic modelling, these terms are further directed to Google’s Knowledge Graph API for Entity Extraction and Entity Enrichment by using SPARQL endpoints. The reason for subjecting Google Knowledge Graph API is to ensure that the density of entities is high such that the framework incorporates realworld knowledge that helps in reducing the cognitive gap between real-world knowledge and the proposed Micro Blog recommendation framework. On the other hand, the data sets are classified using Gated Recurrent Unit (GRU) that are based on deep learning

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and are extensively useful and has been proven to have one of the best deep learning classifiers. In Recurrent Neural Networks (RNN), GRU is a gated mechanism with a Long Short-Term Memory (LSTM) with fewer parameters because of the absence of an output gate and a forget gate. Other similar performances on certain tasks of speech signal modelling, polyphonic music modelling and NLP are found to be on par with LSTM. It has also been found that GRU displays better performance on smaller and less repeated datasets. Similarly, unlike LTSM, it doesn’t possess a separate cell state and only accommodates a hidden state which ultimately helps in training the model faster.

Fig. 1. Architecture of the proposed ISBRNM approach

The two gates it possesses includes the Reset Gate or Short-term memory and Update Gate or Long-term memory. The Reset Gate is the one responsible for the network’s short-term memory and the Update Gate is the one responsible for Long-term memory. Classification of the data sets, as well as the entity enrichment, is done using Google’s Knowledge Graph API. The semantic similarity is done using Normalized pointwise mutual information (NPMI), Normalized Google Distance (NGD) and the Theil Index. p(x | y) p(y | x) p(x, y) = log = log p(x)p(y) p(x) p(y) pmi(x; y) NPMI(x; y) = h(x, y) max{logf (x), logf (y)} − logf (x, y) NGD(x, y) = logN − min{logf (x), logf (y)}

Pmi(x; y) ≡ log

(1) (2) (3)

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S=k

  N  N   1 pi logα = −k pi loga (pi ) i=1 i=1 pi

(4)

Equations (1), (2), (3) and (4) depict Pointwise mutual information (PMI), NPMI, NGD and Theil Index respectively. For NPMI a threshold of 0.5 is considered, for NGD a threshold of 0.75 is taken into consideration and for Theil index, a step difference of 0.25 is taken into consideration. Initially, the semantic similarity is computed to yield the initial solution set. Further, Moth Flame Optimization Algorithm Metaheuristics is integrated to obtain the best solutions by once again using NPMI and NGD as the two objective functions. Moth Flame Optimization (MFO) algorithm establishes as one of the major efforts to simulate the traversal of moths in a computer. This Algorithm is increasingly used in Science and Industry. This algorithm was inspired by moths and their transition phase from larvae to adults. The main key aspect of this moth is the traversal influenced by moonlight which helps us determine the population-based algorithm, where the solutions are related with moths and the problem’s variables as their position in the space. This helps in obtaining the best solution where moths act as the search agents and traverses throughout the search space to reach the flames, which are the best position that the moths need to reach. As a result, the terms that are relevant are arranged in increasing order of the NPMI and all the blogs corresponding and are relevant to these terms are recommended to the user until further clicks are recorded, in which case the process continues as the current user clicks are paused into the extract terms block and only stops the process until further clicks are not recorded.

4 Implementation and Result Analysis The experiment was carried out using an Intel Core i7 processor with an Nvidia Graphic card and 16 GB RAM is used. Google Collaboratory is used for the implantation of this work. Experimentations were conducted for 9457 queries whose ground truth has been collected. Ontology modelling is done using protégé. The Google knowledge graph API is accessed via the standard APIs. The pre-processing is done using python’s NLP toolkit (NLTK) framework. Tokenization is done using a blank space? tokenizer, lemmatization is carried out using the WordNet lemmatizer and stop word removal is done by using the corpus module. A customized algorithm called Moth Flame Optimization. Datasets were obtained from Blog Authorship Corpus that comprises over 600,000 posts from more than 19,000 bloggers from age groups ranging from 13 to 47 with an equal number of male and female bloggers. The data set can be accessed from https://www.kaggle.com/rta tman/blog-authorship-corpus. The ISBRNM approach for Microblog Recommendation has been evaluated using Precision, Recall, Accuracy, F-Measure, Discounted Cumulative Gain (DCG), Normalized Discounted Cumulative Gain (nDCG) and False Discovery Rate (FDR) as potential metrics for which standard formulations were employed. The proposed ISBRNM is baselined on four models namely BRDNM, MBRPI, ICFBR and TOETR respectively.

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Table 1. Comparison of performance of the proposed ISBRNM with other approaches Search technique

Average precision %

Average recall %

Accuracy %

F-measure %

FDR

nDCG

BRDNN [1]

90.47

93.14

91.80

91.78

0.10

0.90

MBRPI [2]

82.44

85.18

83.81

83.79

0.18

0.91

ICFBR [3]

88.45

91.78

90.11

90.08

0.12

0.89

TOETR [4] 86.39

88.43

87.41

87.39

0.14

0.87

Proposed ISBRNM

96.81

95.85

95.84

0.06

0.95

94.89

It is indicative from Table 1 that the reason why BRDNN doesn’t yield a high Precision, Recall, Accuracy and F-Measure are mainly due to its usage of collaborative filtering which demands the ratings of the items where feature network generation happens along with the deep neural network classifier. However, the collaboration of the deep neural network with collaborative filtering where ratings play the most important role that ensures the proposed hybridization doesn’t yield to the maximization of Precision, Recall, Accuracy and F-Measure. Although, the value of nDCG is high because of the incorporation of a diverse topic by DNN. The MBPRI is a personalized recommendation approach for microblogs that uses the partial indexing mechanism. So, the partial indexing verifies the items that are used in the recommendation of the microblogs is excellently tagged which makes the recommendation feasible. The formulation of the user interest vector and the LDA strengthens the information density and improves the nDCG value which creates a high diversity of the results, but the user interest vectors computation demands a proper strategy for vectorization. Moreover, the relevance of results is not high yielding because the relevant computing mechanism causes a lag in this approach. ICFBR also doesn’t perform very well because it uses an improved collaborative filtering method with hybridizing. It requires a rating-based recommendation which always demands a rating for every individual item present in this approach. Every blog cannot be rated and if done, they can be biased because those are done by users with different and varying interests. So, User clustering based on collaborative filtering doesn’t perform very well and lacks a strategy for computing the stringent relevance of the blogs based on user queries and interest. As a result, the Improved Collaborative Filtering has lower Precision, Recall, Accuracy and F-Measure, higher FDR and lower nDCG because of its lack of diversity in the yielded results. The TOETR uses static domain ontologies in Wikipedia categories and WordNet semantic relationships making it a semantic model. Since this approach is highly domain-centric and even though it has static auxiliary knowledge, it fails to perform interdomain queries and datasets with a large diversification with large and diversified domains. The relevance computation is better and even the nDCG value is higher in comparison, but the static model ontology approach shows a lag in its performance which can be rectified by rectifying the

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strategies by computing the relevance of results. The proposed ISBRNM yields the highest Precision, Recall, Accuracy, F-Measure, nDCG and yields the lowest FDR mainly because it is based on LDA. LDA is used for topic modelling, where cognitive real-world knowledge is uncovered and fed into the approach. The GRUs are used for classification, a Deep learning Gated Recurrent Units which is the most powerful yet dependable classifiers that make the classification task magnanimous and efficient. The usage of Google’s Knowledge Graph API for lowering the cognitive gap between real-world entities and entity enrichment. Google’s Knowledge Graph API yields subgraphs so that topic enrichment, as well as entity population, can take place in this approach. Google’s Knowledge Graph API ensures that entity population takes place where real-world verified entities as subgraphs are loaded from Google’s Knowledge Graph API, where entity enrichment and entity population is taken care of. Therefore, making this approach integrative concerning the real-world knowledge and most importantly, the usage of Moth Flame Optimization. The semantic similarity is computed using the Theil index, NPMI measure and NGD measure. Both NPMI and NGD are based on threshold whereas Theil Index is based on the Step difference of 0.25. So, instead of using a singular mechanism for computing the relevance and the semantic similarity, the semantic similarity, in turn, computes the relevance between the recommended blogs as well as the user query which is enriched and ensures that the relevance computation is distinctive, stringent and also effective. The Moth Flame Optimization algorithm makes sure that the initial suggestion recommendable set is refined and optimized into a much more efficient and yet relevant final recommendation set is yielded based on the NPMI, NGD and Theil Index which are further used in objective functions in the Moth Flame Optimization algorithm. The rearrangement based on increasing order of the NPMI measure ensures that the results are yielded based on the needs of the user. The proposed ISBRNM yields the highest nDCG because of the usage of LDA for topic modelling and integration of real-world knowledge for entity populations using Google’s Knowledge Graph API. This ensures the auxiliary knowledge fed into the approach is quite dense which results in a high relevance in the final recommendation with a high nDCG value. From Fig. 2, the Precision-Recall curve is depicted makes it clear that the proposed approach has the better Precision vs no of recommendations curve compared to other approaches with respect to the no of recommendations. The BRDNN is based on a collaborative filtering process that requires the ratings of the various items generated by the deep neural network. Although this approach is built on the combination of DNN with Collaborative filtering, ratings play a pivotal role in its unyielding maximization in Precision, Recall, Accuracy and F-Measure. Even if the value of nDCG is high because of the integration of diverse topics by DNN [1]. The concept of the MBPRI is to provide a personalized recommendation system for microblogs. It uses the partial indexing mechanism to verify the items that are used for recommending the blogs. The LDA and the user interest vector are formulated to improve the information density and produce a high diversity of results. But since there is a lag caused by the relevance computing mechanism, the relevance of the results is not very yielding [2]. The ICFBR uses a rating system that only demands a rating for every item presented in this approach that runs through an improved collaborative filtering method with hybridization. That makes this process completely biased by users of dissimilar

ISBRNM: Integrative Approach for Semantically Driven Blog Recommendation

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Fig. 2. Precision Vs number of recommendations

and contrasting interests and it also lacks the scheme to counter the direct and strict relevance of the blogs that are completely biased on user interest and query. Due to these complications, it doesn’t produce high diversity in results [3]. The TOETR utilizes static domain categories in WordNet and Wikipedia ontologies to perform semantic model computation. However, it cannot perform interdomain queries and large datasets since it fails to perform interdomain queries and datasets with a large diversification with large and diversified domains. Even though the nDCG value and relevance computation is both higher and better, the approach shows a lag when it comes to performance and its relevance of results [4]. The Proposed ISBRNM is a semantically driven integrative approach that incorporates several models namely LDA for topic modelling, GRUs for classification, Google knowledge-based API to reveal the cognitive gap between realworld knowledge entities and entity enrichment. The incorporation of LDA which is an NLP is used to reveal the cognitive real-world knowledge and fed into the system. The Deep Learning model called GRU acts as a classifier that makes the classification of the blogs more efficient. The Google knowledge-based API helps entity enrichment and entity population fed into the system by verified real-world entities. The semantic approach is used by the combination of NPMI, NGD and Theil Index. Where NPMI and NGD are entirely based on thresholds of 0.5 and 0.75 respectively. Theil index is based on a step difference of 0.25. Hence the user query is enriched and secures the relevance computation is distinct, stringent and efficient. Finally, a customized heuristic algorithm called the Moth Flame Optimization algorithm is used to make the initial suggestion recommendable set is refined and optimized into a much more efficient and yet relevant final recommendation set is yielded based on the NPMI, NGD and Theil Index that further makes the algorithm enhanced. This integrative approach makes the system yield precise results and recommendation.

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5 Conclusions A semantically driven blog recommendation system is developed using novel measures. The proposed system focuses on pre-processing the queries input from the users from which they are pre-processed using LDA and classified using GRU into its relevance. NPMI, NGD and Theil index are semantic similarities that are integrated to obtain the relevant blogs using the moth flame optimization algorithm. Finally, the experiments are carried out for blog authorship corpus that yields 95.84% F-Measure with very high nDCG and with low FDR are achieved for the proposed method.

References 1. Gao, J., Zhang, C., Xu, Y., Luo, M., Niu, Z.: Hybrid microblog recommendation with heterogeneous features using deep neural network. Expert Syst. Appl. 167, 114191 (2021) 2. Li, D., Wang, M., Li, L., Zheng, Z.: Micro-blog real time personalized recommendation based on partial indexing. Int. J. Performability Eng. 13(7), 1077 (2017) 3. Sreelatha Kota Hema Naga Sita Ragini, M.: An Improved and Hybrid Collaborative Filtering for Microblog Recommendation. Design Eng. 11888–11904 (2021) 4. Subramaniyaswamy, V., Pandian, S.C.: Topic ontology-based efficient tag recommendation approach for blogs. Int. J. Comput. Sci. Eng. 9(3), 177–187 (2014) 5. Zhang, S., Zhang, S., Yen, N.Y., Zhu, G.: The recommendation system of micro-blog topic based on user clustering. Mob. Netw. Appl. 22(2), 228–239 (2017) 6. Shin, D., Cetintas, S., Lee, K.C., Dhillon, I.S.: Tumblr blog recommendation with boosted inductive matrix completion. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 203–212 (2015) 7. He, Y., Tan, J.: Study on SINA micro-blog personalized recommendation based on semantic network. Expert Syst. Appl. 42(10), 4797–4804 (2015) 8. Zeng, X., Li, J., Wang, L., Beauchamp, N., Shugars, S., Wong, K.F.: Microblog conversation recommendation via joint modeling of topics and discourse. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (long papers), pp. 375–385 (2018) 9. Zhang, S., Liu, W., Deng, X., Xu, Z., Choo, K.K.R.: Micro-blog topic recommendation based on knowledge flow and user selection. J. Comput. Sci. 26, 512–521 (2018) 10. Deepak, G., Gulzar, Z., Leema, A.A.: An intelligent system for modeling and evaluation of domain ontologies for crystallography as a prospective domain with a focus on their retrieval. Comput. Elect. Eng. 96, 107604 (2021) 11. Roopak, N., Deepak G.: OntoKnowNHS: ontology driven knowledge centric novel fybridised semantic scheme for image recommendation using knowledge graph. In: Knowledge Graphs and Semantic Web, pp. 138–152 (2021) 12. Ojha, R., Deepak, G.: Metadata driven semantically aware medical query expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Ayush Goyal, M.A., Jabbar (eds.) Knowledge Graphs and Semantic Web: Third Iberoamerican Conference and Second IndoAmerican Conference, KGSWC 2021, Kingsville, Texas, USA, November 22–24, 2021, Proceedings, pp. 223–233. Springer International Publishing, Cham (2021). https://doi.org/ 10.1007/978-3-030-91305-2_17 13. Yethindra, D.N., Deepak, G.: A semantic approach for fashion recommendation using logistic regression and ontologies. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES, pp. 1–6. IEEE (2021)

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14. Adithya, V., Deepak, G.H.: BlogRec: a hybridized cognitive knowledge scheme for blog recommendation infusing XGBoosting and semantic Intelligence. In: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT, pp. 1–6. IEEE (2021) 15. Surya, D., Deepak, G., Santhanavijayan, A.: KSTAR: a knowledge based approach for socially relevant term aggregation for web page recommendation. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco, pp. 555–564. Springer International Publishing, Cham (2021). https://doi.org/10.1007/9783-030-73882-2_50 16. Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In 2021 7th International Conference on Web Research, ICWR, pp. 232–237. IEEE (2021) 17. Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco, pp. 791–800. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72 18. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019) 19. Roopak, N., Deepak, G.: KnowGen: a knowledge generation approach for tag recommendation using ontology and Honey Bee Algorithm. In: Abdalmuttaleb, M.A., Al-Sartawi, M., Razzaque, A., Kamal, M.M. (eds.) Artificial Intelligence Systems and the Internet of Things in the Digital Era: Proceedings of EAMMIS 2021, pp. 345–357. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_33 20. Deepak, G., Santhanavijayan, A.: UQSCM-RFD: a query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Comput. Appl. 34, 1–25 (2021) 21. Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft. Comput. 25(13), 8337–8355 (2021)

Arabic Broken Plural Model Based on the Broken Pattern Mariame Ouamer(&), Rachida Tajmout, and Karim Bouzoubaa Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco [email protected], {tajmout,karim.bouzoubaa}@emi.ac.ma

Abstract. Broken (or irregular) plurals present a significant issue in natural language processing. It is known that there is not a fixed and general rule assembling all the broken plural forms. Extracting the plurals or reducing them to their singular form is a task that affects the performance of information retrieval, annotation, tagging tasks, and many other NLP applications. This paper describes, on one hand, our own Arabic broken plural list and, on the other hand, the process of extracting the plural and its singular form using several machine learning classifiers. Obtained results show that the Random Forest classifier outperforms the other statistical classifiers with an accuracy of approximately 98%. Keywords: NLP
Arabic NLP
Arabic broken plural
Classification Decision trees
Machine learning
Arabic broken pattern




1 Introduction It is known that the three basic processing levels of any language are the morphological, the syntactic, and the semantic ones. Each of these levels is governed by a set of general linguistic rules. In the case of Arabic, the rule indicating that “the subordinating particle is always followed by a genitive noun” is considered as one of the syntactic rules. Also, one morphological rule states that “the sound plural of a word is formed by adding the suffix “‫ ”ﻭﻥ‬or “‫ ”ﻳﻦ‬to the masculine singular form and “‫ ”ﺍﺕ‬to the feminine singular form”. However, these rules have some exceptions. One example is the Broken Plural (BP) indicating that the base form of the noun is broken either by removing one or more letters, adding one or more letters, changing vocalization, or a combination of these. For example, the broken plural of the noun “‫ﻗﺎﺿﻲ‬/Judge” is “‫ﻗﻀﺎﺓ‬/Judges” instead of being the sound plural “‫ ”ﻗﺎﺿﻴﻮﻥ‬as it is the case for the noun “‫ﻣﺪﺭﺱ‬/Teacher” for which the plural is “‫ﻣﺪﺭﺳﻮﻥ‬/Teachers”. In large Arabic corpora, broken plurals constitute 10% of the text [1] and 41% of all plurals [2]. This important presence of broken forms makes this phenomenon very noticeable, and researchers have always been interested in it. Therefore, authors of [3] have shown that identifying broken plurals and handling them results in improved information retrieval results, while research conducted in [4] has also shown that handling broken plurals can significantly improve the results of semantic tagging systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 22–31, 2022. https://doi.org/10.1007/978-3-031-02447-4_3

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The BPs are formed by altering the singular form through the application of welldefined and known patterns, named BP patterns. For instance, the application of the pattern “‫ ”ﻓﻌﺎﻝ‬to the singular form “‫ﺭﺟﻞ‬/man”, generates the broken plural “‫ﺭﺟﺎﻝ‬/men”. The main difficulty in handling Arabic broken plurals can be attributed to two main factors. First, a word that matches a BP pattern may not be a broken plural at all such as “‫ﺇﻧﺘﻬﺎﺀ‬/End” while this word is in its singular form and its pattern “‫ ”ﺇﻓﻌﻼﺀ‬ca be used as a BP pattern. Second, a BP pattern can have multiple singular patterns. As can be seen in and “‫”ﻓﻌﻴﻠﺔ‬. Table 1, the pattern “‫ ”ﻓﻌﺎﺋﻞ‬has as singular patterns Table 1. The singular pattern possesses serval broken plurals. Plural pattern Plural word Singular pattern Singular word ‫ﻓﻌﺎﺋﻞ‬ ‫ﻭﺳﺎﺋﻞ‬ ‫ﻓﻌﻴﻠﺔ‬ ‫ﻭﺳﻴﻠﺔ‬ ‫ﺭﺳﺎﺋﻞ‬ ‫ﻓﻌﺎﻟﺔ‬ ‫ﺭﺳﺎﻟﺔ‬ ‫ﺯﺑﺎﺋﻦ‬ ‫ﻓﻌﻮﻝ‬ ‫ﺯﺑﻮﻥ‬

On another hand, it is known that researchers use a database to store the list of broken plurals form. However, as will be detailed in related works there is no complete comprehensive broken plural list, and no system covers the detection and the prediction of the BP with high precision and processes all BP pattern forms. Our objective is then threefold. Firstly, we aim to establish the complete list of all BPs alongside their corresponding patterns. Secondly, we want to develop a system that converts the singular word to its broken plural form and extracts the singular form from the plural form. Finally, the third objective is to predict the broken plural of words not previously used in the Arabic language. The remainder of the paper is as follows. Section 2 describes the state of the art regarding methods that handle the broken plural of the Arabic word. Section 3 introduces the description of the learning data by defining the used methodology. Section 4 is dedicated to the description and the evaluation of our approach. The exploitation of this system is described in Sect. 5. In the last section, we conclude the work and discuss future horizons.

2 Related Works In this section, we cite two different parts of related works, the first one on the available lists of broken plurals and the second one on their processing. For the data side and to the best of our knowledge, only a few studies considered providing a complete and comprehensive Arabic broken plural list. The available ones are found in [5] and [6]. In the first work, they automatically extracted the broken plural forms from the electronic version of the Alwassit Arabic Dictionary1. It contains 7,194 singular nouns followed by their broken-plural forms as given by the Dictionary. However, this list contains not only broken plurals but also sound plurals and there are mistakes in some singular

1

2011.

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words. For example, for the plural “‫ ”ﻣﻜﻨﺎﺀ‬the given singular is “‫ ”ﻣﻜﻦ‬instead of the correct one “‫”ﻣﻜﻴﻦ‬. In addition, there are not enough broken plural patterns such as and “‫ ”ﻓﻌﻞ‬since the Al Wassit dictionary does not cover the entire broken plural forms. On the other side, the authors of [6] automatically generated the broken plurals forms from the Arabic Gigaword [7] and the Al-Jazeera website2. This list is limited to 2,561 singular nouns followed by their singular patterns, broken plurals, and broken plural patterns. On the other hand, studies addressing the process of broken plurals differ from each other and can be classified into three categories. The first one focuses on the recognition of broken plurals. The second derives broken plurals from their roots while the last one extracts singulars from plural forms. Many works exist in the first category identifying broken plurals. Some of them are old [1, 8, 9] while the more recent ones and worth to be mentioned are those of [11] and [12]. Authors in [11] carried out a series of experiments and approaches for detecting broken plurals using a test corpus. The best results were obtained using a dictionarybased approach. The dictionary in this instance was semi-automatically built and resulted in a precision value of 81.2% and a recall of 100%. On another hand, the authors of [12] proposed an approach that identifies broken plurals without the need to perform the stemming process on any given word. They applied a decision-tree of the BP algorithm and used four attributes; the three consonants patterns and the fourth feature represents if a pronoun is attached to any given word or not. This approach was evaluated using a 10-fold cross-validation method resulting approximately to a precision of 99%. However, this approach does not gather all broken plural pattern forms and is limited to “‫ ”ﻓﻌﺎﻝ‬and “‫ ”ﺍﻓﻌﻼﺀ‬ones. For the second category, authors of [13] proposed and implemented a system to derive Arabic broken plurals from their singulars. In this system, root entries in the lexicon are associated with a set of nominal patterns, some of which indicate the broken plural. The main drawback of this approach is that it outputs many broken plurals for one input root, and one of these plurals may not represent the correct plural of the single word. For instance, the broken plural generated by the system for the word “‫ ”ﺻﻠﻔﺎﺀ‬is “‫ ”ﺻﻠﻒ‬when the solution should be “‫ ”ﺻﻼﻑ‬according to the Arabic dictionary. In the work of [14], the authors addressed both the last and the first category. They proposed another model based on machine-translation to detect and convert broken plurals to their singular forms. In this model, words that match BP patterns are translated to the English language. If the resulting English word is found to end with an “s” or if the word exists in a list of irregular English nouns, then it is identified as a broken plural. The English term is then stemmed and the stem is retranslated to the Arabic language to obtain the Arabic singular form. They used 10,000 words gathered from different fields (sport, art, and economics) to evaluate their approach. The results of this experiment show that the precision and recall are limited to 90.3% and 91.5% respectively in detection; and to 88.7%, 84.4% respectively in conversion to singular. Besides, some works present a linguistic study of the broken plural. The most recent is that of [18], which generates the broken plural depending on singular’s

2

https://www.aljazeera.net/.

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morphological, phonological and semantic features. They have extracted from a deep linguistic study 108 sets of morphological, phonological, and semantic conditions, which serve to restrict the generation process of the broken plural form from its singular form. The extracted conditions may give one or more broken plural patterns for a given singular form. Then, they implemented inflectional and derivational grammars that generate the broken plural form using the Root-Pattern approach. However, these rules are not available. In summary, according to the preceding works, there is not a correct and integral list of broken plurals that gathers all of their forms. Second, no system covers all the three categories with high precision and processes all broken plural pattern forms. Therefore, the practical challenge is to build a broken plural system ensuring enough training data with high precision.

3 Dataset The availability of an entire and a ground truth BP list is necessary to our work. However, its non-existence led us to create our list. In this section, we present the broken plural list that we built to serve as a dataset to train and test our system. We have used various sources to ensure broad coverage of broken plural forms. Hence, we employ three different sources: the Almaany3 website that contains a compilation of multiple Arabic dictionaries, and the two available broken plural lists from [5] and [6]. First, we extracted data from the Almaany website with a semi-automatic process. In total, we collected about 11,249 singular words alongside their broken plural form. Thereafter, we compared this resulting list with those of [5] and [6] and were able to manually add 1,000 from their list. Our final list named “SAFAR broken plural list” is structured in an LMF file [15], and is available from the SAFAR website4. As an example, Fig. 1 represents a sample of our list encoded in LMF format. The list contains 12,249 singular words with their corresponding BPs, and in some cases, a singular word may correspond to several BP forms. As can be noted from Table 2, this list largely exceeds the other available ones.

3 4

https://www.almaany.com/. http://arabic.emi.ac.ma/alelm/?q=Resources.

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Fig. 1. Example of our list in LMF format. Table 2. Statistics about the available broken plural list. Lists Safar BP list Elghamry list [5] Attia list [6] Words 12,249 7,194 2561

4 Approach Let us remind that our objective is to develop a system that generates the broken plural form from the singular word, and extracts the singular from the plural form. Consequently, we formulate the task as a supervised classification problem for which the broken plural form is the class when the system works as “pluralizer”, and the singular word is the class when the system works as “lemmatizer”. On another hand, as mentioned above, a singular word in our list can have several broken plural forms. For example, the several broken plurals of the noun “‫ﺃﺥ‬/Brother” is “‫ﺃﺧﻮﺍﻥ‬/Brothers”, “‫ﺇﺧﻮﺍﻥ‬/ Brothers”, “‫ﺃﺧﻮﺓ‬/Brothers”, and “‫ﺇﺧﻮﺓ‬/Brothers”. Before the learning phase of the “Pluralizer” system, we go through a preprocessing step where we keep only the most popular BP for each singular word. For the “‫ﺃﺥ‬/Brother” example, we keep only the “‫ﺇﺧﻮﺓ‬/Brothers” broken plural. At this stage, our list for the “pluralizer” system contains 10,000 words. Otherwise, we keep the list as its stands for the “Lemmatizer” system. Moreover, the attributes are the features that describe the singular word in pluralization, and the features of the plural word represent attributes is lemmatization. As detailed below, the creation of these attributes is done by two different methods. 4.1

The First Approach Using Arabic Linguistics Features

The idea of this approach is to find a relationship between the singular word and its plural according to all linguistic features that can be extracted from both the singular and plural forms. To do so, we convert each input noun W (singular or plural) to a vector where its Wi elements are the linguistic features of the noun. Then, we take off those that statistically have no impact and end up with the following linguistic attributes W = (“Number of letters”, “First character”, “Gemination position/‫”ﻣﻮﺿﻊ ﺍﻟﺸﺪﺓ‬,

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“Pattern/‫”ﻭﺯﻥ‬, “Type of noun/‫”ﻣﻴﺪﺍﻥ‬, “Vocalic word/‫”ﺍﺳﻢ ﻣﻌﺘﻞ‬, “Limited noun/‫ﺍﺳﻢ‬ ‫”ﻣﻘﺼﻮﺭ‬, “Incomplete noun/‫”ﺍﺳﻢ ﻣﻨﻘﻮﺹ‬, “Elongated noun/5 ”‫)ﻣﻤﺪﻭﺩ ﺍﺳﻢ‬. For instance, the “Yes”, “No”, “No”, vector of the noun “‫ ”ﻣﺎﻛﺮ‬is (“4”, “‫”ﻡ‬, “No”, “No”). To begin with, the number of letters, the first character, and the gemination position are obvious terms. The Arabic pattern aims to replace each of the original letters with its equivalent in the pattern; the first letter is changed to “‫”ﻑ‬, the second to “‫”ﻉ‬, the third to “‫”ﻝ‬. For example, the pattern of the word “‫ ”ﺑﺎﻫﻞ‬is “‫”ﻓﺎﻋﻞ‬. The type of noun (named also “field”) is used to categorize the nouns. There are two main kinds of field in the Arabic language: inert and derived. The inert noun (‫ )ﺍﺳﻢ ﺟﺎﻣﺪ‬is not derived from another word, such as “‫ﺃﺳ ٌﺪ‬/Lion”. The derived noun (‫ )ﺍﺳﻢ ﻣﺸﺘﻖ‬is taken from a verbal root, which is again divided into subcategories such as “‫ﺍﺳﻢ ﻓﺎﻋﻞ‬/Active participle/” and “‫ﺍﺳﻢ ﻣﻔﻌﻮﻝ‬/Passive participle”. For example, the noun “‫ﻛﺘﺎﺑﺔ‬/Writing” is derived from the verbal root “‫ﻛﺘﺐ‬/Wrote” which represents “‫ﻣﺼﺪﺭ‬/Verbal noun”. The vocalic word has a bi-case and indicates whether the noun contains in the or not. For example, the noun “Writer/‫ ”ﻛﺎﺗﺐ‬is middle one of these letters vocalic while, “Library/‫ ”ﻣﻜﺘﺒﺔ‬is not. The features “‫ “ﺍﺳﻢ ﻣﻤﺪﻭﺩ‬,”‫ “ﺍﺳﻢ ﻣﻨﻘﻮﺹ‬,”‫ ”ﺍﺳﻢ ﻣﻘﺼﻮﺭ‬mean that the noun ends or “‫”ﺀ‬, such as and “‫”ﺻﺤﺮﺍﺀ‬. respectively with To generate these attributes, we first used the SAFAR framework [16] to generate the Arabic pattern and manually verified them. Then, we also manually specified the field and automatically generated the rest of the features for each noun. As previously mentioned, our objective is formulated as a supervised classification problem. Consequently, the training was performed applying decision tree learning algorithms allowing to generate a set of rules in the form of a tree and decision rules. To do so, we used the WEKA data analytics tool that provides several features for machine learning classifiers. It is Java-based, available for free under GNU license, and has a general API that can be embedded in other applications [17]. After building corresponding models of these classifiers, we used 10 cross-validation and train-test splits (80% of data for the train; 20% of data for the test) to estimate the skill of these models when making predictions on new data. A summary of accuracy results for both “Pluralizer” and “Lemmatizer” systems is shown in Tables 3 and 4. Table 3. Accuracy of decision trees for the “pluralizer”. J48 Random tree Random forest REP tree Train-test split 69.3% 70.5% 71.8% 70.3% 10 cross-validation 68.9% 70.1% 71.5% 69.8%

5

For these three last features, we took just a literal translation since they have not an equivalence in the English language.

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M. Ouamer et al. Table 4. Accuracy of decision trees for the “lemmatizer”. J48 Random tree Random forest REP tree Train-test split 50.9% 57.9% 59.6% 57.5% 10 cross-validation 48.7% 57.5% 59.1% 56.8%

After running the two types of experiments, we realized that in general results are not convincing. On one hand, we got different results for the same training dataset when evaluating different classifiers. This may be explained by the fact that machine learning algorithms are stochastic and this behavior of different performances on the same dataset is to be expected. Moreover, our best setup is reached by the Random Forest classifier for both systems with an accuracy of 71.5% and 59.1% respectively. As a result, we have looked for a more effective way to improve the algorithm as explained in the second approach. 4.2

The Second Approach Uses New Features

The main problem with the first approach is that the linguistic features are too general to achieve a good performance and were then too weak to demonstrate the relationship between the singular word and its plural. Another way to improve the algorithm precision is to adopt a new feature to describe our instances. The idea comes from the scrutiny and observation of the Arabic pattern. As explained in the first approach, the pattern allows detecting the letters which constitute the root of a word. For example, the root letters of the word “‫ ”ﻣﻜﺮﻣﻮﻥ‬are “‫ ”ﻙ ﺭ ﻡ‬since the pattern is “‫”ﻣﻔﻌﻠﻮﻥ‬. Indeed, we notice that after extracting the singular pattern, the characters of the root “‫ ”ﻑ ﻉ ﻝ‬are no longer the same in the plural pattern. As an example, let us consider the singular word “‫ ”ﻗﺎﺿﻲ‬and its plural “‫”ﻗﻀﺎﺓ‬. The Arabic pattern of these words is respectively “‫”ﻓﺎﻋﻞ‬ and “‫”ﻓﻌﺎﻝ‬. As can be noticed, the letter “‫ ”ﻝ‬in the singular representing “‫ ”ﻱ‬is not kept in the plural and is changed to “‫ ”ﺓ‬while the patterns indicate the same one. Hence, we propose an adapted pattern named “Broken pattern”. To construct the broken pattern, respectively, and keep the other letters we replace the unchanged letters with as they appear either in the singular or plural noun. Taking the previous example of “‫ ”ﻗﺎﺿﻲ‬and “‫”ﻗﻀﺎﺓ‬, the broken plural is built by replacing the unchanged letters and keep the rest since “‫ ”ﻱ‬is changed to “‫”ﺓ‬. Therefore, the singular and plural broken patterns are respectively “‫ ”ﻓﺎﻋﻲ‬and “‫”ﻓﻌﺎﺓ‬. Thereafter, we have kept the same other attributes of the first approach with the broken pattern. Then, we take off those that statistically have no impact and end up with only two attributes which are the broken pattern and type of noun. Table 5 represents a sample of these attributes.

Arabic Broken Plural Model Based on the Broken Pattern

29

Table 5. Sample of attributes of our database. Singular noun ‫ﺿﺎ ّﻝ‬ ‫ﺿ ٰﺮﺓ‬ ‫ﺟ ٰﺸﺎﺀ‬ ‫ﺟﺜﻮﺓ‬ ‫ﺩﻣﻴﺔ‬

Singular broken pattern ‫ﻓﺎ ّﻉ‬ ‫ﻓ ٰﻌﺔ‬ ‫ﻓ ٰﻌﺎﺀ‬ ‫ﻓﻌﻮﺓ‬ ‫ﻓﻌﻴﺔ‬

Type of noun ‫ﺍﺳﻢ ﻓﺎﻋﻞ‬ ‫ﺍﺳﻢ ﻣﺸﺘﻖ‬ ‫ﺻﻔﺔ ﻣﺸﺒﻬﺔ‬ ‫ﺍﺳﻢ ﻣﻜﺎﻥ‬ ‫ﺍﺳﻢ ﺟﺎﻣﺪ‬

Broken plural ‫ﺿ ٰﻼﻝ‬ ‫ﺿﺮﺍﺋﺮ‬ ّ ‫ﺟ‬ ‫ﺶ‬ ‫ﺟ ًﺜﻰ‬ ‫ﺩ ًﻣﻰ‬

Singular broken pattern ‫ﻓ ٰﻌﺎﻉ‬ ‫ﻓﻌﺎﺋﻊ‬ ‫ﻓ ّﻊ‬ ‫ﻓ ًﻌﻰ‬ ‫ﻓ ًﻌﻰ‬

The performance of this new approach was tested on the same data set using the same methodology. The results of the evaluation show that the accuracy significantly improves approximately 98% and consequently outperforms the first one. Moreover, our best setup is reached by the Random Forest classifier for both systems with an accuracy of 98.1% and 98.6% respectively. A summary of accuracy results for both “pluralizer” and “lemmatizer” systems is shown in Tables 6 and 7. Table 6. Accuracy of decision trees for the “lemmatizer”. J48 Random tree Random forest REP tree Train-test split 98.7% 99.5% 99.7% 99.6% 10 cross-validation 97.7% 98.5% 98.1% 97.8%

Table 7. Accuracy of decision trees for the “pluralizer”. J48 Random tree Random forest REP tree Train-test split 98.1% 99.3% 99.8% 99.4% 10 cross-validation 97.2% 98.4% 98.6% 97.2%

5 Exploitation The evaluation results confirm that the second approach with the Random Forest algorithm outperforms the rest. The resulting Random Forest tree is saved as a model and integrated into a tool to predict the plurals and singulars of Arabic words. This tool has been developed in java and has been integrated into the SAFAR framework6. Our broken plural tool is available both as a web interface and as an API within the framework. In addition, the broken plural tool can be used via the SAFAR API as illustrated in Fig. 2. We first import the package “Safar-modern_standard_arabic-util-broken_plural”. The most important lines are 21 which creates an instance of our broken plural implementation and 24 that uses our implementation. Finally, the result is displayed at line 29.

6

http://arabic.emi.ac.ma:8080/SafarWeb/.

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Fig. 2. A java program of the pluralizer system.

6 Conclusion In this work, we introduced two problems of the Arabic broken plural. We created a broken plural list that covers all BPs alongside their corresponding patterns. Then, we explored two approaches to build a broken plural model. We first followed the approach based on linguistic features to describe our instances reaching an accuracy approximately of 60%. In the second approach, we used a new attribute called Broken Pattern. We experimented with several classification algorithms and showed that the Random Forest classifier performs the best reaching an accuracy of 98%. Future directions of this work include employing our broken plural system in information retrieval.

References 1. Goweder, A., De Roeck, A.: Assessment of a Significant Arabic Corpus, Arabic Language Processing (ACL 2001), pp. 73–79 (2001) 2. Boudelaa, S., Gaskell, M.G.: A reexamination of the default system for Arabic plurals. Lang. Cognit. Process. 17, 321–343 (2002) 3. Goweder, A., Poesio, M., De Roeck, A.: Broken plural detection for Arabic information retrieval. In: SIGIR 2004, pp. 566–567 (2004) 4. El-Beltagy, S.R., Rafea, A.: An accuracy enhanced light stemmer for Arabic text. ACM transactions on speech and language processing 7, 2–23 Association for Computing Machinery. Comput. Rev. 24(11), 503–512 (1983) 5. Elghamry, K.: A lexical-syntactic solution to the problem of (broken) plural in Arabic. In: Arabic Natural Language Processing Track, Georgetown University Round Table (GURT 2010), Washington, 12–14 March 2010 (2010) 6. Attia, M., Pecina, P., Tounsi, L., Toral, A., Van Genabith, J.: Lexical profiling for Arabic. In: Proceedings of eLex, pp. 23–33 (2011) 7. Parker, R., et al.: Arabic Gigaword Fifth Edition LDC2011T11. Web Download. Linguistic Data Consortium, Philadelphia (2011) 8. McCarthy, J.J., Prince, A.S.: Foot and word in prosodic morphology: the Arabic broken plural. Nat. Lang. Linguist. Theory 8, 209–282 (1990)

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9. Kiraz, G.: Analysis of the Arabic broken plural and diminutive. In: Proceedings of the 5th International Conference and Exhibition on Multi-lingual Computing, Cambridge (1996a) 10. Idrissi, A.: Plural formation in Arabic. In: Eid, M., Ratcliffe, R. (eds.) Current Issues in Linguistic Theory, Perspectives on Arabic Linguistics X, vol. 153, pp. 123–145 (1997) 11. Goweder, A., Poesio, M., De Roeck, A., Reynolds, J.: Identifying broken plurals in unvowelised Arabic text. In: EMNLP 2004, Barcelona, Spain (2004) 12. Shafah, A., Goweder, A., Eshafah, S., Rgibi, A.: Irregular Arabic plirals recognition without stemming. In: Proceedings of 2016 4th International Conference on Control Engineering & Information Technology (CEIT-2016), Tunisia, Hammamet, 16–18 December 2016 (2016) 13. Beesley, K.: Computer analysis of Arabic: a two-level approach with detours. In: Omrie, B., Eid, M. (eds.) Perspectives on Arabic Linguistics III: Papers from the 3rd Annual Symposium on Arabic (1991) 14. Goweder, A.M., Almerhag, I.A., Ennakoa, A.A.: Arabic broken plural recognition using a machine translation technique. In: ACIT 2008, Hammamet (2008) 15. Francopoulo, G., et al.: Lexical Markup Framework (LMF). In Proceedings of the LREC 2006, ELRA, pp. 233–236 (2006) 16. Bouzoubaa, K., et al.: A description and demonstration of SAFAR framework. In: Proceedings of the EACL Workshop (2021) 17. Tony C.S., Eibe, F.: Introducing machine learning concepts with WEKA. In: Statistical Genomics: Methods and Protocols, pp. 353–378. Springer, New York (2016). https://doi. org/10.1007/978-1-4939-3578-9_17 18. Blanchete, I., Mourchid M., Mbarki S., Mouloudi A.: Arabic broken plural generation using the extracted linguistic conditions based on root and pattern approach in the NooJ Platform. In: Proceeding of the NooJ 2018 conference: Formalizing Natural Languages with NooJ and Its Natural Language Processing Applications, Palermo, pp. 52–65 (2019)

Performance Improvement of DTC for Doubly Fed Induction Motor by Using Artificial Neuron Network Said Mahfoud1(B) , Aziz Derouich1 , and Najib El Ouanjli2 1 Industrial Technologies and Services Laboratory, Higher School of Technology,

Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] 2 LMIET, Faculty of Sciences and Technology, Hassan First University, Settat, Morocco

Abstract. Due to its numerous advantages, Direct Torque Control (DTC) method is the most extensively adopted technique in the industrial system. However, the ripple torque reduces the strategy’s efficiency as a result of the employment of tree hysteresis comparative models, and the use of the PI speed regulator, and on the other hand the switching tables which generate variable switching frequencies. As a result, driving the machine at low speeds and, more precisely, altering motor resistance has an impact on the machine’s behavior. As a result, this study provides a novel research technique for overcoming the machine’s shortcomings in order to improve control performance. An intelligent DTC approach is applied to two Inverters that supplied the Doubly Fed Induction Motor (DFIM) by employing an Artificial Neuron Network (ANN). The motor and control behaviors were much improved using this technique, which was simulated in Matlab/Simulink. Keywords: DFIM · DTC · ANN-DTC

1 Introduction The DFIM is currently gaining popularity as an alternator for renewable energy sources and as a manufacturing motor for a variety of uses such as rail traction and rolling mills [1]. With the advent of semiconductor-based power electronics, A new approach to variable speed control has arisen, which ensures that all electrical equipment is managed in a progressive manner. In the same way, some academic researchers have been prompted to devise novel methods of controlling machines [2]. However, the main disadvantage of scalar control (SC) is the dynamic and static torque control inaccuracy at low speeds due to the DFIM’s resistive, that cannot be neglected [3], while the needs in industrial applications has become more efficient has created opportunities for academic researchers. Similarly, Blaschke and Hasse [4, 5] developed vector control for AC machine flux orientation. Direct Flux Oriented Control (DFOC) necessitates the use of a flux sensor placed in the air gap, which introduces noise into the measurement owing to physical limits. Although Indirect Flux Oriented Control (IFOC) has largely replaced DFOC [6] these strategies remain extremely sensitive to parametric variation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 32–42, 2022. https://doi.org/10.1007/978-3-031-02447-4_4

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33

In [7] The authors created the Sliding Mode Control (SMC) to address the limitations of conventional controllers and satisfy industrial demands, knowing that this command is known by the chattering phenomenon caused at high frequency, too frequent oscillations can damage the machine and impair the system’s operation and performance. This prompted Takahashi, Noguchi, and Depenbrock [8, 9] to discover controllers that are more efficient, less costly, and less susceptible to parametric fluctuations is named DTC. Its drawbacks include varying stator and rotor resistors, rotation in low speed, and employment of three comparators of hysteresis, which leads to functioning at variable frequency. These variables cause mechanical vibrations and audible sounds, reducing machine life [2]. They are referred to as DTC-based Fuzzy (DTFC) and Direct Torque Neuron Fuzzy Control DTNFC in [10, 11]. This control employs a blocks of fuzzy logic controller and an Adaptive Neuro-Fuzzy Inference System (ANFIS) which combine fuzzy logic rules and ANNs In order to direct the torque and flux towards its setpoint over a predetermined period of time, a voltage vector must be generated. Using these techniques, multivariable controls and identification have shown to be extremely successful, allowing for quick torque and flux responses with reduced distorting. While the fundamental basis of DTC is more sophisticated, it can be accomplished using a fast calculator. The frequency of samples has increased in order to keep up with the complexities of combined artificial intelligence systems [11]. In [12], the authors developed a special DFIM control approach that included modifying the settings of the PID speed regulator to achieve a desired speed. Therefore, under these situations, the DFIM operates like an Induction Motor (IM), making it unable to take use of DFIM advantages such as Overspeed [12]. Using the same strategy as [2], the authors offer a unique control approach that exploits the DFIM’s overspeed on both DFIM sides to take advantage of the DFIM’s overspeed. In [2] the GA-DTC approach, it is hard to determine which algorithm parameters are increasing the algorithm’s execution time and which are decreasing it. In these circumstances, a highly fast calculator is required to fulfill the optimization methods employed in rotating machine controls. A brief overview of the relevant literature is followed by the construction of an artificial intelligence-based control system in conjunction with traditional DTC control on two inverters connected to DFIM. The neural networks used in this study were chosen because of their excellent performance on the structure. Neuronal network controllers replaces the PI speed regulator, three hysteresis comparators, and two switching tables in traditional DTC control in order to solve the constraints of the conventional DTC control. Noise and parametric fluctuations are now less of a threat to the system. This work is arranged along the following axes to guide the reader through a sequential reading process: Sect. 3 studies conventional DTC control, Sect. 4 describes intelligent ANN-DTC control. And Sect. 5 focuses on modeling of ANN-DTC and classical DTC methods and discuss of results interpretation. Finally, this article concludes with a proposal for research in the future.

2 Alpha-Beta DFIM Model The most appropriate model for analyzing dynamic behavior and the design and implementation of DTC applied to the DFIM, is the two-phases model, expressed by Eqs. (1, 2, 3) in (α, β) frame. [1]:

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• Electrical equations: ⎧ v ⎪ ⎪ ⎪ sα ⎨ vsβ ⎪ vrα ⎪ ⎪ ⎩ vrβ

= Rs .isα + d ψdtsα dψ = Rs .isβ + dtsβ = Rr .irα + d ψdtrα + ωm .ψrβ dψ = Rr .irβ + dtrβ − ωm .ψrα

(1)

• Magnetic equations: ⎧ ⎪ ⎪ ψsα ⎨ ψsβ ⎪ ψ ⎪ ⎩ rα ψrβ

= Ls isα + Lm .irα = Ls isβ + Lm .irβ = Lr irα + Lm .isα = Lr irβ + Lm .isβ

(2)

• Mechanical equations: 

Tem = p.(ψsα isβ − ψsβ isα ) J . ddt + f . = Tem − Tr

(3)

3 Artificial Neuron Network-Direct Torque Control strategy Takahashi [8] proposed the DTC control in the mid-1980s, and it has been in use since. The DTC control is less susceptible to fluctuations in the machine’s parametric parameters, and it allows for the generation of accurate and rapid torque dynamics. According to the concept of this control, it is possible to directly regulate the machine’s torque as well as the stator and rotor fluxes. To compare the reference value and the estimated value, a hysteresis comparator is employed, and the situation of an inverter is fully controlled by using a switching table that has been typically predetermined. To address DTC’s shortcomings, the introduction of the artificial neuron network in this control has resolved its shortcomings by replacing the PI speed regulation, hysteresis comparators, and switching tables with neuron network control blocks, respectively. The ANN-DTC control technique for the DFIM is illustrated in Fig. 1. 3.1 Technique of the Rotor and Stator Flux Vector Control In the fixed reference (α, β) attached to the stator, the rotor and stator fluxes are estimated and expressed by Eq. (4) [4] t  ψ s (t) = 0 (V s + Rs .I s ).dt t (4) ψ r (t) = 0 (V r + Rr .I r ).dt Estimated flux are calculated in the same way, and are described by their positions and modules as follows: ⎧  ⎨ψˆ = ψˆ 2 +ψˆ 2 α β (5) ⎩θ = arctg( ψˆ β ) ψˆ α

Performance Improvement of DTC for Doubly Fed Induction Motor

35

Fig. 1. Proposed ANN-DTC applied to a DFIM

The alpha-beta fluxes has been taken into consideration, by using Eq. (6), the electromagnetic torque can be determined: Tˆ em = p.(ψˆ sα .isβ − ψˆ sβ .isα )

(6)

4 Operating Principal and Configuration of ANN The mathematical representation of the neural network is a distributed processing system that is made of many nonlinear computational components (neurons) that operate in parallel and are coupled by forces represented as weights. ANNs are networks of parallel basic systems. In order to learn and improve an ANN’s performance, it need training data. The ANN is made up of the activation functions and summers illustrated in the Figs. 2. A neuron’s equations are as follows.

Fig. 2. Schematic structure of ANN for DTC

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yi = F1 (s) ∗ Oi = F2 (s) ∗

 N i=1

 N i=1

(xi ∗ wi + b)

(7)

(yi ∗ wi + b)

(8)

Here, the input signals are denoted by xi , the synaptic weight of that signal is denoted by wi , the parameter of bias is denoted by b, and the neuron output signals are denoted by yi . F1 (s) presents the activation function of nonlinear hyperbolic-tan, which can be calculated using the following expression. F1 (s) =

eαs − e−αs eαs + e−αs

(9)

And F2 (s) presents the function of linear activation which may be calculated Eq. (10): F2 (s) = βs

(10)

When the gains are denoted by α and β, there is a maximum gain at zero for this activation function since it is monotonic, bipolar, and differentiable. The training of the neural network it is necessary which is described in this work by utilizing the approach of feedforward backpropagation until to have the Mean Square Error (MSE) included between the output and desired patterns is really small. Calculating the MSE is done using the following equation: MSE =

1 N (di (k) − Oi (k))2 i=1 N

(11)

where: di (k): represents the desired response. Oi : represents the actual response provided by a network. N : represents the training data number. The weight update expression is given by (12) and is used to update the weights of each neuron in order to reduce the value of objective function (MSE). wji (k + 1) = wji (k) − η

∂MSE(k) ∂wji (k)

where wji (k + 1): represents the future weight between the neurons i th and j th. wji (k): presents the precedents weight. η: presents the learning rate.

(12)

Performance Improvement of DTC for Doubly Fed Induction Motor

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Table 1. Specification of the optimum proposed ANN models Parameters of ANN

Methods/Values

Neural network

sigmoid_hidden_neurons and Two_layer_feed_forward network

Number of Hidden Layer nodes

10

16

16

16

20

20

Neurons number in the input layer

1

1

1

1

3

3

Neurons number in the second hidden layer

1

Neurons of number in the output layer

1

1

1

1

3

3

Learning rate

0.5

epochs Number

20

100

100

100

200

200

ANN algorithm of training

Backpropagation

Adaption function of learning

Trainlm

Activation function

Tansig

ANN _Speed

ANN _T

ANN _ψS

ANN _ψr

ANN _STS

ANN _STr

Table 1 demonstrate proposed configuration of neural network controllers for ANNSpeed, ANN_T, ANN _ψS , ANN _ψr ANN _STS, ANN _STr, In the suggested model, The hysteresis comparators are used to produce two positions, 0 or 1, for the flux comparators and three positions, −1, 0, 1, for the torque comparator, which will automatically be inputs for the ANN-based switching tables. The structures based on ANN and the results of training are presented in Figs. 3, 4, 5, and 6. The architectures structure utilized for the switching tables, flux (rotor and stator), torque, and speed are 3-20-3-3, 1-16-1-1, 1-16-1-1, and 1-10-1-1, in Figs. (3, 4, 5, 6)a. This is shown in Figs. 3b, 4b, 5b and 6b. The progression of the root of MSE is shown in Figs. (3, 4, 5, 6)b. As shown, the network training reduced the tolerance between the output of anticipated values and target. The inaccuracy first decreases rapidly, reaching a maximum of 1000 epochs for flux and speed controllers and sublimely the switching tables-based ANNs and torque-based ANN are Known 654, 52 epochs, before stabilizing at a low value of 7.5373 * 10–4 , 1.1603 * 10– 3, 1.3777 * 10–3 , and 4.6746 * 10–8 respectively for each controller-based ANN.

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(a)

(b)

Fig. 3. Speed controller ANN architecture and its training of evolution (a) Performance of MSE (Testing, Validation, Training) (b)

(a)

(b)

Fig. 4. Torque controller ANN architecture and its training of evolution (a) Performance of MSE (Testing, Validation, Training) (b)

(a)

(b)

Fig. 5. Rotor and Stator fluxes controllers ANN architecture and its training of evolution (a) Performance of MSE (Testing, Validation, Training) (b)

Performance Improvement of DTC for Doubly Fed Induction Motor

(a)

39

(b)

Fig. 6. Rotor and Stator switching tables controller ANN architecture and its training of evolution (a) Performance of MSE (Testing, Validation, Training) (b)

5 Simulation Procedure and Interpretation of Simulation Results To examine the conventional DTC effectiveness with against the ANN-DTC results in the DFIM, a SIMULINK structure was created. In the DTC, the controllers-based ANNs were used to replace the switching tables, hysteresis comparators, and speed regulator, that were previously used. The dynamic characteristics of the DFIM were investigated using a 1.5 Kw DFIM. The following are the motor parameters: Vr = 130 v, Vs = 230 v, f = 50 Hz, P = 2, Rr = 1.68 , Rs = 1.75 , Lr = 0.104H, Ls = 0.295H, f = 0.0027 kg.m2 /s, M = 0.165H, J = 0.001 kg.m2 . In Figs. 7, 8, and 9, you can see how the speed changes with time, as well as how the electromagnetic torque changes with time, and how the stator and rotor flux change with time. These results were obtained without load as an initial condition and with a dynamique load of 10 nm at 0.5 s as a normal working condition.

Fig. 7. Speed responses

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Fig. 8. Torque responses

Fig. 9. The fluxes of Stator (a) and rotor (b) in alpha-beta frame

The Figs. 7 and 8 show the speed response and electromagnetique torque characteristics of a DFIM controlled by the ANN-DTC, which promptly achieve their reference values without overshooting when compared to a traditional DTC during the beginning of no-load and nominal dynamique load starting, respectively, compared to a conventional DTC. For the study of the speed characteristic in terms of performance, from Fig. 7 it will be noticed that the response time is minimized by a value rate of 86.67% (367.7 ms is noticed for the classic DTC and 49.4 ms is seen for the new ANN-DTC hybrid), and on the other hand, the overshoot is minimized by a value rate of 100% (50.56 rad/s is noticed for the classic DTC and an overshoot null is seen for the new ANN-DTC hybrid). On the one hand, the performance is called the rejection time, and we can notice a reduction of 89.88% (150.2 rad/s is noticed for the traditional DTC and 15.2 rad/s is seen for the new hybrid ANN-DTC). It can be seen from Fig. 8 that in terms of motor torque performance, the torque ripples are minimized by a value rate of 55.83% compared to conventional DTC (2.445 Nm is noticed for conventional DTC and 1.08 Nm is seen for the new ANN-DTC hybrid).

Performance Improvement of DTC for Doubly Fed Induction Motor

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In Fig. 9, we can see the performance of the fluxes in the alpha-beta plane. The fluxes ripples thickness are minimized by values of 69.28% and 37.85% respectively for the stator and rotor fluxes, (0, 05,947 Wb and 0.0121 Wb are noticed for the classic DTC and 0.01827 Wb and 0.00752 Wb are seen for the new hybrid ANN-DTC). The findings reported in Table 2 demonstrated that the ANN-DTC control outperformed the traditional DTC control in terms of overall performance (dynamics, robustness, stability, rapidity and precision). Table 2. Different measures of the classic DTC and ANN-DTC characteristics Characteristics 

Response time (ms)

ANN-DTC

Classic DTC

Enhancement (%)

49.4

367.7

86.67

Overshoot (rad/s)

0

50.56

100

Rejection time (ms)

15.2

150.2

89.88

Undershoot (rad/s)

3.66

5.066

26.24

T

Rippels (wb)

1.08

2.445

ψs

Rippels (wb)

0.01827

0.05947

69.28

ψr

Rippels (wb)

0.00752

0.0121

37.85

55.83

6 Conclusion This study illustrates the relevance of artificial intelligence in systems with multivariable and nonlinear behavior. The novel intelligent direct torque control ANN-DTC, simulated in Matlab/Simulink, improved the conventional DTC in terms of speed, stability, and precision presented by the following points: Artificial intelligence is demonstrated in this work in ANN-DTC structure with multi-variable and nonlinear behavior, demonstrating its use in such systems. ANNDTC is an upgraded version of the conventional direct torque control (DTC) that was simulated in Matlab/Simulink. The following points demonstrate how the innovative intelligent direct torque control (ANN-DTC) outperformed the standard DTC in terms of precision, stability, and precision of all ANN-DTC characteristics: – Enhancement response time of 86.67%. – Removed the speed overshoot. – Reduction of torque ripples by 49.28%. At the completion of this study, our lab’s researcher team created a work plan that includes:-A practical validation of the hybrid ANN-DTC approach on the dSPACE DS1104 board. – Using the hybrid ANN-DTC control in the water pumping application.

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References 1. Mahfoud, S., Derouich, A., El Ouanjli, N., Mohammed, T., Hanafi, A.: Field oriented control of doubly fed induction motor using speed sliding mode controller. In: E3S Web of Conferences, vol. 229, p. 01061. EDP Sciences (2021) 2. Mahfoud, S., Derouich, A., EL Ouanjli, N., EL Mahfoud, M., Taoussi, M.: A new strategybased PID controller optimized by genetic algorithm for DTC of the doubly fed induction motor. Systems 9, 37 (2021) 3. Mahfoud, S., Derouich, A., Iqbal, A., El Ouanjli, N.: ANT-colony optimization-direct torque control for a doubly fed induction motor: an experimental validation. Energy Rep. 8, 81–98 (2022) 4. Blaschke, F.: The principle of field orientation as applied to the new TRANSVECTOR closed loop control system for rotating field machines. Siemens Rev. 34(5), 217–220 (1972) 5. Hasse, K.: Zum dynamischen Verhalten der Asynchronmaschine bei Betrieb mit variabler Standerfrequenz und Standerspannung. ETZ-A 89, 77 (1968) 6. El Ouanjli, N., Derouich, A., Chebabhi, A., Taoussi, M.: A comparative study between FOC and DTC control of the doubly fed induction motor (DFIM). In: 2017 International Conference on Electrical and Information Technologies (ICEIT), pp. 1–6. IEEE, November 2017 7. Abderazak, S., Farid, N.: Comparative study between Sliding mode controller and fuzzy sliding mode controller in a speed control for doubly fed induction motor. In: 2016 4th International Conference on Control Engineering & Information Technology (CEIT), pp. 1–6, December, 2016 8. Takahashi, I., Ohmori, Y.: High-performance direct torque control of an induction motor. IEEE Trans. Ind. Appl. 25, 257–264 (1989) 9. Cirrincione, G., Cirrincione, M., Lu, C., Pucci, M.: Direct torque control of induction motors by use of the GMR neural network. In: Proceedings of the International Joint Conference on Neural Networks, 20–24 July 2003, vol. 3, pp. 2106–2111 (2003) 10. El Ouanjli, N., et al.: Direct torque control strategy based on fuzzy logic controller for a doubly fed induction motor. In: IOP Conference Series: Earth and Environmental Science, vol. 161, p. 012004 (2018) 11. Zemmit, A., Messalti, S., Harrag, A.: A new improved DTC of doubly fed induction machine using GA-based PI controller. Ain Shams Eng. J. (2017) 12. Mahfoud, S., Derouich, A., El Ouanjli, N., Taoussi, M., Mahfoud, M.E.: Improved DTC of the PID controller by using genetic algorithm of a doubly fed induction motor. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 1687–1698. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_153

Knowledge Management Models: Overview and Comparative Study Safaa Essalih1(B) , Mohamed Ramadany2 , and Driss Amegouz1 1 Higher School of Technology, Sidi Mohammed Ben Abdellah University, 30050 Fez, Morocco

[email protected] 2 Faculty of Sciences and Techniques, Sidi Mohammed Ben Abdellah University, 30050 Fez,

Morocco

Abstract. The evolution of manufacturing factories towards Industry 4.0 requires changing the organization of work. Hence, manufacturers must be able to anticipate digital and technological developments. These are enriched by new knowledge issued from new technologies such as augmented reality (AR), additive manufacturing (AM), Internet of Things (IoT), etc... In this paper, we have presented a state of the art of knowledge management models that has characterized these last 15 years according to the different application domains, while focusing on those related to industrial engineering. The state of the art allowed us to make a statistical synthesis. In a second step, we made a comparative study of these different models. The objective is to draw inspiration from them, in order to contribute to the development of a new knowledge management model in the context of Industry 4.0, to identify the necessary approach. Keywords: Industry 4.0 · Models · Knowledge management · Comparative study

1 Introduction Knowledge and knowledge management (KM) have emerged as a major research area in organizational development and management. Knowledge management aims to solve the problem of organizational management knowledge, and its main goal is to facilitate decisions by all organization members to ensure the organization’s effectiveness and efficiency. According to Baizet [1], knowledge can be described as complete and systematic refined information, or information related to the content of use, whereas Alavi and Leidner [2] affirmed that knowledge is the information that is held in the individual’s mind: it is related to procedures, personalized explanations, ideas, facts, observations, concepts, and judgments related information. If the information is processed in the individual’s mind, the information will be transformed into knowledge. If knowledge is expressed and presented in text form, diagrams, or words, it then becomes information. Most authors classify knowledge into two types: explicit knowledge and tacit knowledge (know-how) (Nonaka and Takeuchi [3], Grundstein, Rosenthal-Sabroux, and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 43–53, 2022. https://doi.org/10.1007/978-3-031-02447-4_5

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Pachulski [4]). Explicit knowledge means knowledge that can be transmitted through a “formal and systematic” language [5] and is therefore easy to convey to others. Tacit knowledge is the knowledge that is hard to formulate and share, and can be transferred only through the people’s willingness to communicate their experiences. It is generally obtained through long-term learning and experience [4, 5]. Nonaka and Takeuchi defined the mechanisms of transformation between tacit and explicit knowledge using the SECI (Socialization, Externalization, Combination, and Internationalization) model [3]. Baumard also divided knowledge into two other types: individual knowledge and collective knowledge. Individual knowledge is possessed by an individual. It can be expertise, a private note, or intuition. Collective knowledge is possessed by many people, not by the sum of individual knowledge. It can be a reference book, rules, or social practices [6]. In the words of Tisseyre [7], knowledge management corresponds to the conscious, coordinated, and operational management of all the information, knowledge, and know-how of the members of an organization in the service of this organization. Raman and Muthaiyah [8] state that knowledge management is a process that simply identifies knowledge through past experience and uses it to make decisions in the current process. In line with KM theory, many studies have been conducted in order to propose models dedicated to knowledge management. Our objective is therefore to review the different knowledge management (KM) models that have characterized the last 15 years according to the different application fields (industrial engineering, medicine, education…), this review allowed us to perform a statistical synthesis and a comparative study of these different models.

2 Knowledge Management Models (Literature Review) 2.1 Knowledge Management Models Applicable in the Industrial Field Wheel of Knowledge This knowledge management model is founded on the PDCA (Plan-Do-Check-Act) continuous improvement paradigm [9]. This model first starts with the acquisition of knowledge, the knowledge is then integrated into the organization, and then it is stored. The stored knowledge needs to be transmitted and conveyed throughout the organization, with knowledge application and ultimately innovation being the following step. Through information and communication technology systems, organizational culture, and proper management, these stages form a cycle [10]. Tower of Knowledge Oztemel and Arslankaya proposed an enterprise-wide knowledge management model known as the knowledge tower that aims to integrate KM at the strategic, tactical, and operational levels, this model is organized in a hierarchical manner, with each element needs and requires the abilities of the previous components. The author defined these

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components as knowledge infrastructure, knowledge management processes, knowledge representation, knowledge planning, knowledge management strategies, knowledge organization, knowledge culture, Knowledge Leveraging, and KM Evaluation [11]. Knowledge Management Model Applicable in a Research and Development (R&D) Center of a Small and Medium-Sized Enterprise (SME) The present knowledge management model is elaborated to develop the innovation capacity in a center (R&D) of an SME. It is presented in three phases. The first phase is Basics; the next is Process and the last one is Results [12]. The basics section is constructed from two elements (top management and CEO support and the creation of a cultural context), a good cultural context and the support of top management and the CEO allow to improve and develop the creativity, innovation, and spirit of the company. This phase organizes and applies implicit knowledge in three areas: People, R&D, and Industries [12]. The collected knowledge is then conveyed to the next section to finish the transformation of tacit knowledge into explicit knowledge. Employees who have obtained implicit knowledge are reviewed and categorized as data and information. The information organized for assessment and revision will be presented to the knowledge managers, which are composed of knowledge workers, knowledge engineers, knowledge revisers, and knowledge consultants. In the last section, the outcomes expected from the implementation of the knowledge management model are achieved such as the development of new products and services or the retention of key talents and expertise. At this point, we have a dynamic and strong organization based on knowledge management where staff is the most valuable asset [12]. Model for Incorporating Big Data into Industry 4.0 Oriented Knowledge Management The present model is developed for companies that are adapting to Industry 4.0 using new technologies such as Big Data. This model is composed of Knowledge, Internal and External Recognition (Organizational Recognition Process, Environmental Recognition), Knowledge Management (Database Processing, Information), Information Aspects (Complete Digitization, Cybersecurity, Information Integration, and Flexibility), Applied Knowledge (Innovation and Development, Continuous Improvement, Horizontal Integration, Continuous Learning) [13]. The Knowledge Staircase Model for Industry 4.0 This model is proposed by (North and Maier), is an adaptation of the older knowledge staircase [14] to the requirements of intelligent companies. In this approach, value is created through data, information, and knowledge using digital technologies such as augmented reality, Big Data… Knowledge is seen as the result of information processing and is the basis for action [15]. Competence according to the author is the capacity to act in a structured manner in a difficult environment [15]. Finally, the last staircase step is competitiveness, which can be defined as a combination of many competencies. In addition, North presents the human and organizational dimension that describes the examination of knowledge and skills and is used as a dynamizing factor [19], and the

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technological dimension that is used as a stabilizer to ensure that the proper knowledge is always present in the appropriate place and time [15]. The Assist 4.0 Model In this approach, knowledge is created in textual forms and also multimedia forms through the use of mobile media (smartphone, tablet…) which allows stakeholders to record, evaluate and share their experiences easily with others. Asset 4.0 is a model based on the use of new technologies such as augmented reality, Big Data…, and it also takes into consideration data protection to ensure that the content has not been altered during the transfer between stakeholders and that the knowledge is available at the right place [16]. 2.2 Knowledge Management Models Applicable in the Education Field Dynamic Knowledge Management Model for Higher Education Development This model of knowledge management suggested by Chen and Burstein [17], is based on three major elements: people, policies, and technology. In the author’s view, the successful implementation of knowledge management is realized when technology is combined with suitable policies and human skills using the technological infrastructure. This model comprises six interdependent activities: knowledge capture, knowledge storage, knowledge sharing, knowledge learning, knowledge exploration, and knowledge exploitation [17]. Administrative Model of Knowledge Management Involvement in Higher Education The author proposed a model based on the findings of a study (descriptive survey) that was conducted at Islamic Azad University in Tehran. This model is divided into two sections: knowledge management process and organizational components. The components of the knowledge management process are The System of Entry and Reading of Knowledge, Scoring System, Ordering System, Knowledge Categorization System, Rewarding System, Reporting System [18]. 2.3 Knowledge Management Models Applicable in Medical Field Knowledge Management Model: Implications for Quality Improvement in Health Care Orzano [19] suggested a model for providing knowledge development and sharing in health care practices, which is composed of KM enablers (supportive leadership, trusting climate, reflective practice, robust infrastructure, effective communication, accessible technology, active networks, helpful relationships), critical processes (knowledge seeking, knowledge sharing, knowledge development) and KM consequences (sense of decision making, organizational learning, and organizational performance) [19]. Empirical Model for Knowledge Management in Health Care Organizations The author developed this model [20] based on a qualitative study to create an empirical

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model of knowledge management in healthcare settings. This model consists of four key knowledge processes: knowledge seeking, knowledge synthesis, knowledge use, and knowledge sharing, these knowledge processes are supported by several factors: existing or organizational processes, resources (including information technology), leadership, and organizational culture [20]. 2.4 Knowledge Management Models for Construction Projects Integrated Knowledge Management Model for Construction Projects The integrated KM model was created by Kanapeckiene et al. [21]. This model illustrates the impact of macro, meso, and microscale parameters on the KM process. Macro-level variables like the level of economic, political, and cultural development of a country, governmental policies (regional support programs, competition control, preferential loans), etc. The meso-environment refers to the systematic analysis of an organization’s sector of activity. Micro-environmental elements have a big impact on how a knowledge management model develops. Local infrastructure, life-long learning, a supportive residential environment, and so on are some of the requirements within the organization. Furthermore, the KMM highlights the essential strategic contribution of tacit and explicit knowledge to the development of a cohesive strategy for project and organizational performance [21]. Business Strategy Development Model for Applying Knowledge Management in Construction (the BAN Model) The proposed BAN model operates as a long-term loop through its six phases, applies the main components of KM in construction companies. These phases are Preliminary stage: In this stage, the difficulties and the different barriers the company is facing are determined through meetings, interviews, etc. The participating parties are also identified at this level. Development of an organizational strategy: Includes the formulation of an organizational strategy. It is composed of the following five steps: Aims and objectives of KMSs, Strategic plan, Action plan, Construction business processes, Project financial and human resources. Start-up phase: This step involves creating the knowledge management office, linking the knowledge management office to the organization’s departments, and identifying appropriate knowledge management tools. Implementation: It is about the execution of KM, including KM resources, application activities, and tools. Monitoring and evaluation: All preceding steps are monitored and evaluated at this stage. It is a process that is applied to plans, procedures, and risks on a continuous basis. Derivation of knowledge management values: The application of the BAN model in construction companies achieves short-term results (improved innovation, improved quality of work and performance…) and long-term results (increased profitability, improved productivity…) [22].

3 Statistical Synthesis The models mentioned in this work have been selected on the back of a thorough review of the literature, of which the total number of models presented is 12. Figure 1 shows the distribution of the papers by year:

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Documents

3

2

1

0 2006

2008

2010

2012 2014 Years

2016

2018

2020

Fig. 1. Number of publications by year. Source: Authors elaboration.

On the other hand, Fig. 2 highlights the distribution of first authors according to their origin country. Germany and Iran are the countries that contain the most papers. However, there are no articles from Francophone countries.

China Lithuania Jordan Australia Colombia Turkey Canada United States Germany Iran 0

1

2

3

4

Documents

Fig. 2. Number of papers by county of the first author. Source: Authors elaboration.

Finally, Fig. 3 presents the distribution of the selected models by field of application. It can be seen that 50% of the models are applied in industrial engineering, 16% in medicine, 17% in education and teaching, and 17% in construction projects.

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17%

49

16%

17%

50%

Medecine

Education

Industrial engineering

Construction projects

Fig. 3. Distribution of models by field of application. Source: Authors elaboration.

4 Comparative Study In the context of globalization and digital omnipresence, organizations are irrigated by a continuous flow of data. Information and communication technologies (ICT) have already demonstrated the benefits they can bring to an organization. As a result, they are increasingly integrated into business practices. Since 2011, there has been talked of Industry 4.0, the industry of the future, the smart factory, etc. All these terms refer to the fourth industrial revolution and indicate a radical change in the way things are done, so when factories take the shift to Industry 4.0, the change in the organization of work is imposed and therefore the creation of new models of knowledge Management (Knowledge Management 4.0) is required. By comparing the different models, 10 components were identified as knowledge management activities that can be considered as a knowledge management process, as shown in Table 1, most authors are unanimous for knowledge creation/acquisition, storage, sharing and application. Table 1 below summarizes the results of the comparative study we conducted based on several criteria: knowledge management process activities, staff involvement, technology integration (information systems, internet…), and integration of new Industry 4.0 technologies. From the table, we see that all models involve the different stakeholders and information technologies however, only three models that integrate Industry 4.0 technologies such as Big Data, augmented reality…, which confirms that there is a lack of research in this context. When we compare the traditional models with the new models that use Industry 4.0 technologies, we can notice that in the context of Industry 4.0, knowledge can be available in real-time in the concerned services which allow to share and transfer information and experiences between the different stakeholders and also between people and systems, new technologies such as augmented reality simplify not only knowledge-gathering but also knowledge generation by creating multimedia content using smartphones and smart

X

Model to develop innovativeness in a small

X

X

The administrative model of knowledge management

involvement in higher education [18]

X

A dynamic model of knowledge management for higher education development [17]

X

The Asset 4.0 model [16]

[15]

The knowledge staircase model for Industry 4.0

Industry 4.0 [13]

Model for the Incorporation of Big Data in Knowledge Management Oriented to

X

X

Knowledge tower [11]

and medium-sized enterprise research and development center [12]

X

Acquisition

X

X

X

X

X

Creation

X

X

X

Organisation

Knowledge management process

The wheel of knowledge [10]

KM model

X

X

X

X

X

X

X

Storage

X

X

X

X

X

X

X

X

Sharing

X

X

X

X

X

X

X

X

Transfer

X

X

X

X

X

X

X

Application

X

X

X

X

Evaluation

X

X

X

X

X

Innovation

X

Update

Table 1. A summarization of the comparative analysis results.

X

X

X

X

X

X

X

X

Staff involvement

X

X

X

X

X

X

X

X

Integration of technologies (IS, internet…)

X

X

X

(continued)

Integration of new industry 4.0 technologies

50 S. Essalih et al.

X

Business Strategy Development Model for Applying Knowledge

Management in Construction (the BAN model) [22]

X

X

X

Acquisition

X

Creation

Organisation

Knowledge management process

Integrated Knowledge Management Model for Construction Projects [21]

model for knowledge management in health care organizations [20]

An empirically based

enhancing quality in health care [19]

A knowledge management model: Implications for

KM model

X

X

Storage

X

X

X

Sharing

X

X

Transfer

X

X

X

Application

Evaluation

Table 1. (continued)

X

Innovation

X

Update

X

X

X

X

Staff involvement

X

X

X

X

Integration of technologies (IS, internet…)

Integration of new industry 4.0 technologies

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glasses [16], moreover, the evaluation of knowledge is crucial, as machines and people gather information from a variety of common sources [23]. Knowledge must be evaluated by humans in combination with algorithmic decisions [24] and by combined approaches. In addition, due to the increasing data intensity, the aspect of data security and privacy is of particular relevance, Afterward, data integrity, authentication, authorization, and confidentiality must all be protected [16].

5 Conclusion In this article, we have performed a literature review of knowledge management models, applied in different domains, according to certain criteria. This study will give us an orientation to find new directions to develop these models by adding new ideas. As perspectives, we will conduct a survey with industrialists to identify the barriers and obstacles facing the integration of new technologies of Industry 4.0 in Moroccan companies and draw inspiration from these models to develop a generic model of knowledge management in the context of Industry 4.0.

References 1. Baizet, Y.: Knowledge management in design: application to the computational mechanics at renault-BE. Ph.D. thesis University of Grenoble (2004) 2. Alavi, M., Leidner, D.E.: Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q. 25(1), 107–136 (2001) 3. Nonaka, I., Takeuchi, H.: The knowledge-creating company. In: The Economic Impact of Knowledge, vol. 183 (1995) 4. Grundstein, M., Rosenthal-Sabroux, C., Pachulski, A.: Reinforcing decision aid by capitalizing on company’s knowledge: future prospect. Eur. J. Oper. Res. 145(2), 256–272 (2003) 5. Lalouette, C.: Gestion des connaissances et fiabilité organisationnelle: état de l’art et illustration dans l’aéronautique. Les Cahiers De La Sécurité Industrielle (2013) 6. Baumard, P.: Tacit Knowledge in Organizations. Sage (1999) 7. Tisseyre, R.C.: Knowledge management: théorie et pratique de la gestion des connaissances. Hermès Science Publications (1999) 8. Raman, M., Muthaiyah, S.: Editorial: applied knowledge management in an institutional context. Knowl. Manag. E-Learn. Int. J. 1(2), 81–89 (2009) 9. Deming, E.: Out of the Crisis. Massachusetts Institute of Technology, Center for Advanced Educational Services, Cambridge (1986) 10. Zhao, J., Pablos, P., Qi, Z.: Enterprise knowledge management model based on China’s practice and case study. Comput. Hum. Behav. 28(2), 324–330 (2012) 11. Oztemel, E., Arslankaya, S.: Enterprise knowledge management model: a knowledge tower. Knowl. Inf. Syst. 31, 171–192 (2012) 12. Aghmiyoni, M.T., Salimi, H.: A new applicable knowledge management model to develop innovativeness in a small and medium-sized enterprise research and development center. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1–4 (2015)

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13. Cárdenas, L.J.A., Ramírez, W.F.T., Rodríguez Molano, J.I.: Model for the incorporation of big data in knowledge management oriented to industry 4.0. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943 pp. 683–693. Springer, Cham (2018). https://doi.org/ 10.1007/978-3-319-93803-5_64 14. North, K.: Die Wissenstreppe. In: North, K. (ed.) Wissensorientierte Unternehmensführung, pp. 33–65. Springer, Wiesbaden (2016). https://doi.org/10.1007/978-3-658-11643-9_3 15. North, K., Maier, R.: Wissen 4.0. Wissensmanagement im digitalen Wandel, vol. 55, pp. 665– 681 (2018) 16. Brandl, P., Aschbacher, H., and Hösch, S.: Mobiles Wissensmanagement in der Industrie 4.0. In: Mensch und Computer 2015 – Workshopband, pp. 225–232 (2015) 17. Chen, F., Burstein, F.: A dynamic model of knowledge management for higher education development. In: 7th International Conference on Information Technology Based Higher Education and Training, pp. 173–180 (2006) 18. Farhad Eftekharzade, S.: The presentation of a suitable model for creating knowledge management in educational institutes (higher education). Procedia Soc. Behav. Sci. 29, 1001–1011 (2011) 19. Orzano, A.J., McInerney, C.R., Scharf, D., Tallia, A.F., Crabtree, B.F.: A knowledge management model: Implications for enhancing quality in health care. J. Am. Soc. Inf. Sci. Technol. 59(3), 489–505 (2008) 20. Sibbald, S.L., Wathen, C.N., Kothari, A.: An empirically-based model for knowledge management in health care organizations. Health Care Manag. Rev. 41(1), 64–74 (2016) 21. Kanapeckienea, L., Kaklauskasb, A., Zavadskasc, E., Seniutd, M.: Integrated knowledge management model and system for construction projects. Eng. Appl. Artif. Intell. 23(7), 1200–1215 (2010) 22. Adi, W.A., Hiyassat, M., Lepkova, N.: Business strategy development model for applying knowledge management in construction. J. Civ. Eng. Manag. 27(4), 246–259 (2021) 23. Seeber, I., et al.: Machines as teammates: a collaboration research agenda. In: Hawaii International Conference on System Sciences (HICSS) (2018) 24. Samulowitz, H., Sabharwal, A., Reddy, C.: Cognitive automation of data science. In: ICML AutoML Workshop (2014)

Automatic Story Generation: Case Study of English Children’s Story Generation Using GPT-2 Fatima Zahra Fagroud(B) , Mohamed Rachdi, and El Habib Ben Lahmar Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M’sik, Hassan II University - Casablanca, Sidi Othman, BP 7955, Casablanca, Morocco [email protected]

Abstract. Generative Pre-trained Transformer 2 (GPT-2) has shown aweinspiring effectiveness of pre-trained language models on several tasks, especially the generation of coherent text. Automatic stories generation represents a great research area that has rarely been studied in the past and presents a unique issue for artificial intelligence. For this reason, we present in this work a novel approach to automatic children’s stories generation based on Generative Pre-trained Transformer 2 to increase reading skills. In our implementation, we identified the Simple Transformers library, which is built like a wrapper around the famous Transformers library by Hugging Face. This allows us to train and evaluate Transformer models very quickly. Keywords: Generative pre-trained transformer 2 · GPT-2 · Story generation · Artificial intelligence

1 Introduction Today, recent technologies and computers represent a means that has impacted the transformation of the way of thinking, working, and living. Natural language techniques have shown to be a highly efficient method of exchanging data between computers and humans while requiring less personal information. Natural Language Processing (NLP) is the convergence between computing, artificial intelligence, and linguistics. NLP aims to enable computers, to treat or “understand” natural language to carry out different tasks. NLP is a vital part of Artificial Intelligence (AI). Comprehending and representing verbal meaning is a difficult task. It’s currently one of the most active research disciplines in data science. The process of developing important sentences and phrases in natural language is known as text generation. It consists of automatically generating language that describes, outlines, and explains structured data humanly at a high rate (thousands of pages per second). Recently, using pre-training models and deep learning in several language tasks have demonstrated superb results. Especially, the use of pre-trained models like ELMo (Embeddings from Language Models) [1], OpenAI GPT (Generative Pre-Training) [2], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 54–62, 2022. https://doi.org/10.1007/978-3-031-02447-4_6

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GPT-2 (it represents a successor of GPT) [3], and BERT (Bidirectional Encoder Representations from Transformers) [4] has become among the better practices to give better results. Although both BERT and GPT-2 allow automatic text generation, the elaborated work by Wang and Cho in [5] shows that GPT-2 is much better for text generation in terms of quality. GPT-2 is claimed to be so powerful that the risk of its malicious use is high. For this reason, OpenAI decided to keep its largest model (1.5B parameters) closed so that there is more time to discuss its ramifications [6]. By fine-tuning the released 345M medium version [7], this work attempts to present a new model for automatic children’s story generation. In general, we are impressed by how few training steps are essential to generate a coherent story like a real story, as well as in terms of quality the texts are not generated in the same way. As a result, it is preferable to experiment with GPT-2 and consider its impact on text generation as technology advances. In the next part of this paper, we start with related works, then we move to our approach, that we represent the implementation and the evaluation and we finish with the conclusion.

2 Related Works Recently, several research has focused on the automatic text generation field. In this section, we present various research papers that have been presented in the literature: Ilya Sutskever, James Martens, Geoffrey Hinton [8] implemented a story generation system using a dataset based on a set of short writings. They are showing through automatic and human assessment that the new hierarchical models, self-attention mechanisms, and model fusion dramatically improve the fluidity, timeliness, and overall quality of the generated stories. Xingxing Zhang and Mirella Lapata [9] presented a Chinese poem generation model based on recurrent neural networks. The corpus used is a set of Chinese poems by bringing together several online resources. The model jointly performs content selection and surface realization by learning representations of individual characters and their combinations in and across poem lines. Sivasurya Santhanam [10] implemented a text generator based on The Lord of the Rings dataset. The paper discussed the necessity of contextual information application in language models training for tasks related to text generation. LSTM networks were selected as the language model for the observed use case. Several methods were tried for context extraction, and in the midst of these methods, contexts extracted from groups of words in vector spaces of words worked the best. They use cosine similarity measures to calculate the semantic proximity between the sentences generated and the context provided as an evaluation method. Van-Khanh Tran and Le-Minh Nguyen introduced a GRU-based gate mechanism generator in [11], which included a refinement gateway for semantically refining the native input words. The GRU cell receives the refined entries that include significant information. Samuel Ronnqvist, Jenna Kanerva, Tapio Salakoski, and Filip Ginter in [12] have applied the BERT model on several spots, among them the generation of text, a sequence of symbols [MASK] is generated. BERT is used for several iterations to generate new subwords at random individual positions of that sequence until the maximum number of iterations (500 by default), or convergence is reached. Xing Wu, Shangwen

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Lv, Liangjun Zang, Jizhong Han, and Songlin Hu1 proposed a data-increasing approach based on BERT in [13], which improves performance and can be easily used to both classifiers and convolutional or recurrent neural networks. Jieh-Sheng Lee and Jieh Hsiang used GPT-2 to generate patent claims [6] by fine-tuning the released 345M medium version, and they observed that GPT-2 could be a great method to invent reinforcement.

3 Methodology Before beginning the presentation of our methodology, we start by defining the key elements of our approach that justify our technological choices. 3.1 Data To train our children’s storytelling generator, it is necessary to prepare a specific and good-quality dataset. For this reason, we explain in the subsequent step our Data source. • Data recovery Before starting our process, it’s important to have good data quality, the best way to have verified data is to collect them from reliable resources. There are several methods to do that, we chose in our case to collect a set of children’s stories from tellmeastorymom resource. “It is a fascinating space for stories & interesting illustrations. Especially making it’s easier for busy parents who are trying to figure out a story with good morals. A good story even us adults enjoy” (https://www.tellmeast orymom.com/). • Data preprocessing The dataset could contain different kinds of errors; this will produce a bad result. To avoid this problem, we are starting the process by preprocessing which consists of application some operations specially preprocessing and cleaning up of data to make it more convenient to learn properly and efficiently. The preprocessing that we carried out consists of: • • • • •

Make all characters in lowercase Delete numbers Remove currency symbols Remove all punctuation Remove all special characters

3.2 GPT-2 GPT-2 represents the abbreviation of “Generative Pretrained Transformer 2”: – The model that was trained to predict chips in a sequence of unsupervised chips is referred to as “generative”. To put it differently, the model was given a set of raw textual data and asked to determine the text’s attributes to generate new text.

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– The term “pretrained” refers to OpenAI’s generation of the large and sophisticated language model, which they then fine-tuned for specific applications like machine translation. This is similar to Imagenet’s transfer learning, only it’s for NLP. This method of retraining gained a lot of traction in 2018, and it’s quite likely to be a trend in 2019. – Unlike an RNN, LSTM, GRU, or any other 3/4-letter acronym that comes to mind, “Transformer” means OpenAI used the transformer architecture. – “2” denotes this is not the first trying of this whole GPT thing out. 3.3 GPT-2 Vs BERT Rather than attempting to beat BERT at its own game, the second iteration of GPT, termed GPT-2, modifies the game’s core essence. BERT has been trained to excel in fill-in-the-blanks (Fig. 1), whereas GPT-2 has been trained to excel at trial writing (Fig. 2).

Fig. 1. BERT predicts missing (masked) words [14]

We choose gpt-2 model since it is: – Trained with an easy objective: to predict the subsequent word, taking into account all previous words in a text. – A huge language model with 1.5 billion parameters was trained on an 8 million web page dataset. – It is possible to generate conditional synthetic text samples of exceptional quality due to the variety of the training data set. – Using an arbitrary text as input, the model can generate long texts with precision similar to that of a human.

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Fig. 2. GPT-2 predicts the next word [14]

3.4 Simple Transformers Library The Simple Transformers library is conceived like a wrapper around the famous Transformers library by Hugging face to allow easy access and use Transformer models to the public. The idea was to make it as simple as possible, which means abstracting away many implementations and technical details. The implementation of the library can be found on Github. Many tasks are available in this library, such as: • • • • • • • • •

Sequence Classification Token Classification (NER) Question Answering Language Model Fine-Tuning Language Model Training Language Generation T5 Model Seq2Seq Tasks Multi-Modal Classification – Conversational AI. – Text Representation Generation.

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4 Implementation and Results 4.1 Experiment Setup This section describes the computer environment, the basic code, and the GPT-2 model sizes that were considered. In terms of the number of parameters in the neural network, the four sizes of GPT2 models produced by OpenAI are 117M, 345M, 762M, and 1.5B. OpenAI released the largest version 345M as staged following the original 117M model, based on an intermediate update. Simultaneously, OpenAI has shared the 762M and 1.5B versions with a restricted group of partners in the fields of security, all of which are working to increase the company’s preparation for significant language models. We discovered early on in our project that Model 117M is adequate to produce impressive outcomes. If computer resources are limited, future researchers will be able to start with a modest model. Generally speaking, the larger the model, the better the result. Our investigations in this paper are on the average model 345M at the limit of the computer environment. In computational terms, we use Google Colab for GPU and CPU capabilities. Colab is a Jupyter notebook environment that is free and runs absolutely within the cloud, but it has a boundary of twelve continuous hours by session. For certain of our experiments, it is good enough. For training tasks that need more than we save the TensorFlow control points from in Google Storage and restore them for continuing training for the next session. A manual effort is required to initialize a novel one on Colab. Although it takes time, such a solution nearly can make it simple for researchers to test various experiments. The GPU available on Colab is the NVIDIA Tesla T4 equipped with approximately 15 GB of available memory. The size of the memory is sufficient to refine the layers of the small model, but it is not for the medium model. A public workaround is to use an efficient gradient in memory so that it will run on Colab. 4.2 Implementation The implementation was done using the Simple Transformers library. The creation steps are: 1. Data preparation: load data set, and group them into training data and test data 2. Model creation: change the default parameters by specifying the model name (gpt-2) 3. Tales generation: the user will enter a seed text as the start of the story, and the model will predict a tale. 4.3 Results After creating the model, it’s necessary to test it to ensure that the proposed model produces the desired behavior. Figures 3 and 4 present two examples of history generated by the proposed method based on different seed text: – The child was – I hadn’t seen Oliver

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Fig. 3. The generated tale from seed text: the child was

Fig. 4. The generated tale from seed text: i hadn’t seen oliver

5 Evaluation The evaluation step is very essential which allows us to know the quality of the system and the difference between real writing and automatic generation. Human evaluation is necessary, so when reading the generated tale it is lexically perfect, there is no mistake. Contextually, the generated text follows perfect semantics, which resembles a story written by a human. We apply also an automatic evaluation using current metrics such as Bleu and ROUGE. BLUE is a geometric mean of the precisions n-grams and brevity penalty, i.e. penalty based on length to avoid short sentences as compensation for unsuitable translation. BLEU(n) = exp

N 

wn log pn X BP,

(1)

n=1

where wn is weights for different pn , By calculating words or lexical precision, BLEU depicts two elements of translation quality: adequacy and fluidity.

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Table 1. Evaluation results using Bleu and ROUGE metrics Metric

Value

Ratio

0.97

BLEU-1

0.89

BLEU-2

0.85

BLEU-3

0.81

BLEU-4

0.77

ROUGE

0.88

The BLUE method in our case aims to compare the generated story and a reference one, the value obtained for 1-g is equal to 0.89, which means that the tale written by our generator looks 89% like a tale written by a human. This evaluation presents a very interesting and encouraging result (Table 1) which could be the basis of several research on this subject.

6 Conclusion In this work, we fine-tuned GPT-2 to automatic story generation, especially children’s stories. The appearance of various Transformer models such as GPT-2 represents a paradigm shift and a great opportunity for text generation. We implemented the GPT-2 model to generate children’s stories having a good quality in terms of lexical mistakes and semantics. Our qualitative analysis indicates hopeful perspectives for prospective study, for example: fine-tuning a large pre-trained model, or raising a pre-trained model from patent corpus and scratch. The presented approach represents a real step in the automatic children’s stories generation field, through its intelligent method as well as the results obtained, and it will provide a solid basis for the learning of reading knowledge.

References 1. Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802. 05365 (2018) 2. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018) 3. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019) 4. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) 5. Wang, A., Cho, K.: BERT has a mouth, and it must speak: Bert as a markov random field language model. arXiv preprint arXiv:1902.04094 (2019) 6. Lee, J.S., Hsiang, J.: Patent claim generation by fine-tuning OpenAI GPT-2. World Patent Inf. 62, 101983 (2020) 7. OpenAI, GPT-2 source code (n.d.). https://github.com/openai/gpt-2. Accessed 02 June 2019

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8. Sutskever, I., Martens, J., Hinton, G.E.: Generating text with recurrent neural networks. In: ICML (2011) 9. Zhang, X., Lapata, M.: Chinese poetry generation with recurrent neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 670–680, October 2014 10. Santhanam, S.: Context based text-generation using LSTM networks. arXiv preprint arXiv: 2005.00048 (2020) 11. Tran, V.K., Nguyen, L.M.: Semantic Refinement GRU-based neural language generation for spoken dialogue systems. In: Hasida, K., Pa, W. (eds.) PACLING 2017. CCIS, vol. 781, pp. 63–75. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8438-6_6 12. Rönnqvist, S., Kanerva, J., Salakoski, T., Ginter, F.: Is multilingual BERT fluent in language generation? arXiv preprint arXiv:1910.03806 (2019) 13. Wu, X., Lv, S., Zang, L., Han, J., Hu, S.: Conditional BERT contextual augmentation. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11539, pp. 84–95. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22747-0_7 14. How to Build OpenAI’s GPT-2: The AI That Was Too Dangerous to Release. https://blog.flo ydhub.com/gpt2/. Accessed 24 June 2021 15. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318, July 2002 16. Munkova, D., Hajek, P., Munk, M., Skalka, J.: Evaluation of machine translation quality through the metrics of error rate and accuracy. Procedia Comput. Sci. 171, 1327–1336 (2020) 17. Manaswini, S., Deepak, G., Santhanavijayan, A.: Knowledge driven paradigm for anomaly detection from tweets using gated recurrent units. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 145–154. Springer, Cham (2021). https://doi.org/10.1007/978-3030-73882-2_14 18. Timmi, M., Jeghal, A., EL Garouani, S., Yahyaouy, A.: The review of objectives, methods, tools, and algorithms for educational data mining. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 177–188. Springer, Cham (2021). https://doi.org/10.1007/978-3030-73882-2_17 19. Berrajaa, A., Ettifouri, E.H.: The recurrent neural network for program synthesis. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 77–86. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_8 20. Garouani, M., Chrita, H., Kharroubi, J.: Sentiment analysis of moroccan tweets using text mining. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 597–608. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_54

Prediction of Olive Cuttings Greenhouse Microclimate Under Mediterranean Climate Using Artificial Neural Networks Sanae Chakir1(B) , Adil Bekraoui1 , El Moukhtar Zemmouri2 , Hassan Majdoubi3 , and Mhamed Mouqallid1 1 Department of Energy, ENSAM, Meknes, Morocco

[email protected]

2 Department of Mathematics and Informatics, ENSAM, Meknes, Morocco 3 Laboratory of Scientific Research and Pedagogical Development, CRMEF Meknes, Meknes,

Morocco

Abstract. The Vegetative propagation by cuttings in a greenhouse is a fundamental step in the olive tree production chain. This technique is the best response and most useful production method worldwide, including in the Mediterranean regions. This preliminary step requires some environmental conditions that require a demanding permanent control. In the present study, the ANNs model was developed to predict the parameters inside olive cuttings greenhouse. The prediction model consists of five input parameters with two hidden layers and one output layer (inside air temperature, soil temperature, or relative air humidity). The results show the linear relationships between the measured and predicted parameters with good performance. The correlation results show that some attributes have a high correlation while others are low. The prediction of the temperature and relative humidity of the greenhouse can bring substantial help to advanced climate control and plant productivity. Keywords: Artificial intelligence · Greenhouse · Olive cuttings · Microclimate

1 Introduction The olive is one of the essential fruit trees for the agricultural economy worldwide, especially in Mediterranean countries [1]. Propagation by cuttings in greenhouses is fundamental for the olive tree production chain. It is the first stage towards establishing olive cultivation or renovating existing orchards [2], and its effectiveness depends on the ability of the cuttings to develop adventitious roots. In this process, indoor climate control is a tool to improve the rooting of olive cuttings; it is essential to maintain a high relative air humidity and an ideal air temperature (between 25 and 27 °C), as well as an optimal substrate temperature (between 20 and 24 °C) [3, 4]. The greenhouse is a confined environment involving many physical mechanisms (mass balance and energy transfer). The greenhouse microclimate depends mainly on the outside environment [5]. The primary method to managing the temperature and humidity conditions is to use © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 63–69, 2022. https://doi.org/10.1007/978-3-031-02447-4_7

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ventilation, heating, and misting cooling systems. Predicting and understanding climatic parameters inside the greenhouse will help growers control this crop cultivation. Advances in computing, especially the stationary capacity of calculators, have been used extensively to model complex systems, as they can predict how systems respond to changes in environmental variables. Artificial neural networks are one of the most popular modelling techniques used extensively over the past decades [6]. ANN is a network based on the human brain and nervous system that allows learning and generalisation from representative data to produce meaningful solutions. ANN has a unique characteristic; it can establish empirical relationships between dependent and independent variables extracting subtle information and complex knowledge from representative data sets. These relationships can be found without assuming any mathematical representation of the phenomena. ANN techniques offer several advances; for example, they can handle noisy data, unlike regression models [7]. The calculation of the ANN starts by introducing an array of numbers, xi, into the input layer of the processing nodes. These signals then follow with the connections to every of the adjacent layer nodes and may be inhibited or amplified by connectionspecific weights, wt. The adjacent layer nodes are summation devices for the incoming signals. The incoming signal is transformed into an output (Qj) within the processing units through a function. Equation (1) [8]: f (x) =

1 1 + e−x

(1)

The output f (x), in the Eq. (1), ranges between 0 and 1, and the output from the processing unit is then calculated as: Qj =

1 1 + e−



xj wj

(2)

This Qj is then transported to the next node layer with the weighted connections. The process is repeated until the signal reaches the output layer. The output signal can then be interpreted as the response of the ANN to the given input stimulus [8]. In the present study, an ANN model was used to predict the climatic parameters inside the greenhouse. Five distinct parameters were considered as inputs: outdoor air temperature, outdoor relative humidity, global solar radiation, wind direction, and wind speed, where output was the inside air temperature or soil temperature, or inside relative humidity. The result was validated using experimental data.

2 Materials and Methods 2.1 Prediction Model Architecture The prediction model consists of an MLP neural network with five inputs parameters, two hidden layers with ten neurons each, and one output (Fig. 2). After importing the dataset, attribute selection has been performed. That overall dataset has been split into two subsets (without using cross-validation): training (70%) and testing (30%). A neural network algorithm has been applied to train the model in the training dataset. Then in

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the testing phase, the model application and performance have been performed (Fig. 1).

Fig. 1. The proposed process.

Fig. 2. The architecture of the neural network model. Table 1. Outside and inside greenhouse parameters are used as input and output in the model. Input’s number

Parameter

Abbreviation

Unit

Input.1

Global solar radiation

RAD

w/m2

Input.2

Wind speed

WS

m/s

Input.3

Wind direction

WD

Degree

Input.4

Outdoor air temperature

T_EXT

°C

Input.5

Outdoor relative air humidity

RH_EXT

%

Output.1

Inside air temperature

T_INT

°C

Output.2

Soil temperature

T_SOIL

°C

Output.3

Inside relative air humidity

RH_INT

%

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The following Table 1 summarises the outside and inside greenhouse parameters used as input and output in the model. Table 2. The performance of the model in predicting inside greenhouse climatic parameters. T_INT prediction

RH_INT prediction

T_SOIL prediction

R2

0.977

0.825

0.922

RMSE

1.414 ± 0.000

1.696 ± 0.000

0.816 ± 0.000

MAE

1.045 ± 0.952

1.322 ± 1.062

0.637 ± 0.511

2.2 Recorded Data The experiment was performed in January 2020. The experimental greenhouse was a closed delata9 type tunnel. The dimensions of this greenhouse were 20.67 m in length, 9.52 m in width, and 2.95 m in maximum height. The greenhouse was located in central Morocco (longitude: 31°N, latitude: 5°N). A metrology station was installed outside the greenhouse to measure wind speed and direction, global solar radiation, outside air temperature, and relative humidity. The indoor air temperature and relative humidity were measured with model SHT35 sensors. The soil temperature was measured in the middle position with PT 100 sensors. 2.3 Evaluation of the Model The model’s evaluation was determined using the correlation coefficient (R2 ); this coefficient, presented in Eq. (3), is one of the most critical metrics for assessing the accuracy of prediction models with a magnitude ranging from 0 to 1. Values close to 1 imply highly correlated parameters and vice versa. The mean absolute error (MAE) (Eq. (4)) and the root mean square error (Eq. (5)) represent the error between measured and predicted data, and smaller values reflect better predictions [9]. ⎛ ⎞2   N  ¯ y − y y − y − i ⎠ R2 = ⎝   i=1 (3) N N 2 2 ¯) i=1 (yi − y i−1 (y − y− ) 1 N yi − y (4) MAE = i=1 N

1 N RMSE = (y − y)2 (5) i−1 i N With: 











yi : The measured inside parameter at the time i. y: The mean of the measured inside parameter. y: The predicted inside parameter by the model. y-: the mean of the predicted inside parameter. N: The number of data points in the dataset. 



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3 Results and Discussions

Fig. 3. Regression between predicted and the measured values by the ANN Model for the inside air temperature (a) inside relative air humidity (b) and soil temperature (c).

The model’s regression between the predicted and measured parameters are shown in Fig. 3. The results show good accord between the predicted and measured parameters. The model performance in predicting air temperatures, relative air humidity, and soil temperature inside the greenhouse is summarised in Table 2.

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To highlight the effectiveness of our model, a comparison of the R2 value between our study and other studies in which ANNs were used (Table 3). From this comparison, we can conclude that our model gives good results, even though the prediction is based just on the external variables. Table 3. The result of R2 for MLP neural network between different studies. This study

Taki et al. [10]

Singh et al. [11]

T_INT

0.977

0.76

0.971

RH_INT

0.825

–

0.952

T_SOIL

0.922

0.98

–

Table 4 shows the correlation between all attributes in our dataset. This correlation coefficient measures the relationship between two data attributes. The correlation value ranges from −1 to +1 and quantifies the direction and strength of the linear association between the two variables. Table 4. The correlation between all attributes in our dataset. Attributes

RAD

WD

WS

T_EXT

RH_EXT

T_INT

RH_INT

T_SOIL

WS

0.303

0.215

1

0.258

−0.184

0.268

−0.21

0.118

WD

0.238

1

0.215

0.16

−0.19

0.171

−0.036

0.022

T_SOIL

0.477

0.022

0.118

0.831

−0.711

0.817

−0.651

1

T_INT

0.880

0.171

0.268

0.958

−0.873

1

−0.849

0.817

T_EXT

0.801

0.16

0.258

1

−0.92

0.958

−0.783

0.831

RAD

1

0.238

0.303

0.801

−0.761

0.88

−0.772

0.477

RH_INT

−0.772

−0.036

−0.21

−0.783

0.786

−0.849

1

−0.651

The correlation results show that some attributes have a strong positive correlation, whereas some have a weak correlation. For example, The T_INT is positively correlated, with RAD and T_EXT, with the correlation coefficient value reaching 0.880 and 0.958, respectively, while the correlation coefficient between T_INT and WS is low (r = 0.268). By analysing this table, it can be concluded that the global solar radiation and the outside air temperature are the determining factors of microclimate greenhouse. On the other hand, for closed greenhouses type, the wind speed has no direct effect on climatic parameters greenhouse. Several studies confirm this result [5, 12].

4 Conclusions This research study has proposed an approach based on the ANN technique to predict indoor greenhouse parameters. The ANNs model presented five input parameters,

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two hidden layers, and one output. The inputs included RAD, WS, WD, T_EXT, and RH_EXT, while the T_INT was considered as output variable for the first time after the RH_INT, then the T_SOIL. The results showed good accord between the measured and predicted parameters. Also, in our case, the wind speed has no significant direct effect on greenhouse climate parameters. In contrast, global solar radiation and the outside air temperature substantially impact this microclimate. The study suggests that this developed ANNs model can serve as a helpful tool in managing temperature and relative humidity to reduce water and energy consumption.

References 1. Porfírio, S., Gomes Da Silva, M.D., Cabrita, M.J., Azadi, P., Peixe, A:. Reviewing current knowledge on olive (Olea europaea L.) adventitious root formation. Scientia Horticulturae 198, 207–226 (2016) 2. Schuch, M.W., Tomaz, Z.F.P., Casarin, J.V., Moreira, R.M., Silva, J.B.D.: Advances in vegetative propagation of Olive tree. Revista Brasileira de Fruticultura 41(2) (2019) 3. Sbay, H., Lamhamedi, M.S.: Guide pratique de multiplication végétative des espèces forestière: technique de valorisation et de conservation des espèces à usage multiples face aux changements climatiques en Afrique du nord. Royaume du Maroc, haut-commissariat aux eaux et forêts et à la lutte contre la désertification, centre de recherche forestière, pp. 1–34 (2015) 4. Goldammer, T.: Greenhouse Management: A Guide to Operations and Technology, 1st edn. Apex Publishers (2021) 5. Li, G., Tang, L., Zhang, X., Dong, J., Xiao, M.: Factors affecting greenhouse microclimate and its regulating techniques: a review. IOP Conf. Ser. Earth Environ. Sci. 167, 012019 (2018) 6. El Mghouchi, Y., Chham, E., Zemmouri, E., el Bouardi, A.: Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks. Build. Environ. 149, 607–622 (2019) 7. Nriagu, J.O. (ed.): Encyclopedia of Environmental Health, 5-Volumes Set, Reprint édn. Elsevier (2021). ISBN: 9780444522733 8. Pourghasemi, H.R., Gokceoglu, C.: Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier Gezondheidszorg 9. Genedy, R.A., Ogejo, J.A.: Using machine learning techniques to predict liquid dairy manure temperature during storage. Comput. Electron. Agric. 187, 106234 (2021) 10. Taki, M., Abdanan Mehdizadeh, S., Rohani, A., Rahnama, M., Rahmati-Joneidabad, M.: Applied machine learning in greenhouse simulation; new application and analysis. Inf. Process. Agric. 5(2), 253–268 (2018) 11. Singh, V.K.: Prediction of greenhouse micro-climate using artificial neural network. Appl. Ecol. Environ. Res. 15(1), 767–778 (2017) 12. Baytorun, N.A.: Climate Control in Mediterranean Greenhouses. IntechOpen (2018)

HSCRD: Hybridized Semantic Approach for Knowledge Centric Requirement Discovery Rituraj Ojha1 and Gerard Deepak2(B) 1 Department of Metallurgical and Materials Engineering, National Institute of Technology,

Tiruchirappalli, Tiruchirappalli, India 2 Department of Computer Science and Engineering, National Institute of Technology,

Tiruchirappalli, Tiruchirappalli, India [email protected]

Abstract. There is a necessity for a requirement recommendation system that can eliminate the old tedious recommendation discovery process. In this paper, a hybrid semantic approach for knowledge-centric requirement discovery has been proposed. The proposed HSCRD framework takes stakeholders’ interactions and preprocesses it. The individual keywords obtained are input into the TF-IDF model to yield the documents from the Requirement Specification Document Repository. The index words of these documents are extracted and are linked with Upper Domain Ontologies. The ontologies are grown by computing the semantic similarity measures, namely, Jaccard similarity and SemantoSim similarity. These grown ontologies are submitted to the Wikidata API, Freebase API, and DBPedia API to yield the Enriched Domain Ontologies. The Enriched Domain Ontologies, as features, are passed into Bi-gram and Tri-gram models. Using Bi-gram and Tri-gram of these features, input is given to the Bagging model for classification. Bagging is chosen with SVM and a highly complex Decision Tree classifier. Finally, the recommended documents and ontologies features are passed individually with respect to the classified documents through TF-IDF and semantic similarity pipeline in order to recommend the individual requirements. The proposed HSCRD achieves the highest average Accuracy with the Precision of 93.18%, Recall of 95.69%, Accuracy of 94.43%, F-Measure of 94.42%, a low FDR of 0.07, and a very high nDCG of 0.96. Keywords: Ontologies · Requirement discovery · Requirement engineering · Semantic similarity

1 Introduction Requirement engineering is a process in software engineering that consists of several activities, including defining the requirements, documenting them, and maintaining them. Requirement elicitation is one of the main activities of requirement engineering. It includes gathering information about the project from customers, interviews, manuals, similar software, and other project stakeholders. The process may appear simple, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 70–79, 2022. https://doi.org/10.1007/978-3-031-02447-4_8

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but it has several underlying challenges. Firstly, the customer may not properly understand their needs as per the technologies available. They might get confused when the software is complex. Secondly, they might face difficulty communicating the specific requirements with the software engineer present. Lastly, the customer may describe unnecessary details or omit important information during requirement elicitation. There is a need for a requirement recommendation system to automate the requirement engineering process by eliminating the long old process of tedious meetings. Also, the ontologies and real-world knowledge bases can be mapped to yield the requirements with more accuracy. To align the recommended requirements to stakeholders’ needs, the recommender system should analyze previous stakeholders’ interactions and old validated requirement engineering reports. Motivation: Requirement analysis is quite tedious, and recommending microrequirements for a specific set of stakeholders’ requirements is definitely a tedious task. Therefore, mapping the requirements of similar stature via ontologies from the already existing and validated requirement engineering and requirements analysis reports is the best-chosen practice. Instead of returning the entire requirements, semantic infused learning based on ontology-based criteria mapping and learning based on the users’ opinions on these ontologies is one of the best-suited methods. Contribution: HSCRD, which is a hybridized semantic approach for knowledge-centric requirement discovery, is proposed. The stakeholders’ interactions are recorded and preprocessed, and the individual keywords are input using the TF-IDF model to yield the documents from the Requirement Specification Document Repository. The index words of these documents are extracted and are linked with Upper Domain Ontologies. The ontologies are grown by computing the semantic similarity measures, namely, Jaccard similarity and SemantoSim similarity. These grown ontologies are submitted to the Wikidata API, Freebase API, and DBPedia API to yield the enriched domain ontologies. The enriched domain ontologies, as features, are passed into Bi-gram and Tri-gram models. Using Bi-gram and Tri-gram of these features, input is given to the Bagging model for classification. Bagging is chosen with SVM and a highly complex Decision Tree classifier. Finally, the recommended documents and ontologies features are passed individually with respect to the classified documents through TF-IDF and semantic similarity pipeline in order to recommend the individual requirements. The values of metrics like, Precision, Accuracy, Recall, F-Measure, and nDCG are increased. Organization: The flow of the remaining paper is as follows. A condensed summary of the related works is provided in Sect. 2. Section 3 depicts the architecture of the proposed system. Section 4 describes the implementation and the evaluation of performance. The conclusion of the paper is presented in Sect. 5.

2 Related Works Some of the work related to the proposed approach is discussed in this section. AlZu’bi et al. [1] have proposed a requirement recommender system which is based on Apriori algorithm. The algorithm helps in extracting rules from the user requirements which can

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be used to recommend requirements to stakeholders. Elkamel et al. [2] have proposed a UML based recommender system which suggests items based on the output UML class. The UML classes are classified from UML classes diagrams and contents are recommended to users based on the classified classes. Williams [3] has proposed a recommendation system for security requirement elicitation. The paper presents the ontology-based recommender system and also conducts a study on stakeholders based on the proposed system. Avdeenko and Pustovalova [4] have proposed a methodology for assisting the requirement specification through the help of ontologies. Classes of the ontology are requirement types and instances are requirement statements. Rajagopal et al. [5] have proposed an approach for requirement elicitation during software development. Their model helps in training the stakeholders about the capacity of the software and hardware. The model also gathers the stakeholders’ conversations to extract keywords and the keywords are used for generating the requirements. They have also used methodologies namely, Quality Function Deployment and Capability Maturity Model to evaluate their results. Shambour et al. [6] have proposed a recommender system for requirement elicitation based on collaborative filtering. Their proposed model reduces the amount of time taken to go through the big requirement repositories by extracting the reusable and important requirements. In [7–20] several approaches in support of the literature of the proposed model have been depicted.

3 Proposed System Architecture

Fig. 1. Phase 1 of the proposed system architecture

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The architecture for the proposed system is depicted in Fig. 1 and Fig. 2. The stakeholders’ interactions are recorded and preprocessed using Tokenization, Lemmatization, stop word removal, and Named Entity Recognition (NER). Tokenization is a process of splitting the texts in the dataset into pieces called tokens. Byte Pair Encoding (BPE) is used for the tokenization process. Lemmatization involves grouping together several inflected kinds of the same word so that they can be analyzed as a single term. During stop word removal, the common ubiquitous words are removed as they add no value for the analysis and only increase the dimension of the feature set. NER is the process of finding and categorizing the data or entity into predefined categories. After preprocessing, the individual keywords are input into the TF-IDF (Term Frequency-Inverse Document Frequency) model. The TF-IDF helps in finding the importance of a term in the document. It is used to yield the documents from the Requirement Specification Document Repository which uses versioning and control mechanisms. One of the famous Software Requirements Specification (SRS) repositories is SVN. Every organization has their own or use a requirement specification document repository for versioning and control, which is known as change management or configuration management. For configuration management and change management, the requirement document will be versioned and from that repository, the final version of each document is yielded based on the TF-IDF score. TF-IDF uses the concept of the frequency of occurrence and rarity of occurrence of the word over a document corpus.

Fig. 2. Phase 2 of the proposed system architecture

From the yielded documents, the index words of these documents are extracted and are linked with Upper Domain Ontologies or the Core Domain Ontologies. The indexes are yielded from the documents, by taking keywords from each page of the document and by giving frequent and rare terms more importance. Static Upper Ontologies which are

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relevant to the domain of the stakeholders’ interaction are passed into the framework. So, the ontologies are grown by computing the semantic similarity. The semantic similarity measure used is Jaccard Similarity and SemantoSim Similarity. Parallelly, these grown ontologies are submitted as input to the Wikidata API, Freebase API, and DBPedia API to yield the enriched domain ontologies. The Enriched Domain Ontologies are passed into Bi-gram and Tri-gram models. Using Bi-gram and Tri-gram of these features, input is given to the Bagging model for classification. The indexed dataset of the Software Requirement Document is passed as an input to the Bagging classifier, which has two independent classifiers namely, SVM and Decision Tree (DT), to yield the classified documents. From these classified documents, the document has been ranked. These documents are ranked based on the priority of the classification. From the recommended documents, the features are extracted by employing TF-IDF. The recommended documents and ontologies features are passed individually with respect to the classified documents using TF-IDF and semantic similarity pipeline in order to recommend the individual requirements. Semantic similarity is computed between the contents in the individual requirements and ontology as features. If an individual requirement has more than one keyword, then the combined average of the semantic similarity of all keywords is used. The semantic similarity model used is Jaccard similarity and SemantoSim similarity. The Jaccard similarity coefficient is used for measuring the similarity as well as the diversity of the given sample sets. Jaccard similarity is computed using the formula given in Eq. (1). The SemantoSim measure was proposed by Church & Hanks and is represented by Eq. (2). |A ∩ B| |A ∩ B| = |A ∪ B| |A| + |B| − |A ∩ B|   p(x, y)log p(x, y) + pmi(x, y)   SemantoSim(x, y) = log p(y, x)] + [p(x) ∗ p(y) J (A, B) =

(1) (2)

4 Implementation and Performance Evaluation The dataset used was Software Requirements Dataset from Kaggle. The implementation is done using Python3.8 as the programming language. The operating system used was Windows 10 and Google Colab environment. The backend is used in MySQL Lite. The processor was an i7 Intel core processor with 32 GB of RAM, and 8 GB of Nvidia graphics card. The performance of the proposed HSCRD framework is measured by considering Precision, Accuracy, and Recall. Other measures including False Discovery Rate (FDR), F-Measure, and Normalized-Discounted Cumulative Gain (nDCG) are also used. The performance is evaluated for 6141 queries and their ground truth has been collected. The dataset used was Software Requirements Dataset from Kaggle. Standard formulations for Precision, Recall, Accuracy, F-Measure, FDR, and nDCG in terms of a recommendation system were used. Table 1 presents the performance comparison of the proposed HSCRD approach with other approaches. In order to compare the performance of the proposed HSCRD is

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Table 1. Comparison of performance of the proposed HSCRD approach with other approaches. Search technique

Average precision %

Average recall %

Accuracy %

F-measure

FDR

nDCG

RSRD [7]

86.63

87.69

87.16

87.16

0.14

0.87

Stakeholder + Collaborative Filtering [8]

83.44

86.84

85.14

85.11

0.17

0.82

Content-Based Filtering

84.65

88.25

86.45

86.41

0.16

0.79

Stakeholder Interactions + UML Based Learning

88.16

90.13

89.15

89.13

0.12

0.91

Proposed HSCRD

93.18

95.69

94.43

94.42

0.07

0.96

compared with baseline models namely, RSRD [7], Stakeholder combined with Collaborative Filtering [8], Content-Based Filtering, and Stakeholder Interactions combined with UML Based Learning. The baseline models are evaluated with an exact number of queries in the same environments as the HSCRD approach and the average values of the obtained metrics for 6141 queries for each model are tabulated in Table 1. The RSRD achieves the average Precision of 86.63%, Recall of 87.69%, Accuracy of 87.16%, FMeasure of 87.16%, FDR of 0.14, and nDCG of 0.87. The Stakeholder combined with the Collaborative filtering approach achieves the average Precision of 83.44%, Recall of 86.84%, Accuracy of 85.14%, F-Measure of 85.11%, FDR of 0.17, and nDCG of 0.82. The Content-based filtering approach achieves the average Precision of 84.65%, Recall of 88.25%, Accuracy of 86.45%, F-Measure of 86.41%, FDR of 0.16, and nDCG of 0.79. The Stakeholder Interactions combined with the UML-based learning approach achieves the average Precision of 88.16%, Recall of 90.13%, Accuracy of 89.15%, F-Measure of 89.13%, FDR of 0.12, and nDCG of 0.91. The proposed HSCRD approach achieves the highest average Precision of 93.18%, Recall of 95.69%, Accuracy of 94.43%, F-Measure of 94.42%, a low FDR of 0.07, and a very high nDCG of 0.96. Figure 3 represents the Precision% vs the Number of Recommendations for each model. The content-based filtering approach does not yield a very high average accuracy mainly because content-based filtering alone is not sufficient for recommending the requirements. Based on the contents of the user query or the requirement domain, it is highly difficult and cumbersome to filter out based on matching keywords and a single strategy namely, content-based filtering only. The approach does not learn any relations, rather it filters directly based on the query or domain of the requirement. Moreover, it fails to unveil micro-requirements and fragmented requirements and it yields a very low nDCG among the baseline models since it does not pave the way to include auxiliary knowledge or background knowledge which leads to low diversity.

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Stakeholder Interaction with UML-based learning is quite better than other approaches. This is mainly because stakeholder interactions tend to cover a lot of details that are supplied to the system apart from the query. Detailed stakeholders’ interactions mainly highlight the exact needs of the client’s requirements. The UML diagrams are divided into several diagrams like class diagrams, sequence diagrams, object diagrams, component diagrams, etc., and when these diagrams are learned, the implementation details and domain-based details also come into the model. As a result, combining both stakeholders’ interaction and UML-based learning helps the model to discover the newer requirements along with micro-requirements. But still, there is a scope of improvement of the average precision, accuracy, recall, and F-measure of this model.

Fig. 3. Precision% Vs number of recommendations

In the Stakeholder with Collaborative filtering approach, stakeholder interaction tends to cover a lot of information apart from the query based on the exact requirements of the clients. In this approach, collaborating filtering is a bad choice because the rating matrix has always been deduced. Deriving this rating matrix or criteria matrix requires ratings based on the stakeholders’ interactions for the requirements which is a challenging task and not feasible in practicality. In the RSRD approach, the binary k-nearest neighbors approach with augmenting profiles of several stakeholders’ collaboration are major sources of this approach. They yield less average accuracy because they are relying only on the user profiles which makes the process very tedious and even the difference of opinions among the individual stakeholder users and if it differs in the overall requirement assessment, there is a conflict which happens. The use of only binary KNN is not enough and thus, leads to a need for

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a much better recommendation approach. Also, the maintenance of a product-by-feature matrix and a feature itemset graph makes it quite tedious and computationally expensive. The proposed HSCRD approach has a much better performance compared to the baseline models. There are several reasons for that. Firstly, stakeholders’ interactions are given very high priority along with several versions of the requirements specification document are included in the environment. In versioning, the evolution from one form of requirement to the final version of requirement is also traced and included for inference in the document by means of TF-IDF. TF-IDF ensures that the documents are ranked initially based on the rarity and frequency of occurrence of terms within the document and across the corpus. Secondly, document indexes are formulated and the ontologies are grown by using standard verified lightweight Upper Domain Ontologies. Fourthly, three distinct knowledge bases are used to incorporate real-world knowledge. Fifth, the Bi-gram and Tri-gram are used in order to increase the lateral density of words. Bagging is chosen with SVM and a highly complex Decision Tree classifier. The computation complexity is balanced and the relevance of the classified documents yielded is much higher. Lastly, the document is ranked and the individual requirements are recommended from the documents by semantic similarity computation and TF-IDF. SemantoSim Similarity has a threshold of 0.75 and Jaccard Similarity has a threshold of 0.5. TF-IDF takes care of macro-requirements from document contents and semantic similarity takes care of the similarity of individual micro-fragmented requirements. Hence, this is a much better approach than the existing approaches.

5 Conclusions Requirement engineering is one of the important steps in software engineering which can be long and tedious. To improve this process, HSCRD is proposed which is a hybridized semantic approach for knowledge-centric requirement discovery. The stakeholders’ interactions are recorded and preprocessed and the individual keywords are input using the TF-IDF model to yield the documents from the Requirement Specification Document Repository. The index words of these documents are extracted and are linked with Upper Domain Ontologies. The ontologies are grown by computing the semantic similarity measures namely, Jaccard similarity and SemantoSim similarity. These grown ontologies are submitted as input to the Wikidata API, Freebase API, and DBPedia API to yield the enriched domain ontologies. The enriched domain ontologies, as features, are passed into Bi-gram and Tri-gram models. Using Bi-gram and Tri-gram of these features, input is given to the Bagging model for classification. Bagging is chosen with SVM and a highly complex Decision Tree classifier. Finally, the recommended documents and ontologies features are passed individually with respect to the classified documents through TF-IDF and semantic similarity pipeline in order to recommend the individual requirements. The proposed HSCRD approach achieves the average Precision of 93.18%, Recall of 95.69%, Accuracy of 94.43%, F-Measure of 94.42%, a low FDR of 0.07, and a very high nDCG of 0.96. The overall accuracy of the proposed approach is much better than the existing approaches.

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References 1. AlZu’bi, S., Hawashin, B., EIBes, M., Al-Ayyoub, M.: A novel recommender system based on apriori algorithm for requirements engineering. In: 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 323–327 (2018) 2. Elkamel, A., Gzara, M., Ben-Abdallah, H.: An UML class recommender system for software design. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8 (2016) 3. Williams, I.: An ontology based collaborative recommender system for security requirements elicitation. In: 2018 IEEE 26th International Requirements Engineering Conference (RE), pp. 448–453 (2018) 4. Avdeenko, T., Pustovalova, N.: The ontology-based approach to support the completeness and consistency of the requirements specification. In: 2015 International Siberian Conference on Control and Communications (SIBCON), pp. 1–4 (2015) 5. Rajagopal, P., Lee, R., Ahlswede, T., Chiang, C.C., Karolak, D.: A new approach for software requirements elicitation. In: Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, pp. 32–42 (2005) 6. Shambour, Q.Y., Abu-Alhaj, M.M., Al-Tahrawi, M.M.: A hybrid collaborative filtering recommendation algorithm for requirements elicitation. Int. J. Comput. Appl. Technol. 63(1–2), 135–146 (2020) 7. Hariri, N., Castro-Herrera, C., Cleland-Huang, J., Mobasher, B.: Recommendation systems in requirements discovery. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 455–476. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_17 8. Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures: a large-scale study in the Orkut social network. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 678–684 (2005) 9. Deepak, G., Gulzar, Z., Leema, A.A.: An intelligent system for modeling and evaluation of domain ontologies for Crystallography as a prospective domain with a focus on their retrieval. Comput. Electr. Eng., 107604 (2021) 10. Roopak, N., Deepak, G.: OntoKnowNHS: ontology driven knowledge centric novel hybridised semantic scheme for image recommendation using knowledge graph. In: Iberoamerican Knowledge Graphs and Semantic Web Conference, pp. 138–152 (2021) 11. Ojha, R., Deepak, G.: Metadata driven semantically aware medical query expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds.) KGSWC 2021. CCIS, vol 1459, p. 223. Springer, Cham (2021). https://doi.org/10.1007/978-3-03091305-2_17 12. Yethindra, D.N., Deepak, G.: A Semantic approach for fashion recommendation using logistic regression and ontologies. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1–6. IEEE (2021) 13. Adithya, V., Deepak, G.: HBlogRec: a hybridized cognitive knowledge scheme for blog recommendation infusing XGBoosting and semantic intelligence. In: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–6. IEEE (2021) 14. Surya, D., Deepak, G., Santhanavijayan, A. (2021). KSTAR: a knowledge based approach for socially relevant term aggregation for web page recommendation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 555–564. Springer, Cham. https://doi.org/10. 1007/978-3-030-73882-2_50

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15. Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In: 2021 7th International Conference on Web Research (ICWR), pp. 232–237. IEEE (2021) 16. Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72 17. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019) 18. Deepak, G., Priyadarshini, J.S.: Personalized and Enhanced Hybridized Semantic Algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput. Electr. Eng. 72, 14–25 (2018) 19. Deepak, G., Santhanavijayan, A.: UQSCM-RFD: A query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Comput. Appl., 1–25 (2021) 20. Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft Comput. 25(13), 8337–8355 (2021). https://doi.org/10.1007/s00500-021-057 56-8

The Challenges and Opportunities for Developing the Use of Data and Artificial Intelligence (AI) in North Africa: Case of Morocco Mohamed Oubibi1(B) , Yueliang Zhou2 , Ayoub Oubibi3 , Antony Fute1 , and Atif Saleem1 1 College of Teacher Education, College of Education and Human Development,

Zhejiang Normal University, Jinhua 321004, Zhejiang, China [email protected] 2 Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, Zhejiang, China 3 CSA Paris, 92400 Paris, France

Abstract. Artificial intelligence (AI) represents a significant potential for the African continent. AI can drive progress, development, and democratization governments properly handle the challenges. It can boost productivity growth by extending opportunities in crucial African development areas such as agriculture, healthcare, financial services, and public services. AI will enable employees, entrepreneurs, and enterprises to compete worldwide and be at the vanguard of economic development by providing access to high-quality digital tools. However, the roadblocks necessitate firm policy answers. AI will need significant changes for workers, employers, businesses and open new ethical questions that need thoughtful responses. Higher constraints specific to Africa, such as network limits, educational institution readiness, and the availability of digital data, exacerbate the ethical issues. Africa must make aggressive attempts to address its problems; however, if it succeeds, it will catch up to countries that have previously taken steps to improve AI. These efforts will be complicated, but the road ahead is clear. The government’s effectiveness will be determined by its capacity to facilitate collaboration among all stakeholders, including state and civil society, academics, industry, and national and international stakeholders. Keywords: Artificial Intelligence · Business Intelligence · Big Data · Open data · Data protection

1 Introduction The increasing availability of massive volumes of data, coming from the ability to transform information and recent gains in processing power digitally, has enabled recent developments in artificial intelligence (AI). Artificial intelligence can support new tools in practically every aspect of program analysis and software development [1]. As a result, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 80–90, 2022. https://doi.org/10.1007/978-3-031-02447-4_9

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employing a new generation of artificial intelligence technologies, such as those based on machine learning, to overcome the bottlenecks of old methodologies has become critical [2]. Replicates human intelligence processes, increasing the likelihood of successfully solving issues [3]. Artificial intelligence is the techniques and theories for creating intelligent machines/systems. It is an essential aspect of the technology industry since it allows for low-cost data collection and analysis in a safe working environment [4]. Artificial intelligence has a wide range of applications, including natural language processing, problem-solving, strategy formulation, and so on. It can change the objects to suit its needs [5]. It refers to the duplication of human intellect in computers that have been trained to think and act like humans and any device that demonstrates human-like characteristics such as learning and problem solving [6]. The ability of artificial intelligence to rationalize and execute operations that have the best likelihood of reaching a particular goal is its most valuable feature. Intelligence can be separated into two sorts in this scenario: • The simulation of human intelligence. • The resolution of problems that far exceed human capacity. AI transforms economies worldwide as steam engines and electricity did previously. Despite this, Europe, Asia, and North America account for most AI development activity, expertise, professionals, and funds. Surprisingly, compared to other countries, Africa appears to be a better playground for Artificial Intelligence, owing to fewer legacy technology systems, which frequently hinder the rate of AI adoption in other continents. It fosters a vibrant image and portrays Morocco of the future as a global leader in the interactive virtual displays and e-learning industries [7]. Once Morocco’s Ministry of Education decided to halt face-to-face studies as part of preventative measures to avoid the expansion of the Coronavirus, the alternative of distance learning was implemented, and the rest of the program of information, counseling, and guidance services started to be paid for the benefit of distant students as well. As a result, numerous regional and central administrative structures have been engaged. Those responsible for guidance have organized a group of inspectors, counselors, academic institution directors, and other interveners to ensure the continuity of distance school, vocational, and university assistance in this unusual situation. 1.1 Understanding of Artificial Intelligence The majority of people associate artificial intelligence with robots. This is because bigbudget movies and books depict human-like machines wreaking global havoc. In reality, nothing could be further from the truth. Artificial intelligence is founded on the idea that human intelligence can be defined so that a computer can duplicate it and do tasks that range from simple to sophisticated. Artificial intelligence’s learning, reasoning, and perceiving aims can provide new tools in virtually every program analysis and software development engineering [1].

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AI is constantly evolving to benefit a wide range of sectors. Machines utilize a multidisciplinary method incorporating mathematics, computer science, linguistics, psychology, and other disciplines. Examples of the use of Artificial Intelligence: Automatic translation; Automatic dialogue and dialogue processing; Image processing and facial recognition; Opinion Mining.; Decision support and data science. 1.2 Categorization of Artificial Intelligence There are two sorts of artificial intelligence: basic and smart. Basic artificial intelligence is a system designed to complete a single task. Video games, such as the chess example above, and personal assistants, such as Amazon’s Alexa and Apple’s Siri, are examples of basic AI systems. You ask the helper a question, and it responds. According to the smart Artificial Intelligence Systems, smart artificial intelligence systems can perform tasks that are regarded as human-like. These systems are frequently significantly more complicated and demanding. As an example Fig. 1 of the AI system, IBM developed an approach to help companies create their own AI systems based on a non-linear model based on a continuous loop consisting of three stages that interact at all times: Data, Train and Inference.

Fig. 1. The three stages of the IBM DTI model

1.3 Artificial Intelligence Statistics According to a World Economic Forum survey, by 2025, robots will perform 52% of current human tasks (worldwide), and by 2022, 54% of the workforce will require retraining. Figure 2 shows the evolution of the number of the working between robots and humans. By 2025, 75 M jobs will be converted, and 133 M New jobs will be created and robots will take an important part of our daily lives.

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EvoluƟon of the number of working hours 80

71 58

60

52

48

42 40

29

20 0 2017

2022 Robots

2025

Humains

Fig. 2. The evolution of the number of working hours

1.4 Artificial Intelligence in Africa Data Science Africa (DSA), a non-governmental organization established in Kenya that promotes the affordability, wider deployment ability, and applicability of AI technologies in Africa [8, 9], has been operating since 2013. Through summer schools and workshops offered in Ethiopia, Ghana, Kenya, Nigeria, Tanzania, and Uganda, DSA provides a venue for AI practitioners and academics from around Africa to discuss and share information about the development and utility of AI. Over 200 employees have been trained in machine learning techniques and data science applications leveraging the internet of things (IoT) and social media data analytics as part of DSA’s accomplishments. As a result of these training outcomes, applications for agriculture, disaster management, and healthcare have been developed [8, 9]. WiMLDS (Women in Machine Learning and Data Science) has chapters in Algeria, Botswana, Kenya, Morocco, Nigeria, and Uganda to promote women in AI. Given the concerns voiced about the lack of diversity and gender bias in AI [8, 10], it is crucial in amplifying the voice and input of women in the growth of AI. In addition, in 2013 and 2016, IBM Research launched AI laboratories in Nairobi (Kenya) and Johannesburg (South Africa) [8, 11]. In 2019, Google followed suit and launched an AI lab in Accra, Ghana [8, 11]. 1.5 Artificial Intelligence in Morocco The majority of Moroccan companies have not yet started implementing artificial intelligence solutions. They are in the data preparation phase to facilitate their exploitation. Morocco is establishing the groundwork to become a pioneer in AI development in North Africa and the Middle East. ABI Research forecasts that 1,500 enterprises employed AI in 2018, with that figure likely to climb to over 56,000 by 2022. The sectors that have started to move towards AI as the Banking sector, Insurance, Telecom, Information technology, Agriculture, Industry. The challenge for AI in Morocco (like in the rest of the region) is how people use technology, giving rise to new vocations or replacing others. Morocco must proceed toward training new profiles capable of following this unstoppable trend; the public sector, for example, is beginning to

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invest in AI: Smart Cities and promote artificial intelligence in education, management, and resource building field [12]. At the academic level, the Ministry of National Education, Professional Training, Higher Education, and Scientific Research just launched a call for projects on Artificial Intelligence last March, with a total amount of 50 M MAD. The national center for scientific and technical research currently has a high-performance computing machine made available to Moroccan universities to promote research in the field of AI. In Morocco, not all industries have reached the same level of maturity to support this type of technology. We cannot yet speak of artificial intelligence in Morocco, at least not for most businesses. The country is still in the phase of “Data Engineering”. Companies today are focusing on data, attempting to acquire it, digitize it, make it easier to access, and analyze it. This stage comes before putting an artificial intelligence system into action. Banking/insurance, telecom operators, industry, retail, and the public sector are the sectors that adapt themselves well to this technological phenomenon [13]. AI will make it possible to provide citizens with practical information and thus make their lives easier, modernize administration and public services, and improve participation in public life. AI can boost economic development through better provision and circulation of information, “says the United Nations”.

2 Business Intelligence Business Intelligence is a branch of IT that allows decision-makers to analyze their activities and make good business management with data in hand. Business Intelligence (BI) to help professionals and decision-makers, including all means, techniques, and instruments for gathering, integrating, modeling, and recovering data from internal and external sources [14]. End-users may employ business intelligence tools and processes to extract useful information from raw data, enabling data-driven decision-making in various sectors. Various BI solutions help business users analyze performance indicators and extract insights in real-time. Self-service features are emphasized in these systems, reducing IT reliance and allowing decision-makers to discover performance gaps, market trends, and new revenue opportunities sooner. BI apps are widely used to help companies make better business choices and improve their market position. Meanwhile, Business Intelligence is indisputably crucial in today’s businesses since it allows businesses to analyze market trends and rivals’ and consumers’ actions by supplying data [15]. In a start-up firm, it is vital to look into the impact of Business Intelligence on people’s learning and innovation capacity since this directly impacts its financial success [16]. A process of design, collection, processing, analysis, and restitution of data is necessary to make the right decision (Fig. 3).

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ETL (Data loading)

OLAP (Multidimensional Analysis)

85

Reporting and Visualization

Fig. 3. Business Intelligence process

2.1 Business Intelligence Technologies With its technological innovations that enable advanced informing, BI has emerged as a technology-driven field. For BI activities, virtually all innovations in information management and information systems have been used [17], as examples of ETL tools: SSIS (Microsoft SQL Server Integration Services) –Talend Data Integration - IBM Infosphere DataStage - Informatica - Oracle Data Integrator etc. Examples of OLAP tools: SSAS (Analysis Services) - SAP BO Visualization tools: Microsoft Power BI - Tableau Software - Microsoft SSRS (Reporting Services) - Sisense Software - SAP Crystal Report - QlikView, etc. 2.2 Collection of Free Tools Many sophisticated tools are included in the BI technology set, and numerous estimates place of BI technology market is worth tens of billions of dollars. Because there is no consensus on what constitutes a BI technology family, market projections are based on whether data analytics, artificial intelligence, and other complex new technologies are included. OLAP (On-Line Analytical Processing), ETL (Extract, Transform, Load), SST (Single Source of Truth), in-memory analytics, deep learning, and other technical terminology have developed during BI expansion [17]. Business Intelligence/Statistics/Data Engineering tools in the world market vary. Figure 4 shows Microsoft BI (Developer Edition) – Microsoft Power BI Desktop – Qlik Sense Desktop - SAS University Edition.

Fig. 4. Business Intelligence technologies - free tools

Business Intelligence has been seen as a technology-driven area with its stream of technical innovations that enable improved informing for a long time. BI operations have made use of nearly all data management and information technology [17, 18]. Arnott

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and Pervan compiled a chronology of decision support and business intelligence systems and technology (Fig. 5) [17, 18].

Fig. 5. Business intelligence systems and technologies and a timeline of decision

3 Data 3.1 Open Data In recent years, Open government data has become the most recent international movement, leading to policy innovation in the public sector [19]. Open data is projected to deliver economic(e.g., efficiency) and social (e.g., democratic principles) benefits to many stakeholders in the public and private sectors, as well as improved forms of policy decision-making through data analytics and knowledge-sharing capabilities [20]. Open Data is the set of open and accessible data and statistics for use without constraints or restrictions (copyrights, patents, etc.). The advantages of open data: – – – –

Transparency: allow access to data to anyone. Financial benefits: reduce the cost of data collection. Decision-making: open data allows users to make good decisions. Promote social life and citizenship: public data will be put in place by the government to facilitate access to citizens’ needs and involve them in public affairs (Sense of belonging).

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3.2 Open Data in Morocco and the World Since 2011, the Moroccan government has launched its portal: data.gov.ma, containing a hundred files on the various public departments. This data is sent to all users, researchers, investors, students, journalists, and other governments, and this data is freely available to everyone without restrictions. Open Data worldwide can be checked online and free to access, such as the World Bank open data and others. Table 1 includes some links to the open data around the world. Table 1. Open data links Open data worldwide links http://linkedgov.org/

https://data.europa.eu/euodp/fr/data/

https://data.gov.uk

https://msropendata.com/

https://theodi.org/

http://data.un.org/

https://data.worldbank.org/

https://www.un.org/fr/databases/index.html

3.3 Big Data The emerging technologies with the Internet, Big Data, Cloud Computing and others have improved the way we live our lives as information is always available anytime and everywhere. Also, this advancement has many impacts on the world around us [21]. System performance may suffer if tasks cannot be completed within the required time in soft real-time systems. Big Data, which traditional methods could not handle, has been processable and applicable thanks to a shift in computing paradigm, namely the rise of cloud computing and later the emergence of fog computing and edge computing and an increase in computer power. The knowledge gained from Big Data processing is more thorough, accurate, and has extensive coverage and applicability when it is processed without employing random analytic methods (e.g., sampling) [22]. With the advent of the big data era and the maturing of cloud computing technologies, big data analysis and processing is becoming more reliant on the cloud computing platform. When employing cloud computing platforms to analyze and handle distributed large data, the first difficulty is dispatching distributed data to the right cloud computing platform data center [23]. Big Data in 3 words: Big data analysis. It is characterized by the 3V: volumetry - variety - velocity. It is mass data mining from a variety of sources in minimal time. Critical tools for Data Engineering and Big Data: Python, R, Hadoop, Apache Spark, Cloudera, etc., and the best training sources on this subject (Table 2).

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M. Oubibi et al. Table 2. Best sources of Data Engineering and Big Data training

Sources of Data Engineering and Big Data training www.datacamp.com EDX skillmeup.com app.pluralsight.com LinkedIn Learning

3.4 Data Protection The increasing quantity of data is shared every day in multiple formats, like social media and the internet, internal servers, or physical forms like users’ records or files. Governments and international organizations are increasingly interested in protecting data, especially personal ones, and make several regulations and laws to manage and control the use of personal data with total security. One of the essential data protection regulations is the European GDPR or Global Data Protection Regulation, which aims to protect and administrate data for European citizens by European or international companies, organizations, and governments [24]. In Morocco, data protection was managed by the Moroccan Data Protection Authority (CNDP) and governed by multiple texts, and the objective of these texts and rules is the control of collecting, processing, and storing personal and sensitive data to protect privacy and accessibility and limit the divulgation and the use of these data in the whole data processing operations [25].

4 Recommendations • We have to encourage the integration of theoretical and application courses in AI in the various academic systems. • Orientation of young people to new areas of AI, both in research, higher education and professional training. • We are encouraging entrepreneurship in the fields of AI. • We encourage the professional world and companies to promote research in AI (by financial contributions or by collaboration with academic institutions in African countries). • The role of civil society is essential, the creation of workshop platforms to encourage and familiarize children and young people with new methods and techniques of AI.

5 Conclusion As technology prices fall, accessibility rises; “ready-to-use” algorithms that are more available to the general population make the employment of artificial intelligence a viable option for many Moroccans. Bensouda stated, “There is a real global scarcity for specialist profiles in AI”. “Data scientists, engineers, and architects are all in high demand”.

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Given Morocco’s job predicament, this sector has the potential to be a significant source of employment”. “It is not just for Silicon Valley. Above all, it is a real opportunity for Africa and Morocco to catch up.” As we all know, Africa has evolved dramatically this year. Currently, 350 million people have access to smartphones, a figure likely to quadruple by 2020, according to Sad Amzazi, Morocco’s Minister of National Education, Vocational Training, and Scientific Research. “At the same time, the CLOUD was born, an astonishing instrument that liberated young African coders from the high fees involved with the purchase of servers and the acquisition of licenses that hampered their creative brilliance”. While many see A.I. as a driver of social transformation and economic prosperity, others are concerned that it may exacerbate Africa’s various challenges”. One of the major issues you will face, first and foremost in terms of access, is being able to function on an infrastructure basis that allows everyone to be linked. “Because that is the issue with all of these objects: you need connectivity, which is still a challenge in Africa,” said Firmin Edouard Matoko, UNESCO’s Assistant Director-General for Priority and External Relations. While unemployment remains a problem and many people continue to lose their jobs, artificial intelligence may be the solution the continent is looking for right now. Experts are asking Africans to develop their technology to empower the young.

References 1. Berrajaa, A., Ettifouri, E.H.: The recurrent neural network for program synthesis. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 77–86. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_8 2. El Hajjami, S., Malki, J., Berrada, M., Mostafa, H., Bouju, A.: Machine learning system for fraud detection. a methodological approach for a development platform. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 99–110. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_10 3. Balsano, C., et al: The application of artificial intelligence in hepatology: a systematic review. Dig. Liver Dis. (2021) 4. Kruse, L., Wunderlich, N., Beck, R.: Artificial intelligence for the financial services industry: what challenges organizations to succeed. In: Proceedings of the Proceedings of the 52nd Hawaii International Conference on System Sciences (2019) 5. Choudhary, N., Bharti, R., Sharma, R.: Role of artificial intelligence in chemistry. Mater. Today Proc. (2021) 6. Maaroufi, M.M., Stour, L., Agoumi, A.: Contribution of digital collaboration and e-learning to the implementation of smart mobility in Morocco. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 609–619. Springer, Cham (2021). https://doi.org/10.1007/ 978-3-030-73882-2_55 7. Nagendraswamy, C., Salis, A.: A review article on artificial intelligence. Ann. Biomed. Sci. Eng. 5, 013–014 (2021). https://doi.org/10.29328/journal.abse.1001012 8. Arakpogun, E.O., Elsahn, Z., Olan, F., Elsahn, F.: Artificial intelligence in africa: challenges and opportunities. In: Hamdan, A., Hassanien, A.E., Razzaque, A., Alareeni, B. (eds.) The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. SCI, vol. 935, pp. 375–388. Springer, Cham (2021). https://doi.org/10.1007/978-3030-62796-6_22

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9. Marivate, V.: Why african natural language processing now? A view from South Africa# Africanlp (2020) 10. Feast, J.: 4 Ways to address gender bias in AI. Harv. Bus. Rev. 20 (2019) 11. Hao, K.: The future of AI research is in Africa (2019) 12. Mohamed, O.; Han, J.; Wei, Z.: Intelligent tutoring system: besides the chances and challenges in artifical intelligence era and maker era. Int. J. Recent Sci. Res. 9, 29053–29062 (2018) 13. Marouane, M., Mkik, S., El Menzhi, K.: The challenges of artificial intelligence for Moroccan companies. Int. J. Adv. Res. Innov. Ideas Educ. 7, 781–786 (2021) 14. Azizi, Y., Azizi, M., Elboukhari, M.: Anomaly detection from log files using multidimensional analysis model. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 515– 524. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_47 15. Wanda, P., Stian, S.: The secret of my success: an exploratory study of business Intelligence management in the Norwegian Industry. Procedia Comput. Sci. 64, 240–247 (2015) 16. Ayoub, J., Yang, X.J., Zhou, F.: Combat COVID-19 infodemic using explainable natural language processing models. Inf. Process. Manag. 58, 102569 (2021) 17. Skyrius, R.: Business intelligence technologies. In: Skyrius, R. (ed.) Business Intelligence. Progress in IS, pp. 145–185. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-670 32-0_7 18. Arnott, D., Pervan, G.: A critical analysis of decision support systems research revisited: the rise of design science. In: Willcocks, L.P., Sauer, C., Lacity, M.C. (eds.) Enacting Research Methods in Information Systems, pp. 43–103. Springer, Cham (2016). https://doi.org/10. 1007/978-3-319-29272-4_3 19. Park, S., Gil-Garcia, J.R.: Open data innovation: visualizations and process redesign as a way to bridge the transparency-accountability gap. Gov. Inf. Q. 39, 101456 (2021) 20. Xiang, H., Li, Y., Liao, H., Li, C.: An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers. Struct. Multidiscip. Optim. 55, 701–713 (2017) 21. Mazwa, K., Mazri, T.: IBN TOFAIL UNIVERSITY: from classical university to smart university. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 633–641. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_57 22. Chen, G., Wang, E., Sun, X., Lu, Y.: An intelligent approval system for city construction based on cloud computing and big data. Inte. J. Grid High Perform. Comput. (IJGHPC) 8, 57–69 (2016) 23. Niu, C., Wang, L.: Big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform. Comput. Commun. 181, 173–181 (2022) 24. Makulilo, A.B.: Data protection in North Africa: Tunisia and Morocco. In: Makulilo, A.B. (ed.) African Data Privacy Laws. Law, Governance and Technology Series, vol. 33, pp. 27–44. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47317-8_2 25. Mounia, B., Habiba, C.: Big data privacy in healthcare Moroccan context. Procedia Comput. Sci. 63, 575–580 (2015)

Sustainable Mobility Model for Fez City (Morocco) Mohammed Mouhcine Maaroufi1(B)

, Laila Stour1

, and Ali Agoumi2

1 Process and Environment Engineering Laboratory, Faculty of Science and Technology

of Mohammedia, Hassan II University of Casablanca, B.P. 146, Mohammedia, Morocco [email protected] 2 Civil Engineering, Hydraulics, Environment and Climate Laboratory, Hassania School of Public Works, Casablanca, Morocco

Abstract. Mobility must be intelligent, safe, fair, and adapted to the environment to be sustainable. It must improve the quality of citizen’s life by safeguarding human health and natural ecosystems. Sustainable mobility must optimize resource consumption and public spaces, facilitate accessibility, promote economic dynamism, respect environmental integrity and commit the social responsibility of individuals, companies, and society. Thus, sustainable mobility contributes to the development and responds to the travel demands of present generations without compromising the ability of future generations to meet their own needs. This article presents research results to set up a sustainable mobility model for Fez, a Morrocan booming city. It offers a conceptual approach to simulate the trend in mobility and allows us to measure the positive impact of the corrections made. A supply analysis and the need for trips in a reference situation allows formalizing realistic objectives. Then, a sustainable mobility model prevents social and environmental costs from being higher than the services provided by the transport systems of this city. Keywords: Sustainable mobility · Model · Urban transport systems

1 Introduction The Process of not mastering the urbanization of our cities causes a phenomenon of dispersion of the population and therefore increasing distances. What particularly disadvantage walking and cycling. Economic development causes an increase in disposable income and living standards, as well as two aspects that have a significant influence on mobility: An increase in individual private motorization and an increase in “not compulsory” mobility (different from going working or studying) [1]. These two aspects have a multiplier effect on the uses of private vehicles compared to collective public transport (CPT). The advantages of the private car, compared to the CPT, are based on better travel conditions such as convenience, freedom of schedules, independence, and, in several cases, time-saving. However, we cannot ignore the drawbacks of this mode of transportation [2]. They represent a set of social expenses linked to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 91–102, 2022. https://doi.org/10.1007/978-3-031-02447-4_10

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the growing needs in infrastructure, to the costs of congestion and pollution, to expenses that society cannot afford indefinitely [3, 4]. The current Moroccan cities development, based on increasing using private cars and widening or constructing road infrastructure, is not sustainable. The cities governance has no reliable model for measuring and monitoring the impact of urbanization and the evolution of the mobility system. The main objective of this research is to develop and calibrate a theoretical model that allows a more sustainable vision of mobility in the agglomeration of Fez by applying the following sequence: • First, we will collect the necessary data covering the following aspects: the configuration of the existing road network (hierarchy, intensities), the CPT system by bus and taxis (occupancy, lines, frequencies) as well as the parking system. • Afterward, we will diagnose the current situation or reference situation of the transport system and analyze the data collected to highlight: – Sectorial analyzes and the main current dysfunctions, located in spacetime: access to services/availability/regularity/speeds/comfort/territorial coverage/resources/profitability/security/congestion; – The principal causes associated with these dysfunctions: supply/demand adequacy, complementarity between modes, continuity, and homogeneity of networks, coordination of development/transport, institutional framework; • Then, we will characterize the current social-economic and mobility situation. • After that, we will calibrate a theoretical simulation model, which makes it possible to reproduce the mobility system simply and faithfully from the primary variables of territorial development by first carrying out the zoning of the geographical perimeter. Then, by digitizing the network using Geographies Information System (GIS) and by implementing through the TRANSCAD1 software. • Finally, we will propose an action plan with a sustainable mobility vision.

2 Characterization of the Mobility in Fez City 2.1 Mobility Offer Road Network. A hierarchy of the road structure allows identification of the uses of each road according to functional and geometric criteria: 160 km of main roads (interurban roads, urban arterial roads), 780 km of local streets, and 45 km of pedestrian paths [5]. Parking. The majority of parking is regulated informally. To estimate the potential of this offer, we consider that 40% of the length of the main streets and 60% of local roads can offer parking spaces on both sides, with a ratio of 6 m track length per place. Thus, 1 https://www.caliper.com/tcovu.htm.

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the potential of the offer for the whole urban road can be estimated at 170,000 spaces. The car parks regulated by the Municipality have around 9,350 spaces [6]. Non-motorized Circulation System: Pedestrian – Cyclist. According to the inventory of the urban municipality of Fez in 2015 [6]: • 67% of sidewalks are unable to accommodate continuous foot traffic. • On more than 50% of the roads, pedestrians have no signage. • 92% of the sidewalks do not have the minimum adequacy for the circulation of vulnerable people with reduced mobility or disabilities. Only 12% of the streets have green zones. • 97% of the lanes do not contain a cycle path. The urban center of Fez is accessible on foot and by bicycle (30% of the city surface is within a radius of fewer than 4 km). More than 349,200 inhabitants, 30% of the total population, are within a distance of 20 min by bicycle from the urban center and less than an hour on foot [7]. Bus CPT System. The current network of Fez CPT by bus, characterized by a radial type, has 51 lines (with a fleet of 246 buses). The bus system includes 38 urban (concentration and superposition of rows in the North-South connection) and 13 interurbans. The number of stops is 1,340 stops on a network, spanning a total length of 1,230 km. The linear of urban lines is about 19 km, compared to 39 km of interurban lines. The line length ratio does not exceed 1 km/1000 inhabitants. The total annual shipments (round trip) by bus is 1,235,758 and 21 million vehicle kilometers/year. The places offered per year in the city of Fez reach 134 million [8]. The area covered by the network for a time interval of fewer than 3 min is 22%. Between 3 and 5 min is 23%. This percentage increases for longer intervals at 39% for 5–10 min and 17% for 10–20 min. Taxis System. The taxi sector, made up of first and second category taxis, is the second mode of CPT in Moroccan cities. We registered 1,030 first category taxis (interurban taxis with 6 passengers maximum) and 2,490 s category taxis (urban taxis with 3 maximum passengers) in Fez in 2018 (460 inhabitants/taxi). 56,810 trips were made in interurban taxis and 125,545 in urban taxis [6]. Inter-modality. Currently, mobility in Fez city is characterized by excessive use of private vehicles. The metropolitan CPT system is essentially made up of the second category bus and taxi network. The urban CPT offer is complemented by the existence of clandestine transport. The intercity transport service is satisfied by rail at the railway station, coaches with arrival/departure in 3 bus stations, and first category taxis. International mobility is provided through an airport located 15 km south of the city.

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Urban Goods Distribution (UGD) System.2 Fez has a mobile fleet of 32,138 trucks - vans. This type of vehicle represents more than 83% of heavy vehicles intended for the activities of loading and unloading goods in the city. It represents a potential, which translates into an average growth of 5% per year [7]. 2.2 Current Demand for Mobility Road Requests. The current structure of the road network of the Fez city is radial, constituting a range concentrated in the access to the North zone (Medina). The convergence of flows to or through the city center results in high traffic densities in the radial access roads. This traffic will have to be redistributed in a reticulated scheme, which would confer importance on the west-east peripheral roads, which link the new southern districts of the city. The reasons for traveling by private vehicle users mainly work (50%), accompanying someone else (15%), and leisure (12%). Occupancy for private cars is 1.5 occupants/vehicle [7]. Parking Requests. The peaks in demand for regulatory parking on public roads are between 10:00 am and 3:00 pm. The nocturnal occupation, around 44%, will move it to the periphery, where we find a strong demand from residents. 18% using private cars consider that the number of parking spaces is insufficient [6]. In the case of the Medina, the demand for parking is particularly associated with the “gates” of its historic wall. It presents a constant occupation throughout the day and even the night below 70%. The occupancy of regulatory car parks regulated by the Municipality exceeds 70% throughout the day (demand peaks of over 90%) [6]. Non-motorized Travel Requests: Pedestrian/Cyclist. Non-motorized trips represent 58% of the total trips, with intense demand for pedestrian mobility. Despite the topography and climatology, which favor the use of the bicycle as a mode of transport, currently, bicycle users share the road with motorized transport systems due to the absence of cycle lanes. 16% of citizens who do not use the bicycle in their journeys would be ready to consider this way as an alternative to their current mode of transport [6]. Travel by Bus Requests. A heavy occupancy is observed in city buses, with peak occupancies greater than 100 passengers/vehicle, during the evening peak period. The average occupancy is 46 passengers per expedition. Despite the growth of the population, the number of passengers carried annually has remained frozen around 72 million for the art last two decades due to the modest quality of the fleet, and non-regularity of frequency and travel speed, broken down according to the existing pricing system into ticket passengers (39%) and passengers using season tickets (61%).

2 There is no real regulation of the activities of UGD neither concerning the spaces reserved on

the roads nor concerning the time authorized on the public highway, except certain spaces in parking areas regulated by the municipality.

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The main reasons for traveling by bus are studying (42%), work (23%) and accompanying another person (11%). 25% of bus users had to leave a bus because it was fraught, and 12.5% had to take two or more buses that were not accessible. The average waiting time at a stop is 15 min [6]. Travel by Taxis Requests. According to the association of taxis, on average small taxis provide 40 trips/day (1.65 passengers/vehicle) and travel about 250 km per day. Large taxis provide 30 trips per day (2.8 passengers/vehicle) and cover approximately 300 km/day. As a result, taxis represent 24% of motorized trips in the city of Fez. Mobility obliged by taxi reached 73%: work (48%), accompanying another person (14%), and education (11%) [8]. Urban Goods Distribution Needs. The new areas of the city that will generate and attract mobility associated with the UGD are [9] (Fig. 1 and 2): • • • •

The residential areas: 2,700 Ha to urbanization, housing potential of 123,000 units. The industrial/commercial zone: 364 Ha. The urban center: 400 Ha. The Medina: 235 H.

Fig. 1. Urban planning extensions.

Fig. 2. Main current centers attracting travel. Source: Prepared by the authors using data from [9].

Modal Repartition of the Mobility Demand. Each elderly of more than five years makes 2.3 trips per day with an average of 1.1 step movement. 60% travel from Monday to Friday. 71% of trips are daily. 35% of trips last 15 min or less. 42% 15–30 min. 6% last more than 60 min. 98% of mobility originates/ends at home (Fig. 3).

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Fig. 3. Daily modal split of trips. Source: Prepared by the authors using data from [6].

3 Model of Sustainable Mobility 3.1 Zoning The analysis of the transport system and characterization of the mobility in the city of Fez requires calibrating a general mobility model that can reproduce the current situation and integrate different social-economic settings, urban and infrastructural impacting the evolution of travel. We define 75 areas through GIS. 3.2 Demand Modeling The modeling of mobility demand makes it possible to reproduce the current situation and measure the trend situation and the situation corrected by the new mobility model. For this, we propose to calibrate a general mobility model in four steps: Travel Generation/Attraction Model. We want to fit a mathematical model that can predict with a certain degree of reliability the number of trips (according to the unit of analysis we are using): the motorized trips of origin and destination of each zone. The models used will be of the linear type based on the following formulas: Oi = (a ∗ V1i ) + (b ∗ V2i ) + . . . + (c ∗ Vni )

(1)

Dj = (a ∗ V1j ) + (b ∗ V2j ) + . . . + (c ∗ Vnj )

(2)

where Oi - Number of trips whose origin is zone i; Dj - Number of trips whose destination is zone j; Vi, Vj - Social-economic variables of zones i and j; a, b, c - Adjustment parameters (Table 1).

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We opted for social-economic variables to predict the future, such as population, employment, school places, and the number of vehicles. Table 1. Variables of the displacement generation/attraction model. Model

Variables

Values

Statistical T

R2

Generation(destination vector of the matrix)

Population

0.0119

2.07

0.86

Vehicles

0.2447

4.45

Attraction(origin vector of the matrix)

Jobs

0.0752

5.97

Jobs

0.1779

7.82

School places

0.1054

4.83

0.86

The Motorized Travel Distribution Model. The distribution model allows the reproduction of the number of motorized trips (public + private) between each origin/destination thanks to a mathematical model based on the generation/attraction variables between each transport zone and the generalized cost of the trip between them. The model used will be of the gravitational type based on the following formula: Vij = Oia ∗ Djb ∗ CGijc

(3)

where Vij - Number of trips between zones i and j; Oi - Movements from the starting zone i; Dj - Travel to the finish zone j; CGij - Generalized cost between the two zones i and j; a, b, c - Adjustment parameters (Table 2). For the generalized cost of travel, we adopted the public and private data transport allocation model (examining the time and costs as a function of the demand of these modes). Table 2. Variables of the displacement distribution model Variables

Coefficients Statistical T R2

Generation

0.3988

Attraction

0.2678

Generalized costs −0.4960

14.37

0.92

9.70 −16.33

Modal Distribution Model. To reproduce a modal split model that is easily applicable and consistent with the observed reality, we have chosen to estimate two independent models: PRIVATE TRANSPORT MODEL. This model was estimated using the following linear standard formula: Ratio = (a ∗ R1) + (b ∗ R2) + . . . + (c ∗ Ri)

(4)

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where Ratio - Variable that determines the non-captive trips made by private transport; Ri - Variables; a, b, c - Adjustment parameters (Table 3). We used vehicle per inhabitant ratios in the travel origin and destination areas as a model variable. Table 3. Variables of the private transport model. Model of private transport trip

Variables (vehicle/inhabitant)

Coefficients

Statistical T

R2

Model of non-captivity

Original ratio

1.7089

13.56

0.74

Destination ratio

2.3644

17.85

The accompanying vehicle model

Original ratio

0.9317

15.64

Destination ratio

0.8600

13.75

0.68

PUBLIC TRANSPORT MODEL. The objective of the public transport modal split model is to characterize the mathematical formulas that make it possible to reproduce the TaxiBus modal choice process. For this, we used aggregated models of the logit type based on the definition of utilities, which depend on the types of transport considered and on the user choice variables. The mathematical expression of the utility for an alternative “i” is: Ui = K + a ∗ X1 + b ∗ X2

(5)

where K - constant, which collects the part not explained by the variables of the user’s choice; Xi - are the characteristics of user choice. e the characteristics of choice of the user. These parameters are negatives since they penalize the choice of mode. We estimated the model on the following variables (Table 4) • Travel time between origin and destination, adding in the case of buses, access time, waiting time, and travel. • The cost of the trip: the kilometer rate for the taxi and the rate for public transport. : Table 4. Variables of the public transport model. Variables

Codes

Coefficients

Time value

Constant

K

−0.02745

7.5

Travel time

X1

−0.00234

Cost of travel

X2

−0.01881

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Assignment Model. The allocation model distributes the demand between the various possible roads of private and public transport lines. We create this model using the TRANSCAD network simulator, a GIS environment tool. NETWORK ROAD. The road network includes the road fabric of the city of Fez and the main access roads. Each section of the network has the following intrinsic attributes: Point of origin and destination/Length/Ways number/Vehicle capacity/lane per hour/Speed (km/h)/travel time (min) under optimal free flow conditions/Volumetime function used to represent the behavior of lanes in traffic jams. The delay functions link the volume of vehicles affected and the travel time for each section. The formula adopted is as follows:   (6) T = l t0 + a (i / c)b where T - average travel time in the section (min); l - section length (km); t0 - average travel time of 1 km, free flow (min/km); i - volume or intensity of vehicles (veh/h); c theoretical section capacity (veh/h per track); a, b - adjustment parameters (Table 5). Table 5. Characteristics of the road network. Code

Type

Ways

Capacity c

Speed

a

b

1

Main road

2*

1,800

80–90

2.85

2.90

2

Secondary road

1–2

1,600

60–70

1.10

2.15

3

Urban artery

1–2

1,600

40–50

4.60

2.40

4

collector road

1

1,200

20–30

2.90

1.89

5

Road link

2

1,400

50

4.85

2.50

PUBLIC TRANSPORT LINES. Fez’s public transport network includes 51 urban bus lines and is defined by the following characteristics: • • • •

Route of the line and stops; Service frequency is wait time (min) in peak hours; Travel rates; The real-time bus running is a function of a series of variables, such as network traffic and congestion. These functions collect the overall behavior of the CPT network: function of the private vehicle speeds and the characteristic limitations of the buses. We considered the private traffic/public traffic relationship as follows (Table 6): Vprivate ≤ 10 km/h Vpublic = Vprivate 10 km/h < Vprivate < VpubmaxVpublic = a * Vprivate Vpubmax ≤ VprivateVpublic = Vpubmax = 80 km/h

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Code

Type

Function

1

Main urban road

Vpub = 0.5720 * Vpriv

2

Secondary urban route

Vpub = 0.5473 * Vpriv

3

Expressway

Vpub = 0.6670 * Vpriv

4

Toll highway

Vpub = 0.6670 * Vpriv

5

National road

Vpub = 0.5765 * Vpriv

6

Road connections

Vpub = 1 * Vpriv

3.3 The New Model of Sustainable Mobility The criteria for the territorial and urban reconfiguration of Fez sustainable mobility3 have been distributed according to the following two generic conceptual frameworks: • Integrate sectorial mobility visions, traditionally analyzed separately, by betting on the overall organization of the mobility system capable of both adjusting the different offers according to the characteristics of the different mobility demands and at the same time controlling the evolution of each request by a more intelligent offer. • Redefine the relationships between the different scales of the city (interurban, urban and local) to reconcile between the individual and the space. The improvement of accessibility, as well as the revitalization of urban functions in the city center and historic urban areas, must create proximity between the origins and destinations of travel and respect strategies on sustainable mobility following: • Stimulate livability in residential areas by creating a local road network. • Reduce the demand for motorized travel (particularly private vehicles) and promote the soft modes and CPT use. • Ensure that the physical and geometric conditions of the road network are more functional (access to the road network, road distribution, parking conditions), more secure and that the traffic regulation conditions are more intelligent [11]. • Use parking regulations to deter inappropriate automobile use. These mechanisms will apply different restriction levels (priority for residents, regulation of service movements, and generation of modal transfers to CPT modes for working journeys). Gradually replace free parking on public roads in the most problematic areas of the center and neighborhoods to promote parking for residents.

3 The mobility plan must define the guiding actions to consolidate a city model, balancing pro-

ductive system/economic activity/urbanization/territorial mobility needs. Starting from environmental, social, and economic sustainability principles [6]. The ultimate goal is to have a more human, inclusive, healthy, dynamic and resilient city.

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We set seven objectives for the mobility model proposed for 2036 (Fig. 4): • 20% reduction in private automobile traffic; • 65% development of CPT with 85% of the area covered by the CPT network for a time interval fewer than 5 min; • Encouraging the use of alternative modes (cycling, walking, carpooling); • Improving the operation of road networks in the sense of their increased hierarchy with a view to functional sharing and transverse and longitudinal permeability; • Reorganizing parking and redeveloping more in favor of residents; • Taking into account the delivery of goods with the desire to reduce nuisances; • Integrating intermodality as a continuous chain, in the planning, projection, management, and monitoring of the action plan. Taking into account the objectives and principles mentioned above. A general outline of the urban mobility model is proposed below:

Fig. 4. The new model of urban mobility in the city of Fez.

4 Conclusions This article presents the results of the research work we have carried out to address the issue of sustainable mobility in Fez, through: • The detailed diagnosis of the current situation and the establishment of a certain number of projections on demographic, economic, and sociological evolutions; • The mapping of the Urbanism/Transport interface (the location of activities and the population) and the characterization of current mobility (supply and demand); • Establishing theoretical mathematical models to predict future demand; • The proposal for a new model constitutes a future offer capable of meeting the growing needs of citizens and businesses while safeguarding the well-being of individuals and the development of economic activity.

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The sensitivity analysis of the various theoretical models shows that the variations between the results of these models and the actual development of the situation between 2016 and 2020 vary between −10% and + 10%, which concludes that there is a good prediction capacity. The models could be better calibrated by detailed household surveys and Origin/Destination surveys.

References 1. Directorate-general for sustainable mobility policy and electrification of Quebec: Sustainable mobility policy 2030, p. 54 (2018) 2. European Commission: Towards a new culture of urban mobility. The green paper, p. 25 (2007) 3. European Commission: Towards a competitive and resource-efficient transport system. White Paper Roadmap for a Single European Transport Area, p. 35 (2011) 4. Office for the coordination of sustainable development of Quebec: Quebec indicators sustainable development. The summary document, p. 10 (2010) 5. Ministry of Equipment, Transport, Logistics, Water: METLE 2014 in figures, p. 36 (2015) 6. Urban Commune of Fez: Monograph of the urban agglomeration of Fez. 102 p (2015) 7. Ministry of equipment, transport, logistics, water: METLE 2018 in figures, p. 36 (2019) 8. Ministry of Interior: Regional land use planning plan for the region of Fez, p. 202 (2017) 9. Fez urban agency: Urban development plan (2018) 10. Ministry of equipment, transport, logistics, water: roadmap for sustainable mobility in Morocco, p. 19 (2018) 11. Maaroufi, M.M., Stour, L., Agoumi, A.: Striving for smart mobility in Morocco: a case of lanes designated to heavy goods vehicles in Casablanca. Eng. Manage. Product. Serv. 13(1), 72–86 (2021). https://doi.org/10.2478/emj-2021-0006

Sustainable Mobility Plan Indicators: Application to the Moroccan Case Mohammed Mouhcine Maaroufi1(B)

, Laila Stour1

, and Ali Agoumi2

1 Process and Environment Engineering Laboratory, Faculty of Science and Technology

of Mohammedia, Hassan II University of Casablanca, B.P. 146, Mohammedia, Morocco [email protected] 2 Civil Engineering, Hydraulics, Environment and Climate Laboratory, Hassania School of Public Works, Casablanca, Morocco

Abstract. Sustainable mobility adopts as principal criteria: rationalizing motorized transport use, upgrading to more energy-efficient means of transport, and ensuring the balanced distribution of the available space; Sustainability results in lower emissions of Green House Gas (GHG), pollutants, and noise. Sustainable mobility aims to reduce accidents and congestion and improve the accessibility levels associated with collective public transport (CPT). It would reconcile social cohesion, economic prosperity, and quality of life. It’s would also ensure sustainability that would prevent the benefits of today from being damaged tomorrow. Thereby, the development of Moroccan cities, based on an increase in using private vehicles and on a widening or constructing road infrastructure, is not enough “sustainable”; The territorial and transport actual policy need a more sustainable vision. This article presents the research results to set up a sustainable mobility plan for Fez. It sets realistic objectives to correct the current trend. The sustainability indicators set makes it possible to quantify the improvement made and decide on the action’s opportunity. Keywords: Sustainable mobility · Smart mobility · Indicators · Urban transport systems · Socio-economic development · Environment

1 Introduction The lack of sustainability focuses on the following three factors [1]: • Costs of infrastructure needed to channel increasing private mobility; • Costs of gridlocks caused by the lack of capacity at certain times of peak and in some places the road network; • Environmental costs. Indeed, the capacity of road infrastructure cannot keep pace with the growing demand for motorized travel [2]. The priority is to give to “the most sustainable means”, collective © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 103–113, 2022. https://doi.org/10.1007/978-3-031-02447-4_11

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public transport (CPT), walking, and cycling. To this end, we must take into consideration as a priority the sustainability criteria as follow: • The accessibility: a modal system with a balanced distribution where all people have possibilities of access to the transport system and the main centers of city activity [3]; • The city’s livability: to improve the quality of life of all citizens by promoting and stimulating the various uses of urban land [1, 4]; • The quality of the public spaces and the urban environment: we must propose actions to recover streets and squares from a functional point of view [2]. To establish the assessment and quantification of the effects that a new model will have on Fez city it was necessary to define quantifiable indicators that make it possible to detect any successes or failures. A scenario 0, or baseline for 2016, with which future scenario up to 2036 is compared. From this current scenario, all the strategies and actions that make up the target scenario are defined, including the most conflicting aspects, in which it is necessary to intervene. To visualize the expected effects, we establish two scenarios: • Trend, in which the sustainable mobility model is not implemented; • Corrected, where the proposals considered in the new model have been deployed. After describing the problem, we will first review some strategies concerning sustainable mobility. Afterward, we will define the criteria and the objectives of a new mobility model. Then the analysis of the improvement brought about by the measures adopted (concerning the trend situation without intervention) makes it possible to assess the added value of the proposed model. Finally, we will conclude with a few recommendations and constraints.

2 Literature Review A benchmark of good practices adopted in favor of sustainable mobility in Europe and Canada during the last decade has inspired us and allowed us to set realistic objectives for the model to be adapted to Morocco and particularly to Fez city: 2.1 Urban Mobility Policy for 2050 in Europe At the European level, urban traffic is the source of 40% of CO2 emissions and 70% of emissions of other pollutants from road transport. The number of road accidents in cities is also increasing each year: today, one in three fatal accidents occur in urban areas, and it is the most vulnerable, pedestrians and cyclists, who are the first victims [2]. If it is at the local level that these problems appear, it is at the continental level that their impact is felt: global warming, increase in health problems, bottlenecks in the logistics chain, etc. It is why Europe puts its power of reflection, proposal, and mobilization at the service of policies decided and implemented locally to shape a new culture of urban mobility.

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To have fluid and less polluted cities, the European Commission (EC) plans to promote walking and cycling, optimize the use of private cars and promote increasingly intelligent toll systems. Clean, safe, accessible, and adapted urban transport, through innovative solutions, a balanced network of territories, and an integrated approach will contribute to better mobility [2]. Given that, restricted mobility is not an option. The EC promotes a quality, competitive and reliable transport system to reduce 60% of emissions by 2050 [3]. 2.2 Sustainable Mobility Policy by the year 2030 in Quebec In 2018, the government of Quebec made public the 2030 sustainable mobility policy, carried out by the Directorate General of Sustainable Mobility Policy and Electrification (DGSMPE). This policy aims to respond to the citizen and business transport concerns and needs of people and goods in all regions of Quebec. The goal of the Quebec sustainable development approach is to meet the present needs of Quebec society while safeguarding the potential for improving the quality of life and the well-being of future generations [4]. In 2030, Quebec aims to become a North American leader in sustainable and integrated mobility for the 21st century. A territory developed with sustainable mobility has an efficient, safe, connected, and low-carbon transport ecosystem, which contributes to the prosperity and meets the needs of citizens and businesses [1]. The orientations targeted by the policy for 2030 consist of [4]: • More options for moving: 70% of the Quebec population will have access to, at least, four services sustainable mobility; • Shorter travel times: 20% reduction in average travel time between home and work; • Even safer roads: 25% reduction in the number of fatal and seriously injured accidents compared to 2017; • Fewer individual car trips: 20% decrease in the share of individual car trips; • Limited consumption of fossil fuels: 40% reduction in oil consumption in the transport sector below the 2013 level; • Much less Green House Gas (GHG) emitted into the atmosphere by transport: 37.5% reduction in GHG emissions in the transport sector below the 1990 level; • A strong and innovative industrial sector of sustainable mobility equipment: 15 billion dollars in annual sales for the Quebec land transport equipment sector; • Optimal use of transport modes for improved business competitiveness: increase 25% of freight tonnage transshipped at ports and intermodal rail centers; • Companies less affected by congestion: reduction of the cost of congestion for businesses in metropolitan areas of Montreal and Quebec; • Decrease in household expenditure allocated to transport 20% reduction in gross household expenditure allocated to transport. 2.3 Roadmap for Sustainable Mobility in Morocco Morocco experienced the last decade advances and reforms in the environment sector, sustainable development, and the fight against change climate. Several sectoral strategies,

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including that of transport and logistics, thus integrate these three dimensions. The economic stake is primordial since the cost of the pollution of the area represents in Morocco more than 10 billion DH (1% of the GDP) [5]. The National Sustainable Development Strategy (SNDD) 2030 identified in 2014 the transport sector as the third energy consumer in Morocco. It accounts for 16% of total emissions and 28% of energy module emissions. Sustainable mobility is defined as “a transport policy which seeks to reconcile accessibility, economic progress and the reduction of the environmental impacts of the selected transportations” [6]. In addition, Morocco is the first country to have initiated an adaptation of the global macro-roadmap for the transformation of transport resulting from the Paris Process on Mobility and Climate (PPMC). The Moroccan 2018 roadmap support national strategies, particularly the SNDD 2030. It recommends reducing the modal share of the private car to 16%, reaching 80% of inhabitants within 500 m of a bus stop, having less than 10% of income spent on travel by at least 80% of low-income households, and reducing GHG emissions by 13% [6]. All modes of transport emitted in Morocco in 2014: 16 million tons of CO2 equivalent represent 19.2% of the country’s total emissions. Morocco has set itself a target of 42% for reducing GHG emissions from the year 2030 with two scenarios: unconditional objective: 17%, and conditional objective: 25%. Transport should contribute 14.8% of the mitigation effort in 2030 [7].

3 Research Results The detailed analysis of the socio-economic, territorial, mobility infrastructure environment and the characterization of trips in Fez have made it possible to distinguish the principal dysfunctions preventing to migrate to more intelligent and sustainable mobility. 3.1 Criteria of a New Model of Sustainable Mobility To assess sustainable mobility, the following 10 assessment criteria will be used: • • • • • • • • • •

Sustainability: social/environmental/economic. Quality of life: habitability/urban environment/accessibility. Road safety: accidents/congestion. Awareness: follow-up/involvement/collaboration/participation. Ability to solve problems. Ability to respond to demand. Complementarity between the different modes: inter-modality. Conditions of implementation: organization/technical feasibility. Financial costs associated with investments, operation, and operation. Economic costs for users and operators.

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3.2 Objectives and Consistency of the New Mobility Model The mobility model proposed for 2036 sets itself seven objectives: • 20% reduction in private automobile traffic; • 65% development of CPT with 85% of the area covered by the CPT network for a time interval fewer than 5 min; • Encouraging the use of alternative modes (cycling, walking, carpooling); • Improving the operation of road networks in the sense of their increased hierarchy with a view to functional sharing and transverse and longitudinal permeability; • Reorganization of parking for residents; • Taking into account the delivery of goods with the desire to reduce nuisances; • Integrating intermodality as a continuous chain, in the planning, projection, management, and monitoring of the action plan [8]; • Reducing non-essential travel by encouraging digital collaboration and e-learning; • Optimizing traffic management by using smart mobility and ITS. The approach of livable public space for the city of Fez requires a set of technical, organizational, normative, educational, participatory, and economic instruments for its development. This model establishes a new hierarchy in the city’s road network, adaptation to urban morphology, and the distribution of public space from an integrated vision of mobility planning and space management public. The mobility management model that we propose is a management model by 30 zones or calm traffic zones. Zones 30 are environmental zones of multifunctional homogeneity of 500 m. Within these areas, the dominant modes are pedestrians and cycling. The streets can receive car traffic, but with a maximum of 30 km/h. This management model aims to slow down, prevent and reverse the trend of the current mobility model. It aims to create a more habitable public space and calm traffic zones. Public space inside neighborhoods will be used for communicating between citizens. To verify the relevance of the proposed model, we wish to use a set of indicators that scan the economic, environmental, and social aspects of mobility through [9]: • The incurred costs: – Environmental: The GHG and pollutants emissions, the energy consumption, and the space consumption by transport. – Economic: The unit cost of mobility (cost of a journey km and collective cost of mobility, measured for one person and per year). – Social: The costs transport by the income of households and their location. • The produced services: – The economic accessibility: employment and services. – Social accessibility: The average transport time budget per person per day and per mode of transport.

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4 Discussion of the Results 4.1 Impacts of Current and Tendency Mobility The qualification and quantification of the impact of current mobility (baseline situation in 2016) and the mobility without actions (trend situation in 2036) make it possible to measure the sustainability of mobility. It allows assessing the contribution or otherwise of the new mobility model through three indicators related to safety, quality of life, and the following environmental consequences: Accidents. There were 420 accidents in the agglomeration of fez, of which 8% were fatal (against 4% of the national average). The 43 victims were mainly pedestrians and private vehicle and motorcycle users. The main reason for accidents, which occur more in working days, is the lack of control over speeding [10] (Fig. 1). Congestion and Points of Conflict. Congestion between 8 a.m. and 10 a.m. 29 conflicting points and 14 road sections with a very high concentration of congestion have been identified in Fez [10] (Fig. 2). Environmental Consequences of Motorized Traffic. The use of private cars causes consumption and degradation of public space and affects the life quality of residents and that of the urban environment. Traffic jams, pollution, noise, accidents, or energy dependence are the most consequences generated by the current mobility model. Acoustic Pollution. Regarding the noise evaluation, we carried out a theoretical model considering that the sound level equivalent to one hour (Laeq, 1 h) increases logarithmically with the increase in the average daily intensity of the vehicles [11]. Over 17% of the urban area of the city of Fez suffers from episodes of noise pollution capable of causing some discomfort in people (Fig. 1). The model allows us to see that over 31% of the urban area will undergo a noise pollution medium and high in the case of a scenario trend on the horizon in 2036 (Fig. 2).

Fig. 1. Theoretical spatial model for assessing acoustic pollution in 2016.

Fig. 2. Theoretical spatial model: trend assessment of acoustic pollution in 2036.

Air and GHG Pollution. To analyze the levels of the current concentration of air pollutants and those trends, we adopted a model simulation of statistical distribution depending (Fig. 3 & 4). The trend concentrations at the 2036 horizon will increase by more than 70% compared to 2016 (Fig. 5 & 6) [12].

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Fig. 3. Average concentrations current of NOx (g/m3) at 2016.

Fig. 4. Trend average concentrations of NOx (µg/m3) at 2036.

Fig. 5. Average concentrations of MPS10 (µg/m3) at 2016.

Fig. 6. Trend average concentrations of MPS10 (µg/m3) at 2036.

Energy, economic and environmental balance associated with the current mobility of Fez: To estimate the city’s energy balance, we consider the fleet according to the distribution in the chapter characterization of motorization with the distances considered in the Origin/Destination matrix, and we take the average consumption of each type of vehicle according to the values below. (Table 1): Table 1. Fuel consumption per year and per type of vehicle in the city of Fez. Vehicle type

Consumption in l/100 km

Park

Distance traveled/day (km)

Consumption/year (l)

Private vehicle–Gasoline

7.7

29,000

700,000

17,248,000

Private vehicle–Diesel

6.3

80,000

Motorcycle–Diesel

5.7

1,450

16,000

Utility vehicle–Diesel

10

44,000

400,000

1,900,000

38,304,000 291,840 12,800,000

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To transform the consumption (in liters) into Tons of Oil Equivalent (Tep), we use the following factors (Table 2): Table 2. Tep consumption and emission factor are produced according to energy sources [13]. Energy source

Consumption

Emission factor

Tep/l

tCO2/Tep

1 L Gasoline

1,290

2.89

1 L Diesel

1,181

3.09

Currently, the energy total consumed annually by the Fez motorized transport system is 57,000 Tep. The annual energy cost is 643 Million DH. The annual contribution of the road mobility system to global warming amounts to 173,500 tons of CO2. 4.2 Contribution of the Proposed Mobility Plan To assess the effects of the programs of the new sustainable mobility model and in what proportion they contribute to achieving the objectives defined by the new mobility plan, the simulation of the model by introducing the adopted measures makes it possible to visualize the different indicators. In 2036, the number of trips by car (private vehicle + taxi) will be reduced by 19.6% with a sustainable mobility plan. The average speed of the trend scenario of 24 km/h will increase by 21%. Total véhicules.kilomètres (veh.Private + taxi) will decrease by 18%. • The number of CPT trips will increase by 63.8%. The total of vehicles kilometers (CPT) will increase by 12% and that of passenger kilometers (CPT) by 73%. • The modal share of CPT will increase to 34% with the sustainable mobility model against 20% for the trend scenario when the private cars will be reduced to 42% against 52% without corrective measures. The calculations impact confirm saving 300,000 tons of equivalent CO2 until 2036. The savings from fuel consumption will be 124 million liters or 1.1 Billion DH (10% of the overall cost of implementing the sustainable mobility plan) (Fig. 7 & 8). In addition, sustainable mobility will reduce over 25% emission pollutants and more than 45% of an urban area undergoing a risk of medium noise and high (Fig. 9). Furthermore, the reduction of accidents is associated with a trend downward vehicular intensity resulting from a sustainable mobility plan (Fig. 10).

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Fig. 7. NOx concentrations (µg/m3) in 2036 with a sustainable mobility plan.

Fig. 8. MPS10concentrations (µg/m3) in 2036 with a sustainable mobility plan.

Fig. 9. Risk of noise pollution by 2036 with the implementation of the plan.

Fig. 10. Evolution of the accident risk level with a new model of mobility.

5 Conclusions This article presents the results of the research work we have carried out for the verification, through sustainability indicators, of the contribution of the sustainable plan mobility to reversing the current trend of modal split and the pressure introduced by the private vehicle. Although the performance indicators of the model were not all calculated, due to the lack of availability of complete and reliable data, nevertheless those estimated made it possible to confirm the achievement of the main objectives, namely: • Protect the most vulnerable and sensitive urban networks from vehicle intrusion; • Rationalize the use of private vehicles by organizing the entire road space of the city functionally; • Prioritize the road network. It will suppose the formal and functional determination of the entire road space of the city and will result in the balanced sharing of the road section transverse and longitudinal permeability through a multifunctional design of the track; • Diversify the forms of travel in the city by offering the possibility of modal choice to the user with conditions favorable to inter-modality; • Redefine the concept of road network capacity. The physical capacity (the maximum of vehicles that can pass by a road for a time) will be complemented by the environmental

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capacity (reflects the quality of the urban space and the positive effect it has on the quality of citizen life); Limit the presence of the private vehicle in public space, by promoting and favoring parking at the origin that is to say in buildings in residential areas; Increasing accessibility and safety of pedestrians and cyclists; Promote the use of CPT within the city by improving its quality, reliability, and level of safety; Plan a more efficient urban distribution of goods that generates minimal interference with the use of road space; Take advantage of the arrival of smart mobility [14].

The main constraints to be taken into account in the implementation of the sustainable mobility model are: • The intrinsic characteristics of the city. The model will not be able to change the city, but can make the city’s mobility system more efficient; • The development of the socio-economic activity of the city. The model will not be able to intervene in the level of city growth, but will be able to promote it and stimulate it in the long term; • Limitation of external funding and available local budgets; • Administrative and legal restrictions. Laws can be reformed to adapt to the needs of each moment; • Citizen participation and engagement. Citizens must know, understand, accept the culture of the new model and adapt to the measures of its implementation since the model will be constantly dynamic.

References 1. Directorate-general for sustainable mobility policy and electrification of Quebec: Sustainable mobility policy 2030, p. 54 (2018) 2. European Commission: Towards a new culture of urban mobility. Green paper, p. 25 (2007) 3. European Commission: Towards a competitive and resource-efficient transport system.White Paper Roadmap for a Single European Transport Area, p. 35 (2011) 4. Office for the coordination of sustainable development of Quebec: Quebec indicators of sustainable development. The summary document, p. 10 (2010) 5. Ministry of Equipment, Transport, Logistics, Water: Roadmap for Sustainable Mobility in Morocco, p. 19 (2018) 6. Secretary of State to the Minister of Energy, Mines and Sustainable Development: National Strategy for Sustainable Development 2030, p. 138 (2017) 7. Ministry of Energy, Mines, and Environment: Second updated Biennial Report of Morocco within the framework of the UNFCCC, p. 212 (2019) 8. Maaroufi, M.M., Stour, L., Agoumi, A.: Contribution of digital collaboration and E-Learning to the implementation of smart mobility in Morocco. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 609–619. Springer, Cham (2021). https://doi.org/10.1007/ 978-3-030-73882-2_55

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9. Transport Economics Laboratory - National School of State Public Works (ENTPE): Sustainable mobility indicators: from the state of the art to the definition of indicators in the SIMBAD project. Interim report No. 2, p. 99 (2005) 10. Ministry of Equipment, Transport, Logistics, Water: METLE 2018in figures, p. 36 (2019) 11. French Ministry of the Environment and the French Living Environment - Ministry of Transport: Guide to noise from land transport, p. 90 (1978) 12. Department of surveillance and risk prevention: The cadastre of atmospheric emissions in the Fez region, p. 54 (2015) 13. MED-IEE project: Energy Efficiency Indicators for the Mediterranean, p. 80 (2014) 14. Maaroufi, M.M, Stour, L., Agoumi, A.: Striving for smart mobility in Morocco: a case of lanes designated to heavy goods vehicles in Casablanca. Eng. Manage. Product. Serv. 13(1), 72–86 (2021). https://doi.org/10.2478/emj-2021-0006

Demand Driven DRP vs DRP: An Empirical Study Yassine Erraoui(B)

and Abdelkabir Charkaoui

Faculty of Science and Techniques, University Hassan 1, Settat, Morocco {y.erraoui,abdelkabir.charkaoui}@uhp.ac.ma

Abstract. Demand Driven Distribution Resource Planning (DDDRP) is a proposed method for managing flow in distribution networks that is supposed to manage customer demand uncertainties better than traditional management methods. This paper proposes a case study allowing to compare between this model and the conventional push-flow systems like Distribution Resource Planning (DRP). A discrete event simulation (DES) is adopted to measure efficiency of each model towards demand fluctuations, using supply chain Key performance indicators (KPI). Results present empirical comparison and show the benefits and interests behind the pull-flow approach - Demand Driven DRP - in distribution networks, in terms of Working Capital (WC) and service level. Keywords: Supply chain · Distribution network · Demand-driven distribution resource planning

1 Introduction Companies, nowadays, are required to properly manage product and information flow through distribution networks in supply chain. Flow management consists of directing all the successive activities carried out during the distribution of the product, which is a crucial key as it affects directly inventory situation in every distribution unit and consequently the entire working capital and the service level. Bad flow management may lead to a gap between the achieved sales to the buyer and the produced quantity in the factory [1]. Moreover, optimizing flow must take in consideration many parameters like the lead time, storage policy, accuracy of forecasted customer demand [3], capacity limits and other issues [4]. In this paper, we present – in a comparative way - the models of flow management in distribution networks. In this respect, Distribution Resource Planning (DRP) is one of the well-known push systems that rely on demand forecasting and decide time and quantity of product replenishments for downstream locations [5]. On the other hand, pull systems have been developed as part of concepts like LEAN, Theory of Constraints and Just-In-Time, offering approaches based on real demand, where the product is delivered after justification of the customer demand. Nevertheless, Demand Driven Distribution

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 114–123, 2022. https://doi.org/10.1007/978-3-031-02447-4_12

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Resource Planning (DDDRP) is a concept that takes advantages from both approaches, by implementing buffers at strategic points in distribution networks, and pulling flow between those points. It combines LEAN [6], Theory of Constraints [7], and DRP logics [8]. This article offers an empirical study, using a real industrial case, to test the efficiency of adopting a demand driven approach in distribution networks. For this, we compare between Demand Driven DRP (DDDRP) model on one hand, and DRP model on the other hand. The approach consists on subjecting the models to multiple variability scenarios, and analyzing the impact on inventory situation and service levels. The paper starts with the case study. Then, it focuses on the models implementations and finally the interpretation of the results.

2 Empirical Study 2.1 Case Study Data and Models Implementation The case study deals with the distribution network of a supply chain in a Moroccan company specialized in milk industry. The distribution network includes three echelons: Urban Distribution Centers (UDC), Regional Distribution centers (RDC) and Factory echelon (Fig. 1).

Fig. 1. The studied Distribution network

To conduct the study, the input parameters for all the UDCs and RDCs are initial On hand inventory, forecasted average daily usage (ADU) for every buffered location, and the Decoupling Lead time (DLT). Moreover, lead time (LT) (from RDCs to UDCs is 3 days, as well as the Lead Time from Factory to RDCs). Costs for holding inventory and selling prices are also given. Table 1 illustrates an example of input data for the first three UDCs considering the hypothesis of a stable demand over the year. LT and variability factors are determined based on the length of LT and size of demand variability, ADU is concerning the first month.

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Y. Erraoui and A. Charkaoui Table 1. Some Input Data for the first three urban distribution centers

Distribution unit

Initial on hand inventory

ADU

LT factor

Variability factor

UDC1

2000

400

50%

20%

UDC2

15000

3100

50%

20%

UDC3

15000

3500

50%

20%

2.2 Models Implementation DPP Model Implementation DRP model takes in consideration Forecasted demand which are elaborated locally, every echelon compiles a DRP grid (Table 2) considering its own security levels. Resultant orders are transmitted to suppliers, triggering replenishment operations. The final push decision is about timing and quantities of replenishments of all downstream locations. Table 2 represents an elaborated DRP for UDC1, with a variable forecasted demand, for the first 10 days of the year. Table 2. Elaborated DRP for UDC1 considering the first 10 days Security stock On Hand Start Supply Quantity Lead time (days)

1

2

3

4

5

6

7

8

9

10

Demand

550

550

550

550

550

2200

2200

550

550

550

8000

End Inventory

8000

7450

6900

6350

5800

5250

3050

850

2300

1750

2000

Projected On-Hand

8000

7450

6900

6350

5800

5250

3050

2850

2300

1750

Schedule Receipt

0

0

0

0

0

0

0

2000

0

0

Schedule Start

0

0

0

0

2000

0

0

2000

0

2000

550

3

Week

DDDRP Model Implementation As regard the Demand Driven DRP implementation, Strategic Buffer Positioning is referred to the ‘Hub and Spoke’ Configuration [9], which consists on placing an ‘Inventory Hub’ in the source unit, and small stock points on the warehouses (Fig. 2). All the UDCs and RDCs are Buffered. RDCs are considered with enough capacity to feed UDCs demand. Table 3 illustrates the second step of Buffer sizing, presenting part of the results of the first month, for all the UDCs units. these calculated levels of Buffers are the base of the step of demand driven planning (Table 4). Column 1 represents the real demand, then future spikes are checked for the horizon of three days. After that, net flow position (NFP) in the Buffer is daily specified to calculate the order amount to be replenished. The request day is the day at which the order amount is expected to arrive. Planning priority shows the NFP position with regard to the Buffer sizes, a supply is planned if the situation under the Top of Green, and the order amount is Top of Green minus the NFP. The buffer profile and levels exploit the following DDMRP formulas (1 to 5) [8].

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Sourcing Unit

Warehouse 1

Warehouse 2

Warehouse 3

Fig. 2. Hub and spoke configuration

Red Base = ADU ∗ DLT ∗ lead time factor

(1)

Red Safety = Redbase ∗ variability factor

(2)

Total red zone = Red Base + Red Safety

(3)

Yellow Zone = ADU ∗ DLT

(4)

Green Zone = ADU ∗ DLT ∗ Lead Time factor

(5)

OH + OS − QS = net flow position

(6)

OH = On Hand Quantity, considering the available physical stock; OS = Open Supply Quantity, considering the ordered but not received Stock. QS = Qualified Sales, considering Sales orders past due, sales orders due today, and qualified spikes. The last step is the Execution of the planned orders; it is based on Buffer status; this information is determined by dividing the On-Hand Status by the Top of the red (TOR). It makes the decision possible about the execution of the planning made in the previous step. The item that is in the smallest status forms an emergency of execution. As we were only interested in one product, we did not give much interest to this part.

Buffer Levels

Parameters

960

1200

960

969

2169

3129

Yellow zone

Green zone

TOR*

TOY**

TOG***

20%

LT Factor

9

50%

Variability Factor

Red Safety

3

DLT

Red Security

400

ADU

1

UDCs

24254

16814

7514

7440

9300

74

7440

20%

50%

3

3100

2

27384

18984

8484

8400

10500

84

8400

20%

50%

3

3,500

3

4694

3254

1454

1440

1800

14

1440

20%

50%

3

600

4

9388

6508

2908

2880

3600

28

2880

20%

50%

3

1,200

5

7824

5424

2424

2400

3000

24

2400

20%

50%

3

1000

6

7824

5424

2424

2400

3000

24

2400

20%

50%

3

1000

7

17995

12475

5575

5520

6900

55

5520

20%

50%

3

2,300

8

Table 3. Buffer sizing result

27384

18984

8484

8400

10500

84

8400

20%

50%

3

3500

9

6259

4339

1939

1920

2400

19

1920

20%

50%

3

800

10

25036

17356

7756

7680

9600

76

7680

20%

50%

3

3200

11

18777

13017

5817

5760

7200

57

5760

20%

50%

3

2,400

12

16430

11390

5090

5040

6300

50

5040

20%

50%

3

2100

13

9388

6508

2908

2880

3600

28

2880

20%

50%

3

1,200

14

18777

13017

5817

5760

7200

57

5760

20%

50%

3

2400

15

118 Y. Erraoui and A. Charkaoui

Demand Driven DRP vs DRP: An Empirical Study

119

Table 4. Demand driven planning result Day Sales order due today

Total On-hand On-order Showing-up Qualified NFP future order demand spike

Order Request Planning amount date priority

1

34604 16749 80000

0

0

51353

28647 23461

4

54.98%

2

0 16749 45396

23461

0

16749

52108

0

-

100.00% 100.00%

3

16749

0 45396

23461

0

16749

52108

0

-

4

13029

0 28647

23461

23461

13029

39079 13029

7

75.00%

5

0

0 39079

13029

0

0

52108

0

-

100.00%

0

52108

100.00%

6

0 39079

13029

0

0

0

-

7

913 21648 39079

13029

13029

22561

29547 22561

10

8

0 21648 51195

22561

0

21648

52108

0

-

100.00%

0 21648 51195

100.00%

9

56.70%

22561

0

21648

52108

0

-

10

21648

0 51195

22561

22561

21648

52108

0

-

100.00%

11

9535

0 52108

0

0

9535

42573

0

-

81.70%

12

764

0 42573

0

0

764

41809

0

-

80.24%

13

0

0 41809

0

0

0

41809

0

-

80.24%

14

5581 14838 41809

0

0

20419

21390 30718

17

41.05%

15

0 14838 36228

30718

0

14838

52108

0

-

100.00%

16

0 14838 36228

30718

0

14838

52108

0

-

100.00%

17

14838

0 36228

30718

30718

14838

52108

0

-

100.00%

18

10815

0 52108

0

0

10815

41293

0

-

79.25%

19

3666

0 41293

0

0

3666

37627 14481

22

72.21%

20

776

0 37627

14481

0

776

51332

0

-

98.51%

21

5566

0 36851

14481

0

5566

45766

0

-

87.83%

0 23301 31285

14481

14481

23301

22465 29643

25

43.11%

23

0 23301 45766

29643

0

23301

52108

0

-

100.00%

24

1133 23301 45766

29643

0

24434

50975

0

-

97.83%

29643

29643

23301

50975

0

-

97.83%

22

25

23301

0 44633

26

0 18096 50975

0

0

18096

32879 19229

29

63.10%

27

12189 18096 50975

19229

0

30285

39919

0

-

76.61%

28

0 41036 38786

19229

0

41036

16979 35129

31

32.58%

3 Models Comparison 3.1 Scenarios of the Simulation This study proceeds to challenge the Demand Driven DRP under several demand variabilities and compare it with a traditional DRP concept. The approach consists on using multitude of scenarios resumed in Table 5, Fig. 3 and Fig. 4.

120

Y. Erraoui and A. Charkaoui Table 5. Scenarios of demand variability

Scenario

Particularity

1

Stable demand along 12 months

2

Variable demand characterized with 2 spikes every week. Each spike is 5 time the ordinary demand. (Fig. 3)

3

Monthly Seasonality, and the demand is fix along one month. (Fig. 4)

600 500 400 300 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Fig. 3. Variable demand with spikes

5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

Fig. 4. Seasonal demand

Demand Driven DRP vs DRP: An Empirical Study

121

The simulation with ARENA achieved results on key performance indicators (KPI) related to service and inventory levels. The amount of annual demand for all the scenarios is kept the same. Then, the variation of Working Capital (WC) is measured for each model, as well as the On-time Shipping (OTS) rate. Table 6 resume the results with regard to all the studied scenarios considering a deterministic lead time. Table 6. Simulation results by ARENA simulation Scenario

Stable

Model

DRP

Seasonal

KPI

WC

OTS

WC

OTS

WC

OTS

WC

OTS

WC

OTS

WC

OTS

RDC1

5437.7

99.53%

1173

100%

7594

99.02%

1702

89.77%

5126

95.59%

2393

96.37%

UDC1

1637.54

DDDRP

Spikes

DRP

1302

DDDRP

1483.46

DRP

1615

DDDRP

1895

3278

UDC2 UDC3 RDC2

3324

UDC4

1389.72

98.25%

346

100%

551

3505

91.58%

1075.91

1143

90.00%

1125

3574

100.00%

954

1619

95.38%

1581

UDC5 UDC6 RDC3

5375.2

UDC7

2258.05

100.00%

1064

100%

1319

7897

92.28%

808.18

1612

97.69%

1940

5276

99.30%

1556

2919

97.03%

2879

UDC8 UDC9 RDC4

5329.2

UDC10

1963.59

100.00%

978

100%

1234

5091

85.61%

694.23

2720

91.75%

2486

5861

94.39%

1478.44

3439

94.72%

3323

UDC11 UDC12 RDC5

4939.9

UDC13

2180.49

100.00%

882

100%

1153

5416

99.30%

1583.93

2006

97.36%

2015

4321

100.00%

1427.33

2296

99.01%

2681

UDC14 UDC15 Total KPI

33835.39

99.56%

10002

100%

35148.71

93.56%

18364

93.31%

31468.77

97.86%

26408

96.50%

3.2 Interpretations Table 6 is an exhaustive illustration for the spent capital in each distribution unit, as well as the resumed OTS for every three UDCs separately. For stable demand, both models present an ideal OTS, but the remarkable difference lies in the level of stock required to ensure this rate. The safety stock used for the DRP model is constant throughout the year. Otherwise, the factors used for the DDDRP model (LT factor = 0.5, variability factor = 0.2) reflect the low level of variability and the average LT of three days. The end result shows the advantage of demand-driven distribution in terms of Working Capital.

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On the other hand, the seasonal aspect has changed the results slightly. In fact, an increase in the level of inventory is noticed for both models. It is valorized at 4% for the DRP model and at 83% for DDDRP. LT factor remains the same as before, but the variability factor must increase taking into account the variation in demand from a month to another. The safety stock policy remains the same as before, while the Buffer levels are adjusted monthly. The variable demand scenario (Spikes) shows that inventory level has decreased for DRP, and increased for DDDRP. These fluctuations are due to the estimation of LT and variability parameters. Also, the stability of DRP inventory level is due to the good level of precision adopted for the forecasted demand. To resume, DDDRP remains better than DRP – for all scenarios - in term of necessary WC that ensure an acceptable service rate.

4 Conclusion This article offers an empirical study and focuses on the comparison between flow management policies in distribution networks. The purpose is to test the efficiency of supply chain in terms of inventory situation and service levels under several scenarios of demand variability. A case study was conducted to compare between conventional DRP and DDDRP approach. To do this, a model based on real demand, (Demand Driven DRP) was developed, describing its theoretical elements and specifying the necessary steps for its implementation. Several demand variability scenarios were set up, and a discrete event simulation showed results on service level and Working Capital for each model. The results showed that demand-driven DRP has good responsiveness to demand variability. Conventional DRP requires good forecasting accuracy, and the WC level is always at better levels in DDDRP than in DRP. In this work, DDDRP model has shown its advantages. However, it presents difficulties at certain levels of modelling. In fact, the choice of lead time and variability factors is crucial, and affects the Buffer levels, and subsequently the overall inventory level. Works should be conducted, using heuristics, to seeking for the optimal parameters in this context. Otherwise, the study adopted a strategy of Buffering all the units of the echelon (UDCs and RDCs). The optimal choice of strategic points for setting up buffers remains a challenge, especially for distribution networks with more than three levels. As a perspective, the study of the process variability, which could consider a stochastic processing time, can lead for more optimality of the management policy in distribution networks.

References 1. Lee, H.L.: Taming the bullwhip. J. Supply Chain Manag. 46(1), 7–8 (2010) 2. Lee, H.L., Padmanabhan, V., Seungjin, W.: The bullwhip effect in supply chains. Sloan Manage. Rev. 38(3), 93–102 (1997) 3. Heydari, J., Kazemzadeh, R.B., Chaharsooghi, S.K.: A study of lead time variation impact on supply chain performance. Int. J. Adv. Manuf. Technol. 40(11–12), 1206–1215 (2009)

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4. Alony I, Munoz A.: The bullwhip effect in complex supply chains. 2007 International Symposium on Communications and Information Technologies Proceedings ,Darling Harbour, Sydney, Australia, 17–19 October, pp. 1355–1360 (2007) 5. Watson, K., Polito, T.: Comparison of DRP and TOC financial performance within a multiproduct, multi-echelon physical distribution environment. Int. J. Prod. Res. 41(4), 741–765 (2003) 6. Jaca, C., Santos, J., Errasti, A., Viles, E.: Lean thinking with improvement teams in retail distribution: a case study. Total Qual. Manag. Bus. Excell. 23(3–4), 449–465 (2012) 7. Cyplik, P., Doma´nski, R.: Implementation of the theory of constraints in the area of stock management within the supply chain-a case study. LogForum 5(3), 1–12 (2009) 8. Miclo, R., Fontanili, F., Lauras, M., Lamothe, J., Milian, B.: An empirical comparison of MRPII and demand-driven MRP. IFAC-Papers On Line 49(12), 1725–1730 (2016) 9. Ptak, C., Smith, C.: Demand Driven Material Requirements Planning (DDMRP). Industrial Press, Norwalk (2016)

Effect of Questions Misspelling on Chatbot Performance: A Statistical Study Rachid Karra(B) and Abdelali Lasfar LASTIMI Laboratory, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco [email protected]

Abstract. In recent years, the size of NLP models, the number of parameters they use, and their performances have gotten all the attention. The race for gigantism increases the score of the models at the expense of the environment and high energy consumption. However, the quality of the data with which they interact influences the reliability of these models. To our knowledge, this is the first paper to investigate the influence of misspelling on question-answering agent responses. It is an important aspect of chatbot seen as a whole and complex system with its dynamic relations and interactions. According to the results, the R-NET model performs well against misspelling. Keywords: Chatbot · Data quality · Deep learning · Education · Human-machine Interaction

1 Introduction 1.1 Background It is difficult to define artificial intelligence (AI). Kaplan and Haenlein [1] define AI as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” Another definition is the science of making machines do things that would require intelligence if done by humans” [2]. But we can also say it is a set of techniques allowing machines to perform tasks and solve problems reserved for humans and some animals. AI can recognize objects in an image, plan the movements of a robot driving a car [3], enhance energy transport (smart grid) [4, 5], translate a text, lead a dialogue on different topics and play some complex game like chess or Go. Machine learning as a subdomain of Applied Artificial Intelligence or narrow Artificial Intelligence focuses on creating algorithms that use prior experience with a specific task to improve its performance (ex: self-driving car, web search). Machine learning is subdivided into three main categories: Supervised learning, Unsupervised learning, Reinforcement learning. Deep learning is a subset of machine learning. It is a neural network composed of an input layer, a hidden layer where the learning happens, and an output layer [6]. The process of learning gets through analyzing enormous amounts © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 124–132, 2022. https://doi.org/10.1007/978-3-031-02447-4_13

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of data. Deep learning provides personalized teaching by analyzing student reactions, and interactions in real-time, offering better learning conditions. Machine learning also plays a key role in monitoring assessment and reviews by removing bias and simplifying this process [7]. A chatbot is “A computer program designed to simulate conversation with human users,” according to the Oxford English Dictionary. It is an application of machine learning and a Human-Computer Interaction (HCI) model [8]. Fields like education, ecommerce, and entertainment utilize chatbots. Service companies (cleaning, food delivery) use chatbots to coordinate their services. Conversational interfaces carry out this task. Chatbots are increasingly playing a vital role in healthcare. For example, they provide personalized therapy and advice to patients. They can offer a diagnosis and suggest treatments based on the symptoms. It includes chatbots such as “OneRemission” (cancer information) or Youper, which treats the emotional health of patients. More recently, chatbots provided information on COVID 19 (HealthBuddy). Chatbots can also encourage sports and help treat sleep disorders [8]. In education, chatbots allow students to be taken on individually, especially in massive open online courses (MOOCs). At the teaching level, chatbots can learn from a wide variety of sources in addition to learning from interactions with students. Chatbots can gain valuable data and insights on user behavior [9] and thus constitute a source to improve the teaching experience. They help administration by interacting with students on different topics such as assignment due date, course registration, exam schedule, communicating grades, graduation [10]. 1.2 The Problem Sensitive areas such as education or health use chatbots, so it is important to check their robustness and behavior when faced with atypical questions such as long and complicated sentences or those containing errors. We talk about the textual adversary. One of the first to explore the textual adversary and its impact on Understanding Neural Networks models was Jia and Liang [11]. Szegedy et al. [12] evaluated the robustness of an image classification model by introducing a disturbance at the input (adversarial examples). Unlike humans, the model failed in classification. Concatenation, editing, paraphrasing, and substitution are examples of adversarial attacks on chatbots by altering questions [11]. Spelling errors are part of mistakes made by chatbots users, especially in the education context. Therefore, chatbots should be able to overcome them. 1.3 The Contribution Working on adversarial attacks is recent. Alzantot et al. [13] generated both semantically and syntactically adversarial examples against models. These models were trained on the IMDB sentiment analysis task and the Stanford Natural Language Inference (SNLI). On the one hand, [14] proposed adversarial attacks by paraphrasing sentences, whereas [15] published the findings of text classification attacks on a BERT model by identifying the model’s influential words and replacing them with the most semantically similar words until the prediction is changed. Ebrahimi et al. [16] attacked on a character level

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a CharCNN-LSTM architecture. Andriushchenko et al. [17] use Random Search to approach target perturbation with each iteration (square attack). Our research proposes a statistical study of chatbot behavior, based on the R-NET self-attention model, in case of a misspelling (character substitution) in the questions and how it reacts to this degradation. It also compares the robustness of this model against one, two, and three errors in the examples specially created for this study concerning the fields of education and computer science. The remainder of this paper is structured as follows: We introduce the methodology and data of our study. We consider chatbots as a black box. We apply our approach to the dataset by generating adversaries in questions partitioned into three levels {1–2 or 3 misspelling}. We discuss the results and evaluate the robustness of the QA agent. Finally, we conclude the study by reminding the results and pointing out some open issues.

2 Experiments 2.1 R-NET Model R-NET is an end-to-end neural network model for reading comprehension and question answering. It uses pre-trained GloVe for word and character embedding [18]. GloVe was trained on Wikipedia and the English corpus and had 300-dimensional vectors. R-NET offers a “gated attention-based recurrent network,” which considers the importance of each word in context to answer the question [19]. It, therefore, assigns a different weight to each part of the context according to its importance to the question. Then R-NET proposes a self-matching mechanism that can efficiently deduce the answer by analyzing the entire context. The gated matching layer efficiently encodes the question by considering each word in the context. In addition, R-NET uses a “gated attention-based recurrent network” to get the dependencies between the words of the context between it [19] (Fig. 1). Chatbots use several approaches to guide a conversation such as Disambiguation, Digression Prompts, and Dialogs in the Event of Failure. However, using these approaches requires user intervention and involves active interaction between the user and the chatbot. On the other hand, anomalies detection and automatic correction do not require any intervention by the system’s user. It is therefore essential to see the impact of data entry errors on the responses of a chatbot. 2.2 Data Description and Methodology We use 79 questions related to the Python programming language. Each question has its context as well as the corresponding correct answer. Afterward, we design an application that inserts errors randomly in the questions. We extracted ten questions with {one, two, or three} errors from each question. As a result, we have three databases with 790 questions each. The first have questions with only one misspelling, the second with two errors, and the third with three errors. We fed the questions and their context to the model to get the new answer. Then, we compare it to the initial response to ensure its accuracy. The length of each original

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Fig. 1. R-NET architecture overview.

question is the number of words in the sentence [19]. We use this measure to calculate the correlation between the sentence length and the number of incorrect answers. Statistical analysis is given below for each of the scenarios. 2.3 Context Architecture Figure 2 describes how we can get the context of the current course. The courses are organized in modulestores in a document-oriented database (MongoDB). There are three collections in modulestores: active_versions, structures, and definitions. Blocks of chapters, verticals, problems, and HTML elements are the components of a typical course [20]. The course structure is immutable, stored in a single structure collection. It adds a new document, whenever there is a change [21]. The content of the course is stored separately in a definition document.

acve_versions

structure

definion

course blocks:

data/context: chapter vercal

Franklin Delano Roosevelt …… to by his inials FDR, was an American lawyer ……. and polician who served as the 32nd president of the United States from 1933 unl his death in 1945….

html

Fig. 2. Structure of the course’s context based on Open edX database architecture.

The QA system searches for the active version of the course. It points to its published structure. In our study, the HTML block is equivalent to the context.

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3 Results and Discussion Questions with One Error. The analysis of the answers to the questions with a 1-Error allowed us to draw up the following table of results: Table (1) shows that out of 79 questions, 24 were not affected by the errors introduced, i.e., 30.4% of the questions. The number of questions with only one incorrect answer is 16.5%, on par with those with two wrong answers. We found that 86% of the questions have less than 4 out of 10 incorrect answers after entering a single error. Calculating the Pearson correlation1 between the number of incorrect responses and the length of the sentence showed a negative but weak correlation with a correlation coefficient of −0.2719. Although it did not show any correlation, the plotting representing the number of wrong responses (on the x-axis)/question length (on the y-axis) confirms the observations (Fig. 3). 14

1-Error

Number of words

12

10

8

6

4

2

0 0

1

2

3

4

5

6

7

8

9

10

Number of wrong answers

Fig. 3. The number of incorrect answers by the length of the 1-error question.

Questions with Two Errors. Table (1) shows an increase in questions with incorrect answers. Indeed, the questions with no wrong answers represent only 16.5%. As for the questions, the number of incorrect answers is less than or equal to four. Their proportion no longer represents 65.8%. The calculation of the Pearson correlation between the number of incorrect responses and the length of the sentence as the graph of these two indicators showed a negative but weak correlation with a correlation coefficient of −0.3379 (Fig. 4).

1 Correlation measures a link between two variables. A change in one variable has an impact on

another variable in the same direction (positive correlation) or the opposite direction (negative correlation). The Pearson correlation coefficient varies between 1 and −1. The meaning of its values is as follows: 0.00–0.10 no correlation, 0.10–0.39 weak correlation, 0.40–0.69 moderate correlation, 0.70–0.89 strong correlation, 0.90–1.00 very strong correlation.

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Table 1. Categorization of questions by the number of wrong answers. 1-Error

2-Error

3-Error

NWR2

NQ

0

24

0

13

0

6

1

13

1

7

1

8

2

13

2

9

2

7

3

6

3

12

3

11

4

12

4

11

4

8

5

5

5

13

5

8

6

5

6

5

6

12

–

–

7

6

7

6

8

1

8

1

8

5

–

–

9

1

9

6

–

–

10

1

10

2

NWR

NQ

NWR

NQ

14

2-Error

Number of words

12

10

8

6

4

2

0 0

1

2

3

4

5

6

7

8

9

10

Number of wrong answers

Fig. 4. The number of incorrect answers by the length of the 2-error question.

Question with Three Errors. The deterioration in the ratio of correct answers continues to increase when we choose three random errors. As a result, the share of questions with no incorrect answer has fallen to 7.6%, and questions with four or fewer wrong answers now only represent 50.6%. As in the first two cases, the Person coefficient reaches −0.349. It reveals a negative correlation.

2 NWR: Number of Wrong Responses and NQ: Number of Questions.

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The graph retracing the length of the sentence as a function of the number of questions with incorrect answers does not indicate any other correlation between these two variables. We notice that the model R-NET has good robustness even with three errors (in the question). It is due to the character embedding aspect of the R-NET architecture (character n-grams [18]) (Fig. 5). 14

3-Error

Number of words

12

10

8

6

4

2

0 0

1

2

3

4

5

6

7

8

9

10

Number of wrong answers

Fig. 5. The number of incorrect answers by the length of the question with 3 errors.

In summary, the negative correlation between question length and the number of incorrect answers shows that the longer the sentence, the more the impact of an error is diluted. However, this causality remains relatively weak in the three cases studied. The three scenarios show, what emerges instinctively, that the number of incorrect answers decreases if the number of errors occurring in the sentence increases. As shown in the table below (Table 2): Table 2. Distribution of questions according to their categories (correct, partial, wrong) and the number of committed errors. 1 error

2 errors

3 errors

613

506

423

Partial

9

15

12

Wrong

168

269

355

Correct

The coefficient of 0.9989 confirms the positive correlation. We cannot identify any pattern to explain the mechanisms that lead to an error. However, it is sure that the use of layers of character embedding by R-NET allowed having a good percentage of correct answers despite the errors which sometimes affected several keywords as shown in the examples below (Table 3):

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131

Table 3. Examples of 3-Error questions. Question with 3-error

Original question

Which opDrator is used to select valueshwithin a rakge

Which operator is used to select values within a range

What Moeszpython use to selimit blocks What does python use to delimit blocks Dhoeare the pythoristas

Who are the pythonistas

wSo aCe the pythoiistas

In some cases, the analysis of the sentences remaining correct shows that all the words of the sentence were affected by an error during the iterations. Sometimes the result is different, despite the same word is wrong. For example: ‘Ohat is Expressions’ gives a correct result, whereas ‘WhRt is Expressionsr’ gives an incorrect response.

4 Conclusion This work aims to present the effect of misspelling on dialogue system responses. The reliance on the NLP model only cannot always give reliable results. We show that filters for misspelling and grammar error detection and correction have great utility as a chatbot component. It gives an idea about how the Question-Answering agent behaves in degraded mode and how the entire system should react to the situation. The results show the great sensitiveness of misspelled questions on chatbot responses. We suggest using a misspelling pipeline that can correct sentences in one of two ways: first, it can fix single words, and second, it can fix words based on their syntactic context. In future works, we will test the chatbot on real student mistakes instead of artificially generated errors [22] and transformer-based chatbots like BERT or GPT-2.

References 1. Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manage. Rev. 61(4), 5–14 (2019). https://doi.org/10. 1177/0008125619864925 2. Minsky, M. (ed.): Semantic Information Processing. MIT Press, Cambridge (1969) 3. Narasipuram, R.P., Mopidevi, S.: A technological overview & design considerations for developing electric vehicle charging stations. J. Energy Storage 43, 103225 (2021). https://doi.org/ 10.1016/j.est.2021.103225 4. Narasipuram, R.P.: Modelling and simulation of automatic controlled solar input single switch high step-up DC-DC converter with less duty ratio. Int. J. Ind. Electron. Drives 3(4), 210–218 (2017). https://doi.org/10.1504/IJIED.2017.087611 5. Narasipuram, R.P.: Analysis, identification and design of robust control techniques for ultralift Luo DC-DC converter powered by fuel cell. Int. J. Comput. Aided Eng. Technol. 14(1), 102–129 (2021). https://doi.org/10.1504/IJCAET.2021.111640

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6. “Natural Language Processing with TensorFlow 2.0,” springerprofessional.de. https://www. springerprofessional.de/en/natural-language-processing-with-tensorflow-2-0/17513238. Accessed 05 December 2021 7. Asthana, P., Hazela, B.: Applications of machine learning in improving learning environment. In: Tanwar, S., Tyagi, S., Kumar, N. (eds.) Multimedia Big Data Computing for IoT Applications. ISRL, vol. 163, pp. 417–433. Springer, Singapore (2020). https://doi.org/10.1007/978981-13-8759-3_16 8. Adamopoulou, E., Moussiades, L.: Chatbots: History, technology, and applications. Mach. Learn. Appl. 2, 100006 (2020). https://doi.org/10.1016/j.mlwa.2020.100006 9. Vogel, J.: Chatbots: development and applications. p. 67 10. Hien, H.T., Cuong, P.-N., Nam, L.N.H., Nhung, H.L.T.K., Thang, L.D.: Intelligent assistants in higher-education environments: The FIT-EBot, a Chatbot for administrative and learning support. In: Proceedings of the Ninth International Symposium on Information and Communication Technology, New York, NY, USA, pp. 69–76, December 2018. https://doi.org/10. 1145/3287921.3287937 11. Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. ArXiv170707328 Cs, July 2017. http://arxiv.org/abs/1707.07328. Accessed 28 Nov 2021 12. Szegedy, C., et al.: Intriguing properties of neural networks. ArXiv13126199 Cs, February 2014. http://arxiv.org/abs/1312.6199. Accessed 06 Dec 2021 13. Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., Chang, K.-W.: Generating natural language adversarial examples. ArXiv180407998 Cs, September 2018. http://arxiv. org/abs/1804.07998. Accessed 02 Jan 2022 14. Iyyer, M., Wieting, J., Gimpel, K., Zettlemoyer, L.: Adversarial example generation with syntactically controlled paraphrase networks. ArXiv180406059 Cs, April 2018. http://arxiv. org/abs/1804.06059. Accessed 02 Jan 2022 15. Jin, D., Jin, Z., Zhou, J.T., Szolovits, P.: Is BERT really robust? A strong baseline for natural language attack on text classification and entailment. ArXiv190711932 Cs, April 2020. http:// arxiv.org/abs/1907.11932. Accessed 02 Jan 2022 16. Ebrahimi, J., Rao, A., Lowd, D., Dou, D.: HotFlip: white-box adversarial examples for text classification. ArXiv171206751 Cs, May 2018. http://arxiv.org/abs/1712.06751. Accessed 02 Jan 2022 17. Andriushchenko, M., Croce, F., Flammarion, N., Hein, M.: Square attack: a query-efficient black-box adversarial attack via random search. ArXiv191200049 Cs Stat, July 2020. http:// arxiv.org/abs/1912.00049. Accessed 02 Jan 2022 18. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543, October 2014. https://doi.org/10.3115/v1/D14-1162 19. Wang, W., Yang, N., Wei, F., Chang, B., Zhou, M.: Gated self-matching networks for reading comprehension and question answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, pp. 189–198 (2017). https://doi.org/10.18653/v1/P17-1018 20. edx/edx-platform. edX (2021). https://github.com/edx/edx-platform. Accessed 10 Oct 2021 21. Karra, R., Lasfar, A.: Enhancing education system with a Q&A Chatbot: a case based on open edX platform. In: International Conference on Digital Technologies and Applications, pp. 655–662. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-030-73882-2_59 22. Jansen, B.J., Spink, A., Saracevic, T.: Real life, real users, and real needs: a study and analysis of user queries on the web. Inf. Process. Manag. 36(2), 207–227 (2000). https://doi.org/10. 1016/S0306-4573(99)00056-4

Contribution to the Economic Analysis of Numerical Data of Road Accidents in Morocco Abdelaziz Zerka(B) and Fouad Jawab Industrial Technologies and Services - High School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco {abdelaziz.zerka,fouad.jawab}@usmba.ac.ma

Abstract. The main objective of this paper is to analyze road accidents in Morocco. This analysis could be effective in assessing the level of road insecurity and planning appropriate countermeasures to solve specific road safety problems. The approach adopted for this analysis is based on the deployment of cross-referenced data on road traffic accidents as recorded by the Moroccan National Road Safety Agency as well as data from the Moroccan High Commission for Planning and on data from the International Transport Forum and the World Bank. These different databases were explored because they include official data for the analysis of road accidents in Morocco. The analysis shows that the distribution of road traffic deaths and injuries in Morocco varies according to gender, age, month, time and city. The 15–44 age group is the most likely to be involved in road traffic accidents in Morocco, although men face a higher number of deaths and injuries than women. In addition, road accidents are relatively more numerous in certain extreme weather conditions, during working hours and holiday periods. The Wilaya of grand casa recorded the highest number of fatality risk and mortality rate. Human behavior is the most frequent source of road accidents in Morocco. The most effective way to reduce the number of deaths and injuries would be an integrated approach involving close collaboration between many sectors and focusing on multi-factorial causes: human, vehicular and environmental factors. Taking these factors into consideration helps reducing the risk of a road accident occurrence. Keywords: Road accidents · Injuries · Deaths · Road safety · Morocco

1 Introduction Road insecurity weighs heavily on the Moroccan economy. Traffic accidents, by their number, generate catastrophic, economic, health, social and environmental consequences, impacting on the whole community. According to the report of International Transport Forum (2019) [1], road accidents represent a significant cost for the Moroccan society. In 2017, accidents cost almost 2 billion Euros. Similarly, according to World Bank estimation, losses from traffic accidents account for nearly 2% of Moroccan GDP [2]. It is true that road safety measures aim at mitigating the negative consequences of traffic accidents. The benefits they generate are not the same as the reduction of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 133–144, 2022. https://doi.org/10.1007/978-3-031-02447-4_14

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damage or cost these accidents cause. In order to improve this situation, many strategies need to be implemented. They concern all road safety measures and issues, be them technological, legal, political, health or economic [3]. There is still a need to further improve road safety measures by taking into account the various components of the accident phenomenon, i.e. behavior, vehicle, infrastructure, etc. The analysis of postcrash damage data also makes it possible to assess the level of road safety in order to take appropriate measures [4]. To improve road safety in Morocco, it is necessary not only to define strategies and action plans, but above all, to diagnose accident data on the roads, namely: the analysis of road accidents at the national level, especially at the level of cities, the distribution of data on deaths and injuries according to gender, age, months, time and the analysis of the causes of accidents [5]. This can contribute to the optimization of road safety. The approach adopted here is based, among other things, on a scientific analysis and scanning of numerical accident data, which has made it possible to identify strategic issues for which the potential for reducing the number of victims or the severity of the after-effects of accidents is greater [6]. The following sections compose this paper: Research’s Context in Sect. 2. In Sect. 3, Research method. Section 4 elaborates on Analysis of road accidents in Morocco. Finally, Sect. 5 details the way forward, followed by a conclusion.

2 Research’s Context 2.1 Traffic Accidents: From Accidents to Accidentology Traffic accidents are collisions that occur on the road network between a vehicle and any other object (other vehicle, person, etc.). They can result in a loss of life, injury and material damage. Many factors contribute to its severity. Road accident is analyzed and treated as a system. Every road accident involves three factors that determine, in varying proportions, the severity of the accident: vehicle – infrastructure human behavior [2, 3] Considers accidents as the consequence of transgressions and drivers being responsible for accidents. Historically, road accidents were considered to be fortuitous events and an inevitable consequence of road traffic. According to [4], the term “accident” gives the meaning of something inevitable and unpredictable and an uncontrollable event. However, today, a traffic accident and its related data can be the subject of a rational scientific analysis and an indispensable element for planning road safety interventions. Since the 1960s, in the scientific literature, several research works have been conducted to analyze this phenomenon of road accidents [5–11]. These researchers have tried to diagnose the causes of their occurrence, predict the means and preventive measures and reduce their number and severity. This work involved all scientific disciplines in the field of engineering, psychology, medicine, economics and management etc. All these skills have been combined to give birth to a scientific discipline called “accidentology”. “Accidentology” is mainly the detailed study of accidents that is most developed. As in economics, a distinction can be made between a “micro” analysis, which consists of studying the mechanism of the individual accident, and a “macro” approach, which consists of studying all accidents [12]. This means that even a minimal amount of accident data can be used to

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assess the possible safety level of a road network and to discover black spots with a high number of accidents, in order to understand their genesis and course. This allows the implementation of effective and preventive actions and measures in road safety. Like accidentology, road safety is a relatively young science that only started in 1972 [12]. Today, it has become a major issue and concern of our contemporary societies. Their evolution over time has been based on this system: man-vehicle-environment. This eclectic approach has enabled road safety research to benefit from progress in the fields of psychology, physiology, ergonomics, education sciences and economic and management sciences. 2.2 Road Safety Situation in Morocco In Morocco, traffic accidents cause an average of nearly 3,700 deaths and 12,000 serious injuries per year, i.e. an average of 10 deaths and 33 serious injuries per day. Since 2000, the number of annual road deaths has fluctuated between 3,500 and 4,300, with no visible tendency. The number of road deaths per 100,000 inhabitants in Morocco has known a decrease of about 17% between 2000 and 2018. In 2018, 10.37 road deaths per 100,000 inhabitants were recorded, compared to 12.6 in 2000. For comparison, according to the International Transport Forum report, the European Union average was 4.9 deaths per 100,000 inhabitants in 2018. Despite a slight improvement in some key indicators in Morocco, the country is still suffering from the consequences of serious road insecurity. With a fleet of nearly 4.3 million vehicles on the road, Moroccan roads remain among the deadliest in the world. Deaths per 100 000 inhabitants are more than 12 times higher than in France, 14 times higher than in Italy and 17 times higher than in Denmark, Japan and Spain. According to the International Transport Forum report, losses by type of road users in Morocco, vulnerable uses come in the first place of victims. In 2017, they accounted for nearly 63% of all road fatalities (29% motorcyclists, 28% pedestrians and 6% cyclists). Car users accounted for 30% of road deaths. The 162% increase in the number of motorcyclists killed between 2000 and 2017, is a serious concern. It is linked to the very large increase in the number of motorcycles in the motor vehicle fleet, coupled with a low rate of helmet use. It is in the spirit of improvement, and in relation to all stakeholders of road safety that Morocco has decided to implement a pragmatic and ambitious national road safety strategy spanning 2017–2026 to combat road accidentology in all its forms.

3 Research Method The methodology adopted to analyze road accidents in Morocco will be based on the deployment of road accident data through the examination of several indicators and percentages (Table 1). We will use these indicators and percentages to study the evolution of accidents’ situation in Morocco from 1990 to 2018 and to compare it with the one of developed countries (2018). Then, we will focus on the study of the distribution of fatalities and injuries according to gender, age, months and time, for the period from 2006 to 2018. Then, we will study the distribution by cause of accidents in 2018 and

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establish an accidental map by city in 2018. The focus will be on identifying the main road safety problems and discussing the appropriate countermeasures which can solve specific road safety problems. Table 1. Main indicators adopted to analyze road accident Indicators

Reports

Risk of accident

Number of accidents per 100,000 people

Accident severity index

Number of deaths per 100 accidents

Risk of death

Number of deaths per 100,000 people

Mortality rate

Number of deaths per 10,000 registered vehicles

Motorization rate

Number of vehicles per 1,000 people

The main sources of data for the study are: road traffic injury statistics published by the Moroccan National Road Safety Agency [14], Morocco in Figures data published by the Moroccan High Commission for Planning [15], the annual road safety report data published by the International Transport Forum [1], and the total population data published by the World Bank [16]. These different databases were explored because they include official data for the analysis of road accidents in Morocco. The whole process was audited and supervised by a second author.

4 Analysis of Road Accidents in Morocco 4.1 Road Traffic Deaths and Injuries The number of deaths due to road accidents in Morocco has been increasing slowly over the years, without any visible tendency in relation to the evolution of the population and the number of cars in circulation. The number of accidental deaths on the roads has increased by about 34.5%, from 2777 in 1990 to 3736 in 2018. During these years, the number of deaths between 1990 and 2000 increased by 30.61%, between 2000 and 2010 increased by 4.16% and between 2010 and 2018 decreased by −1.11%. Regarding injuries, the number has been multiplied by about 3; from 47301 in 1990 to 137 998 in 2018 (see Table 2). From 2006 to 2018, the number of fatalities was almost stable, while the country’s population and the number of vehicles on the road increased by 16.96% and 115.5% respectively. As a result, the risk of death (the number of road accident deaths per 100,000 people) decreased from 12.19 in 2006 to 10.37 in 2018. Despite a low level of motorization, Morocco faces a very high risk of death compared to developed countries (see Table 3). The risk of death in Morocco is three times higher

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than in Denmark and Japan, and almost twice as high as in Spain, France and Italy. Although the fatality rate (the number of road deaths per 10 000 vehicles) has decreased from 29 in 1990 to 8.66 in 2018, it is still quite high com- pared to developed countries. In many developed countries, the fatality rate is less than one death per 10,000 vehicles, despite the very high motorization rate for developed countries (see Table 3). Table 2. Evolution of the accidental situation in Morocco: 1990–2018. Year

Number of accidents

1990

32992

2000

48370

2006 2010

Number of casualties

Number of kills

Risk of accident

Accident se- verity index

Risk of death

Mortality rate

47301

2777

132,99

8.41

11.19

29.0

74265

3627

167,99

7.50

12.6

21.7

56426

82651

3754

183.17

6.65

12.19

18.76

65461

98472

3778

202.93

5.77

11.68

13.54

2015

78003

115042

3776

225.03

4.84

10.89

10.52

2018

94944

137998

3736

263.52

3.93

10.37

8.66

Table 3. International comparison of fatal accident rates. Country

Motorization Mortality Risk of rate rate death

Morocco(2018) 119.68

8.66

10.37

Spain (2018)

764.4

0.5

3.9

Japan (2017)

721.4

0,5

3.5

Danmark (2018)

558.9

0.5

3

France (2018)

674.7

0.7

5

Italy (2018)

854.5

0.6

5.5

4.2 Age and Gender Distribution of Road Fatalities and Injuries Table 4 presents the distribution of fatalities according to age. This table clearly shows that the most productive age group, the 25–34 year olds, are the most likely to be victims of road accidents in Morocco. The 25–34 age groups represents only 15.48% of the Moroccan population, but is faced with almost 20.50% of the total number of road traffic fatalities. In recent years, from 2006 to 2018, the number of deaths in this age group has also decreased relatively from 820 (21.84% of total deaths) to 766 (20.50% of total deaths). The middle age group of 15–24 years and 35–44 years is also highly

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exposed to road accidents. These age groups represent only 17.04% and 13.76% of the total population, respectively, but account for almost 16.78% and 16.25% of the total number of deaths, respectively. Therefore, the economically active age group 15– 44 years records more than half of the deaths due to road traffic accidents: (53.53%) concerns this population group, which represents less than half of the total population (47.06%). This could be explained by the fact that people in this age group are in the prime of their lives and are therefore more likely to be on the roads. The proportion of fatalities in the 00 - 04, 05 - 09 and 09–14 age groups is almost similar to their respective representation in the total population. The same is true for the 45–54, 55–64 and over 65 age groups. Table 5 represents gender distribution of road traffic accident deaths and injuries in Morocco for the years 2006 and 2018. This table shows that males accounted for 83.38% of all deaths and 77.78% of all injuries in 2018. Over the last twelve years, the number of male fatalities increased by 2.47% from 3040 in 2006 to 3115 in 2018. This is significantly higher than the increase in female fatalities; female fatalities decreased by -11.82% from 677 in 2006 to 597 in 2018. However, the tendency in injuries is quite the opposite of the trend in deaths. Over the last twelve years, the number of injuries related to males has increased by 68.66% from 63638 in 2006 to 107330 in 2018. This increase is relatively higher than the increase in female injuries; the number of female injuries has increased by 60.65% from 18367 in 2006 to 29506 in 2018. Table 4. Distribution of road traffic deaths in Morocco according to age Age group

Number of deaths (2006)

Percentage share (2006)

Number of deaths (2018)

Percentage share (2018)

0–04 years

109

2.90

85

2.28

05–09 years

194

5.17

102

2.73

10–14 years old

152

4.05

99

2.65

15-24years old

629

16.76

627

16.78

25–34 years old

820

21.84

766

20.50

35–44 years old

611

16.28

607

16.25

45–54 years old

469

12.49

475

12.71

55–64 years old

303

8.07

405

10.84

Over 65 years old

388

10.34

482

12.90

79

2.10

88

2.36

3754

100

3736

100

Unspecified Total

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Table 5. Distribution of deaths and injuries in road accidents in Morocco according to Gender Type

Deaths

Injuries

2006 Share 2018 Share Change in % 2006 of of of deaths total total (2006–2018) deaths deaths in in 2006 2018 (%) (%) Male Woman Unspec Total

3040 80.98

3115 83.38

2.47

677 18.03

597 15.98

−11.82

37 0.99

24 0.64

−35.14

3754 100

3736 100

−0.48

Share 2018 of total injuries in 2006 (%)

63638 77

Share Change in % of total of injuries injuries (2006–2018) in 2018 (%)

107330 77.78

68.66

29506 21.38

60.65

646 0.78

1162 0.84

79.88

82651 100

137998 100

66.96

18367 22.22

4.3 Distribution of Road Accidents by Month and Period Figure 1 represents the monthly distribution of road accidents in Morocco in 2006 and 2018. Although the monthly variation in road accidents is not substantial, road accidents are relatively higher in the last six months of the year. This shows that vacation periods and extreme weather conditions impact on the frequency of road accidents. Holidays and temperature, being quite high in June, July and August in Morocco, could have an impact on road accidents. The school break, the arrival of Moroccan immigrants and vacation periods are the main cause of congestion on the roads. High temperatures have both a physiological and psychological effect on drivers. According to [17], fatigue increases with temperature, drivers lose their concentration on the road and their reaction time is slower. This is the reason why the number of road accidents is relatively higher in summer compared to other months of the year. The number of road accidents in October, November and December is also generally higher than in the first six months of the year. This can be explained by the fact that these months face a period of early precipitation and slippage. In addition, some areas of the country, especially the coastal cities of Morocco, face poor visibility on the roads in December due to foggy weather. Figure 2 shows the time distribution of road accidents in Morocco (2006 and 2018). The most dominant temporal slot when accidents occur is between 19:00 and 20:00 with 7344 accidents and 250 fatalities. It is followed by the time from 8 pm to 9 pm with 6447 accidents and 245 fatalities. This figure clearly shows that there is a substantial variation in road accidents depending on the time of day. Accidents remain relatively constant and high between 9 am and midnight, but variable and low in the middle of the night and in the early hours of the day. However, this does not mean that daytime driving is more risky than nighttime driving. Culturally speaking, Moroccans tend to be more active during the day than at night.

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Fig. 1. Distribution of road accidents by month of the year in 2006 and 2018

Fig. 2. Distribution of road accidents by time of day in 2006 and 2018

4.4 Analysis of Road Accidents in Cities Figure 3 represents the risk of death and mortality rate in Moroccan cities for the year 2018. On average, the risk of death in cities is 0.16 deaths per 100,000 people in 2018. However, the risk of death varies greatly across cities in Morocco, ranging from 0.005 deaths per 100,000 people in Assa Zag to 0.9 deaths per 100,000 people in the Wilaya of grand casa in 2018. In the same year, the Wilaya of grand casa (0.9), Mar- rakech (0.52), the Wilaya of Rabat Salé (0.49), Kenitra (0.42), BeniMellal (0.4), Safi (0.39), El-Jadida (0.36), and Tangier (0.34) had a risk of death that was twice as high as the average for cities (0.16). Figure 4 represents the mortality rate, the number of deaths per 10,000 vehicles, in Moroccan cities for the year 2018. In 2018, the fatality rate varied from 0.005 fatali- ties per 10,000 vehicles for Assa Zag to 0.75 fatalities per 10,000 vehicles for Wilaya de grand casa. However, as shown in Figure 4, the fatality rate of the 9 cities (Wilaya of Rabat Sale, Wilayaof Grand Casa, Tangier, Safi, Marrakech, Kenitra, Fez, El- Jadida and BeniMellal) is 100% higher than the average of the cities (0.14 fatal acci- dents per 10,000 vehicles).

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Fig. 3. Risk of death and death rate due to road accidents in Moroccan cities in 2018

5 The Way Forward The consequences of road traffic accidents in terms of death and injury are largely preventable, as the risk of being injured in an accident is largely predictable. Several effective countermeasures exist. The most effective way to reduce the number of deaths and injuries would be an integrated approach involving close collaboration between many sectors and focusing on multi-factorial causes: human, vehicular and environmental. These factors play a role before, during and after a road accident. A modifiable factor can contribute to reducing the risk of a road accident occurring. Road safety is a shared responsibility. Progress is being made in many countries where multisectorial strategic plans are progressively reducing the number of deaths and injuries in road crashes [18]. The problem of road accidents in Morocco is aggravated by the concentration of public and private administrations in the big cities and city centers, the concentration of leisure areas in well-defined regions, while Morocco has more attractive unexplored leisure areas. In other words, the road network, between most cities, is characterized by roads, round trip, used by different categories of vehicles (fast, slow, heavy, light, motorized, non-motorized) and also of variable width and speed. To reduce exposure to accident risk, it is necessary not only to classify vehicles in homogeneous categories, but also to enforce the speed limit on fast vehicles. Let us now analyze human behavior, the vehicle, the infrastructure and its environment as factors causing road accidents. The concept of the human behavior factor refers to the set of variables related to the individual that may affect driving behavior and crash occurrence [19]. Variables may be

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demographic, such as gender or age; psychological, such as inattention; or physiological, such as health problems, fatigue and alcohol. In addition to these variables, there is experience and practice. This factor is particularly important since driving is primarily dictated by human behavior. Road safety experts agree that the human element is the main factor behind road accidents and their consequences [19]. Despite the complexity of the system, the driver is the final regulator: [19, 20] showed that this activity involves, for the driver, the accomplishment of many tasks, namely control of the vehicle, identification of hazards and circulation. Drivers choose the path of their vehicle and must adapt their behavior to road regulations, weather conditions, and road and traffic conditions. Alcohol consumption was cited as a contributing factor in 2% of cases. Among the growing road safety problems in Morocco is distraction, for example through the use of mobile phones while driving. A survey of 3,031 motorists revealed that 75% of motorists admitted to having used their phone while driving in the past 12 months. Of particular concern is the share of drowsiness and fatigue as a causal factor in collisions. In 2017, based on police data, it was estimated that around 1.5% of collisions were caused by drowsiness and fatigue. According to another study, conducted in September 2017, the rate of seat belt use in urban areas was 60% for drivers and 57% for front seat passengers. In rural areas, the usage rate is 72% for drivers, 65% for front seat passengers. Despite progress, these usage rates are still too low. Many lives could be saved if seat belts were used. Research on the “vehicle” factor is the first line of research in road safety. The first aim is to make the vehicle more reliable (lights, braking, power steering, etc.) in order to prevent accidents, but also to improve the protection it offers in the event of a traffic accident (seat belts, airbags, etc.). Thus, the vehicle is increasingly called upon to become an aid in the prevention of traffic accidents, thanks to new technologies and advanced equipment. Recent research on vehicle technology "intelligent vehicle" has made it possible to replace the driver failing or having lost the ability to act on certain behaviors (speed seeking, risk taking, etc.) and to compensate for certain human weaknesses (fatigue, decreased attention, sleepiness, etc.), [21]. The infrastructure and its environment are generated mainly by failures of visibility, legibility and integration of the road in the territory. According to [21], the elements related to the infrastructure and their equipment can contribute to the occurrence of traffic accidents. In addition to the measures mentioned above, it is important to stress that legislation plays a cardinal role in preventing and reducing road accidents. Indeed, the level of enforcement of traffic laws and the severity of penalties for violations also influence the behavior of road users. Low levels of enforcement often negate efforts to improve road safety through legislation. As an example, a decrease in the number of deaths on Moroccan roads was recorded in 2010 (3778 in 2010 against 4042 in 2009 and 4222 in 2011). The decrease is explained by the implementation of a memorandum from the Minister of Justice ordering the withdrawal of driving licenses for traffic violations. However, the lack of enforcement of the law led to an increase in the number of road traffic victims recorded in 2011. Legislation without enforcement and public awareness remains ineffective. These are essential to establish common social rules for road safety. In addition, post-crash health care is becoming increasingly important in saving victims. It has been shown to significantly reduce the number of deaths and serious

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injuries following a road accident. In addition, the speed of rescue, emergency care, skilled care delivery, technology used, etc., are important for effective care of injured people, starting with the activation of the care system during transport, in hospital and at home.

6 Conclusion The analysis of road accident data in Morocco shows that the distribution of deaths and injuries varies according to age, sex, month and time. We found that the most productive age group is the most vulnerable population one. Also, men face a higher risk of death and accident than women. In addition, road accidents are relatively more numerous in JuneJuly and October-December, which shows that the cessation of schooling, the arrival of Moroccan immigrants, holiday periods and extreme weather conditions influence the frequency of road accidents. Accidents remain relatively constant and high from 9 a.m. to midnight and variable but low in the middle of the night and in the early hours of the day. Several factors are responsible for accidents but driver fault is the most important one. This study has also analyzed the phenomenon of road accidents at the city level. However, the risk of death varies greatly from one city to another in Morocco, ranging from 0.005 deaths per 100,000 people in Assa Zag to 0.9 deaths per 100,000 people in Wilaya of Grandcasablanca in 2018. In spite of the accident situation, road safety in Morocco is not allotted a particular attention, especially in what concern the multifactorial causes (human, vehicular and infrastructure factors), either at the level of the central government, cities and local communities in order to create safer standards for road safety, especially that human behavior is the first responsible for road accidents. In fine, road safety is a shared responsibility. Risk reduction and the responsibility to address the various aspects of road traffic system problems require commitment and responsibility in decision making by all stakeholders: governments, NGOs, industry, researchers, professionals and communities.

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10. Latreche, A.: Estimation of the probability of accident by the Probity model in the case of car insurance. Les Annales ROAD 15, 27–53 (2007) 11. Singh, S.K.: Road traffic accidents in India: issues and challenges. Transp. Res. Proc. 25, 4708–4719 (2017) 12. Chapelon, J.: The economic impact of road safety. Les Tribunes de la sante (4) 65–70 (2008) 13. World Health Organization: Global status report on road safety (2018) 14. Compendiums of road traffic injury statistics (2000, 2006, 2010, 2015 and 2018) published by the Ministry of Equipment, Transport, Logistics and Water of Morocco 15. World Bank, population data. http://data.worldbank.org/indicator/IS.VEH.NVEH.P3 16. Bijleveld, F., Churchill, T.: The influence of weather conditions on road safety: an assessment of precipitation and temperature, vol. 2009, no. 9 (2009) 17. Evans, L.: A new traffic safety vision for the United States, pp. 1384–1386 (2003) 18. Elvic, R., Vaa, T.: The Handbook of Road Safety Measures. Elsevier Science, Oxford (2004) 19. Sabine, D.: Accident data and road safety; World Road Association, Routes roads no. 335 (2007) 20. Guilbot, M.: Accidents de la route, infrastructure et responsabilités. Documentation Française (2008) 21. Zerka, A., Jawab, F.: Calculation of the costs of health care services for road accident victims in TDABC: a systematic review of the literature. In: 2020 IEEE 13th International Colloquium of Logistics and Supply Chain Management LOGISTIQUA), pp. 1–7. IEEE, December 2020

Study and Analysis of Accidents Database for the Years 2015–2016 Halima Drissi Touzani1(B) , Sanaa Bollouz2 , Sanaa Faquir1,2 , and Ali Yahyaouy1 1 LISAC Laboratory, Faculty of Science Dhar Mehraz, Sidi Mohamed Ben Abdallah University,

Fez, Morocco [email protected] 2 LSED Laboratoire Systemes et Environnements Durables, Faculty of Sciences of Engineering, Private University of Fez, Fez, Morocco

Abstract. In Morocco, road traffic accidents are presenting a big problem because it causes loss of human life and fatal injuries. So to work on this phenomenon we focus our study on real database for the years 2015–2016 that the Ministry of Transport gave them to us, to apply data visualization that can give us a clear idea of what the information means by giving it visual context through graphs. This makes the data more natural for the human mind to comprehend and therefore makes it easier to identify trends, patterns, and outliers within large data sets, all its efforts will be made to find solutions to reduce the enormous number of accidents and save human life. Keywords: Road accidents · Data analytics · Data visualization

1 Introduction No one can deny that road accidents are one of the major causes of mortality among individuals, is one of the essential areas of research, so to fight this problem, divers road safety strategies methods and countermeasures have been recommended and used [1], Kumar et al. analyzed different source of road accident data in India using Emergency Management Research Institute (EMRI) database, which serves and keeps track of each accident file on each type of road, and the main goal of this analysis was to put some accident avoidance efforts in the areas identified to control the number of accidents [2], Velivela et al. use statistical and data mining techniques to analyze attributes that affect the road accidents to provide the information about the role of these attributes which can be practiced to reduce the accident percentage [3]. Morocco is one of the countries taking the highest rate of deadly accidents resulting in fatalities and various levels of injuries. According to a report of the Ministry of Equipment and Transport, a total of 3,384 people were killed in road accidents in Morocco in 2019. [4], for that reason, many countries including Morocco needed to focus their efforts on improving road safety and ameliorating road conditions. In our previous work, we used Data mining techniques to analyze traffic accidents data for 2014, through the results

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 145–152, 2022. https://doi.org/10.1007/978-3-031-02447-4_15

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found we proposed precious suggestions that will help decrease the problem of road accidents [5]. To enjoy those results we proposed a solution based on fuzzy logic control to train a semi-autonomous car to take the right decision when the driver didn’t react to prevent the accident from occurring [6]. In this paper, efforts have been made to study the role of predictive analytics and big data to anticipate the severity of accidents. The purpose is to predict injuries and deaths of an accident based on 2015 and 2016 accidents databases. Our role is to collect and organize the data, determine which type of mathematical model implements the case at hand, and then draw the fundamental conclusions from the outcome. First of all, data visualization is needed to give us an observable idea of what the information means by giving it visual context over the chart. This makes the data more ordinary for the human mind to assimilate and then makes it easier to determine trends, patterns, and outliers within large data sets.

2 Data Visualization Results The database we have contains information about accidents of the years 2015 and 2016 such as COD_TUE, COD_BLE, which give us a vision about deaths and injuries if exist, HEU_ACC that gives us an idea about the time of an accident, and so on. Here are some examples of what can be done to visualize this database represented in Fig. 1.

Fig. 1. Scatter plot of time of accidents

With a sample of 100 accidents, the scatter plot in Fig. 1 represents the time of each one; we can obviously conclude that the most accidents happen between 10 am and 8 pm. However it’s barely when an accident happen between midnight and 5 Am (Fig. 2).

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Fig. 2. Scatter plot of injuries

The above graph gives us an idea of injuries resulting from each accident, so we can read this graph as follows: COD_BLE = 0: there are no injuries, COD_BLE = 1: there are injuries and they require hospitalization, COD_BLE = 2: Slightly injured requiring light care.

Fig. 3. Scatter pot of deaths according to accidents

The scatter pot in Fig. 3 gives us a vision of deaths resulting from an accident and informs us about the time of death. COD_TUE = 0: there are no deaths COD_TUE = 1: died immediately on the accident field COD_TUE = 2: died during transfer to hospital COD_TUE = 3: died within 30 days of the accident.

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Fig. 4. Histogram of drivers’ ages

According to the histogram of drivers’ age in Fig. 4, we can see that most of them are under the age of 40, so they are obviously young people.

Fig. 5. Scatter plot of Drivers Sex

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The scatter in Fig. 5 presents the sex of the driver of a vehicle that had an accident; SEX_CON = 1: Driver is a male SEX_CON = 2: Driver is a female Out of 600 accidents, only 20 of them where the driver was female.

Fig. 6. Circle Graph of surface condition

To recognize the impact of the road surface condition in an accident, we consider the circle graph in Fig. 6. With a total of 600 accidents this graph displays three different road surface conditions at the time of the accident; • The blue part in the graph represents a dry surface with a percentage of 72%. • The red part represents a wet surface with a percentage of 6%. • The green part represents other conditions with a percentage of 22%. According to the graph, in the most cases the road surface is in a good condition.

Fig. 7. Circle graph of lighting condition

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The graph in Fig. 7 shows the consequence of lighting on the number of accidents as follows; LIT_COD = 1: an accident happened at night LIT_COD = 2: an accident happened in daylight Out of 760 accidents, 634 were in daylight which means only a percentage of 20% were at night (Fig. 8).

Fig. 8. Circle graph of the weather condition at the time of the accident

Considering a sample of 740 accidents during the year 2015, this graph investigates the impact of several weather conditions on the number of crashes. The blue part represents the number of accidents that happened in mild weather with a percentage of, the red one represents the number of crashes in rainy weather and the green part is when there was a snowfall, while the purple color represents the number of crashes in foggy weather. We show in the graph of Fig. 9, the type of driving license that was available with the driver during the accident. We can read the graph as follows: Value = 0: license A (Motorcycle), Value = 1: license B (vehicle with a maximum of 9 seats), Value = 2: license C (Truck, weight > 3.5T), Value = 3: license D (vehicle with more than 9 seats), Value = 4: license E (B, C, or D with a trailer weighs more than 750 kg), According to the graph, we can conclude that most vehicles having had an accident are those with a maximum of 9 seats (license B category).

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Fig. 9. Circle graph of license categories

3 Conclusion From a collection of 178 countries, road accidents problem are the 8th cause of loss of life according to the World Health Organization (WHO), Morocco is one of the countries taking a maximum percentage of deadly accidents resulting in mortality and diverse levels of injuries. In this paper, efforts have been made to study the data on accidents based on the 2015 and 2016 accidents database, collect and organize this input, identify which type of mathematical model applies to the case at hand, and then draw the crucial conclusions from the results, like for example the time of accidents, were can conclude that the most accidents happen between 10 am and 8 pm, also from the attribute sex driver we note that out of 600 accidents, only 20 of them where the driver was a female, the light code shows that out of 760 accidents, 634 were in daylight which means only a percentage of 20% were at night not like what can people might think that the most accidents occur when there is no light or at night because of lack of sleep and lack of light, and many other results which clarify our database to predict injuries and deaths of an accident based on 2015 and 2016 accidents using the appropriate algorithm in our future work.

References 1. Olutayo, A., Eludire, A.: Traffic accident analysis using decision trees and neural networks. Int. J. Inf. Technol. Comput. Sci. 6(2), 22–28 (2014) 2. Kumar, S., Toshniwal, D.: Analysing road accident data using association rule mining, pp. 1–6 (2016) 3. Gopinath, V., Purna Prakash, K., Yallamandha, C., Krishna Veni, G., Krishna Rao, D.S.: Traffic accidents analysis with respect to road users using data mining techniques. Int. J. Emerg. Trends Technol. Comput. Sci. 6(3), 15–20 (2017)

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4. Morocco World News|Morocco Current Events, Latest News. https://www.moroccoworld news.com/. Accessed 11 Nov 2019 5. Drissi Touzani, H., Faquir, S., Yahyaouy, A.: Data mining techniques to analyze traffic accidents data : case application in Morocco. In: The Fourth International Conference on Intelligent Computing in Data Sciences, ICDS 2020 Conference, pp. 1–4 (2020) 6. Touzani, H.D., Faquir, S., Senhaji, S., Yahyaouy, A.: A proposed solution to road traffic accidents based on fuzzy logic control, pp. 1–8

The Behavioral Intention of Healthcare Professionals to Accept Remote Care Technologies: 20 Years of Scientific Production Mohammed Rouidi1(B) , Abd Elmajid Elouadi1 , Amine Hamdoune2 , and Khadija Choujtani3 1 Ibn Tofail University, Kenitra, Morocco

[email protected]

2 Hassan 1er University, Settat, Morocco 3 Mohammed V University, Rabat, Morocco

Abstract. With the spread of COVID-19 throughout the world, remote care technologies have played an important role in addressing pressure on health systems worldwide, strategies that aim to promote the use of these tools often focus on the technological dimension and neglect other important dimensions for successful implementation. One of the documented dimensions for successful integration of these tools into clinical practice is acceptance by health care professionals. Two models are used mainly to identify acceptance factors: TAM and UTAUT, based on a literature search on three databases PubMed, Scopus, and Web of Science, we have identified all studies that have used the original or a modified version of these two models to study the factors of acceptance of remote care technologies by health professionals, this article presents a summary of 20 years of scientific production in this area and identifies the factors that have been validated by the different researchers and that have influenced the behavior of health professionals to accept remote care technologies. Keywords: Technology acceptance · TAM · UTAUT · Health professionals

1 Introduction With the emergence of the COVID-19 pandemic, remote care technologies have played an important role in mitigating the spread of the virus that began in Wuhan, China, and is currently widespread around the world. For example, the University of California, San Francisco (UCSF) has mandated the use of telemedicine to care for palliative and nonpalliative patients in outpatient settings whenever possible [1]. “The US Office of Civil Rights and Department of Health and Human Services issued a ruling to allow health care professionals to use any available remote communication product to communicate with patients, even if those products do not fully comply with the Information Security Act [2]. The World Health Organization has published guidelines, which contain recommendations on the scope of remote care technologies intervention that have shown evidence © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 153–162, 2022. https://doi.org/10.1007/978-3-031-02447-4_16

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to address specific challenges in the health sector [3], according to the same source, these digital technologies have allowed the introduction of new opportunities for health systems and offer the potential to improve the coverage and quality of practices. Reducing direct contact face to face and, at the same time, continuing the process of care has been the challenge of all countries in the world, and it is in this sense that these digital tools have allowed us to strengthen health systems and respond to the challenges posed by the COVID-19 pandemic. Several initiatives have been recorded to introduce remote care solutions in an attempt to cope with this pandemic. [4] have cited some use cases such as teleconsultation to protect hospitals from being overwhelmed by cases of mild or moderate illness that can be managed at home. Remote monitoring with real-time tracking tools to reduce the risk of transmission in the hospital setting. Training of health care workers with online training to build capacity, and inform them of new management protocols, especially for staff in rural areas. Psychological support for those confined and providing them with counseling to reduce stress and panic. Exploiting the large mass of data and analyzing it by artificial intelligence algorithms to draw relevant conclusions… Despite the progress and benefits made possible by the implementation of digital solutions in the health sector, the level of acceptance and use of these health solutions remains low and health professionals have not yet taken the step of integrating them into their clinical routines. In South Africa, a telemedicine system has been set up, but despite significant investments, only a third of the sites are operational. The technological problems have been solved; however, the acceptance of this technology remains low, one of the documented barriers to the successful implementation of telemedicine is the acceptance of this technology by health professionals [5]. In Germany, a technology project that aimed to provide a secure infrastructure for the dissemination of telemedicine was built nationwide, and although most German physicians recognize the potential benefits of telemedicine, the implementation of this project was delayed for more than five years due to the non-acceptance of the technology by German physicians [6]. In South Korea, despite the introduction of telemedicine technology in 1988, its widespread use remains very slow due to resistance from physicians, who perceive the technology as a threat to their expertise [7]. Three examples in three different countries share the same issue, that of the acceptance of these technologies by health professionals. This leads us to ask a central question: what are the factors that impact the acceptance of new technology by health professionals? The authors [8] suggest that to avoid wasted investment by governments in new technologies, it is necessary to address not only the technical challenges but also the management challenges, including acceptance and use by health professionals. In this perspective, our objective in this paper is to try to answer our central question and to identify based on a literature review the variables or factors that have been validated by different researchers and that have influenced the behavior of health professionals to accept the technologies of remote care. This paper is structured as follows: first, a presentation of the theoretical framework, then the objectives of our research, then the methodology and the results obtained. Finally, the paper ends with a conclusion.

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2 Theoretical Frame Over the past several decades, various models have been developed and applied to examine the acceptance and use of information technologies. The Technology Acceptance Model (TAM) [9] is among the most popular theories for studying perception and the factors that contribute to the acceptance of new technology. The central idea of TAM is to increase the use of new technologies by promoting their acceptance, and acceptance can only be promoted if the factors that influence it are known. The researchers of the model hypothesized that “perceived usefulness” and “perceived ease of use” are the main factors that determine users’ attitudes, they also hypothesized that the intention to accept technology is influenced by the individual’s attitude and perceived ease of use. In addition to being validated several times by various researchers, the TAM model has also been subject to various improvements and adaptations, starting with its author. In 2000, the same researchers proposed a new version of TAM named TAM2 [10] that is based on the identification of the determinants of “perceived usefulness” and the moderating variables. Subsequently, other researchers proposed TAM 3 [11], which includes all the determinants of TAM 2 with the addition of a set of determinants for the variable “perceived ease of use”. In 2003, and following a comparative review of the literature and more specifically, the eight theories and models that have been proven to explain the variance of acceptance and use of information technology, researchers formulated and validated the Unified Theory of Acceptance and Use of Technology (UTAUT) [12]. These theories and models are the theory of planned behavior [13], the theory of reasoned action [14], the technology acceptance model [9], the combined model of the theory of planned behavior with the technology acceptance model, the applied motivational model [15], the theory of diffusion of innovation [16], the social cognitive theory [17] and the computer acceptance theory [18]. By consolidating and improving the previous models of information technology acceptance, the model created is based on the conceptual and empirical similarities of these eight models. In this new UTAUT model (Fig. 2) the authors estimated that the determinants of the intention to use the technology can be grouped into four dimensions: “Performance Expectancy”, “Effort Expectancy”, “Social influence” and “facilitating conditions” (Fig. 2).

Perceived usefulness Attitude

Behavioral intention

Perceived ease of use

Fig. 1. The Technology Acceptance Model (TAM)

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Performance Expectancy Behavioral intention

Effort Expectancy

Use behaviora

Social influence Facilitating conditions Gender

Age

Experience

Valuntariness of use

Fig. 2. The unified theory of acceptance and use of technologies (UTAUT)

3 Goal Many efforts have been made by researchers to evaluate, predict and analyze the acceptance and use of new distance care technologies. TAM and UTAUT are among the most widely used models in this field, researchers have tried to extend these two models to increase their explanatory power, either by introducing variables from other theoretical models or by examining the antecedents or moderators of their original variables. Our objective in this paper is to identify the published research that TAM and UTAUT have used in the area of new distance care technologies. And based on the census to identify the factors that have impacted the acceptance behavior of health care professionals.

4 Method We conducted searches on 3 databases: PubMed, Scopus, and Web of Science between the period of 2001 and 2021, that is to say for 20 years of scientific production in this field, the keywords used are: TAM, UTAUT plus a set of keywords that concern remote health care such as telemedicine, E-health, M-health, telehealth, and mobile health. The abstracts, titles, authors, journals, and publication years of each article were recorded in an Excel file. After deletion of duplicates, the abstracts of all these articles were evaluated by two authors of this paper, which excluded articles that did not meet the inclusion and exclusion criteria. The remaining articles were read in their entirety, and eligible publications were retained in a list. Our primary inclusion criteria were: • Qualitative studies that use the TAM model and its extensions or the UTAUT model and its extensions as the theoretical framework for analysis; • The study population must be healthcare professionals (doctors, nurses, nurses’ aides, etc.); • Studies wrote in English or French; • Studies examining the technologies used to provide patient-centered health services;

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• Studies aimed to explain acceptance by healthcare professionals as a primary objective.

5 Results A total of 636 articles were retrieved from the databases. After the deletion of 105 duplicate articles, the titles and abstracts were evaluated by two authors, 488 articles were excluded after applying the inclusion criteria, the main reasons for the exclusion of articles in this phase were: the study population was not health professionals but another category such as patients or families, theoretical articles. In the event of disagreement on the exclusion of an article, the authors redid their evaluation until a consensus was reached. 43 articles were selected for full-text reading. 11 articles were excluded in this phase, the two main reasons for exclusion, 4 were qualitative studies and 3 could not be found in full text. 32 studies were included in this analysis. 7 studies used the original TAM model [19– 25], while 6 studies used the original UTAUT model [26–31]. Other researchers have tried to extend the original models, adding other variables to predict health care professionals’ behavior to accept new remote care technologies, 13 studies used a modified version of the TAM model [7, 32–43], while 7 studies used a modified version of the UTAUT model [44–49]. A wide variety of remote care technologies were addressed in all of these studies. Our main objective in this paper is to identify the factors and variables that have impacted the behavior of health professionals to accept a new technology of remote care, Fig. 3 traces the set of variables that have been validated in the different studies that we have analyzed, in the rest of this paper we will try to make a brief description of these factors according to the researchers. In the rest of this paper, we will try to make a brief description of these factors according to the researchers. [7] and [44] have validated the variable “Reinforcement Factor”, a concept that was developed by psychologists to understand the factors that impact or prevent a behavior, according to [44] who conducted a study to identify the factors that influence the behavior of acceptance of telemedicine by the clinicians in Nigeria, this variable which is translated into financial incentives was one of the strongest variables in their model, the study showed that telemedicine technology should not be considered as an additional workload, but a responsibility that requires financial reinforcement. [7] conducted a study on the acceptance of telemedicine by physicians in South Korea, according to the researchers the appropriate payment of physicians for the use of this technology must be approved urgently to develop this new service. [20] tried to examine the acceptance of telemedicine technology by physicians in China, the study validated the variable “Perceived behavioral control”, which means an individual’s perception of the availability of resources and opportunities necessary to use technology, according to the researchers, this variable has a significant but modest effect on the behavioral intention of physicians to accept telemedicine technology. [31] conducted a study to identify the factors that impact the acceptance of telemedicine by physicians in Ethiopia, the researchers validated 3 variables “Compatibility with values”, “Compatibility with preferred work style” and “Compatibility

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with practice”, according to the results of the study, these variables are the most important in their model, and they should be taken into account when designing and implementing the technology, to improve the accessibility of existing technologies and better align them with the medical practices of a country. In a study [32] conducted to analyze the acceptance factors of the use of a remote consultation tool (eConsulta) by health professionals in Spain in the context of postCovid-19, the researchers validated 6 variables: “Pressure from other groups”, “The professional profile”, “A health care professional’s age”, “A health care professional’s gender”, “The degree of implementation of eConsulta” and “Level of use of eConsulta”. According to the study, the acceptance behavior of this technological tool is affected by the pressure of the environment, such as colleagues or the organization in which a professional works, as well as by the profile, age, and gender of this healthcare professional. Acceptance is also affected by the place or work environment and the level of implementation of the technological tool in that workplace. [37] tried to explain the factors that determine the acceptance of telemedicine in clinical practice by the medical staff of the health care institution in Spain, validated 3 variables “The perception of reduced costs associated with clinical practice”, “The perception of information security and confidentiality offered by telemedicine use” and “The medical staff”, according to the researchers the acceptance of this technology is significantly impacted by its potential to reduce costs, and its ability to protect the privacy and confidentiality of patients, as well as by the opinion of other physicians. [In an attempt to assess physicians’ attitudes and intentions to use telemedicine technology to provide health care services in India, the researchers validated the variable “Recognized Risk”, which refers to the degree of risk recognized by physicians who are considering telemedicine. According to the study, this risk must be taken into account by stakeholders who are required to work and resolve technical issues and provide appropriate infrastructure support. In a study conducted by the European Commission [40], the intention of primary care healthcare professionals was measured to accept telemonitoring technology for the treatment of chronic care patients in Spain. The researchers validated the variable “compatibility”, which was one of the most influential variables in predicting the intention of nurses, general practitioners, and pediatricians to use this technology. [42] tried to analyze the impact of some cultural factors on the acceptance of ehealth in Bangladesh, the researchers validated 3 variables: “Restraint”, “Masculinity” and “Power Distance”, according to the study results, people with enough money, high position in an organization and power have a higher intention to accept e-health, so acceptance behavior is affected by belonging to a culture with high masculinity and level of income. Finally [48] examined perceptions of physician acceptance of mobile health applications in clinical practice in Turkey, researchers validated 3 variables: “Technical support and training”, “Perceived service availability” and “anxiety”, according to the study, the acceptance behavior of physicians is affected by the concept of technical support and training in the use of technology, by the concept at which this technology is perceived to support use in the health context, as well as by the concept of physicians’ fear of using this technology.

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Compatibility with values

Compatibility with preferred work style

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Technical support and Training

Perceived behavioral control

Recognized Risk

Reinforcement Factor /Perceived incentives

Perceived service availability

The medical staff

anxiety

Restraint Behavioral intention

Level of eConsulta use

Masculinity

Compatibility with practice

Power Distance

The degree of eConsulta implementation

Pressure from other groups The perception of information security and confidentiality offered by telemedicine use

A health care professional’s gender

The professional profile

A health care professional’s age

The perception of reduced costs associated with clinical practice

Fig. 3. Summary of the various factors validated by the researchers and which impacted the behavioral intention of healthcare professionals to accept remote care technologies

6 Conclusion Policies to promote the use of remote care technologies must go beyond purely technological dimensions and take into account other factors such as professional and social determinants, as well as those of a contextual and organizational nature [37], in this sense we have tried in this article to summarize scientific production over the last 20 years, analyzing qualitative studies that have studied the acceptance behavior of remote care technology by health professionals. Understanding the acceptance process is important to help managers better capitalize on their investment budgets and to promote better integration of these new technological tools into the hospital sector.

References 1. Calton, B., Abedini, N., Fratkin, M.: Telemedicine in the time of coronavirus. J. Pain Symptom Manage. 60(1), e12–e14 (2020) 2. Notification of the enforcement discretion for remote health communications during the COVID-19 Nationwide Public Health EmergencyURL: https://www.hhs.gov/hipaa/for-pro fessionals/special-topics/emergency-preparedness/notification-enforcement-discretion-tel ehealth/index.html. Accessed 09 Oct 2021 3. WHO guideline Recommendations on Digital Interventions for Health System Strengthening. https://www.ncbi.nlm.nih.gov/books/NBK541905/#ch1.s2. Accessed 09 Oct 2021

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4. Mahmood, S., Hasan, K., Colder Carras, M., Labrique, A.: Global preparedness against COVID-19: we must leverage the power of digital health. JMIR Public Health Surveill. 6(2), e18980 (2020). https://doi.org/10.2196/18980 5. Cilliers, L., Flowerday, S.: User acceptance of telemedicine by health care workers a case of the eastern Cape Province, South Africa. Electron. J. Inf. Syst. Dev. Count. 65(5), 1–10 (2014) 6. Dünnebeila, S., Sunyaevb, A., Ivo, B., Jan, M.L., Helmut, K.: Determinants of physicians’ technology acceptance for e-health in ambulatory care. Int. J. Med. Inform. 81, 746–760 (2012) 7. Rho, M.J., Choi, I.Y., Lee, J.: Predictive factors of telemedicine service acceptance and behavioral intention of physicians. Int. J. Med. Inform. 83(8), 559–571 (2014) 8. Croteau, A.M., Vieru, D.: Telemedicine adoption by different groups of physicians. In: Proceedings of the 35th Hawaii International Conference on System Sciences, HICSS, vol. 6, p. 151 (2002) 9. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–339 (1989) 10. Venkatesh, V., Davis, F.D.: A theoretical extension of the Technology Acceptance Model. Manag. Sci. 46(2), 169–332 (2000) 11. Venkatech, V., Bala, H.: Technology Acceptance Model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008) 12. Venkatesh, V., et al.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003) 13. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211 (1991) 14. Fishbein, M., Azjen, I.: Belief, Attitude, Intentions, and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975) 15. Davis, F.D., Bagozzi, R.P.: Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22(14), 1111–1132 (1992) 16. Rogers, E.M.: Diffusion of Innovation. Free Press, New York (1995) 17. Bandura, A.: Social Foundations of Thought and Action: A Social Cognitive Theory. PrenticeHall, Englewood Cliffs (1986) 18. Thompson, R.L., Higgins, C.A., Howell, J.M.: Influence of experience on personal computer utilization: Testing a conceptual model. J. Manag. Inf. Syst. 11(1), 167–187 (1994) 19. McKee, G.B., Pierce, B.S., Donovan, E.K., Perrin, P.B.: Examining models of psychologists’ telepsychology use during the COVID-19 pandemic: a national cross-sectional study. J. Clin. Psychol. 77(10) (2021) 20. Chau, P.Y.K., Hu, P.J.-H.: Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf. Manag. 39, 297–311 (2002) 21. Kim, J., DelliFraine, J.L., Dansky, K.H., McCleary, K.J.: Physicians’ acceptance of telemedicine technology: an empirical test of competing theories. Int. J. Inf. Syst. Change Manag. 4(3), 210–225 (2010) 22. Monthuy-Blanc, J., Bouchard, S., Maïano, C., Séguin, M.: Factors influencing mental health providers’ intention to use telepsychotherapy in First Nations communities. Transcult. Psychiatry 50(2), 323–343 (2013) 23. Monthuy-Blanc, J., Bouchard, S., Maïano, C., Seguin, M.: Factors influencing mental health providers’ intention to use telepsychotherapy in First Nations communities. Transcult. Psychiatry 50 (2013). https://doi.org/10.1177/1363461513487665 24. Bunnell, B.E., Barrera, J.F., Paige, S.R., Turner, D., Welch, B.M.: Acceptability of telemedicine features to promote its uptake in practice: a survey of community telemental health providers. Int. J. Environ. Res. Public Health 17(22), 8525 (2020)

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Leveraging the Automated Machine Learning for Arabic Opinion Mining: A Preliminary Study on AutoML Tools and Comparison to Human Performance Moncef Garouani1,2(B) and Kasun Zaysa3 1 LISIC Laboratory, Univ. Littoral Cote d’Opale Calais, Calais, France

[email protected]

2 CCPS Laboratory, ENSAM, University of Hassan II, Casablanca, Morocco 3 Informatics Institute of Technology, University of Westminster Colombo, Colombo, Sri Lanka

[email protected]

Abstract. Despite the broad range of Machine Learning (ML) algorithms, there are no clear guidelines on how to identify the optimal algorithm and corresponding hyperparameters configurations given an Opinion Mining (OM) problem. In ML, this is known as the Algorithm Selection Problem (ASP). Although Automatic Algorithm Selection or AutoML has proven to be successful in many areas of ASP, it has hardly been explored in OM. This paper explores the benefits of using AutoML in this field. To this end, this work examines to what extent AutoML can be competitive against ad hoc methods (manually select and tune ML pipelines) on Arabic opinion mining modeled from a supervised learning perspective. We compare four state-of-the-art AutoML tools on 10 different popular datasets to human performance. Experimental results show that the AutoML technology can be considered as a powerful approach to support the ML algorithm selection problem in opinion mining. Keywords: AutoML · Machine learning · Opinion mining · AutoML benchmark · Arabic text

1 Introduction Human decision-making is extensively influenced by the opinions and perceptions of others [1]. A customer tends to gather as much information about a product as possible before making any sort of decision [2, 3]. A stock market analyst analyses and predicts the stock market movements of a particular company by analyzing its popularity and feedback among its customers before investing in its shares [4]. The advent of social media has made gathering data for analysis and evaluation easier and less time consuming [5]. Twitter, Facebook, LinkedIn, and other social media platforms all house useful data such as reviews, comments, evaluations, etc. [1, 5]. As a form of opinion mining, sentiment analysis is a language-independent technology that extracts and analyses useful informations from textual data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 163–171, 2022. https://doi.org/10.1007/978-3-031-02447-4_17

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The increasing data availability and the greater computing capacity have enabled machine learning (ML) to address opining mining. From a machine learning perspective, opinion mining is a technique that uses historical data to create predictive models using textual data to make predictive or classification decisions. Opinion mining literature reports a wide range of ML methods and tools. However, in spite of such availability, there is no ML algorithm that would perform well across all types of textual data, such as is stated by the no free lunch theorem [6]. Therefore, selecting the most suitable machinelearning method or algorithm is a complex process requiring expert knowledge on ML, along with high computational power, time, and human effort [7, 8]. Automated machine learning or AutoML [8] concept has emerged as a promising process for reducing effort and coast of ML, particularly in areas where specialized ML knowledge may not always be available or affordable. AutoML attempts to find or identify the optimal set of preprocessing techniques, machine learning algorithms, and hyperparameters to maximize a performance criterion on the data without being specialized in the problem domain where the data comes from; the latter is known as the general purpose of the AutoML [8]. Automated machine learning methods have been successfully used in various fields such educational data analysis [9], health care applications [10] and manufacturing industry [8, 11]. However, a comprehensive analysis of their strengths and weaknesses has not been carried out in extremely learning tasks, such as opinion mining especially for complex languages as the Arabic one. In this work, our main objective is assess to what extent the AutoML technology can be competitive against the ad hoc method in Arabic opinion mining applications. For this purpose, we examine 4 current AutoML methods (Auto-Sklearn [12], TPOT [13], AutoWeka [13] and AMLBID [8]), whose internal strategies for identifying competitive ML methods are (1) meta-learning, ensemble learning, (2) genetic programming, (3) stacking and ensemble learning, and (4) meta-learning respectively. The AutoML tools are compared to methods used in traditional opinion mining, which commonly consist on manually selecting the best of a set of commonly used ML algorithms. Concretely, we compare AutoML results to human tuned models in multiple opinion mining tasks. The main contributions of this study are: – Identify the benefits of the AutoML for Arabic opinion mining in terms of performance against human effort; – Characterize the performance of AutoML against a set of manually selected and tuned ML algorithms in Arabic opinion mining. The remainder of this paper is organized as follows: Sect. 2 presents the ML background and related works in opinion mining and covers the fundamental concept of AutoML and introduces the available tools and techniques. Section 3 outlines the followed methodological approach to carry out the study. Then, Sect. 4 analyzes the empirical study results. Finally, conclusions and future directions are discussed in Sect. 5.

2 Background and Related Works This section discusses the literature on Machine Learning and Automated ML related to the Arabic opinion mining. We start by summarizing common modeling approaches and

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recent works of ML in OM. Then, in Sect. 2.2, we introduce the background, purpose, and application areas of AutoML. 2.1 Machine Learning in Opinion Mining Machine learning has been applied to a plethora of opinion mining application areas, ranging from sentiment analysis [1, 2, 14, 15], fake news detection [16], to Advertising [4]. Analyzing companies products or services in order to improve them based on users and customers feedback and expectations is one of the most common uses. Not being restricted to this, it also provides insight into how political parties are perceived by the public, and is used to uncover their leadership potential [1]. To judge the public mood concerning new policies or reforms, government policy-making bodies use opinion mining tools as well [17]. Over the past few years, efforts have been made to provide informative surveys on trends and challenges in Arabic text analysis in [14, 18]. Aiming to extract the polarity of Moroccan Arabic tweets and the practice of tweeting in dialectical Arabic, [1] used Support Vector Machines classifier, Logistic Regression, CNN and LSTM models on a corpus of 13550 tweet. The general accuracy is of 92.09%. To investigate the performance of Arabic language text classification, [19] conducted a comparative study between eight text classification methods (SVM, RF, KNN etc.). The study results showed that algorithms that employ Latent Semantic Indexing features outperform TFIDF-based algorithms. 2.2 Automated Machine Learning As stated above, AutoML addresses the ASP as an optimization problem that consists of finding the more adequat ML algorithm with related hyperparameters configuration that maximizes or minimizes a performance criterion on a given predictive problem. Current literature [20, 21], reports a variety of approaches to automate the machine learning. Among the most popular systems, we find AutoWeka [13], Auto-Sklearn [12], TPOT [13], and AMLBID [8]. A short description of each of these AutoML tools is given below. Auto-WEKA is among the first AutoML systems to address the selection of ML algorithms and hyperparameters simultaneously. By using Bayesian optimization [22], it searches for the best pair of ML algorithms and hyperparameters, and its supports 39 ML algorithm implemented in the WEKA ML software1 . In later works, Feurer et al. [12] and Olson et al. [13] developed Auto-sklearn and TPOT to automatically select ML algorithms along with related hyperparameters values respectively. Auto-sklearn uses meta-learning [23], Bayesian optimization, and ensemble selection to identify promising ML pipelines from a search space of 15 classifiers, 14 features preprocessing techniques, and 4 data preprocessing methods. Meanwhile, TPOT uses genetic programming [13] to address the algorithms selection problem. At its core, TPOT incorporates a search space of 6 classifiers and 7 data preprocessing techniques.

1 https://www.cs.waikato.ac.nz/ml/weka/.

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More recently, AMLBID [8, 24] has emerged as a meta-learning based decision support system for the selection and optimization of hyperparameters. It consists of a knowledge base of +4 million evaluated ML pipeline. At its core, AMLBID has a search space of 8 Scikit-lean classifiers and a range of 8000 hyperparameters configuration of the supported algorithms. In opinion mining applications, to the best the authors’ knowledge, AutoML methods haves not yet been used for the Arabic language in this knowledge domain.

3 Methodology This section describes our approach and experiment settings for evaluating AutoML tools on Arabic text mining datasets. First, we collected a range of relevant datasets for related tasks together with human performances on them. We then used the benchmarked AutoML tools in the second step to allow each tool generate its best model based on the obtained preprocessed datasets. Finally, we conducted an analysis to assess tools performances and compare overall AutoML scores to those achieved without the use of AutoML (human performance). The Fig. 1 below illustrates the adopted evaluation process.

Fig. 1. Evaluation workflow.

3.1 Case Study and Raw Data To conduct the experimental study, we considered 10 publicly available corpora from past Kaggle competitions and research works. The language is the Arabic with both varieties, standard and vernacular. Sentiment analysis, fake news detection, and text categorization are some of the topics covered by the datasets. Each dataset contains an average of 74137 samples, ranging from 900 to 510600, and target classes varies from two (binary classification) to 4. An overview of the datasets used in the experiment is given in Table 1.

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Table 1. Datasets description. Dataset

Number of instances

Number of classes

Arabic

D1 [25]

18000

4

Moroccan

D2 [26]

49864

2

Algerian

D3 [27]

900

2

Jordanian

D4 [28]

9901

2

Moroccan

D5 [29]

17000

2

Tunisian

D6 [30]

510600

3

Egyptian

D72

4462

2

Arabic

D83

66666

3

Arabic

D94

56862

2

Arabic

D105

11751

2

Egyptian

3.2 Experimental Set-Up In this section, we present the Data preparation and representation process, the benchmarked AutoML tools, and the hardware choice to carry out the experimentation. Data Preparation and Representation. All datasets were prepared in such a way that only two columns remained text and target column. In order to do so, a pre-processing stage is done to minimize the effect of text informality on the classification. The preprocessing stage includes Emojis removal, repeated letters elimination, Arabic characters normalization and finally the stemming as done in [1]. For the features representation, we used the TFIDF (Term Frequency-Inverse Document Frequency) representation [1]. AutoML Methods and Baseline. To achieve the aforementioned analysis, we used the TPOT, AutoSklearn, AutoWeka and the AMLBID as AutoML tools on the benchmarked datasets, considering three execution times (15, 30, 60 min) for each of TPOT, AutoSklearn and AutoWeka as they are based on Genetic programing, Bayesian optimization, and Ensemble learning respectively. Lastly, it is important to realize that we did not perform any optimizations or extra-adjustments to the AutoML methods’ hyperparameters. The latter is justified, as our objective is to carry out a fair comparison between AutoML’s performance and human effort.

2 https://kaggle.com/youssefelasery/sentiment-analysis-tfidf-word2vec-rnn. 3 https://kaggle.com/abedkhooli/ar-reviews-100k-nbsvm-binary. 4 https://kaggle.com/imranzaman5202/arabic-language-twitter-sentiment-analysis. 5 https://kaggle.com/mksaad/sentiment-analysis-in-arabic-tweets-using-sklearn.

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Hardware. The benchmark was run on a dedicated server equipped with two Intel Xeon E5-2690 v4 - 35M cache - 2.60 GHz - 14 cœurs, 128 DDR4 Synchronous 2666 MHz memory modules and two NVIDIA GeForce GTX 1080 Ti.

4 Experimental Study For the aforementioned analysis, we present the results obtained by the benchmarked AutoML tools. Table 2 presents the achieved results of each method on the benchmarked datasets. Moreover, in the training process, we use a five-fold cross-validation strategy. Each split was created in a stratified fashion and using random shuffling of samples. Complementarily, Fig. 2 demonstrates the distribution of the results shown in Table 2. The Y-axis represents the obtained Accuracy value after a 05 fold cross-validations for each tool. While the X-axis has the datasets ID in which the recommended pipelines were evaluated to calculate the reported Accuracy values. Table 2. Performance results of each evaluated method. Dataset

AutoML results

Human configuration results

TPOT

Auto-Sklearn

Auto-Weka

AMLBID

D1 [25]

85.73%

90%

87.07%

91.47%

92.09%

D2 [26]

86.71%

65.89%

50.21%

83.91%

86%

D3 [27]

81%

79.22%

75.07%

80.62%

86.89%

D4 [28]

73.11%

83.91%

80.49%

79.46%

84.33%

D5 [29]

86.02%

83.99%

69.21%

89.71%

78%

D6 [30]

79.55%

78.36%

68.67%

77%

79.05%

D7

88.51%

83.72%

65%

88.80%

87%

D8

79.30%

80.73%

70.45%

87.03%

91%

D9

53.09%

78.16%

62.51%

73.61%

77%

D10

80.74%

82%

65.23%

86.37%

78%

According to the results summarized in Table 2 and in Fig. 2, it can be noted that all AutoML solutions achieved promising performances on the benchmarked datasets. Overall, the AutoML results are significantly better or very close to those of humanmade. The AMLBID improves Human tuned models in 03 out of 10 cases, obtaining the best overall results among the benchmarked AutoML tools.

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Fig. 2. Comparative results of the efficiency of AutoML against ML experts.

5 Conclusion This study has investigated the effectiveness of AutoML techniques on the Arabic opinion mining. We have systematically focused on deepening into the benefits of AutoML for supervised classification in the field of Arabic OM. To accomplish such a goal, we have empirically studied to what extent the results of AutoML differ from the general approach of OM. We used Auto-WEKA, TPOT, AutoSklearn and AMLBID as AutoML tools on 10 benchmarked datasets. The empirical comparison provides evidence that the AutoML tools for algorithms selection and hyperparameters optimization rather than choosing default values or manually tune hyperparameters achieve state-of-the-art performance in OM settings as well. The results indicate that, the existing AutoML solutions are not able to outperform human text classification experts (ML experts). Nevertheless, there are OM cases that can be handled relatively better or equally well by AutoML tools. On this basis, we believe on the potential of AutoML in future development for specific text classification modules within AutoML tools. Such solutions will make machine learning more accessible to beginners, as well as providing a baseline for advanced ones. Futures research plans include: (I) comparing the performance of more AutoML tools in different opinion mining cases to assess the consistency of the AutoML in such application field; and (II) studying the pre-processing and features importance of the Arabic text to determine what affects the performance of AutoML.

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Day-Ahead Photovoltaic Power Production Forecasting Following Traditional and Hierarchical Approach Ali Agga(B)

, Ahmed Abbou , and Moussa Labbadi

Electrical Engineering Department, Mohammadia School of Engineering, Mohammed V University in Rabat, 10090 Rabat, Morocco [email protected]

Abstract. Forecasting power production in photovoltaic plants is becoming one of the leading research areas, owing to its potential for electricity production stability, as precisely estimated predictions are crucial for power systems operations and planning. In this work, three machine learning models are used (MLP, SVR, ANN) to predict the power production of a self-consumption PV plant following the traditional and hierarchical approach. The data used correspond to a PV plant located in the Moroccan city of Settat collected from December 2019 to October 2020. The performance of the models was measured using different evaluation matrices, RMSE (root mean squared error), MSE (mean squared error), and MAE (mean absolute error). Based on the MAPE error indices, the suggested (hierarchical) approach showed an improvement of 7% using the ANN model. This work represents the benefits of using outputs of individual inverters for forecasts in PV plants where more than one inverter is installed. Keywords: PV forecasting · Machine learning · Hierarchical approach · MLP · SVR · ANN · PV plant

1 Introduction In recent years, the development of photovoltaic technology has seen significant improvements and has become the core of power generation in many countries for many reasons. Such as the initial cost of solar power plants [1], technological innovations [2], the decrease in greenhouse gas emissions [3, 4], and payback time in terms of energy [5]. In 2020, and according to the REN21 report, global energy consumption from renewable energies represented 19.3% and in 2017, produced electricity from renewable energies accounted for 26.4% [6]. The use of renewable energy in the electric power grid is increasing steadily, necessitating improved and accurate forecasting models for better planning, management, and operation [7]. To date, the development of forecasting methods is overgrowing, such as physical models, statistical methods, machine learning, and deep learning models to improve the prediction accuracy of renewable energy [8].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 172–180, 2022. https://doi.org/10.1007/978-3-031-02447-4_18

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However, with this comes many challenges and difficulties due to the complex nature of renewable energy data [8]. Among these methods, machine learning models show promising results in discovering non-linearity and complex data characteristics [9]. The forecasting of energy production varies from the short-term horizon of a few minutes in advance [10] to forecasting days ahead of time, following different approaches [11]. The main objective of Morocco’s energy strategy is to reduce the energy bill and reduce dependence on energy imported from other countries by producing its energy using the strong potential of the kingdom in terms of renewable energy sources [12]. Several projects have been launched with more than 2,000 MW of renewable energy plants already installed. More plants with a generation capacity of 2,000 MW are currently under construction. Their launch is scheduled for 2021 to ensure stability and faster transition [13]. In 2019, electricity produced from ERS sources accounted for 42%, with the goal by 2030 to have 52% of electricity from renewable energy sources. In [14], a one-year dataset was divided into various training and testing sizes to understand and create a hybrid model’s most effective training method. While in [15], the ANN and SVR models were used to forecast power output over three different time windows 15 min, 1 h, and 24 h ahead of time. Also, they employed some weather variables as inputs and inverters output records to train the ML models. In addition to that, they followed the hierarchical approach as their PV plant contains more than one inverter. In [16], the analysis presents how to set up the suitable parameters for the ANN model, such as the count of neurons and trials, to get reliable forecasts using the physical hybrid method (PHANN). In this work, three machine learning models were trained and tested on data combinations of 70% and 30% for testing. Moreover, the day ahead predictions were obtained following two different approaches. The first method focuses on following a traditional route for making future predictions based on the total PV plant power production records, while the second approach focuses on working with power generation records of each inverter in the plant separately to make the day ahead forecasts. The organization of this paper is as follows: In Sect. 2, we provide an overview of the working methodology of used machine learning models. Section 3 defines evaluation indexes to measure models performance. Section 4 presents the case study in more detail. Section 5 shows the results and discusses the validity of the hierarchical approach. Section 6 draws conclusions.

2 Methodology In this section, the proposed approach is described in detail and applied for PV plants with more than one inverter. Then, the three ML models are represented and explained, the ANN and SVR, which are widely used for time-series predictions, and the MLP model that runs well on this topic. All three models predict power output 24 h ahead of schedule.

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2.1 Hierarchical Forecasting Historical data of power production could easily be fed into machine learning methods as inputs, as clearly shown in Fig. 2. Traditionally, forecasting techniques and algorithms use historical power production records to train machine learning models, then focus on manipulating model parameters to get better predictions. When a PV installation incorporates more than an individual inverter it could be accessed from its micro-level power generation records. The use of micro-level data from photovoltaic power plants presents high potential and helps implement new techniques, such as the hierarchical approach for predicting power production. This technique focuses on deploying predicting tools at a micro-level, using the data from each inverter to make predictions. The resulting forecasts are used to assess the performance at the macro-level of the entire photovoltaic plant through the addition of the predictions, as illustrated in Fig. 1. The hierarchical method allows us to attend and witness the operation of each inverter of the photovoltaic plant and its impact on the overall energy produced.

Fig. 1. Hierarchical approach.

2.2 Artificial Neural Networks Neural network systems were developed based on how the human brain works and its learning process for recognizing objects. The different artificial neurons in the ANN model engage in how neurons function in a human brain. The other neuron nodes inside the layers of each model (input, hidden, output) are connected using a network model [15]. For this work, FFNN (feed-forward neural network) has two hidden layers with 50 neurons, one output neuron (the power production prediction), and seven input neurons (meteorological inputs, historical power production, and grid power consumption). A simplified diagram of the proposed ANN architecture is illustrated in Fig. 2.

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Fig. 2. Proposed ANN architecture.

2.3 Support Vector Regression SVMs are statistical models that are frequently used in regression and classification problems [17]. The idea behind SVRs is to receive the input data and map it to higher feature space dimensions by finding a nonlinear map of the input space to the output space. Then, an estimation function is used to perform linear regression in this feature space [18]. 2.4 Multilayer Perceptron Applications that use neural networks have shown that the capability of these models is good for having multiple hidden layers and performing many nonlinear activation functions. MLP (Multilayer perceptron) model contains one input layer, more than one hidden layer, and one output layer. The approach is frequently employed for supervised learning problems of classifications and predictions [21]. The MLP model works on power production forecasting by training on input temporal data to predict a target power production output.

3 Evaluation Indexes The evaluation of the performances of the methods previously described was done using four different error metric calculations, RMSE, MSE, and MAE: Root mean squared error (RMSE)      N P − Pˆ 2  i=1 i i (1) RMSE = N Mean absolute error (MAE) MAE =

ˆ i N Pi − P  i=1

N

(2)

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Mean absolute percentage error (MAPE) N 1  Pi − Pˆ i MAPE = × 100 Pi N

(3)

i=1

where Pi is the predicted energy production, Pi is the weighted power output, P stands for the mean value of energy production and N denotes the count of data points that the predicted errors calculate. MAE is the mean value of the sum of absolute differences between actual and forecasted it give insights into how close the predictions are to the saved measurements on an absolute scale. On the other hand, RMSE intensifies and penalizes big errors through the square form.

4 Case Study This study was carried out on a photovoltaic plant located in the Moroccan city of Settat. The dataset has data roughly over one year from 16/12/2019 till 31/10/2020. The PV plant contains 211 polycrystalline panels with a rated power of 275 W, and these panels are connected to two Fronius inverters of 27 kW each, resulting in a 54 kW PV plant. The Helinatha Company provided us with that dataset belonging to a Morrocan factory based in Settat that plans to enlarge its photovoltaic plant soon. The main intention was to develop a couple of Machine learning models that help forecast the plant’s power output. The dataset was normalized to the range 0 to 1 before feeding the ML algorithms by following the equation below: yi∗ =

yi − ymin ymax − ymin

(4)

The dataset consists of six columns. Five of the input variables are weather variables (Wind Speed, Temperature, Humidity, Cloud Coverage, and Sun Hours) collected from an online service that provides monthly weather conditions. In contrast, the sixth input variable is the facility grid power consumption measured with a smart meter. The temperature impact in this PV plant was not that big influential since the PV panels cover the roofs of the factory, which are high enough, so they receive enough winds that cold them down.

5 Results and Discussion This section illustrates the results obtained for the proposed approach using different models to ensure better efficiency in getting an accurate forecast. Table 1 presents the models’ performances employed for the data combination (70% Training, 30% Testing), comparing the hierarchical approach to the traditional method. Indeed these three models performed well by following the hierarchical system, with the ANN model scoring the lowest MAE error value 29.93 and (34.78, 34.89) for SVR and MLP, respectively. Although the MLP model is less adept in these datasets, it still yields better RMSE 55.29 for the hierarchical approach than the traditional approach 59.07.

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Table 1. Case A errors table Approach

Model

RMSE

MAE

MAPE

Hierarchical

ANN

42.66

29.93

28.42

SVR

49.08

34.78

36.96

MLP

55.29

34.89

54.62

ANN

45.06

30.51

35.06

SVR

49.33

35.89

37.26

MLP

59.07

37.54

54.42

Traditional

Table 2 provides deeper insights into each inverter, allowing for micro-level analysis. In Table 2, where (70%) of data is reserved for training, both inverters had results so close to each other that ANN produces finer results for the S2 inverter due to their same power size. While on the other hand, MLP and SVR models forecasts were more reliable for inverter S1. Table 2. Case A inverters errors Model

Inverter

RMSE

MAE

MAPE

ANN

S1

22.13

16.27

31.68

S2

23.79

16.10

27.24

SVR

S1

23.25

16.39

35.69

S2

25.93

18.55

16.95

MLP

S1

27.74

17.90

55.82

S2

28.49

18.90

55.66

The following graphs represent the predictions of the different models; these models were analyzed using the performance indices discussed in Sect. 3. The graphs in Fig. 3 show both ANN and MLP models’ predictions and compare the results obtained for the traditional and hierarchical approaches. In Fig. 3-a, the ANN model excels with the hierarchical approach in following general trends, whereas in Fig. 3-b, the hierarchical approach stills achieve better performance than the traditional technique. Still, the model performance has not improved that much, which makes forecasts of the ANN model closer to the ground truth than the MLP model. The graphs in Fig. 4 show each inverter’s micro-level predictions. Thus, Fig. 4 illustrates the forecasts set for the ANN model, while Fig. 4-b shows predictions of the MLP model. Both models show predictions close to the underlying truth and follow trends perfectly, but as can be seen, the performance of the ANN model exceeds the MLP model.

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Fig. 3. (a) ANN traditional vs ANN hierarchical forecasts, (b) MLP Traditional vs MLP Hierarchical Forecasts

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Fig. 4. (a) ANN vs MLP Inverter S1 Predictions, (b) ANN vs MLP Inverter S2 Predictions

6 Conclusions This work compares two commonly used machine learning models, ANN and SVR, to a third machine learning MLP to forecast a PV plant’s power production. Hence, we use different machine learning models to prove that the hierarchical approach is more appropriate for PV plants that incorporate more than one inverter since this helps in improving the quality of the forecasts. Thus, using a couple of ML models was for comparison purposes and more clarity. The hierarchical approach was evaluated at the level of a self-consumption plant, using micro-level power production data records. To sum up the hierarchical approach accuracy exceeds that of the traditional method using machine learning models. Thus, the hierarchical approach makes it possible to understand how each inverter in the plant affects the total production and which inverter is much easier to forecast.

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Short-Term PV Plant Power Production Forecasting and Hyperparameters Grid Search for LSTM and MLP Models Ali Agga(B)

, Ahmed Abbou , Moussa Labbadi , and Rachid Touileb

Electrical Engineering Department, Mohammadia School of Engineering, Mohammed V University in Rabat, 10090 Rabat, Morocco [email protected]

Abstract. In recent years, the use of green energy resources has known a considerable growth due to many factors such as improved performance and reliability, which lead to more investments in these technologies, especially photovoltaic. Photovoltaic technology represents an alternative to none environmentally friendly sources such as fossil fuels and coal-fired electricity in today’s world because it is abundant, clean, and inexhaustible. Although the generated PV power is clean, however, this power is highly unconnected and unstable. To overcome this issue, the use of deep learning methods helps predict the day ahead power production of PV plants. This work grid search five hyperparameters of two deep learning models LSTM (Long Short-Term Memory Network), and MLP (Multi- layer Perceptron) for forecasting power production. The performance of the used methods was demonstrated with a case study using an actual dataset collected from Rabat, Morocco. The values of the three performance evaluation indicators, RMSE, MAE, and MAPE, show that the MLP model exhibits roughly superior performance in forecasting accuracy, stability, and speed. Keywords: Deep learning · LSTM · MLP · PV plant

1 Introduction Global warming and climate change have encouraged several legislation and incentives promoting green energy sources for electricity production [4, 5]. Solar technologies fall into two categories: thermal and photovoltaic, with photovoltaic being more widely known and deployed with a relatively higher annual growth rate which makes it a promising alternative to fossil fuel-generated electricity as it is abundant, clean, and inexhaustible [6]. Globally installed PV capacity is growing at a rate of 30% every year [7], thanks to the rapidly increasing rated power of PV modules and constantly decreasing prices. Therefore, the continuous depletion of fossil fuels leads PV plants to further integrate into modern power and electric power systems. However, because of the randomness of light and the periodicity of the day and night, the power output of PV plants is highly chaotic and uncertain due to its dependencies on © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 181–189, 2022. https://doi.org/10.1007/978-3-031-02447-4_19

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unstable weather conditions [8, 9]. Therefore, a promising solution is using PV power forecasting methods to improve operational plant quality [3]. Technological evolution, access to resources, and economic growth are the drivers for the energy transition [11]. The energetic shift opens up new opportunities for businesses and individuals towards cleaner energy [12, 13]. In 2017, and according to the REN21 report, global energy consumption from renewable energies represented 19.3%, and in 2016, produced electricity from renewable energies accounted for 24.5% [10]. Germany is willing to reduce 80% of its greenhouse gas emissions by 2050 [14, 15]. Denmark is also taking part in this global transition by setting policies for years to come [16]. China is the world’s largest consumer and producer of energy, and it is a crucial player in the global transition. More than half of the world’s solar photovoltaic capacity is planted in China [17]. Accordingly, Morocco has increased its investments and dependencies on renewable energy sources for electricity production in the last decade. The widespread implementation of solar energy systems in the country is encouraged by factors such as weather conditions (Sunny days throughout the year) and the government’s strategy to produce 52% of electricity from green sources by 2030 [20, 21]. Therefore, solar forecasting is the “foundation” of a decent and firm solar energy industry [1]. The predicting models are developing rapidly and fall into physical, statistical, machine learning, and deep learning architectures [19]. The forecasted periods of power production vary from the ultra-short-term horizon of a few minutes in advance [22] to months [23]. In [24], the authors suggested a hybrid model that merges the ELM, LSTM, singular spectrum analysis, and variational decomposition to forecast wind speed. The developed model was compared to eight other models. The findings reveal that the suggested architecture produces accurate forecasts and well extracts the trends. In [25], they used a CNN model for wind power output forecasts, the SVR and BP shallow methods were used for comparison purposes. The findings indicate that the proposed architecture can learn the data ambiguity more effectively and perform better. In [26] the ANN model was developed to forecast the intra-day power output. The suggested model successfully predicted the PV production one hour ahead by utilizing the global horizontal irradiance and cell temperature as feature inputs. Authors in [27] try a different approach to predict the power production of a photovoltaic plant where they merged the day-ahead and intraday time windows. They investigated four situations, and the forecasts’ quality was good with an nRMSE varying from 2 to 17% for the different studied PV plants. When working with deep learning models, one of the challenges when setting up a model is choosing the values of the right hyperparameters for the model. So, to overcome that issue in this work, we provide a solid background on how to launch a grid search topology for the most influential parameters of a deep learning method. After finishing the data preparation and data processing, the researcher should launch some trial tests that give him more information about the data, he’s using. After that, he has to set up various values for each parameter to find out the best configuration that fits its dataset. In this work, a simplified approach using a univariate data set with no meteorological parameters was followed. The proposed models aim to forecast the day-ahead power production of a photovoltaic plant. The forecast models used are MLP and LSTM; the two deep learning methods are suitable for forecasting time series based on PV plant’s historical records.

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The paper has the following structure: Sect. 2 provides a brief introduction about the deep learning architectures employed to predict power production and the followed approach to grid search the hyperparameters; Sect. 3 defines the performance metrics in terms of standard error criteria; Sect. 4 provides a description of the case study and the dataset used; Sect. 5 discusses the results, and Sect. 6 draws the conclusion.

2 Methodology What makes deep learning neural networks reliable is their capability of automatically extracting features from raw data. This feature of neural networks can be used for time series forecasting problems, where raw data can be used directly. Impressively with no pre-processing on raw data with both trend and seasonal components, simple deep learning neural network models are efficient in making skillful forecasts. 2.1 Multilayer Perceptron Applications that use neural networks have shown that the capability of these models is good for having multiple hidden layers and performing many nonlinear activation functions. MLP (Multilayer perceptron) model contains one input layer, more than one hidden layer, and one output layer. The approach works well on supervised learning problems of classifications and predictions. Considering that MLP is a class of artificial neural networks, it uses connection weights and nonlinear activation functions as properties of its learning process. MLP includes weights and biases of the model during the training process to minimize the prediction error. Usually, the MLP method has the intention to self-learn the complex model and reduce prediction errors. The way the MLP model is employed for load forecasting is to train it on an input temporal data X(t) to predict a target load L(t). 2.2 Long Short-Term Memory Network LSTM models are part of the deep recurrent neural networks that are generally applied to various applications like language processing and time series models [2]. When the time dependence in the sequential data has an essential implicit characteristic, the performance of LSTM models tends to be high. LSTM provides previous time steps as inputs to the model activations to impact predictions at the recent time step compared to standard feedforward networks. The LSTM architecture consists of recurrently connected subnets, known as memory blocks. These blocks can be thought of as a differentiable version of the memory chips in a digital computer. Each block contains one or more self-connected memory cells and three multiplicative units, the input, output, and forget gates, that provide continuous analogs of write, read and reset operations for the cells [18].

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3 Evaluation Indexes A variety of performances evaluation techniques exist in the literature, and many papers have described in depth these indexes; Thus, we will use the methods most used in the literature [33–35]. The performance evaluation of the methods previously described was done using three different error metric calculations, RMSE, MAE, and MAPE.

4 Case Study This study was carried out on a photovoltaic power plant located in the Moroccan capital Rabat, the PV plant has the following characteristics: PV technology: Silicon Polycrystalline, Each Module Rated power: 275 W, Number of PV panels: 40, Solar panel tilt angle (Beta): 30, The data was collected over the period from 21 July 2019 till 27 September 2020. Moreover, the models are fed with a univariate dataset that contains previous days’ production records. The PV strings are connected to the electricity grid by a Fronius Symo 15 inverter, guaranteeing production optimization. The main contribution of this work is instead of adopting pre-configured settings for the proposed models, a grid search methodology with a variety of hyper-parameters combinations was followed to get the configuration that better predicted the day ahead power production. 70% of data was used for training and 30% for testing. The proposed architectures are well suited for time-series applications such as energy production predictions since they produce reliable forecasts in terms of accuracy and efficiency.

5 Results and Discussion In the following section, we will discuss and represent the obtained results; for simplicity and brevity, we choose 52 configurations over the 243 that both models went through. Each hyperparameter influences the configuration performance. Thus, both models share the exact five hyperparameters grid search (ninput, nnodes, nepochs, nbatch, ndiff ). ninput: Represent the number of prior inputs to use as input for the model. nnodes: Represent the number of nodes to use in the hidden layer. nepochs: Represent the number of training epochs. nbatch: Represent the number of samples to include in each mini-batch. ndiff: Represent the difference order. Figures 1 and 2 illustrate the impact of the two hyperparameters (ninput, ndiff ) on the performance of the 52 different configurations of LSTM and MLP models using RMSE and MAE error metrics. Overall, there was a steady decrease in the error values over these configurations. Starting with the least skillful configurations in graphs (a) followed

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Fig. 1. The impact of the two hyperparameters (ninput, ndiff ) on the performance of the LSTM model, (a) poorly performed configurations, (b) tolerable performance configurations, (c) well performed configurations

by moderate ones in graphs (b) and graphs (c) show well-performed configurations. Both the (ninput) and the (ndiff ) witnessed changes during their different configurations; in fact, by using the previous 16 days as input and a differentiation order of 24, produced inaccurate predictions with high error values (LSTM: (21.49, 14.86), MLP: (12.91, 9.33)). After that, when the value of (ndiff ) reached zero, the error values went down by tripled for the LSTM model and by doubled for the MLP model, resulting in much better RMSE and MAE values (LSTM: (6.76, 5.32), MLP: (6.26, 4.48)), also the (ninput) varied according to the different configurations. Thus, the latest arrangements with (ninput) equal to 12 resulted in lower error values than the initial configurations with values of 16 and 14. Therefore, the two hyperparameters reveal that our dataset has no trend or seasonality, and using the previous 12 days gives accurate predictions. The bar charts in Figs. 3 and 4 illustrate the correlation between the five grid search hyperparameters and their influence on the overall performance of both models. On the other hand, (nepochs) and (nnodes) have less impact on the different configurations, as the chosen values fit the dataset perfectly. Table 1 has the best five configurations that forecast the power production of the PV plant using the LSTM model. Table 2 shares the same structure as the previously mentioned table, and it is clearly noted that the MLP model better predicts power output than the LSTM model. These outcomes confirm the results from Figs. 1 and 2 of using 12 days as input for the models and that the dataset has no trend or seasonality. The (nbatch) has a crucial role, as it is the main difference between the two models where the lower it get, the better the LSTM model performs. Meanwhile, the MLP model required more samples in each mini-batch to get better predictions.

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Fig. 2. The impact of the two hyperparameters (ninput, ndiff ) on the performance of the MLP model, (a) poorly performed configurations, (b) acceptable performance configurations, (c) well performed configurations Table 1. Best LSTM model configurations Config

48

49

50

51

52

ninput

12

12

16

12

12

nnodes

50

100

50

75

50

nepochs

150

100

100

200

100

nbatch

1

1

1

1

1

ndiff

0

0

0

0

0

RMSE

7.193

7.09

7.073

7.07

6.76

MAE

5.333

4.808

5.143

5.025

5.32

MAPE

26.591

25.529

28.22

25.368

23.667

Fig. 3. LSTM grid search hyperparameters values, and their influence on MAE

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Fig. 4. MLP grid search hyperparameters values, and their influence on MAE Table 2. Best MLP model configurations Config

48

49

50

51

52

ninput

12

12

12

12

12

nnodes

100

100

100

75

100

nepochs

200

150

100

150

200

nbatch

50

50

50

50

100

ndiff

0

0

0

0

0

RMSE

6.306

6.303

6.292

6.288

6.267

MAE

4.476

4.436

4.485

4.46

4.481

MAPE

22.183

23.153

23.394

22.882

22.803

In conclusion, most skillful and unskillful configurations share common hyper parameter values such as (ninput), (ndiff ) and (nbacth), the worst configurations had a (ninput) equal to 16 and a (ndiff ) equal to 24 and their (nbacth) equal to 100. On the other hand the well performed configurations had different values for their (ndiff ), (ninput) and (nbacth).

6 Conclusions This work compares two deep learning models, LSTM and MLP, to predict power production 24 h ahead of time for a 15 kW PV plant. The dataset used is made of a univariate variable (Power Production). Meteorological parameters were not used to reduce model complexity and measure the performance and ability of univariate models to predict power output. Based on the analysis of the errors between real values and predicted values, it is clear that the MLP and LSTM model both performed well on predicting the day ahead power output. The analysis of these hyperparameters gives insights into the dataset and lead to choosing the appropriate value for each hyperparameter. Therefore, based on the obtained results from the one-year historical dataset, the best performing parameters that minimize the RMSE error metric for the one day ahead forecasts are as follow : (ninput = 12, nnodes = 50, nepochs = 100, nbatch = 1, ndiff = 0) for the

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LSTM model and (ninput = 12, nnodes = 100, nepochs = 200, nbatch = 100, ndiff = 0) for MLP model. The approach we follow is intended to be adopted and applied for different PV installations with univariate datasets. In future work, we will focus on using hybrid models such as CNN-LSTM and ConvLSTM, including weather conditions, and adding some features that affect PV plant power production.

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19. Wang, H., Lei, Z., Zhang, X., Zhouc, B., Peng, J.: A review of deep learning for renewable energy forecasting. Energy Convers. Manage. 198, 111799 (2019) 20. Interpretations of Energy Indicators, Moroccan Ministry of Energy, Mines, Water, and the Environment (2019) 21. Energy Transition: Morocco’s strategic choice. In: 14th Energy Conference, 19 November 2018. Skhirat, Morocco (2018) 22. Sobri, S., Koohi-Kamali, Sam, K.-K., Rahim, N.A.: Solar photovoltaic generation forecasting methods: a review. Energy Convers. Manage. 156, 459–497 (2018) 23. Florian, B., Sumedha, R., Arindam, G.: Very short-term photovoltaic power forecasting with cloud modeling: a review. Renew. Sustain. Energy Rev. 75, 242–263 (2017) 24. Liu, H., Mi, X., Li, Y.: Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Convers. Manage. 159, 54–64 (2018) 25. Wang, H., Li, G., Wang, G., et al.: Deep learning based ensemble approach for probabilistic wind power forecasting. Appl. Energy 188, 56–70 (2017) 26. Almonacid, F., Perez-Higueras, P.J., Fernandez, E.F., Hontoria, L.: A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Convers. Manag. 85, 389–398 (2014) 27. Zhang, J., et al.: A suite of metrics for assessing the performance of solar power forecasting. Sol. Energy 111, 157–175 (2015) 28. Hyndman, R., Koehler, A.B., Ord, J.K., Snyder, R.D.: Forecasting with Exponential Smoothing: The State Space Approach. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3540-71918-2 29. Janacek, G.: Time series analysis forecasting and control. J. Time Ser. Anal. 31, 229–303 (2010) 30. Bouktif, S., Fiaz, A., Ouni, A., Serhani, M.A.: Optimal deep learning, L.STM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11, 1636 (2018) 31. Bouktif, S., Fiaz, A., Ouni, A., Serhani, M.: Single and multi-sequence deep learning models for short and medium term electric load forecasting. Energies 12, 149 (2019) 32. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997) 33. Monteiro, C., Fernandez-Jimenez, L.A., Ramirez-Rosado, I.J., Munoz-Jimenez, A., LaraSantillan, P.M.: Short-term forecasting models for photovoltaic plants: analytical versus softcomputing techniques. Math. Probl. Eng. 2013, 767284 (2013) 34. Ulbricht, R., Fischer, U., Lehner, W., Donker, H.: First steps towards a systematical optimized strategy for solar energy supply forecasting. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013), Riva del Garda, Italy, 23–27 September 2013 35. Kleissl, J.: Solar Energy Forecasting and Resource Assessment. Academic Press, Cambridge (2013)

Computational Analysis of Human Navigation in a VR Spatial Memory Locomotor Assessment Using Density-Based Clustering Algorithm of Applications with Noise DBSCAN Ihababdelbasset Annaki1(B) , Mohammed Rahmoune1 , Mohammed Bourhaleb1 , Noureddine Rahmoun1 , Mohamed Zaoui2 , Alexander Castilla2 , Alain Berthoz2 , and Bernard Cohen3 1 Research Laboratory in Applied Sciences, National School of Applied Sciences, PB 669,

60000 Oujda, Morocco [email protected] 2 Collége de France, CIRB. 11, Place Marcelin-Berthelot, 75231 Paris Cedex 05, France 3 Faculty of Medicine Bd de L’Hopital, Sorbonne University, 75013 Paris, France

Abstract. In this study, we explore human navigation as evaluated by the VR Magic Carpet TM (VMC) [1], a variation of the Corsi Block Tapping task (CBT) [2, 3], employing Density-based spatial clustering of applications with noise (DBSCAN) [4]. As a result of the VMC, we acquired raw spatial data in 3D space, which we processed, analyzed, and turned into trajectories before evaluating them from a kinematic standpoint. Our previous research [5] revealed three unique groupings. However, the categorization remained ambiguous, showing clusters with diverse people (patients and healthy). We utilized DBSCAN to compare patients’ navigation behavior to healthy individuals, highlighting the most notable differences and assessing our existing classifiers. Our research aims to produce insights that may help clinicians and neuroscientists adopt machine learning, especially clustering algorithms, to identify cognitive impairments and analyze human navigation behavior. Keywords: Artificial intelligence · Machine-learning · DBSCAN · Virtual reality · Neuropsychological assessments · Cognitive impairments · Human navigation

1 Introduction Artificialintelligence(AI)referstocomputingsystemsthatreplicatehumancognitiveabilities to analyze complex medical data in healthcare. Machine learning (ML) and other AI technologies integrate physical, behavioral, and social components in disease diagnosis, prognosis, and treatment [6]. Through training, AI adjusts to identify abnormalities in diverse datasets. (i.e., pattern recognition) [7]. AI has the potential to increase the interpretability and therapeutic value of massive amounts of unexploited medical (i.e., neurophysiological) data. AI has the potential to teach us new elements of brain function, such as © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 190–198, 2022. https://doi.org/10.1007/978-3-031-02447-4_20

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navigation and connectivity, and fresh ideas about brain-based processes. AI can identify cognitive impairment and anticipate prognosis trajectories in various groups [8, 9]. Traditional statistics focus on fitting a specific model and hypothesis testing to discover basic processes. ML algorithms do not need a priori hypotheses about the connections between variables and instead concentrate on accuracy, which often uncovers unexpected linkages and complicated nonlinear interactions within data [10]. We are mainly interested in Unsupervised Learning (UL) algorithms in our study. Although the data may include people with and without cognitive impairment, the algorithm is entirely oblivious of this. Instead, the algorithm searches for correlations or clusters in data (for example, neuropsychological assessment outputs) to classify the data or identify anomalies that do not fit into any categories. Identifying clusters usually need clinical expertise to judge their relevance [11]. Researchers in other fields, including robotics, neuroscience, and mathematics, are interested in extracting descriptive elements that might help them better understand invariance in human navigational behavior. Several studies have established the existence and development of conceptual models of human movement [12]. According to experts in robotics and physics, the formation that occurs during goaloriented movement in humans is stereometric when observing a person’s path from A to B [13]. Many studies focus on building and duplicating human locomotion models in robots, enabling researchers to compare the simulated human locomotor models and robots [14]. In contrast, attention to path aspects such as smoothness and movement planning led to the development of different models (i.e., the maximum smoothness model [15]). The emphasis on motor coordination, gait analysis, musculoskeletal system [16], and energy optimization [17] inspired divergent conceptual models. Mathematicians evaluated human locomotion and found that activities are planned and regulated with the need to combine various motions and senses. The focus is on analyzing the geometrical component of human locomotor movement [18] and emphasizing the trajectory by implying multiple models such as stochastic [19]. As previously stated, machine learning clustering algorithms are involved in a methodological approach to assist researchers in other fields by providing insights into the model’s existence, conception, categorization, and detection of the numerous parallels and dissimilarities observed during human navigation. The primary goal is to identify the indicators that distinguish archetypal human navigation. In this paper, we present The VR Magic Carpet TM assessment. Then we describe our exploratory data analysis based on the assessment’s output [1]. Furthermore, we apply DBSCAN, an unsupervised learning algorithm [4], to delve deeper into the classes identified by our early kinematic data analysis [5] and examine the validity and involvement of the variables used in this study.

2 Materials and Methods 2.1 VR Magic Carpet TM As previously said, we focus on the VMC [1], a CBT variant. The Corsi block tapping test (CBT) [3] is a short-term memory assessment described as follows:

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– The researcher (the experiment administrator) places nine blocks in front of the subject. – The experiment administrator displays the sequence to the participant by tapping on a series of blocks. – The subject must tap the blocks that the experimenter showed in the same sequence. – Those procedures are done several times with varied block lengths. This task culminated in the creation of a large-scale locomotor variant known as the walking Corsi test (WTC) [20] by following the exact instructions but altering the table to a room (3 m × 2.5 m) and the spans with tiles (30 cm × 30 cm). The participant is required to walk rather than tap. Furthermore, another variant known as the magic carpet replaced the experimenter by lighting the tiles to reveal the sequence. Propelled by technological advancements and the urge to record the participant while taking the test, virtual reality tools were incorporated into the WCT by constructing an environment in Blender, which also led to the construction of the VR Magic Carpet TM in Unity3D. 2.2 Participants Dr. Bernard Cohen’s pilot test supplied the experimental data. Paris University ethics committee approved the evaluation carried out under the Helsinki Declaration [21]. Each of the twenty-two participants was assessed independently. As a consequence of the experiment, no neurological or neuropsychological difficulties were developed. To avoid a priori prejudice in the study, any personal information, from demographics to pathologic features, was omitted. Participants’ information is anonymized for data accessibility and in-depth research of the topic. 2.3 Experimental Setup Two PCs, a video projector, and the HTC Vive kit comprise the experimental setup. The video projector is linked to a laptop, which projects the tiles onto the group. The HTC Vive VR Kit is connected to the second machine. The HTC Vive kit includes two SteamVR controllers and two SteamVR base stations that detect movement in the play area. 2.4 Data Acquisition The play area and numerous calibrations were adjusted after installing SteamVR software on the laptop connected to the HTC Vive. Afterward when the experiment is launched by launching the unity-built environment. A first controller was attached to the head and a second to the waist; calibration aided us in establishing the origin of the play area and the axis of rotation based on the arrangement of Euler angles. The 3D spatial data was acquired using the SteamVR tracking software and the OpenXR API, making it possible to integrate the device into Unity3D. Furthermore, Unity c scripts were created to extract spatial data from Unity3D. The information was saved in text files known as session data, which show the participant’s trajectory and rotation. In addition, we kept the tile coordinates as JSON files. The adjustments produced by the HTC Vive calibration were mirrored in each file.

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2.5 Data Preprocessing We use “Pandas, NumPy,” Python data analysis and processing tools to unify the data structure to prevent differences across file formats. Moreover, a local target identification algorithm was developed to detect the tiles that were visited and identify the tiles as targets where the subject indicated a stop inside it or the participant just passed by to comprehend the participant’s behavior once on a tile. The algorithms assisted us in retrieving crucial information, such as the time spent within a target and the behavior before moving on to the next tile. The data was collected at a sample rate close to 1 kHz. We utilized the SciPy library’s thresholding and filtering algorithms to reduce noise in the resultant product. Furthermore, we adjusted our dataset to be physically meaningful in the context of human bipedal navigation research while also boosting consistency and reliability. We added additional columns to our datasets, such as the Euclidean distance and speed, generated from a kinematic analysis approach. 2.6 Visual Replication We depicted the clinical trials using Matplotlib, a powerful tool for producing dynamic, rich Python visualizations, and we double-checked the findings of our target recognition algorithm. The use of animation to duplicate the trajectory and targets allowed us to acquire a better knowledge of the kinematic behavior of the participants during each session of the experiment, allowing us to deepen our exploratory analysis (see Fig. 1).

Fig. 1. Visual replication of a session using Python, the participant’s trajectory is represented in cyan color. The squares represent the tiles. After visiting the tile, we highlighted it with a red dot as the closest point from the trajectory to the target’s center. The purple mark is when the participant leaves the confidence area around the target, and the yellow cross indicates when the participant enters the confidence area of the target. The black rectangle delimits the navigation area.

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2.7 Data Aggregation As described in the data acquisition section, each subject participates in 10 sessions. The sequence varied from two to nine targets. Instead of a session-oriented analysis, we intended to establish a participant-based study to concentrate on the bigger picture. In addition, we aggregated the data via SQL Server and Microsoft SSIS. This method helped us create two key columns: the participant’s overall average speed and the average time spent within a tile. This aggregation aided us in feature selection and extraction, on which we applied the clustering algorithm DBSCAN. We specifically utilized kinematics as the analytical pillar since it allowed us to determine the core characteristics for our study without resorting to feature extraction techniques. 2.8 Density-Based Spatial Clustering of Applications with Noise DBSCAN [4] describes clusters as high areas flanked by low-density areas. Unlike KMeans, which maximizes the within-cluster sum-of-squares and is best suited for convex forms, DBSCAN can find clusters of any shape. DBSCAN also automatically determines the number of clusters. DBSCAN relies heavily on the concept of core samples. Core samples are data points within an eps distance of at least minPts neighbors. The existence of at least minPts in some distance eps implies that the core samples are located in a dense area of the vector space. A cluster is a collection of core samples that may be built by iteratively taking a core sample and determining all of its neighbors who are also core samples and then identifying their neighbors who are also core samples. Non-core samples resemble but are not identical to core samples. Because they appear to be outside a cluster, these samples are sometimes referred to as border samples or border points. Finally, there is a third type of sample: noise samples frequently referred to as outliers. DBSCAN Algorithm Pseudo-code. The algorithm is expressed in pseudo-code, as demonstrated in (Fig. 2): Determination of EPS and MinPts. We employed the default value of MinPts, which is defined as the number of points that must be near to a point to be deemed a core sample in the “Scikit-learn” module. It primarily affects the algorithm’s noise susceptibility. Furthermore, we computed the average distance between each point and its k neighbors to obtain the maximum distance between two points that may be considered neighbors (eps). The average k-distances were sorted and presented on the distance graph, with the optimal value obtained by evaluating and experimenting with the various slopes [23] (see Fig. 3).

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Fig. 2. DBSCAN pseudo-code [22]

Fig. 3. K-Distances line plot to determine eps

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3 Results According to DBSCAN, this study found four distinct clusters, with four individuals deemed outliers (see Fig. 4). Although we established three groups based on our previous data analysis, we could not differentiate individuals who did not fall into any categories. We emphasize that the clusters formed are based on relevant information that led to aggregate variables; in other words, we focus more on the participant’s average speed and the average time spent near recognized tiles (targets) across all sessions. Furthermore, the two measures used may be considered reliable indicators of variations in human navigational behavior between control people and patients. These findings have piqued the interest of neuroscientists, who believe they will help create profiles based on clustering studies, especially when other variables are included.

Fig. 4. DBSCAN clustering result, Estimated number of clusters: 3 and estimated number of noise points: 4

4 Discussion We established groups or categories and outlined in broad strokes the varied actions of individuals who completed the VMC in our initial exploratory data analysis [5] using a kinematic approach. The goal of this paper was to corroborate preliminary analytical findings while also delving deeper into the unclear categories that included persons from both groups. It might be beneficial for developing criteria and describing classifiers that

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define human navigation behavior, particularly in producing different profiles. Furthermore, the methodological approach applying a machine learning clustering algorithm may aid in, if not modeling, demonstrating the model’s existence and providing us with crucial information that may help in modeling.

5 Conclusion The current work is part of a larger project to define human behavior in a sophisticated visuospatial evaluation to test memory and navigation abilities. The purpose is to extract useful information from raw 3D spatial data, such as speed and time spent inside the target, and to provide a methodological approach to help in the development of a human navigational stereotypic model. On the other hand, our study attempts to combine innovative analytics approaches (such as Unsupervised Learning) into the decoding of neuropsychological exams. Therefore, supporting neuropsychologists in making decisions about various rehabilitative and therapy alternatives.

References 1. Berthoz, A., Zaoui, M.: New paradigms and tests for evaluating and remediating visuospatial deficits in children. Dev. Med. Child Neurol. 57(Suppl 2), 15–20 (2015). https://doi.org/10. 1111/dmcn.12690 2. Corsi, P.M.: Human memory and the medial temporal region of the brain Ph.D. McGill University (1972) 3. Berch, D.B., Krikorian, R., Huha, E.M.: The Corsi block-tapping task: methodological and theoretical considerations. Brain Cogn. 38(3), 317–338 (1998). https://doi.org/10.1006/brcg. 1998.1039 4. Martin, E., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231. AAAI Press (1996) 5. Annaki, I., et al.: Computational analysis of human navigation trajectories in a spatial memory locomotor task. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications, ICDTA 2021. LNNS, vol. 211, pp. 233–243. Springer, Cham (2021). https://doi.org/10.1007/ 978-3-030-73882-2_22 6. Graham, S.A., et al.: Artificial intelligence approaches to predicting and detecting a cognitive decline in older adults: a conceptual review. Psychiatry Res. 284, 112732 (2020). https://doi. org/10.1016/j.psychres.2019.112732 7. Ahmed, R., et al.: Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects. IEEE Rev. Biomed. Eng. 12, 19–33 (2019). https://doi. org/10.1109/RBME.2018.2886237 8. Fan, M., Yang, A.C., Fuh, J., Chou, C.: Topological pattern recognition of severe Alzheimer’s disease via regularized supervised learning of EEG complexity. Front. Neurosci. 12(12), 685 (2018). https://doi.org/10.3389/fnins.2018.00685 9. Graham, S.A., Depp, C.A.: Artificial intelligence and risk prediction in geriatric mental health: what happens next? Int. Psychogeriatr. 31(7), 921–923 (2019). https://doi.org/10.1017/s10416 10219000954 10. Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7

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Analysis of UNSW-NB15 Datasets Using Machine Learning Algorithms Hakim Azeroual(B) , Imane Daha Belghiti, and Naoual Berbiche LASTIMI, EST Sale, Mohammed V University in Rabat, Rabat, Morocco [email protected]

Abstract. To improve the existing intrusion detection systems, we propose the use of a set of ML classification algorithms to determine the best one capable of recognizing the maximum number of intrusions in a system. To do this, we used the data set UNSW-NB15 to which we applied data cleaning, then we determined the most important characteristics, and finally we passed to the evaluation of the classifiers with PCA, to apply the different ML algorithms, and conclude that the DT-PCA model gave good results for the classification of the attacks, but which remains to be optimized to preserve more of the characteristics of our data set. Keywords: Machine Learning · Random Forest · Decision Tree · KNN · XGB · Accuracy · UNSW · NB15 · Gaussian Naïve Baye

1 Introduction Cyber defense is about anticipating adversary actions to counter intrusions. Securing this technology, which has penetrated nearly every aspect of our lives, including social, economic, and political systems, is critical. Research continues to develop Intrusion Detection Systems (IDS) to secure cyber technology that addresses the problems of attack in today’s Big-Data environment [1]. This secure defense can result in normal operations for organizations that reach a certain threshold while facing persistent threats and developed attacks. Therefore, it is essential to create a proactive secure cyber defense system with these evolving technologies. Proactive intrusion detection monitors a network for malicious activity and optimizes it based on the new information it learns. The proposed solution in this framework is the creation and development of Machine Learning (ML) and Deep Learning (DL) models for normal and anomalous activities to build anomaly-based IDS [2]. Developing a robust and optimized ML or DL model again requires training and testing on current intrusion-based datasets that must be constantly updated with new threats [3]. Thus, the development of datasets and optimization of ML/DL models built on these datasets is the key to developing effective IDSs. In the research presented in this paper [4], we used a hybrid dataset between actual modern normal activities and current synthesized attack activities of flow-based labeled network traffic, UNSW-NB15 [5], for the evaluation of anomaly-based IDSs. Finally, we implement ML models on this dataset to conclude by creating robust IDSs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 199–209, 2022. https://doi.org/10.1007/978-3-031-02447-4_21

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The structure of our paper is as follows: Sect. 2 gives an overview of the work done by other authors; Sect. 3 includes the different methods we used in our work. Section 4 presents the tests and the results obtained, and finally, the conclusion is represented in Sect. 5.

2 Related Work In this section, we provide examples of previously published papers aimed at improving intrusion detection techniques using ML and DL models. Sarika Choudharya and Nishtha Kesswani used a deep neural network (DNN) to identify attacks on the IoT [4]. The performance of DNN to correctly identify attacks was evaluated on the most widely used datasets, including UNSW-NB15. The experimental results showed the accuracy rate of the proposed method is above 90% for each dataset. Quamar Niyaz et al. [6], presents a new deep learning technique for intrusion detection. It is a deep learning classification model evaluated using the KDD Cup ‘99 and NSL-KDD benchmark datasets. The technique demonstrates improvements over existing approaches and strong potential for use in modern NIDS. Ripon Patgiri and all [7], used the NSL-KDD dataset to evaluate machine learning algorithms for intrusion detection. In their work, they reduced the dimensionality of the dataset using RFE which improved the execution speed and prediction accuracy. Thus, it demonstrated the comparison between the performance of the model before and after the selection of the features of Random Forest and SVM, based on the confusion matrices.

3 Methodology 3.1 Data Description The UNSW-NB15 train database was released in 2015, it includes modern attacks compared to older databases. It is dedicated to traffic analysis and intrusion detection. It includes a variance of normal and attacked events, as shown in Fig. 1. UNSW-NB15 includes different characteristics like basic, flow, content, and others [8]. There are some general-purpose features and some connection ones. In addition, the UNSW-NB15 train dataset includes nine types of attacks: Analysis, Fuzzers, Backdoors, DoS Exploits, Reconnaissance, Generic, Shellcode, and Worms [9]. Their distributions are displayed in Fig. 2. We start by cleaning our database. this consists of avoiding redundancies and deleting duplicates. In our case, we delete the [‘id’] column. Then we extract the numerical attributes and scale them to have a zero mean and unit variance. Similarly, we encode categorical attributes.

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Fig. 1. Distribution of the flow between attacks and normal.

Fig. 2. Distribution of attack types.

3.2 Methods • Random Forest Classifier Since our database contains too many features, we focus our work on the most important features. For this, we used the Random Forest classifier to determine them. To estimate the importance of the variable m, the number of votes for the correct class is counted in each tree [10]. • Nearest Neighbor (KNN) The K-nearest neighbors algorithm says for a given value of h. The algorithm will try to find the h-nearest neighbors of an unseen data point and then assign the class to the

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unseen data point by having the class that has the largest number of data points among all classes of h neighbors [11]. • Logistic Regression Logistic regression is a classification model that makes predictions based on a set of independent variables. Logistic regression is used when we are working with binary data [12, 13]. • Gaussian Naïve Bayes Naive Bayes Gaussian is a variant of Naive Bayes that takes into account continuous data. Naive Bayes is one of the supervised classification algorithms. It is a simple classification technique with high functionality. It is often used when there is a lot of input data [9]. • Decision Tree Decision tree (DT) algorithms are used in classification, which is considered a predictive model that maps observations about an object to a tree structure composed of nodes and branches [14]. • XGB eXtreme Gradient Boosting It is a boosting algorithm based on the gradient boosted decision tree algorithm. XGBoost applies a better regularization technique to reduce overfitting. • PCA Principal component analysis, or PCA, is often used for dimensionality reduction of large data sets. It requires decreasing the dimensionality while preserving many of the characteristics of our database.

4 Test and Result 4.1 Determination of Significant Features 4.1.1 Random Forest Classifier To reduce the dimensionality of our database, we used the RF algorithm. As a result, we obtained 15 important features, as shown in Fig. 3, and their description are shown in Table 1.

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Fig. 3. Important features extracted with RF.

Table 1. Description of the important features extracted by Random Forest. Features Duration Sbytes Dbytes Rate Sttl Sload Smean Dmean Ct_srv_src

Ct_src_dport_ltm Ct_dst_sport_ltm

Ct_srv_dst

Label Service

Description Record total duration Number of data bytes from source to destination Number of data bytes from destination to source Rate Source to destination time to live value Source bits per second Mean of packet size transmitted by the source Mean of the packet size transmitted by the destination Number of connections that contain the same service and source address out of 100 connections according to the last time Number of connections of the same source address and the destination port in connections according to the last time Number of connections of the same destination address and the source port in 100 connections according to the last time Number of connections that contain the same service and destination address in 100 connections according to the last time 0 for normal and 1 for attack records used service

4.2 XGB Classifier Secondly, we used the XGB classifier. This gave us 7 important features, as shown in Fig. 4, and their description are presented in Table 2.

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Fig. 4. Important features extracted with XGB.

Table 2. Description of the important features extracted by XGB.

Features Protocole State Sinpkt Dinpkt Sjit Sload Ct_dst_ltm

Description Transaction protocol Indicates to the state and its dependent protocol Source interpacket arrival time Destination interpacket arrival time Source jitter Source bits per second Number of connections of the same destination address in 100 connections according to the last time

4.3 Decision Tree Classifier Thirdly, we used the Decision Tree classifier. This gave us 4 important features, as shown in Fig. 5, and their description is presented in Table 3. The green lines represent the features extracted in common by the RF, XGB, and Decision Tree classifiers. We conclude that the XGB and Decision Tree classifiers have four features in common: sload which is part of the basic features, protocol which is part of the flow features, and dinpkt sjit which are part of the time features. On the other hand, the only feature that has been extracted in common from the 3 classifiers is sload.

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Fig. 5. Important features extracted with DT.

Table 3. Description of the important features extracted with DT.

Features Protocole Dinpkt Sjit Sload

Description Transaction protocol Destination interpacket arrival time Source jitter Source bits per second

4.4 Evaluation of Classifiers with PCA In the second phase of our work, we evaluate which is the best classifier according to RF, XGB, and DT. To do this, we will combine PCA with the classifier sets. For each classifier, we define only two principal components. Then, following the percentage of variance assigned by each of the selected components, we will choose the best model. The results are presented in Table 4. Table 4. Results after applying the PCA. Explained variance ratio %Preserved features

%Lost features

Number of detected attacks/9

RF-PCA

44

56

4

XGB-PCA

54

46

6

DT-PCA

70

30

6

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Fig. 6. Applying the RF-PCA.

Fig. 7. Applying the XGB-PCA.

Fig. 8. Applying the DT-PCA.

Based on Figs. 6, 7, and 8 and the percentages of the variance ratio explained, we can see that the DT-PCA model is the most performing model. Still taking into account that the DT classifier has detected only four important features, their return has been the best because the PCA-DT result has kept a good percentage of the characteristics of our database. 4.5 Application of the Different ML Algorithms After choosing the best classifier, DT-PCA that gave us good results, we will apply different ML algorithms to classify the attacks. We first apply the different classification algorithms, in our study, we apply KNN, Decision Tree, Gaussian Train Naive Baye, and logistic regression. As metrics, we use cross-validation mean square and classification accuracy. Classification accuracy is a measure for evaluating classification models. Its equation is represented in (1).

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Cross-validation is a technique used to estimate the test error of a predictive number. The observations are divided into N sets and the model is trained on N-1 sets; the test error is predicted on the partition left out. Accuracy =

Number of correct predictions Total number of predictions

(1)

To perform these tests, we divided our database as follows: 0.7 for training and the rest for testing. The results of our tests are shown in Table 5. Table 5. Application of the different ML algorithms. Cross-Validation Mean Square

Model Accuracy

KNN

0.8631

0.9027

Decision Tree

0.8839

1.0

Gaussian Naïve Baye

0.7738

0.7733

Logistic Regression

0.8633

0.8644

As the scores in the table show, DT is the best in accuracy, and Gaussian Naive Baye is the best in the Cross-validation mean. The classification reports for each model are displayed below. The classification report contains the metrics precision (2), recall (3), accuracy (4), and F1 (5). Precision = Recall = Accuracy = F1 = 2 ·

tp tp + fp

(2)

tp tp + fn

(3)

tp + tn tp + tn + fp + fn

precision · recall precision + recall

Table 6. Confusion matrix. Observed

Predicted

TP: True Positive

Positive

Positive

FP: False Positive

Negative

Positive

FN: True Negative

Positive

Negative

TN: False Negative

Negative

Negative

(4) (5)

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Fig. 9. KNN classification report.

Fig. 11. Gaussian Naïve Baye classification report.

Fig. 10. DT classification report.

Fig. 12. LR classification report.

From our study, we can conclude that the DT-PCA model has given good results for attack classification using different ML techniques. But we are still motivated to optimize the results even more, especially by trying to find a model even better than DT-PCA that will preserve more of the features of our database (Figs. 9, 10, 11, 12 and Table 6).

5 Conclusion Our study detected the 4 most important features from the 44 features of the Train dataset UNSW-NB15 using a combined decision tree and principal component analysis fusion algorithm. In future work, we see that we can further optimize the performance by decreasing the error rate and increasing the prediction accuracy. For this, we see that the use of deep learning algorithms gives more optimized results.

References 1. Kotenko, I., Saenko, I., Branitskiy, A.: Applying big data processing and machine learning methods for mobile internet of things security monitoring. J. Internet Serv. Inf. Secur. (JISIS) 8(3), 54–63 (2018)

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2. Thapa, N., Liu, Z., Shaver, A., Esterline, A., Gokaraju, B., Roy, K.: Secure cyber defense: An analysis of network intrusion-based dataset CCD-idsv1 with machine learning and deep learning models. Electronics 10(15), 1747 (2021) 3. Mahbooba, B., Sahal, R., Alosaimi, W., Serrano, M.: Trust in intrusion detection systems: an investigation of performance analysis for machine learning and deep learning models. Complexity 2021, 1–23 (2021) 4. Choudhary, S., Kesswani, N.: Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT. Procedia Comput. Sci. 167, 1561–1573 (2020) 5. Al-Daweri, M.S., Ariffin, K.A.Z., Abdullah, S., Senan, M.F.E.M.: An analysis of the KDD99 and UNSW-NB15 datasets for the intrusion detection system. Symmetry 12(10), 1–32 (2020). https://doi.org/10.3390/sym12101666 6. Niyaz, Q., Sun, W., Javaid, A.Y., Alam, M.: A deep learning approach for network intrusion detection system (2015). https://doi.org/10.4108/eai.3-12-2015.2262516 7. Patgiri, R., Varshney, U., Akutota, T., Kunde, R.: An investigation on intrusion detection system using machine learning. In: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, January 2019, pp. 1684–1691 (2019). https://doi. org/10.1109/SSCI.2018.8628676 8. Moustafa, N., Slay, J.: UNSW-NB15: A Comprehensive Data set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). https://cve.mitre.org/ 9. Choudhary, S., Kesswani, N.: Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT. Procedia Comput. Sci. 167(2019), 1561–1573 (2020). https://doi.org/10.1016/j.procs.2020.03.367 10. Zhou, Y., Cheng, G., Jiang, S., Dai, M.: Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput. Netw. 174, 107247 (2020). https://doi. org/10.1016/j.comnet.2020.107247 11. Li, W., Yi, P., Yue, W., Pan, L., Li, J.: A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J. Electr. Comput. Eng. 2014, 1–8 (2014). https://doi.org/10.1155/2014/240217 12. Subba, B., Biswas, S., Karmakar, S.: Intrusion detection systems using linear discriminant analysis and logistic regression, pp. 1–6 (2015) 13. Mahmood, D.Y.: Classification trees with logistic regression functions for network based intrusion detection system. IOSR J. Comput. Eng. 19(03), 48–52 (2017). https://doi.org/10. 9790/0661-1903044852 14. Al, A., Salameh, J.B.: A model for cloud intrusion detection system using feature selection and decision tree algorithms. Int. J. Sci. Technol. Res. 10, 2258–3233 (2021)

OntoFusionCrop: An Ontology Centric Approach for Crop Recommendation Based on Bagging and Semantic Alignment Aparna Chandramouli1 and Gerard Deepak2(B) 1 Department of Computer Science and Engineering, SRM Institute of Science and Technology,

Chennai, India 2 Department of Computer Science and Engineering, National Institute of Technology,

Tiruchirappalli, India [email protected]

Abstract. Agriculture is a crucial source of livelihood. There are many farmers who take up farming as their main source of income. The major difficulty present among farmers is that their crop selection is not based on the soil and weather conditions. Thus, it is essential for farmers to know the necessary information regarding the different facets of crop production, best agricultural practices, suitable crops for a specific soil type to be grown in a particular weather, etc. A crop recommender system, which has accurate and precise information, serves as a means for the farmers to farm appropriate crops and get the best yield. This paper proposes an ontology-based recommender system to provide the appropriate details regarding crops and other information associated with them. The proposed OntoFusionCrop uses ontology cluster that includes soil ontology, crop ontology, geographical ontology and agricultural ontology. This strategy uses the crop recommendation dataset from Kaggle which is classified using bagging. The classified instances along with the ontology cluster are semantically aligned using spider monkey optimization algorithm from which we get facts after suitable verification. The query is asked by the user and the recommender system gives the output. The proposed model achieved 93.87% accuracy and A minimum of 0.05 FDR compared to the existing models. Keywords: Bagging · Spider Monkey Optimization · Support Vector Classification · Random Forest

1 Introduction An expert system, also known as a recommendation system, is a system that anticipates and recommends services and products to the users based on their needs and interests. Crop recommendation systems are used to recommend specific crops based on the different factors such as soil properties, weather, agricultural practices, etc. There is a need for a specialized recommendation system which is domain specific, focusing on recommendation of crops, which helps agriculture. The existing recommendation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 210–219, 2022. https://doi.org/10.1007/978-3-031-02447-4_22

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systems are not semantically driven, not semantically compiled and not domain specific. They cater to the broad range of needs wherein a lot of technical contents to improve agriculture is always neglected and the process to make agriculture precise is also left out. Domain based recommendation systems can assist recommendations in a target domain based on knowledge learned. Specific domain-based recommender systems require knowledge representation and reasoning, and retrieving the information based on the recent knowledge. Motivation: It is becoming necessary to have a semantically driven or semantically compiled recommendation systems for specialized crop recommendation in order to improve the agriculture and increase good product yield. The recommendation system can improve agricultural yielding and prospects. Contribution: The features are extracted using Bag of words model and TF-IDF method. The entropy is computed and the features are weighed. Next, the data is classified using Bagging. After classification, Semantic alignment is done using Spider Monkey optimization algorithm where the given ontologies are used and modelled. The multiobjective functions (Web overlap, Normalized Google Distance, Lesk similarity) are executed here and the ontologies are semantically aligned. The facts are generated, and the information thereby produced is fed to the recommender system interface to give suggestions to the users. The proposed OntoFusionCrop is 93.87% accurate. Organization: The remaining content of the paper is given as follows. Related Works are depicted in Sect. 2. Section 3 describes the Proposed System Architecture. Section 4 provides a detailed overview of the Implementation and the evaluation of the performance of the model. Finally, in Sect. 5, the paper is concluded.

2 Related Work Nidhi et al. [1] puts forward a recommendation system that uses ensembling technique of machine learning incorporating Random Forest, Linear SVM, and Naïve Bayes. Doshi et al. [2] proposed an intelligent system called AgroConsultant which uses Random Forest, Decision tree, Neural Networks, and K-Nearest Neighbors for crop recommendation. Madhusree Kuanr et al. [3] put forward an expert system that utilized cosine similarity measure to find similar users with respect to the geographical location. Fuzzy logic is used for the prediction of the rice crop specifically. A three input two output Mamdani Fuzzy inference model had been used for estimating the rice crop produce. S. Bangaru Kamatchi et al. [4] put forward a predictive analysis to analyse the best crop to be produced for particular weather conditions. A hybrid recommender system is also proposed which uses a combination of CBR- Case-Based Reasoning, for increasing the ratio of the success of the system, and collaborative filtering technique. Artificial Neural Networks (ANN) is employed for achieving weather prediction. Jacqueline et al. [5] proposes a semantic approach which is set in motion to recommend items semantically close to the items of those users who has used and appreciated, and the final approach,

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which is the collaborative approach, is used to recommend related products that similar consumers have a preference for. Suresh et al. [6] puts forward a recommendation system that utilizes Help Vector Machine in order to enhance the efficacy of the suggestion. Avinash et al. [7] designed a recommendation system which utilized classification models such as SVM Algorithm and Decision Tree for the crop recommendation which resulted in the fact that SVM classification gave a better accuracy. Devdatta et al. [8] put forward an expert system to predict crop yield by applying Support Vector Machine and Random Forest on agricultural data. In [9–26] several knowledge centric models in support of the literature of the proposed work have been proposed.

3 Proposed System Architecture

Fig. 1. Proposed system architecture

The system architecture of the OntoFusionCrop is depicted in Fig. 1. The dataset is pre-processed by various techniques. These include lemmatization, tokenization, and stop word removal. Tokenization is a process of breaking long strings of sentences into smaller segments called tokens. Huge blocks of texts are tokenized to sentences, and sentences are tokenized to words, etc. Further pre-processing is done by the process of lemmatization. Lemmatization refers to the removal of inflected endings of words and changing them to their original root word which is called lemma. Stop words are common words which do not add much meaning to the information of the text. Stop

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words removal is also necessary to ignore the commonly used words. Hence, these words are filtered out in this process. After the pre-processing of the dataset, feature extraction is done by Bag of words model and TF-IDF method. Bag of words is a depiction of a text that describes the frequency of the words in the document. The structure of the document is not considered in this context. The model is concerned only about the occurrence and the frequency of the word in the document, but not its position. Term Frequency-Inverse Document Frequency model (TF-IDF) is used for retrieval of information. In this model, frequency of words in the document is calculated (Term Frequency). If the frequency is high, the word is considered significant else it is considered non-relevant. If a word has high frequency in a document but also in other documents, this word is considered irrelevant. If the recurrence of that word is low in other documents, the word is considered significant for information retrieval. The TF-IDF result for a term n in a document d in association with the document set S is studied and determined from the Eqs. (1), (2) and (3). TF IDF(n, d , S) = TF(n, d ) . IDF(n, S)

(1)

N ) count(d ∈ S : n ∈ d )

(2)

IDF(n, S) = log(

TF(n, d ) = log(1 + freq(n, d ))

(3)

After the features are extracted, the entropy is computed and the features are weighed based on the entropy. The classification of the dataset is done using the Bagging. The algorithms used include - Support Vector Classification and Random Forest. Support Vector Classification is a learning algorithm used for classification and it is one of the supervised machine learning algorithms. We utilize this algorithm to discover a hyperplane present in n-dimensional space which provides a decision boundary to classify and separate two classes. Different planes of different margin lengths are possible but the plane identified should have a maximum margin between data points of both the classes. Random Forest is used to give the final prediction which is done by relying on many decision trees and its predictions. The voting average of all the decision trees is given as the final prediction. From the classified instances, Semantic alignment is done. Semantic alignment is the computing of semantic similarity between the classified instances of the dataset and instances from the specified domain ontologies i.e., soil ontology, crop ontology, geographical ontology and agricultural ontology. This process is done using Spider Monkey optimization algorithm. The ontologies are used as the input here. Spider Monkey Optimization Algorithm is an algorithm used for the semantic alignment of the data set. This is a population-based method. The spider monkeys, living in groups, divide themselves into sub-groups and search for food and other resources by the process of fission. After getting the appropriate resources, the leaders decide to combine, which is called as the fusion process. The optimization algorithm has multi-objective functions which are: Web overlap, Normalized Google Distance, Lesk similarity. Web overlap is a semantic similarity measure. It is a natural modification to the Overlap (Simpson) coefficient, is

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defined as in Eq. (4).  Web Overlap(A, B) =

0

H (A ∩ B) min(H (A),H (B))

if H (A ∩ B) ≤ c otherwise

(4)

Normalized Google distance is a semantic search measure, calculated based on the number of search results returned by Google based on the keywords. If the keywords have similar meaning, or are used in a similar context, then they might occur in high number in many webpages. On the other hand, if the keywords are not related to one another, there will be relatively fewer web pages containing those key words. Next algorithm is Lesk Similarity. In a phrase, a word’s dictionary definition of each of its senses is compared to the dictionary definition of other word in the same phrase. Then, a word is assigned the sense whose definition contains many common phrases with the definition of the other words. After the semantic alignment of the ontologies, the facts are obtained. The facts are then verified from the Wikidata and this information will be yielded to the recommender system interface. The user asks a query, the facts are given as output.

4 Implementation and Performance Evaluation The implementation has been carried out using Google Collaboratory environment in Python. The ontology was modelled using Web Protégé and it was dynamically generated using OntoCollab. The potential metrics used in their standard formulation include Accuracy, Recall, FDR (False Discovery Rate), F-measure and Precision. The dataset taken into consideration in the proposed model is a crop recommendation dataset. The dataset was created by adding to the datasets of weather, precipitation, and fertilizer data available for India. The dataset utilized here in the proposed OntoFusionCrop is Crop Recommendation Dataset from Kaggle. The crops considered include cash crops, cereals, and food crops. Soil properties such as ratio of nitrogen, potassium, and phosphorous in the soil, topography, ph value, information regarding the crops and weather (including temperature and precipitation) are the factors studied. The ontologies necessary here are soil ontology, crop ontology, geographical ontology, and agricultural ontology. The ontologies are generated using OntoCollab with the information from related web documents and eBooks. The soil ontology has 1024 instances, which consists of 427 concepts and 597 individuals. The crop ontology has 2028 instances, which consists of 1427 concepts and 601 individuals. The geographical ontology has 2184 instances, which consists of 1427 concepts and 757 individuals. The agricultural ontology has 3384 instances, which has 1857 concepts and 1527 individuals. The above-mentioned crops and factors constitute a dataset. To compare the proposed approach’s performance, CRSMCY [27], CRSPA [28] and CRS [29] are used as the baseline models. From Table 1, it is clear that CRSMCY yields only 81.25% of average precision, 78.85% of average recall, 79.45% of accuracy, 80.03% of F-measure, 0.19 of FDR, and 0.80 of nDCG. CRSPA delivers 84.27% of average precision, 81.29% of average recall, it is 82.28% accurate, 82.75% F-measure, 0.16 FDR, and 0.82 nDCG. CRS using C4.5 and SMA gives an average precision of 87.65%, average recall of 84.69%, 86.84% of accuracy,

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86.14% of F-measure, 0.13 of FDR, and 0.83 of nDCG. Finally, the proposed OntoFusionCrop produces an increase of 13.87% average precision compared to CRSMCY, an increase of 10.85% average precision compared to CRSPA, and an increase of 7.47% average precision compared to CRS. It yields an increase of 12.71% average recall in contrast with CRSMCY, an increase of 10.27% average recall in contrast with CRSPA, an increase of 6.87% average recall in contrast with CRS. OntoFusionCrop produces 14.42% accuracy higher than that of CRSMCY, 11.59% higher than that of CRSPA, and 7.03% higher than the accuracy of CRS. Table 1. Performance evaluation of the proposed OntoFusionCrop Search technique

Average Precision % Average Accuracy F-Measure FDR nDCG Recall % % %

CRSMCY [27]

81.25

78.85

79.45

80.03

0.19

0.80

CRSPA [28]

84.27

81.29

82.28

82.75

0.16

0.82

CRS using C4.5 and 87.65 SMA [29]

84.69

86.84

86.14

0.13

0.83

Proposed OntoFusionCrop

91.56

93.87

93.31

0.05

0.97

95.12

We can see that there is a decrease of 0.14 in the FDR in comparison with CRSMCY, a decrease of 0.11 in comparison with CRSPA, and a decrease of 0.08 compared to CRS. The nDCG of OntoFusionCrop is 0.17 higher than the nDCG of CRSMCY, 0.15 higher than that of CRSPA, and 0.14 higher than that of CRS. Hence, we can conclude that the proposed OntoFusionCrop gives a better performance when compared to the other approaches. This is since one of the techniques used by CRSMCY is Support Vector Machines which is a linear classifier that is highly complex. The algorithmic complexity and memory requirements of SVM are also high which is also another disadvantage. CRSMCY also utilizes ANN (Multi-layer perceptron). Here, ANN requires a processor with parallel processing power with respect to the structure. So, the hardware equipment is very dependent. CRSPA uses Naïve Bayes, K-nearest Neighbor and CHAID. The disadvantage of this baseline model is that the usage of Naïve Bayes assumes that all the attributes are mutually independent which is practically impossible. The main predictions are also chaotic and hence they cannot be made. K-nearest Neighbor used here requires high memory to store training set. Hence it is very expensive with respect to computers. The usage of CHAID is completely based on statistical principles which computes simple moving averages that is more expensive computationally for determining the chi square along with the simple moving averages. CRS employs Simple Moving Average (SMA) and C 4.5. Although, the idea of computing moving average is quite good, but there is a rate in which the accuracy fails at a point of time. The employment of C 4.5 is only suitable for datasets that can reside in memory. The proposed model focuses on ontology which gives more auxiliary knowledge, the background knowledge is higher, and the data density is also high. It also helps in the integration of entities which are highly relevant. Another advantage of this model is that

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it uses bagging. Bagging is a classification technique which uses two different forms of machine learning algorithms. Random forest and decision tree are the algorithms used in this model. Apart from this, the feature extraction is also done using two different techniques which are TF-IDF and Bag of Words. This extraction process limits the features by taking the required ones. This improves the quality of features and the quality of classification as well. Adding on to this, this model is also semantically aligned which uses three measures namely Web Overlap, Normalised Google Distance and Lesk similarity which ensures that the entities which are highly relevant are only considered are only considered into the framework. The use of Wikidata and the fact verification process made sure that even the global knowledge is accumulated which is relevant to the framework. Hence the proposed OntoFusionCrop is one step ahead when compared to the other baseline approaches.

Fig. 2. Accuracy vs number of recommendations

The Fig. 2 depicts Accuracy vs the Number of recommendations of the baseline approaches and the OntoFusionCrop. We can infer from the graph that, the proposed OntoFusionCrop yields the highest despite of the number of recommendations. The proposed model is efficient since it uses ontology and the features are further semantically aligned. The CRSMCY is less efficient because, getting an appropriate network structure with respect to ANN is achieved through trial, error and experience as there is no rule for determining the structure of artificial neural networks. There is also a need for the processor to compute instructions parallelly. SVM utilized also requires a lot of memory since all the support vectors are stored in the memory and this number grows abruptly with the training dataset size. Another constraint includes, feature scaling of the variables needs to be done every time before applying SVM which becomes tedious. CRSPA provides less accuracy because the Naïve Bayes technique used is also known as a very bad estimator and the attributes are considered mutually independent which is impracticable. Also, the standard of data that is loaded is dependent on the accuracy of KNN. If a large volume of data is loaded, the predictions tend to become very slow. CHAID, utilized in this baseline model, would require huge amounts of data

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to get proper results because numerous splits break the variable’s range. Also, it becomes very expensive with respect to computers. CRS uses C 4.5 which is only best suited for data that can be stored in the memory and hence, the program cannot compute results if a large training set is forced into the memory. The accuracy also fails when SMA is employed.

5 Conclusions The proposed model implements an ontology-based crop recommendation system using semantic alignment and bagging. The crop recommendation dataset taken from Kaggle is pre-processed and the features are extracted using Bag of words model and TF-IDF method. Classification is done using Support Vector Classification and Random Forest. Spider Monkey Optimization Algorithm is used to semantically align the ontologies given as input. The user gives a query based on his own interests and the necessary facts and appropriate information are obtained regarding the crop. OntoFusionCrop has achieved an overall accuracy of 93.87% with a minimum FDR of 0.05, which makes OntoFusionCrop the best compared to other baseline models.

References 1. Kulkarni, N.H., Srinivasan, G.N., Sagar, B.M., Cauvery, N.K.: Improving crop productivity through a crop recommendation system using ensembling technique. In: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (2018) 2. Doshi, Z., Nadkarni, S., Agrawal, R., Shah, N.: AgroConsultant: intelligent crop recommendation system using machine learning algorithms. In: 2018 Fourth International Conference on Computing Communication Control and Automation (2018) 3. Kuanr, M., Rath, B.K., Mohanty, S.: Crop recommender system for the farmers using Mamdani fuzzy inference model. Int. J. Eng. Technol. 7(4.15), 277 (2018) 4. Bangaru Kamatchi, S., Parvathi, R.: Improvement of crop production using recommender system by weather forecasts. Procedia Comput. Sci. 165, 724–732 (2019) 5. Konaté, J., Diarra, A.G., Diarra, S.O., Diallo, A.: SyrAgri: a recommender system for agriculture in Mali. Information 11, 561 (2018) 6. Suresh, G., Senthil Kumar, A., Lekashri, S., Manikandan, R.: Efficient crop yield recommendation system using machine learning for digital farming. Int. J. Modern Agric. 10(1), 906–914 (2021) 7. Kumar, A., Sarkar, S., Pradhan, C.: Recommendation system for crop identification and pest control technique in agriculture. In: 2019 International Conference on Communication and Signal Processing (2019) 8. Bondre, D.A., Mahagaonkar, S.: Prediction of crop yield and fertilizer recommendation using machine learning algorithms. J. Eng. Appl. Sci. Technol. 4(5), 371–376 (2019) 9. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019) 10. Deepak, G., Rooban, S., Santhanavijayan, A.: A knowledge centric hybridized approach for crime classification incorporating deep bi-LSTM neural network. Multimedia Tools Appl. 80(18), 28061–28085 (2021). https://doi.org/10.1007/s11042-021-11050-4

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11. Deepak, G., Santhanavijayan, A.: UQSCM-RFD: a query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Comput. Appl. 1–25 (2021) 12. Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In: 2021 7th International Conference on Web Research (ICWR), pp. 232–237. IEEE (2021) 13. Roopak, N., Deepak, G.: OntoKnowNHS: ontology driven knowledge centric novel hybridised semantic scheme for image recommendation using knowledge graph. In: VillazónTerrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds.) Iberoamerican Knowledge Graphs and Semantic Web Conference, KGSWC 2021. Communications in Computer and Information Science, vol. 1459, pp. 138–152. Springer, Cham (2021). https://doi. org/10.1007/978-3-030-91305-2_11 14. Ojha, R., Deepak, G.: Metadata driven semantically aware medical query expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M.A. (eds.) KGSWC 2021. CCIS, vol. 1459, pp. 223–233. Springer, Cham (2021). https://doi.org/10.1007/978-3030-91305-2_17 15. Surya, D., Deepak, G.: USWSBS: user-centric sensor and web service search for IoT application using bagging and sunflower optimization. In: Noor, A., Sen, A., Trivedi, G. (eds.) Proceedings of Emerging Trends and Technologies on Intelligent Systems. ETTIS 2021. Advances in Intelligent Systems and Computing, vol. 1371, pp. 349–359. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-3097-2_29 16. Arulmozhivarman, M., Deepak, G.: OWLW: ontology focused user centric architecture for web service recommendation based on LSTM and Whale optimization. In: Musleh AlSartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds.) EAMMIS 2021. LNNS, vol. 239, pp. 334–344. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_32 17. Adithya, V., Deepak, G., Santhanavijayan, A.: HCODF: hybrid cognitive ontology driven framework for socially relevant news validation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 731–739. Springer, Cham (2021). https://doi.org/10.1007/978-3030-73882-2_66 18. Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 791–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72 19. Aditya, S., Muhil Aditya, P., Deepak, G., Santhanavijayan, A.: IIMDR: intelligence integration model for document retrieval. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 707–717. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_64 20. Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft Comput. 25(13), 8337–8355 (2021). https://doi.org/10.1007/s00500-021-057 56-8 21. Abhishek, K., Pratihar, V., Shandilya, S.K., Tiwari, S., Ranjan, V.K., Tripathi, S.: An intelligent approach for mining knowledge graphs of online news. Int. J. Comput. Appl. 1–9 (2021) 22. Krishnan, N., Deepak, G.: KnowSum: knowledge inclusive approach for text summarization using semantic alignment. In: 2021 7th International Conference on Web Research (ICWR), pp. 227–231. IEEE (2021) 23. Deepak, G., Gulzar, Z., Leema, A.A.: An intelligent system for modeling and evaluation of domain ontologies for Crystallography as a prospective domain with a focus on their retrieval. Comput. Electr. Eng. 96, 107604 (2021) 24. Chatrati, S.P., et al.: Smart home health monitoring system for predicting type 2 diabetes and hypertension. J. King Saud Univ. Comput. Inf. Sci. (2020) 25. Deepak, G., Priyadarshini, J.S.: Personalized and enhanced hybridized semantic algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput. Electr. Eng. 72, 14–25 (2018)

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26. Singh, S., Deepak, G.: Towards a knowledge centric semantic approach for text summarization. In: Shukla, S., Unal, A., Varghese Kureethara, J., Mishra, D.K., Han, D.S. (eds.) Data Science and Security. LNNS, vol. 290, pp. 1–9. Springer, Singapore (2021). https://doi.org/ 10.1007/978-981-16-4486-3_1 27. Anantha Reddy, D., Dadore, B., Watekar, A.: Crop recommendation system to maximize crop yield in Ramtek region using machine learning. Int. J. Sci. Res. Sci. Technol. (2019) 28. Pudumalar, S., Ramanujam, E., Rajashree, R.H., Kavya, C., Kiruthika, T., Nisha, J.: Crop recommendation system for precision agriculture. In: 2016 Eighth International Conference on Advanced Computing (2017) 29. Gavas, P., Jamsandekar, M., Meghani, P., Sabnis, S.: Crop recommendation system. Int. J. Creative Res. Thoughts

Employee Attrition by Ensemble Approach Arifa Shamim(B) , Ramsha Khan, and Sadia Javed Department of Computer Science, Jinnah University for Women, Karachi, Pakistan [email protected]

Abstract. Machine learning and Artificial Intelligence have revolutionized many important sectors’ including human resources (HR). As the artificial intelligence increases all over the world there are many ways discovered that are used to improve the organization’s business strategies, employees working capabilities and their qualities. The employees are the most essential source in any organization and well trained employee’s turnover is a major loss for an organization. The HR department facilitates the organization’s employees need and creates an environment that reduces attrition. While training, the loyalty of employees with an organization does matter and can effects the future progress of organization. Therefore, to predict that how many employees will stay and who will leave or the prediction of the employee attrition is important. In this research work we intensely focus on the employee attrition prediction through machine learning Ensemble approach. IBM dataset is used in this research work. The major goal of this process is to provide a way to any company for improving their employees’ satisfaction and to manage the best practices for employees’ attrition so that the company would be well prepared for the future hurdles. Keywords: Employee attrition · Ensemble approach for attrition · Machine learning · Random Forest · Gradient Boost · SMOTE

1 Introduction 1.1 Background and Related Work Key employee attrition is a major problem for an organization and it results in a financial loss to an organization [1]. It is important for the organizations to take the feedback of their employee’s satisfaction. In these days, organizations use the social platform and online surveys to collect feedback from their employees. Most of the business organizations use opinion mining [2] to get the knowledge of the employees through different platforms. It is important for any organization to have a high level of knowledge and feedback of employees especially about the employees who left the organization and who are willing to leave the organization. The reason behind the turnover of the employees must be in the knowledge of an organization’s manager and the HR department because it impacts the performance and the reputation of an organization. Some organizations use the data mining process for the employee data gathering it includes all types of data [3]. Employee attrition can be of two types: voluntary and involuntary attrition [4]. In © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 220–228, 2022. https://doi.org/10.1007/978-3-031-02447-4_23

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Involuntary attrition organizations take resignation from the employees, dismiss or suspend them and other one is employees’ data is not exactly the same or inaccurate data record so their turn over must be very clear and essential. The important task for an organization is to be aware of the employee’s performance and their staying plan in an organization. This task is now performed through different techniques like some organization uses the opinion mining and so on. Our vision was to propose a standard way of analyzing the employee attrition. Although this work is already done by other researchers also. So, we have decided to work on this problem by applying different machine learning techniques. Our core focus was on the ensemble technique (Table 1). Table 1. Related study review Title

Problem definition

Dataset

Result

Algorithm

Preprocessing

Prediction of employee turnover using ensemble learning [2]

To find out the major features of employee turnover and how it can be overcome before the problem

Only voluntary employee turnover from the kaggle

Support vector machine 77.65 Logistic regression 81.77 Random forest 82.64 Ensemble model 83.87

Support Vector Machine, Random Forest Classifier, Logistic Regression

Uses under sampling to made the data 60% 40%. Biasing reduces and sensitivity increases

Employee attrition prediction using neural network cross validation method [5]

To Predict employee attrition implements feed-forward neural network along with 10-fold cross validation procedure under a single platform

1470 number of sample records from kaggle

SVM = 85.6%. K-NN = 83.74%. Naïve Bayes = 83.74%. Decision Tree = 78.4% AdaBoost = 85.8%. Random Forest Accuracy = 85.8%. Neural Network with Cross-Validation = 87.01%

Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree Classifier and ensemble classifiers such as Random Forest (RF) and AdaBoost Classifiers

Checking and handling missing values, scaling of some attributes performed

(continued)

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Title

Problem definition

Dataset

Result

Algorithm

Preprocessing

Prediction of employee attrition using machine learning and ensemble methods [6]

To predict the employee attrition automatically and accurately

IBM HR Analytics Employee Attrition Performance dataset used from Kaggle.org. 1470 observations with 35 different attributes

The based model results were DT 82.31%, 0F 85.03% LR 88.43% GB 84.01% AB 86.7% The ensemble techniques results were shown as: DT + LR 86.39%, AB + RF 86.05%, SG + GB 81.23%

Decision Tree, Random Forest Regressor, Logistic Regressor, AdaBoost Model, and Gradient Boosting Classifier models

Feature selection, Missing value imputation, Data type conversion, feature scaling

2 Methodology 2.1 Dataset Our dataset comprises on both numerical and categorical type of values in the dataset. Following snap will provide an overview of our dataset (Fig. 1).

Fig. 1. Visualization of dataset

2.2 Data Pre-processing Initially, our dataset consists of 35 features including both numerical and categorical type of data but after the dataset feature engineering we get 26 numerical data features and 29 dummy features obtained by converting categorical features into dummy variables by using get_dummies method. Hence the total number of features becomes 55.

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But, from this set of total 55 features, we include 44 features that we have finalized on the basis of Pearson’s correlation (which can be used to evaluate the linear relationship between two dataset variables). We used 500 (n-estimators) weak learners. As the data of our main target variable was in highly imbalance form so here we use the oversampling technique SMOTE (Synthetic Minority Oversampling Technique) to balance our imbalance data. Our data has skewed values so we implement the SMOTE technique by using the imblearn python packages. For our research work first we take the dataset of IBM we split the data first into 80% and 20% where 80% of data taken as training data and 20% taken as testing data. Our targeted variable was the employee attrition. We implemented the Random Forest Classifier which is an ensemble approach using ubiquitous Decision Tree Classifier as a base estimator. Then we implemented Gradient Boosting ensemble technique with weak tree learner combination working as base estimators to form a stronger estimator. On the same data we implemented AdaBoost Classifier ensemble technique with three different ways and got different results for each. We implemented these entire machine learning ensemble techniques by using “python” programming language. KDE Plots for Features KDE which stands for Kernel Density Estimate is a method used for the clear and easily interpretable visualization of the distribution of values of a dataset. We have generated bivariate plots to get the basic understanding of the features of selected dataset (Fig. 2).

Fig. 2. KDE plots for features

Pearson’s Correlation of Features Pearson’s correlation of features had been found in order to decide and select the final features from the selected dataset (Fig. 3).

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Fig. 3. Pearson’s correlation of features

Imbalance Dataset of Target Variable The dataset values of our target variable i.e. “Attrition” are also imbalanced (Figs. 4, 5, 6 and 7).

Fig. 4. Visualization of imbalance data values of target variable

Employee Attrition by Ensemble Approach

Random Forest Classifier (Feature Importance)

Fig. 5. Random Forest classifier feature importance

Gradient Boosting Classifier (Feature Importance)

Fig. 6. Gradient boosting model feature importance

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AdaBoost Classifier (Feature Importance)

Fig. 7. AdaBoost classifier model feature importance

3 Result We have applied Random Forest Classifier based on ubiquitous Decision Tree as base estimator and 500 weak learners were selected. It gave 84% accuracy results. Then we applied Gradient Boosting Classifier which is an ensemble technique based on the combination of different weak tree learners which ultimately form a relatively stronger learner. 500 n-estimators have been selected as weak learners and we got accuracy score equals to 85.4%. We have used AdaBoost Classifier two times. AdaBoost Classifier is an ensemble boosting classifier. First time we have selected the default classifier for AdaBoost which is Decision Tree Classifier and it acts as a base estimator. We have selected 500 n-estimators as weak learners and we have got the accuracy score equals to 85%. Second time, when we use AdaBoost Classifier, the base estimator which we have used was SVC. While using SVC as a base estimator, first we have selected only 2 n-estimators as weak learners and we have got the accuracy score results equal to 75% almost after 4 h. Then, when we have selected 10 n-estimators as weak learners we have got the accuracy score results equals to 84%. We have got these results almost after 2 h. The results which we have got after applying ensemble approaches in multiple ways for employee attrition are shown in the following table (Table 2):

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Table 2. Accuracy of Applied Ensemble Approaches Ensemble approach

Base estimator

Number of weak learners

Output

Gradient Boosting Classifier

Weak tree learners

500

85.4%

AdaBoost Classifier

Decision Tree

500

85%

AdaBoost Classifier

SVC

2

75%

AdaBoost Classifier

SVC

10

84%

Random Forest Classifier

Ubiquitous Decision Tree

500

84%

4 Conclusion and Discussion From the implementations that we have done, we can conclude that our dataset was divided into 80% training set and 20% testing set, Gradient Boosting Classifier performs the best. AdaBoost Classifier based on Decision Tree base estimator remained the second best. We can also suppose that both approaches i.e. Gradient Boost and AdaBoost (Decision Tree as base estimator) approximately perform the same as there is only a minor difference of 0.4% in between their accuracy results. AdaBoost Classifier based on SVC with 10 weak learners and Random Forest Classifier both remained at the third best position. AdaBoost Classifier based on SVC in case of 2 weak learners came at fourth position. It did not perform so well hence came at last position. From our research and analysis we can complement that, Gradient Boosting Classifier is said to be the best performing ensemble approach for employee attrition.

References 1. Shankar, R.S., Rajanikanth, J., Sivaramaraju, V.V., Vssr Murthy, K.: Prediction of employee attrition using datamining. In: 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN 2018), pp. 1–8. IEEE (2018). https://doi.org/10.1109/ ICSCAN.2018.8541242 2. Mahajani, A., Pandya, V., Maria, I., Sharma, D.: file:///D:/big data/chapter1/pradeep2017.pdf, vol. 904. Springer, Singapore (2019) 3. Alao, D.A.B.A., Adeyemo, A.B.: Analyzing employee attrition using decision tree algorithms. Inf. Syst. Dev. Informatics 4(1), 17–28 (2013). http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.1012.2947&rep=rep1&type=pdf 4. Alduayj, S.S., Rajpoot, K.: Predicting employee attrition using machine learning. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 93–98 (2019). https://doi.org/10.1109/INNOVATIONS.2018.8605976 5. Dutta, S., Bandyopadhyay, S., Kumar Bandyopadhyay, S.: Employee attrition prediction using neural network cross validation method. Int. J. Commer. Manag. Res. 6(3), 80–85 (2020). www.managejournal.com 6. Qutub, A., Al-Mehmadi, A., Al-Hssan, M., Aljohani, R., Alghamdi, H.S.: Prediction of employee attrition using machine learning and ensemble methods. Int. J. Mach. Learn. Comput. 11(2), 110–114 (2021). https://doi.org/10.18178/ijmlc.2021.11.2.1022

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7. Khedkar, S., Shinde, S.: Deep learning and ensemble approach for praise or complaint classification. Procedia Comput. Sci. 167(2019), 449–458 (2020). https://doi.org/10.1016/j.procs. 2020.03.254 8. Zhao, Y., Li, J., Yu, L.: A deep learning ensemble approach for crude oil price forecasting. Energy Econ. 66, 9–16 (2017). https://doi.org/10.1016/j.eneco.2017.05.023

Internet of Things, Blockchain and Security and Network Technology

Bigdata Applications in Healthcare: Security and Privacy Challenges Maida Ahtesham(B) Department of Computer Science and Software Engineering, Jinnah University for Women, Karachi, Pakistan [email protected]

Abstract. In the current era, big data represents a new technological paradigm, and data is supposed to be generated with high volume, velocity, and variety. The health care industry is also subjected to generate massive volume of data historically, it is derived by record keeping, compliance, patient care, and regulatory requirements. The health care services are growing smart each day with the latest advancements such as Telehealth, IoMT (Internet of Medical Things), telecommunication technologies, and smart sensors. However, it has remained the most complex task to decide on allowable uses of data while also considering the security of data and patients’ right to privacy. It is most substantial to deploy all advancements of health care when security and privacy issues are correctly addressed. The trustworthy big data environment in health care can only be developed if limitations and envision directions of the latest advancements are clear. The research highlights privacy and security challenges in big data such as data breaches in wireless body area networks or securing unauthorized transmission of patients’ sensitive information, and compromised nodes. Along with an assessment of security and privacy issues, the paper also presents ways to address these challenges. Some of them include techniques such as data encryption, data masking, access control, authentication. The emphasis is on techniques proposed by relevant literature to address the security and privacy risk along with making evaluations about what benefits it can provide towards overcoming the big health care data challenges. Keywords: Big data · Big data analytics · Internet of Medical Things (IOMT) · Wearable sensor

1 Introduction The current digital world is observing a storm in data being produced each passing day [1]. There is a constant increase in data being produced due to heterogeneous sources of data generation which includes sensors that are used in the Internet of Things (IoT), social media, Radiofrequency Identification (RFID), Global Positioning System (GPS), images, videos, texts and many other sources [2]. Since the data generated from such

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sources holds massive volume, a significant degree of variety, and high velocity making it complex and big. It has become quite challenging to keep up with the pace. Modern organizations are utilizing big data analysis techniques to get useful information from big data and utilize it for their benefit. Such techniques are leading industrial revolution to get smarter each day. Big data in health care refers to data sets obtained by smart sensors and other electronic assets and the dataset is so complex and huge which makes it nearly impossible to manage using old techniques and software [3]. The idea of health care has been greatly revolutionized or transformed due to increase in use of technologies and increasing expectations to make it smarter each day. Considering the scenario, big data can be a helping hand in unprecedented ways to meet expectations. The big data potentials in health care rely on its capability of detecting patterns while transforming huge amounts of data into working knowledge for decision-makers and precision medicine [4]. Including this, the health care domain while utilizing big data has put forward several solutions that help in the improvement of patient care and value generation in health care companies. Big data analytics in health care is indeed a great breakthrough in providing benefits but on the same plane, it raises manifold challenges and barriers [5]. The fundamental and dramatic shift has also increased several concerns over the security and privacy of big data applications. A reactive, technological-centric approach is not sufficient for the privacy and security of big data in health care. Considering the fact, there is a need for new approaches and information systems to secure violations of delicate information as well as other type of privacy issues while using big data applications efficiently. The research presents broad view of privacy and security challenges of big health care data is presented from some relevant related works. In addition to this, the research has also discussed proposed techniques or solutions including Encryption, Data Masking, Authentication, Access control, and others. Privacy and security breaches in any domain can cause massive threats towards individuals or organizations sensitive information and if the data is being manipulated it can also cause financial or other sorts of risk. The organization of the rest of the sections is as follows; section two includes a literature review that is conducted on smart health care and big data applications domain. The literature review follows with an illustration of potential areas of applications in the smart health care domain and evaluating open problems and challenges. The paper marks its end with concluding remarks. Figure 1 summarizes the potential security and privacy challenges that are faced by the healthcare domain with respect to big data.

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Data Breach in WBAN Compro mised Node

Data Leakage Big Data Security and Privacy Challanges

Data Storage & Transaco n Logs

Unauthori zed Access

Real Time Data Analycs

Fig. 1. Big data security and privacy challenges in healthcare

2 Related Work This section discusses some of the relevant researches that have made their contributions towards the big data applications and smart health. Saidulu and Sasikala (2017) [1], have enlightens the issues as well as opportunities of big data applications over smart health care system to identify services that are patient-centric. The research has discussed data challenges in traditional health care domain and its self-possessed data capture, analysis and transfer practices. Big data offers opportunities in smart health care systems by using big data efficiently and improving performance. It can also help to lessen the cost of health care and promote value and innovation. There are several challenges and privacy concerns in big data over smart health care system including organization threats, systematic threats and authentic data disclosure. Similarly, Syed et al. (2019) [6], has also presented smart health care Ambient assisted living (AAL) framework which is known for combining the concept of living with modern technology in order to anticipate in quality of living for entire human community. The framework is based on the concept of monitoring elderly people from remote by using intelligent machine learning algorithms and IOMT for faster analysis. The research is based on the idea that aging often brings several challenges such as caregivers, health care and government. Increase in rate of senior citizens requires inexpensive, unobtrusive and easy to use health care solutions. Major advancement in wireless technology, miniaturizations, computing power and processing are driving innovation in health care. The research has also discussed the driving forces and challenges that are associated with AAL. W. Li et al. (2021) [7], has offered a detailed review based on machine learning techniques and its application for big data analysis in healthcare. Research study also

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highlights several strength and weakness of existing machine learning techniques and research challenges. It aims to provide a break through for health care practitioners as well as government agencies in order to make them well-equipped while utilizing most recent trends in ML based big data analytics for smart health care. This comprehend research study goes round with introducing recent developments in IoT which has now been considered as essential cyber-physical system and network of networks. Despite all the hype of smart application in smart health, big data is still considered as the challenging aspect. Modern sensors and other medical devices produce tremendous amount of data that is difficult to manage. Several ML based recommendations systems are also discussed that has been proposed in other research work and evaluates its efficiency. A complete and comprehensive review of literature provided in paper allows researchers to have the insight and make a reliable selection from pool of techniques. Digitalization in health care domain has been undergoing a rapid change in clinical or business operations. This change is spurred by aging population and innovative treatments and evidence based medicines. All of which are leading towards significant opportunities but also being the reason behind generating massive amount of data which is complex to manage, analyse and secure. To evaluate security and privacy in big data environment, Abouelmehdi et al. (2018) [5] have presented its research on security and privacy challenges of big data in health care industry. The research also discuss some of the successful technologies that are currently in use to overcome the challenges. Some of the prominent techniques includes encryption, Authentication, Data masking and many others. Successful related works that has proposed recent approaches along with their limitations has also been made part of the research. In health care domain, the data is collected from several different sources such as sensors and mobile devices can raise significant challenges regarding data leakage, security, privacy and integrity of data. The research presented by Aqeel-ur-Rehman et al. (2017) [8] has highlighted some of the broad and pre-deployment issues that big data face in health care. The concept of E-health has been presented as the theme that aims to bring improvement in health care quality with the use of technology. On the same plane, some segments of research are also dedicated towards highlighting security and privacy concerns in big data such as data control in body sensing networks, the challenges of emerging IOT platforms in order to deploy privacy in large number of IoT enabled devices. Data leakage is also mentioned as one of the most important challenge while managing numerous amount of data from several different sources. The possible solution of data leakage could be authorization techniques or multifactor verification. The next section assessed Security and privacy concepts from several different perspectives, since at many scenarios it is being used intervenable while it holds two different perspectives.

3 Security and Privacy Challenges in Big Data The concept of privacy and security is quite complex to define. One of the perspective that can connect to privacy to current context is the management of information flow that is based on the process or manner in which information is accessed and the actors involved, the purpose of access and frequency of access [5]. It requires establishing

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authorization requirements and policies and to assure that the data is utilized in the appropriate manner. Security refers to protection against unauthorized access including availability and integrity. It is concerned with securing data from data stealing for profit or from other pernicious attack. The table below presents differentiation among security and privacy concept (Table 1). Table 1. Differentiating among concepts of security and privacy Security

Privacy

Security is about providing protection in case Privacy relates to protection of personally of unauthorized access identifiable information It is meant for providing confidentiality to an It is concerned with individual’s rights of agency or enterprise safeguarding their personal information from other parties It refers to security protocols for providing confidentiality, availability and integrity of information assets

Securing privacy rights while any personal data is being processed

Security cannot be achieved without privacy

Privacy cannot be achieved without security

Security programs are concerned with all sorts of information that an organization can collect

Privacy programs are concerned on personal information such as social security numbers, addresses, names, financial information etc.

Security is about being building surety that decisions are being respected

It decides what and where information goes

4 Security and Privacy Challenges of Big Data Applications in Health Care Big data in medicines and healthcare undergoes security and privacy challenges in many folds. Since big data in health care is based on many distinct devices, platforms and systems. In Health care, data breaches there is a huge threat of exposing highly sensitive information that includes personal identifiable information including names, social security number, address and other sensitive healthcare data like Medical ID number, medical history or health insurance information [9]. It is the reason that privacy and security is a big challenge for healthcare data and requires adoption of right strategies and measures. The adoption of new available technologies or platforms for big data in health care related applications has increased concerns related to patients’ privacy and security issue. Since, Statistics of patients are kept in data centers. Many health related data centers holds certification of health Insurance portability and accountability Act’s (HIPAA) [10], but such certifications does not provide assurance of patients record security and integrity [5]. It is a big challenge to deploy security resolutions on dissimilar data collection. Some of the challenges that big data in health care face with respect to security and privacy are listed below.

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4.1 Develop Secure Infrastructure There are large number of tools or frameworks that has been developed to handle and process large volume of big data. Some of tools include Hadoop, Map reduce, Storm, Cassandra, Mahout but these tools lack suitable security and privacy mechanism. Hence it is a huge challenge to provide security to health care data while using such tools. 4.2 Data Breach in Wireless Body Area Network (WBAN) Medical sensors collect patient information and send msg to doctor or hospital server. It is a challenge to secure the information or msgs from being attacked by intruders. Hence security should be paramount while implementing technology in health care. 4.3 Data Leakage It is most prominent challenge in the digital world with respect to security and privacy of data. Health care data is collected or distributed from number of heterogenous sources and hence it possesses a huge challenge to secure each source from any sort of manipulation or possible attacks. 4.4 Compromised Nodes WBAN are considered to be vulnerable to frequently open towards huge number of attacks. It is a huge challenge to secure nodes from being attacked, by misusing the availability of WBAN and incapacitating or capturing particular node. It can lead to loss of patient’s life.

5 Solutions/Technologies in Use There are number of technologies that are being used as a solution to provide privacy and security of big health care data. Some of them are mentioned below: 5.1 Data Encryption Data encryption is considered as one of the most prominent techniques to prevent data from any sort of unauthorized access. It can help secure any sort of breaches including storage devices or packet sniffing in networks. In health care domain, it is crucial to assure that patients sensitive data is secured by utilizing efficient encryption schemes. Encryption can also be applied to different network layers to encrypt information that passes between two nodes within a network. 5.2 Data Masking Data Masking is a technique of replacing sensitive data information with unidentifiable value. It is commonly used to mask personal identifiers including names, zip codes and date of birth. K-anonymity is data masking technique that protects identity but is not well supported with attributes. While p-sensitive anonymity works with both identities and attributes.

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5.3 Access Control Access control refers to the most common technique used to secure information. It refers to giving access to only authorized individuals. It is governed by access control policy that works on privileges. A sophisticated authorized control that enables individuals to perform actions only as much as he is eligible to perform. 5.4 Authentication Authentication is a technique of verifying the details for being true. In any organziation such as health care, It plays an important role in restricting any inappropriate access from patients personal information. The table below tries to cover security and privacy challenges of big data in health care domain along with its possible solutions (Table 2). Table 2. Privacy and security challenges and solutions in big data in health care Challenges

Domain

Description

Possible security requirements and Solutions

Data breach in Wireless Body Area Network (WBAN) [11]

WBAN

Sensors used in treatment can be attacked by intruders and this can put patient’s life to risk by manipulating information and sending wrong information to doctor

Random key distribution can be a solution. An undisclosed key can be shared to assure that the message is only conducted by the reliable individual [12]

Adoption of comprehensive physical security [4]

Cloud

The data of most Health information systems (HIS) uses cloud computing architecture, and hence there is a huge challenge of securing patients’ data from unauthorized access by providing comprehensive physical security

One of the solution is Anonymity, in which identities of patients are made anonymous by storing health data over cloud server no one could learn about the patients identity [13]

Compromised node [11]

Wireless body Area Network (WBAN)

In WBAN, it is another challenge to secure node from adversary, as it can capture an EEG sensor and send false information to physician

Possible solution is making node resilience to compromise by proofing method [11]

(continued)

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Challenges

Domain

Description

Possible security requirements and Solutions

Secure data storage and transaction logs [14]

-

The healthcare data is obtained from several different domains and levels and hence it is a challenge to protect transaction logs and other sensitive information

Typical solutions to such challenges entails: Access control [5, 13] & Data Anonymization [5, 13]

Open shared environment of cloud [13]

Cloud

Big Data in healthcare contain sensitive information which is now being stored at cloud which is open and shared environment and possess challenges towards data security

Identity based or attribute-based encryption security techniques can be applied [15]

Unauthorized access of IOT devices [16]

IOT

It is a challenge to protect Devices such as mobile, sensors that collects patients’ personal information

Monitoring and auditing technique can be applied to monitor network events and to catch intrusions [5]

Message disclosure [11]

Wireless body Area Network (WBAN)

It is a challenge to Link/Network layer secure patient’s personal encryption and Access health information from control [11] being electronically interfere by intruders

Develop Secure infrastructure or providing security in Hadoop [14]

Cloud

Most of the health care Hashing technique applications use Hadoop named SHA-256 is used platform for big data for authentication [9, 15] analytics and providing security to Hadoop is also a challenge

Real Time Data [17]

Clinical care, IOT,

In health care domain, data from sensors is obtained in real time. It can be theft and manipulated and can lead to risking human life

Multifactor Authentication Scheme

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6 How Data Breach can Affect any Organization: A Case of Medical Informatics Engineering: For Several past years, health care industry has been the most prominent target of cyber criminals or attackers. In the duration of last five years data breaches frequency has increased to an alarming situation. Health care data contains extremely sensitive information that can harm patient in many ways. The information includes security number, name, health records, health insurance information and others. The world has observed several data breaches in health care industry that has put millions of patient’s identities at risk. One of the cases happened in year 2015, This year is also known as the banner year for health care data breaches. A company named medical informatics engineering that works for creating software for electronic medical records had to face massive data breach incident. In this incident at least 3.9 million patient’s personal information such as names, social security number, addresses, medical histories and many other sensitive information were being stolen. Such incidents are the alarm for health care industry to utilize efficient techniques in order to provide security and privacy towards patient’s personal information.

7 Conclusion In summary, big data or big data analytics is said to be a multi-disciplinary information processing system in several areas including government, business, media and healthcare. The health care domain has to deal with massive amount of data that holds information that is sensitive towards patients’ identity. Any sort of data breach can cause a huge threat towards patients emotional or financial aspects. One of the most prominent challenges in Smart health care is that, it collects information from several IoT sensors or devices and it possess challenge to monitor each sensor or device for any possible data breach, manipulation or attack. In Latest Health care applications cloud is being utilized to store patients’ data which is a centralized shared platform. It has also raised a question towards security of patient’s data. In order to resolve many of such challenges, some solutions have been discussed including encryption, anonymization, hashing technique, access control and others that can provide a useful assistance in providing security and privacy against challenges. Present study forms the basis for future research to understand the big data applications in healthcare under the perspective of security and privacy issues. The research has addressed the challenges and solutions on just the tip of the iceberg and it still needs to be studied further.

References 1. Saidulu, D., Sasikala, R.: Understanding the challenges and opportunities with big data applications over ‘Smart Healthcare System.’ Int. J. Comput. Appl. 160(8), 23–27 (2017). https:// doi.org/10.5120/ijca2017913075 2. Sakr, S., Elgammal, A.: Towards a comprehensive data analytics framework for smart healthcare services. Big Data Res. 4, 44–58 (2016). https://doi.org/10.1016/J.BDR.2016. 05.002

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3. Saranya, P., Asha, P.: Survey on big data analytics in health care. In: International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 46–51 (2019). https://doi.org/10. 1109/ICSSIT46314.2019.8987882 4. Sarkar, B.K.: Big data for secure healthcare system: a conceptual design. Complex Intell. Syst. 3(2), 133–151 (2017). https://doi.org/10.1007/s40747-017-0040-1 5. Abouelmehdi, K., Beni-Hessane, A., Khaloufi, H.: Big healthcare data: preserving security and privacy. J. Big Data 5(1), 1–18 (2018). https://doi.org/10.1186/s40537-017-0110-7 6. Syed, L., Jabeen, M.S., Alsaeedi, A.: Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques. Future Gener. Comput. Syst. 101, 136–151 (2019). https://doi.org/10.1016/j.future.2019.06.004 7. Li, W., et al.: A comprehensive survey on machine learning-based big data analytics for IoTenabled smart healthcare system. Mob. Netw. Appl. 26(1), 234–252 (2021). https://doi.org/ 10.1007/s11036-020-01700-6 8. Aqeel-ur-Rehman, Khan, I.U., Sadiq ur Rehman: A review on big data security and privacy in healthcare applications. In: García Márquez, F., Lev, B. (eds.) Big Data Management, pp. 71–89. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45498-6_4 9. Khaloufi, H., Abouelmehdi, K., Beni-Hssane, A., Saadi, M.: Security model for big healthcare data lifecycle. Procedia Comput. Sci. 141, 294–301 (2018). https://doi.org/10.1016/j.procs. 2018.10.199 10. Edemekong, P.F., Annamaraju, P., Haydel, M.J.: Health Insurance Portability and Accountability Act. StatPearls Publishing (2021). https://europepmc.org/article/NBK/nbk500019 11. Al-Janabi, S., Al-Shourbaji, I., Shojafar, M., Shamshirband, S.: Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications. Egypt. Informatics J. 18(2), 113–122 (2017). https://doi.org/10.1016/J.EIJ.2016.11.001 12. Ding, Y., Xu, H., Zhao, M., Liang, H., Wang, Y.: Group authentication and key distribution for sensors in wireless body area network. Int. J. Distrib. Sens. Netw. 17(9), 155014772110443 (2021). https://doi.org/10.1177/15501477211044338 13. Al-Issa, Y., Ottom, M.A., Tamrawi, A.: EHealth cloud security challenges: a survey. J. Healthcare Eng. 2019, 1–15 (2019). https://doi.org/10.1155/2019/7516035 14. Goel, P., Patel, R., Garg, D., Ganatra, A.: A review on big data: privacy and security challenges. In: 2021 3rd International Conference on Signal Processing and Communication, ICPSC 2021, pp. 705–709, May 2021. https://doi.org/10.1109/ICSPC51351.2021.9451749 15. Chenthara, S., Ahmed, K., Wang, H., Whittaker, F.: Security and privacy-preserving challenges of e-health solutions in cloud computing. IEEE Access 7, 74361–74382 (2019). https:// doi.org/10.1109/ACCESS.2019.2919982 16. Zeadally, S., Siddiqui, F., Baig, Z., Ibrahim, A.: Smart healthcare. PSU Res. Rev. 4(2), 149–168 (2019). https://doi.org/10.1108/prr-08-2019-0027 17. Kumar, R., Kiit, B., Abhaya, B., Sahoo, K., Pradhan, C.: Big Data Analytics in Real TimeTechnical Challenges and its Solutions Software Engineering View project Data Visualization in Big Data View project Big Data Analytics in Real Time-Technical Challenges and its Solutions (2017). https://www.researchgate.net/publication/321965323

Ensure the Confidentiality of Documents Shared Within the Enterprise in the Cloud by Using a Cryptographic Delivery Method Hamza Touil1(B) , Nabil El Akkad1,2 , and Khalid Satori1 1 LISAC, Faculty of Sciences, Dhar-Mahraz (FSDM), Sidi Mohamed Ben Abdellah University,

Fez, Morocco {Hamza.touil,nabil.elakkad}@usmba.ac.ma 2 Laboratory of Engineering, Systems and Applications (LISA), National School of Applied Sciences (ENSA), Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. Cloud-based software services have recently gained popularity due to their architecture, which provides numerous benefits to their users. These services, for ex-ample, enable remote users to work transparently on legacy software provided by an enterprise, with all the resources required. Consider office documents or databases that are used by internally developed software. In this paper, we are interested in the specific case where multiple users must work concurrently on shared office documents while reaping the benefits of the cloud without compromising the confidentiality of the manipulated data. We propose a process to protect the confidentiality of data passing through the provider. This technique allows us to use cloud services without relying on the cloud’s confidentiality of our data. Our method also enables us to digitally sign the changes made to the document, preventing a malicious cloud from altering the data. The cloud can alter the data. The presented technique is also compatible with using a secure element that encapsulates cryptographic operations in hardware. At the hardware level, operations are carried out. The proposed process applies to content that does not necessitate interpretation by the service provider. Keywords: Confidentiality · Cloud · Cryptography

1 Introduction In contrast to the traditional architecture in which a company’s IT infrastructure is installed locally, cloud computing focuses on using remote servers with extensive storage and computing capabilities. These servers are owned by companies specializing in leasing IT services and can be physically located in different places. The cloud is an abstraction of these servers: the resources are located somewhere. The user (an individual or a company) does not have to worry about the means put in place by the hosting company to access them [1–5]. The role of the hosting company is to provide its customers with resources (storage, bandwidth, CPU time, etc.) and allow them to access and use them from anywhere. Cloud-based software services allow users to access a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 241–250, 2022. https://doi.org/10.1007/978-3-031-02447-4_25

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wide range of low-cost or even free IT services from anywhere in the world. Despite the undeniable advantages of this infrastructure, some weaknesses prevent it from being a perfect solution. For example, reliability, the fact that a company’s resources are all centralized within a cloud constitutes a risk for the company in case of failure. Companies are heavily dependent on their Internet connection, and if that connection fails, their IT services go down with it. Similarly, a service provider could suffer an outage and not offer a service for a certain period [6–10]. The financial losses to the company could then be significant. Another disadvantage is confidentiality, as users share resources with other companies. As a result, preserving the confidentiality of the data entrusted to the service provider is a key issue for most companies. Indeed, one would not want a service provider to be the victim of a computer attack by one of its clients and for the latter to consult the data of other clients. Similarly, it is unclear whether a cloud could behave maliciously and access its customers’ data without their consent. One way to address this is to use data encryption technologies. This is one of the approaches we will be used to mitigate these types of threats to data privacy. Another aspect not to be overlooked is that centralizing operations to a remote cloud requires a tremendous amount of bandwidth than if the operations were carried out internally, especially if we are talking about a company that would use it [11–14]. On the other hand, modern cryptography is available in many areas of operation, including cloud services; almost all, or the best of them, are built from components that are well known in terms of design and operation. The only secret component of these services is the encryption key. A set of secret cryptographic protocols for all sorts of internationally known organizations, all sorts of strange secret encryption algorithms are used to some extent. In the rest of this article, we will see the related work. Then we will dissect our method and end with the fields of application [15–18].

2 Related Works A Set of studies has been proposed in the security of data stored in the cloud. Adkinson et al. propose an additional security layer to Google Docs in their paper [19], so that the Google server never has access to the actual data in the shared document, but users can still use all of its features transparently, i.e., without having to change their habits, because their implementation does not change the functionality of the primary Google Docs graphical interface. Adkinson et al. implemented their method using a Firefox browser extension. Their response: It is worth noting that Google Docs’ graphical interface comprises web technologies, including HTML, CSS, and Javascript [20]. In addition, The browser may interpret the GUI’s content and behave like a web browser. Interface and operate as a proxy server to change the way it looks. Two configuration documents are created and maintained by the extension on the server. One server is dedicated to each secure document, while the other is dedicated to all papers. These configuration documents provide information on the security of each document, such as the encryption mode, passwords for access to the Google service, and alternatives for the chosen encryption mode, if encryption mode is required. Shi et al. [21] describe an implicit authentication system that keeps track of the user’s usual tasks and habits, such as making phone calls. After collecting data such as phone calls or visits to close locations, the system creates a profile for each user that describes his activity. For the first

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time, the system goes through a learning phase that learns from previous user behavior. Patterns are gathered and then used to create a user model. We can conduct a comparison based on this user model and some recent perceived user behavior to determine if authentication is authorized or not. The comparison is based on a probability value representing an authentication score that rises or falls in response to fresh observed actions. Usual occurrences and duties are regarded as positive events that boost the score. When the score drops below a predetermined threshold, the user is required to authenticate manually. Sathish and Venkataram [22] propose TBAS (Transaction-Based Authentication Scheme), an authentication scheme based on mobile transactions that operate on the application level to classify user behavior and transaction sensitivity using intelligent agents for perceiving information the environment and reasoning about these perceptions. The TBAS uses mobile Cognitive Agents (MCA) to collect information about user activities. Static agents (SCA) evaluate transaction sensitivity and determine the appropriate authentication process based on the security level required.

3 Proposed Method There are collaborative document solutions such as team management software such as Microsoft SharePoint, eXo Platform, Producteev, and others. However, this software does not implement security protocol on the same document simultaneously. Instead, they are macro collaboration software that works not at the file level but the station and set sets of files. It will first be a matter of formalizing the developed concept, including the properties respected by the software and the various entities. The document represents a file and its various constituents, which we will refer to as blocks, in the same way, that traditional data blocks are. The hierarchical organization of these blocks allows for various uses for applications that use them at a higher level. Internal documents must have the following properties (Table 1): Table 1. Properties to be respected for the document. Property

Description

Server-side authentication

Access to the document and its components is authorized following the authentication of the user to a server. The authorizations are defined in the metadata linked to the document and its components

Integrity

It can be verified that the data contained in the document has not been altered and document has not been altered and has not been injected into the document in a previous procedure. This makes the reuse of data impossible

Privacy

Data can be encrypted on the client-side so that that only users with the correct access rights can access, read can access, read and modify it

Non-repudiation

A legitimate user cannot deny the alteration of data. An electronic signature system usually implements this property

Coherence

The structure of the confidential data can be proven to be valid. This property is not implemented yet, as it can also be ensured to a lesser extent by the Integrity property

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3.1 Notation The BNF (Backus-Naur Form) [23] notation is used to formalize the principles presented in this document:

:= | [symbole] {symbole}

symbole

Is defined as OU An optional symbol A symbol that can be repeated (from 0 to N times). A non-terminal symbol that can be defined by subtones. A terminal symbol cannot be defined by one or more sub-state(s).

A symbol is defined via the operator «: = "and any symbol can be terminal or not. A terminal symbol is atomic and cannot be redefined by other symbols in the case of a terminal symbol. In the case of a non-terminal symbol, on the contrary, it can be redefined by other symbols, terminal or not. In addition to this basic notation, some elements have been taken from the BNF Extended notation to characterize symbol repetitions:

3.2 Document A document is a representation of data that the system can manage. As determined by the system, this could mean a file or a portion of a file in concrete terms. Still, it could

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also mean connected objects receiving configuration instructions via our method or other less conventional applications. A document is made up of a series of elements. It is defined as follows:



:= : := :

{} , | {} | {} ,

A is a hierarchical structure. It is an organization of nodes where each node contains the information necessary to construct the . At the storage level, a may consist of multiple files (or any other type of possibly heterogeneous container for that matter). When accessing this part of the through the application layer, the necessary information will be extracted to reconstruct the into an entity. This coherent representation can then be manipulated directly by the user in the same way as any other file, in addition to notifications when users modify a said file. The following diagram (Fig. 1) illustrates a possible organization of the data:









….

Fig. 1. Possible example of a structure for a .

The various attributes (, , ) required for a coherent document are defined as follows:

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:



, , , [] : , []

:= , [] ,

The different symbols used to define the above attributes are defined as follows:

:=

:= , {”.” | }

:= {}

:= := , , , , ,

is a unique identifier associated with an at the node to which it is associated. If the is the document’s root node, it identifies the record (relative to other documents). identifies the Version of the node it is associated with. If multiple versions of a document exist, this attribute is used to determine the components belonging to a specific version of a document or node. It is used when a particular version of the document or node is requested to access the correct data. Data is accessed. defines the type of document. On the server side, this is used internally to determine what structure to give the file to maintain consistency for the application layer that will use it. This attribute can be assimilated to a MIME-Type [7].

:

Text | Image | {...}

identifies nodes at a level below strictly a single level of the node. This is the attribute of those same nodes if present. defines the list of access conditions associated with an .

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is a specific symbol defining the access conditions to the root node of the document. These and elements are defined more specifically in a later section. is used to define the cryptographic algorithm used to perform encryption/decryption operations and electronic signatures. This parameter is open and is set to nil if the document is neither encrypted nor signed. [] defines the metadata used to structure and represent the document. Document. The structure of this metadata is dependent on . This metadata is free to be defined by the user according to the needs of the software application that uses it. 3.3 Principle of Spreading As this method aims to facilitate the work on the company’s internal documents in a secure manner, it is also necessary to the data layer a way to share information between users in real-time. The implementation of this protocol is left to the user to choose the medium to store the data structure where network protocols are available physically. The propagation of messages is done in the following way: A user sends an action to the server. On his part, the server answers with an ACK request. On the other hand, it sends a NACK if the action is impossible or an error (if the user tried to delete the document, for example). The user has tried to delete a non-existent node or read a node for which he has no access rights. Once the action is validated by the server installed on the cloud infrastructure, it propagates it to other users. These users, in turn, respond with an ACK when the action is effective on their side. This means that they are ready to receive new steps from the server. In terms of implementation, the most direct way would be to use simple TCP connections. One potential difficulty is that clients must maintain a TCP connection without having a public IP address not to have problems traversing NATs as shown in Fig. 2.

Fig. 2. Illustration of action propagation used by our method in the case of an UPDATE action.

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3.4 Fields of Application To all kinds of data, from the typical office documents used by almost all types of used by nearly every type of business, or more specific detailed data such as GPS coordinates, for example: – Office Document The main inspiration for offering this method is the popular Google Docs service described above. The main problem with this service is that it cannot be used for documents that need to remain confidential. Confidential. One thinks, of course, of most corporate documents and those of the various public services that are faced with confidentiality obligations and privacy issues. In addition, large enterprises often want to be accountable for their infrastructure, either to remain consistent with their overall IT infrastructure strategy or to meet specific commitments that may have been made with secure customers or vendors. Therefore, using a third party, even a trusted one, is not always an option. It is important to note that most popular office solutions do not allow multiple users to access the same document simultaneously (think of the offline version of the famous Microsoft Office, for example). How many times have we tried to find out who has this or that file open so that another user can edit it? From this point of view, a text editor using our method could be considered a private and secure Google Docs. – File Sharing One could also think of using this method as a simple, secure file container like a classic file server (Windows Share, Samba…) whose disk would be encrypted. The most direct way to do this would be to implement a Fuse driver, for example. An advantage of using this approach compared to a conventional system is that one does not have to worry about the security of the link between the service provider and the user because the data is encrypted and decrypted on the client-side. As a result, an SSL link is not an absolute necessity, although it is not an absolute necessity. Still, it is recommended to protect against metadata leakage. An improvement to the system in this respect could be encrypting the metadata via an additional layer. However, it should be noted that the system was not designed for this type of use, and it would not be the most optimal way to share files in a secure securely. Indeed, a simple SFTP [18] server installed on a machine whose disk is encrypted using software such as dm-crypt [17] would do the trick if users want to share files without exploiting the collaborative capabilities of our method. Another more straightforward and more decentralized way of sharing files way to share files would be to use software that controls the BitTorrent protocol. – Embedded Systems One of the most exciting aspects of our approach is that data encryption is handled entirely on the client-side. The information is processed entirely on the client-side. Thus, one could imagine a modular architecture that would use secure containers (smart

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cards, “Secure Element”, USB key, etc.) to store keys, certificates and especially to perform certain fully secure cryptographic operations on a processor that is independent of the primary system used by the client so as not to expose the keys. This physical segregation of the encryption via a secure container would allow a very high level of security to be obtained compared to the protection of symmetric keys. In this context, the use of a secure element, which is becoming more and more widespread, mainly thanks to ARM processors, could be relevant. It is interesting to note that this method could take advantage of the TrustZone [3] technology and its Trusted Execution Environment on ARM processors. ARM processors. This is a processor technology whose architecture includes a “secure” zone inaccessible to applications for which the trust link has not been trusted has not been established. A use case would be the possibility to code an application asking the user to enter a PIN code: the application would control a trusted person. The application would give control to a trusted application that would display the screen to validate the PIN, and the application would only see the result. This way, no application can find this way, and no application can listen to or store this PIN since it is never in contact with it. The PIN. An application could implement our method to use this secure area of the processor.

4 Conclusions Our method is in line with the era where software operations are increasingly removed and centralized to simplify management. We started with a service similar to Google Docs but which would allow the user to manage his keys and we ended by showing the scope of application. We believe that the principles we have developed can be used to build applications that we have not yet thought of. We hope that the developer community will be able to exploit these principles, and hope-fully help create the secure applications of the future.

References 1. Arachchige, N.H., Perera, S.N., Maas, H.-G.: Automatic processing of mobile laser scanner point clouds for building façade detection. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXIX-B5, 187–192 (2012) 2. El Akkad, N., El Hazzat, S., Saaidi, A., Satori, K.: Reconstruction of 3D scenes by camera self-calibration and using genetic algorithms. 3D Res. 6(7): 1–17 (2016) 3. Nurunnabi, A., West, G., Belton, D.: Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data. IEEE Trans. Geosci. Remote Sens. 54(4), 2181–2193 (2016). Art. no. 7339614 4. El Akkad, N., Merras, M., Baataoui, A., Saaidi, A.: Satori K Camera self-calibration having the varying parameters and based on homography of the plane at infinity. Multimedia Tools Appl. 77(11), 14055–14075 (2018) 5. Nurunnabi, A., Teferle, F.N., Li, J., Lindenbergh, R.C., Hunegnaw, A.: An efficient deep learning approach for ground point filtering in aerial laser scanning pointclouds. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch. 43(B1-2021), 31–38 (2021) 6. El Akkad, N., Saaidi, A., Satori, K.: Self-calibration based on a circle of the cameras having the varying intrinsic parameters. In: Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS, pp 161–166 (2012). Google Scholar

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7. Roynard, X., Deschaud, J.-E., Goulette, F.: Paris-Lille-3D: a large and high-quality groundtruth urban point cloud dataset for automatic segmentation and classification International. J. Robot. Res. 37(6), 545–557 (2018) 8. Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13(18), 1587–1611 (2013) 9. Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, GCE 2008, art. no. 4738445 (2008) 10. El Akkad, N.E., Merras, M., Saaidi, A., Satori, K.: Robust method for self-calibration of cameras having the varying intrinsic parameters. J. Theor. Appl. Inf. Technol. 50(1), 57–67 (2013) 11. El Akkad, N.E., Merras, M., Saaidi, A., Satori, K.: Camera self-calibration with varying parameters from two views. WSEAS Trans. Inf. Sci. Appl. 10(11), 356–367 (2013) 12. Merras, M., El Akkad, N., Saaidi, A., Nazih, A.G., Satori, K.: Camera calibration with varying parameters based on improved genetic algorithm. WSEAS Trans. Comput. 13, 129–137 (2014) 13. Xiao, L., Li, Q., Liu, J.: Survey on secure cloud storage. J. Data Acquis. Process. 31(3), 464–472 (2016). Shuju Caiji Yu Chuli 14. Merras, M., Saaidi, A., El Akkad, N.: Satori K Multi-view 3D reconstruction and modeling of the unknown 3D scenes using genetic algorithms. Soft Comput. 22(19), 6271–6289 (2018) 15. Touil, H., El Akkad, N., Satori, K.: Secure and guarantee QoS in a video sequence: a new approach based on TLS protocol to secure data and RTP to ensure real-time exchanges. Int. J. Saf. Secur. Eng. 11(1), 59–68 (2021) 16. Touil, H., El Akkad, N., Satori, K.: Text encryption: hybrid cryptographic method using Vigenere and Hill Ciphers. In: 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, pp. 1–6 (2020) 17. Touil, H., El Akkad, N., Satori, K.: H-rotation: secure storage and retrieval of passphrases on the authentication process. Int. J. Saf. Secur. Eng. 10(6), 785–796 (2020) 18. Touil, H., El Akkad, N., Satori, K.: Securing the storage of passwords based on the MD5 HASH transformation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 495–503. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_45 19. Adkinson-Orellana, L., Rodríguez-Silva, D.A., Gil-Castiñeira, F., Burguillo-Rial, J.C.: Privacy for Google Docs: implementing a transparent encryption layer. In Proceedings of the 2nd International Conference on Cloud Computing, pp. 20–21 (2010) 20. Goyal, P.: Application of a distributed security method to end-2-end services security in independent heterogeneous cloud computing environments. In: 2011 IEEE World Congress on Services (SERVICES), pp. 379–384. IEEE (2011) 21. Shi, E., Niu, Y., Jakobsson, M., Chow, R.: Implicit authentication through learning user behavior. In: Burmester, M., Tsudik, G., Magliveras, S., Ili´c, I. (eds.) ISC 2010. LNCS, vol. 6531, pp. 99–113. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-181 78-8_9 22. Babu, B.S., Venkataram, P.: A dynamic authentication scheme for mobile transactions. Int. J. Netw. Secur. 8, 59–74 (2009) 23. Laros, J.F.J., Blavier, A., den Dunnen, J.T., Taschner, P.E.M.: A formalized description of the standard human variant nomenclature in Extended Backus-Naur Form. In: Proceedings of the European Conference on Computational Biology 2010 (2010)

Survey IOT Systems Security Based on Machine Learning El Mahdi Boumait1(B) , Ahmed Habbani1 , Mohammed Souidi1 , and Zaidan Didi2 1 Smart Systems Laboratory, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

{elmahdi_boumait2,ahmed.habbani,mohammed_souidi}@um5.ac.ma 2 Computer Science Research Laboratory (LaRI), Ibn Tofail Kénitra University, Kenitra, Morocco [email protected]

Abstract. IoT technologies are essential for creating a variety of smart applications in the real world that will improve people’s lives. The focus of this study is on the security of IoT systems using machine learning. It’s about a thorough examination of the security challenges and threats that applications face. It’s a survey of machine learning techniques that can be utilized to boost IoT system confidence and security. Keywords: IoT · Security · Artificial Intelligence · Machine Learning

1 Introduction Increased IoT devices have led to an increase in IoT botnet attacks on the internet. The Mirai botnet attacked 100,000 IoT devices (most notably cameras on CCTV channels) in October 2016 to carry out an attack on the Dyn DNS infrastructure using a distributed denial of service (DDoS) [1]. Multiple famous websites have been made unavailable several times, including Twitter, PayPal, CNN, Twitter, and Amazon. The source of Mirai was published in January 2017 and the number and intensity of DDoS attacks utilizing IoT-botnets developed from Mirai has escalated [2]. In addition to the large diversity of IoT applications and scenarios, it is a challenge to determine which methodologies are appropriate for securing IoT systems. The development of appropriate approaches to IoT security should be a research priority [3]. Machine Learning and Deep Learning techniques are effective data exploration approaches to understand how IoT components and devices interact and communicate in the IoT ecosystem and learn about ‘normal’ and ‘abnormal’ behaviors. For the purpose of determining typical patterns of interaction and, therefore, the harmful actions, input data for each part of the IoT system may be collected and evaluated. The remaining sections of this paper are organized as follows: • Section 1 introduction to the Internet of Things. • Section 2 expands on the general notions of the IoT frameworks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 251–258, 2022. https://doi.org/10.1007/978-3-031-02447-4_26

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• Section 3 elaborates the implementation of the AI-Centric IoT Security deploying Machine Learning. • Section4 expands on open issues and research directions.

2 Internet of Things The Internet of Things (IoT) is a network of physical objects having embedded technology for communicating, perceiving, and interacting with their internal or external states [4]. The ability to connect assets, processes, and personnel allows a company to collect data and events from which to learn about behavior and use, implement prevention measures, and improve or transform business operations. The Internet of Things (IoT) is a critical capability for establishing a digital business. In order to provide smart services to users, the Internet of Things (IoT) architecture is made up of physical items that are connected to a communication network and backed by computational equipment [5]. The three tiers of the IoT architecture are application, network, and perception [5]. This design, as shown in Fig. 1, can be taxonomized further for simplicity of usage and analysis. Each level is described in the subsections that follow. 2.1 IoT Layers Architecture 2.1.1 Sensing Layer The sensing layer’s principal function is to perceive, gather, and perhaps process data from the surrounding world.

Fig. 1. IoT layers architecture [6]

A multitude of sensors make up this layer. IoT devices use a variety of sensors, including temperature, humidity, motion, and acceleration sensors, to implement different sensing functions for different applications [6].

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2.1.2 Network Layer One of the main goals of the IoT platform is to collaborate with heterogeneous sensors and provide intelligent services. The Network Layer serves as a conduit for data acquired in the sensing layer to be transmitted to other connected devices. For data transfer between devices on the same networks, a variety of communication technologies (e.g., WI-FI, Bluetooth, Zig-bee, etc.) are implemented into the network layer of IoT devices [6]. 2.1.3 Data Processing Layer IoT sensors capture or record large volumes of data, which can be quite useful. The data processing layer examines the information gathered by the sensing layer and makes judgments depending on what it discovers. The data processing layer of many IoT devices (e.g., smartwatches, smart home hubs, etc.) recalls the findings of previous studies to improve the user experience. The network layer enables this layer to share data processing results with other connected devices [7]. 2.1.4 Application Layer The Internet of Things (IoT) can be used for a variety of purposes. Smart healthcare, smart transportation, smart grid, and smart buildings are just a few examples of well-known applications [8]. To accomplish a variety of IoT device applications, the application layer implements and presents the data processing layer’s results. The application layer directly deals with and provides services to the end user [9]. 2.2 IoT Security The Internet of Things (IoT) connects the physical reality to the Internet so that the physical components and its environment interact intelligently. In general, IoT devices operate in a variety of environments to achieve a variety of objectives. However, in both cyber and physical states, its operation must fulfill a complete security requirement. IoT systems are complex and interdisciplinary in nature. As a result, maintaining the security need while dealing with the IoT system’s large attack surface is difficult. The solution should take into account all aspects of security in order to meet the specified level of protection [3].The subsections that follow describe the different security aspects. Security aspects: • Authentication: One of the most critical parts of trust in the IoT layers is ensuring that users and devices are who they say they are. Without this verification, fraudsters may be able to impersonate someone or something trustworthy in order to get access to and alter illegal data [10]. • Authorization: The authorization process is a tool that is used to verify the authenticity of each IoT system endpoint. The certification procedure is specified upon registration and notifies the service provider of the approach to employ when verifying the system’s identification [11].

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• Data Encryption: It entails encoding a message in such a way that anyone who does not have access to the decoding key cannot read it. Although encryption is frequently used by a gateway or smart edge device for power-constrained IoT devices, the data and communications channel should be secured [12]. • Availability: Service availability, or ‘uptime’, solutions try to protect against unanticipated attacks from both external and internal sources [13]. • Integrity: Data integrity protection is strongly tied to the concept of confidentiality. That is to say, if a device or system is effectively secured, an attacker will have a tough time accessing and manipulating the data produced by that device or system [13].

3 Artificial Intelligence and IoT Security 3.1 Artificial Intelligence (AI) is an advanced computer science subject capable of doing actions that typically require human intelligent. AI can flourish in a variety of domains thanks to its high prediction and automation skills [14]. Machine Learning and Deep Learning are two major subcategories of Artificial intelligence (see Fig. 2) shows the differences between them [15].

Fig. 2. The difference between AI, ML and Deep Learning [16].

Artificial Intelligence aims to emulate human cognition, whereas Machine Learning goes a step farther. Machine learning is a branch of artificial intelligence that enables machines to learn without the assistance of humans. In reality, the “computer” that recognizes patterns is an algorithm that analyzes massive amounts of data that a human could never comprehend. To put it another way, machine learning allows a machine to be trained to automate tasks that are difficult for a human to do, and it can then use this information to generate predictions.

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3.2 Machine Learning and IoT Security Machine Learning algorithms aim to improve performance when a task is performed through training. There are four types of machine learning algorithms - supervised, unsupervised, semi supervised, and reinforcement learning. For example, the goal of learning intrusion detection is to classify system activity as good or bad. • Supervised Learning: When established goals must be attained from a number of inputs, supervised learning is implemented. The data tagged and then trained in this type of learning using labeled data (having inputs and desired outputs). It aims to find rules automatically, establish different classes and predict if elements, persons and criteria are part of a certain class. • Unsupervised Learning: When the environment provides inputs instead of planned results. They can evaluate unlabeled data similitudes and categorize data into separate categories without the need for marked data [15]. • Semi-supervised Learning: Either all of the observations in the dataset have no labels or all of the observations have labels in the preceding two types [15]. Semisupervised learning is located somewhere in the middle. In many circumstances, the cost of labeling is fairly significant because it necessitates the hiring of skilled human specialists. As a result, semi-controlled algorithms are the best technique to build a model when the majority of observations lack labels but only a few do. • Reinforcement Learning: A kind of enhancement learning in which the agent learns through feedback following environmental interaction. It carries out specific measures and decides on the award it receives. The learning processes for humans and animals have a major effect [15]. Machine learning is recognized as one of the most relevant IoT-intelligence computational paradigms to improve the security layer of IoT devices. When answering a given problem evolves over time, such as routing a computer network or recognizing software or programs with harmful code, machine learning is one of the best solutions. It may also be used to detect and remove malware from affected devices (see Fig. 3), those applications become a venue for researchers to disclose new innovative ideas. The number of papers devoted to the use of machine learning in IoT security is increasing rapidly.

Fig. 3. Documents talk about machine learning’s uses in IoT security on Scopus databases.

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IoT network layer Chatterjee et al. [17], The authors of this study presented an ANN-based artificial neural network authentication approach. The physically unclonable authentication function (PUF), which looks at the physical features of the transmitter, can make IoT more effective. To detect the transmitters, the authors employed a machine learning in-situ technique that combined several communication non-idealities. At the receiver end, the ML technique is used to precisely identify and/or categorize the transmitters, and entropy is gathered. Attack detection and mitigation Ozayet et al. [18] the authors examined different ML attack detection systems in a profound way in smart grid. The authors studied supervised education, semi-supervised study, fusion space and online algorithms for attacker detection. The authors divided the networks into large and small networks. The results showed that K-nearest Neighbors algorithm (k-NN) was better in small data, whilst Support vector machine(SVM) did a better job in large data. Doshi et al. [1] compared the existing machine learning algorithms for DDoS detection in IoT networks. Their results propose the automated identification of local IoT device sources from DDoS assaults using low cost machine learning, gateway routers or other Network Middle boxes. The weakest performer was the SVM classifier, indicating that the data is not linearly separable. With an accuracy of 0.99, the decision tree classifier worked admirably. Anomaly and intrusion detection Canedo et al. [19] For this paper, the researchers used two machine learning approaches, the ANN algorithm and genetic algorithms to detect intrusions at IoT networks. The training data as well as the two ANN input neurons indicated that 99% accuracy anomalies had been anticipated. Three input neurons were also examined by the authors and the prediction rate maintained over 95% with barely 1% false negatives. Viegas et al. [20] designed to build energy-efficient intrusion detection systems that are sound to hardware for IoT devices. During their intrusion detection studies the authors used three classifiers: Decision Tree, Naive Bayes, and Linear Discriminant Analysis. The authors initially looked at the influence of a single grading system among the classifiers listed above to investigate how they affected the detection using a combination of those classifiers. The experimental findings showed that all classifiers were outstanding in accuracy of more than 99% with baseline testing; nonetheless, the accuracy of new attacks was reduced by more than 30%. Malware Analysis Alamet et al. [21] used a supervised technique for learning to identify malware on Android devices using a Random Forest classifier. Several random forests, including tree size and number of trees, have been tested. Random Forest has a lower misclassification rate, according to the findings of research, with an accuracy of more than 99% than other classifiers, such as BayeNet, NaiveBayes, Decision Stump. Suet et al. [22] presented a DDoS malware detection methodology in IoT networks based on image recognition. The researchers of this method prepare two main malware families, Mirai and Linux Gafgyt, and then convert the IoT applications program binaries to grey-scale pictures. The photos are then classified using the Convolutional Neural Network algorithm into goodware and malware. The authors’ testing data demonstrate that the CNN-based technique obtains a classification accuracy of 94%.

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4 Conclusion In this paper, we have examined the various IoT layers, the security and privacy concerns of IoT systems, and the history of attacks. We examined the roles and applications of ML in the IoT from the standpoints of security and privacy. This state of the art seeks to provide a useful manual that can motivate researchers to progress IoT system security from just providing safe communication among IoT components to building intelligent end-to-end IoT security-based approaches. The rest of the research will be dedicated to the realization of a comparative study between machine learning and deep learning to test the effectiveness to achieve the planned objectives in terms of forecasting and detection, a future work will be focused on to the application and validation of a dataset to detect attacks against MQTT on an industrial IoT network.

References 1. Doshi, R., Apthorpe, N., Feamster, N.: Machine learning DDoS detection for consumer internet of things devices. In: 2018 IEEE Symposium on Security and Privacy Workshops (2018). https://doi.org/10.1109/SPW.2018.00013 2. Seaman, C.: Threat Advisory: Mirai Botnet (2016) 3. Al-Garadi, M.A., Mohamed, A., Al-Ali, A., Du, X., Guizani, M.: A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun. Surv. Tut. 22(3), 1646–1685 (2020). https://doi.org/10.1109/COMST.2020.2988293 4. Hung, M.: Leading the IoT Gartner Insights on How to Lead in a Connected World. Gartner, Inc. (2017) 5. Zhao, K., Ge, L.: A survey on the internet of things security. In: 2013 9th International Conference on Computational Intelligence and Security (CIS), pp. 663–667. IEEE (2013). https://doi.org/10.1016/j.comnet.2018.03.012 6. Sikder, A.K., Petracca, G., Aksu, H., Jaeger, T., Uluagac, A.S.: A survey on sensor-based threats to internet-of-things (IoT) devices and applications. In: Security for Cyber-Physical Systems (CPS) and Internet of Things (IoT), February 2018 (2018). https://doi.org/10.1109/ COMST.2021.3064507 7. IoT Security: The Key Ingredients for Success. THALES (August 2020) 8. McDermott, B.: 5 of the Worst IoT Hacking Threats in History (So Far). Dogtown Media (March 2020) 9. Sengupta, J., Ruj, S., Bit, S.D.: A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. J. Netw. Comput. Appl. 149, 102481 (2020). https:// doi.org/10.1016/j.jnca.2019.102481 10. Westby, J.: The Great Hack: Cambridge Analytica n’est que la partie visible de l’iceberg. Amnesty International (July 2019) 11. Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., Sikdar, B.: A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7, 82721–82743 (2019). https://doi.org/10.1109/ACCESS.2019.2924045 12. Kolias, C., Kambourakis, G., Stavrou, A., Voas, J.: DDoS in the IoT: Mirai and other Botnets. Computer 50(7), 80–84 (2017). https://doi.org/10.1109/MC.2017.201 13. Abdul-Ghani, H.A., Konstantas, D., Mahyoub, M.: A comprehensive IoT attacks survey based on a building-blocked reference model. Int. J. Adv. Comput. Sci. Appl. 9(3), 355–373 (2018). https://doi.org/10.14569/IJACSA.2018.090349

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14. Xiao, L., Wan, X., Xiaozhen, L., Zhang, Y., Di, W.: IoT security techniques based on machine learning: how do IoT devices use AI to enhance security? IEEE Sig. Process. Mag. 35(5), 41–49 (2018). https://doi.org/10.1109/MSP.2018.2825478 15. Hussain, F., Hussain, R., Hassan, S.A., Hossain, E.: Machine learning in IoT security: current solutions and future challenges. IEEE Commun. Surv. Tut. 22(3), 1686–1721 (2020). https:// doi.org/10.1109/COMST.2020.2986444 16. The difference between Artificial Intelligence, Machine Learning and Deep Learning. Data Catchup (May 2019) 17. Chatterjee, B., Das, D., Maity, S., Sen, S.: RF-PUF: enhancing IoT security through authentication of wireless nodes using in-situ machine learning. IEEE IoT J. 6(1), 388–398 (2019). https://doi.org/10.1109/JIOT.2018.2849324 18. Ozay, M., Esnaola, I., Vural, F.T.Y., Kulkarni, S.R., Vincent Poor, H.: Machine learning methods for attack detection in the smart grid. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1773–1786 (2016). https://doi.org/10.1109/TNNLS.2015.2404803 19. Caedo, J., Skjellum, A.: Using machine learning to secure IoT systems. In: 2016 14th Annual Conference on Privacy, Security and Trust (PST), December 2016, pp. 219–222 (2016) 20. Viegas, E., Santin, A., Oliveira, L., França, A., Jasinski, R., Pedroni, V.: A reliable and energyefficient classifier combination scheme for intrusion detection in embedded systems. Comput. Secur. 78, 16–32 (2018) 21. Alam, M.S., Vuong, S.T.: Random forest classification for detecting Android malware. In: IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 663–669, August 2013 (2013) 22. Su, J., Vargas, D.V., Prasad, S., Sgandurra, D., Feng, Y., Sakurai, K.: Lightweight classification of IoT malware based on image recognition. CoRR, vol. abs/1802.03714 (2018)

Reduce the Energy Consumption of IOTs in the Medical Field Mohammed Moutaib1(B) , Tarik Ahajjam2 , Mohammed Fattah1 , Yousef Farhaoui2 , Badraddine Aghoutane3 , and Moulhime El Bekkali4 1 IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco

[email protected]

2 L-STI, T-IDMS, Faculty of Science and Technics, University of Moulay Ismail,

Errachidia, Morocco 3 IA Laboratory, Science Faculty, Moulay Ismail University, Meknes, Morocco 4 IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Abstract. The Internet of Things or IOT is a technology that has a great impact on human development. The Internet of Things has made advancements in the medical field, where each person is monitored by a set of nodes that collect data acquired from the human body. This makes energy consumption a major issue. However, reducing energy consumption revolves around three main axes. The collision domains that occurs when two nodes send information simultaneously. The second axis is the retransmission of data produced by the effect of the first axis. The distance between nodes is the third axis that increases energy consumption. In this article, we propose a solution for managing the flow through the shortest path between nodes with a hierarchical method in order to avoid collision domains, which will decrease data retransmissions. The first part is a complete study, with a demonstration diagram of the architecture. In the second part, an architecture is proposed to demonstrate the difference between the centralization and the distribution of nodes. Third, algorithms for routing data through nodes have been proposed with a final discussion. Keywords: IoT · Energy consumption · Cloud computing · Data storage

1 Introduction The Internet of Things is one of the most powerful technologies that have developed computing [1], ensuring standards of reliability and communication. They are part of the family of technologies that can be trusted to use In the IoT environment, everything is tied to the Internet because of its processing, computing and communication capabilities. The Internet of Things has changed the concept of the Internet network and made it universal. It allows communication between several types of objects [2, 3]. The IOt is represented by several objects in the form of a dedicated and easy-to-use node on several types. Indeed, connected objects have changed several areas and made it more creative, in our case in the medical field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 259–268, 2022. https://doi.org/10.1007/978-3-031-02447-4_27

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The IoT involved in the medical field have adapted several types of sensors, which act as nodes on the human body, in order to measure and collect data. These sensors are implanted and portable in the human body, allowing people to benefit from existing medical services at any time and in any place and to have a good follow-up of their health [4, 5]. A human body is equipped with several sensors implemented in several organs. These will provide data that will help to monitor the patient’s condition. Each node is equipped with a processor. This implies a similar impact on energy consumption. These energy costs are distributed among the battery-powered nodes that need to be recharged regularly (the integrated nodes), the constant power bases, and the servers and routers necessary to ensure the connectivity of the objects. There are several solutions to reduce the energy consumption of connected objects. A continuous data transfer was carried out in a desire for hyper-connectivity. However, in many cases a device only needs to be connected to the network occasionally, as is the case with all devices operating in automation. In this article, we propose a new solution for IoT applications in the medical field, comprising the three steps detailed in Fig. 1. Our architecture is explained as follows:

Fig. 1. Application of IoT in the field of health.

(1) In this step, the knot is placed on the patient’s body. These sensors have the function of collecting data on the human body. In order to know the state of the body, etc. The data collected by the node is sent to the master node via the IEEE 802.15 standard [6]. (2) This step acts as an intermediary between step 1 and step 3. In this part, we use a mode of communication according to our needs (Wi-Fi connection or access point). The data collected in the previous step will be sent to the next step for processing, storage and prediction.

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(3) Storage stage. In this step, the data collected in step 1 and transmitted in step 2 is stored in this step to be analyzed in order to help us make decisions. In this work, we improve the use of IoT in the medical field in step 1, which will be detailed later. The nodes of the architecture use a wireless communication method to exchange data, the human body could disrupt the transfer of data between the nodes, which should be taken into account in our solution (step 1). In addition, reducing energy consumption is essential in our case, since the sensory node is implanted in the human body [7, 8]. Among the solutions adapted to meet the challenges of bodily IoB systems, is the use of the physical part. In this step, there are several methods such as channel coding, network coding, modulations, etc. [9–11]. In our article, we are interested in virtual diversity. We can achieve the latter thanks to distributed communication: let’s take our case study where the sensors work together and collect the data to provide to the Base.

2 Literature Review So far, a lot of research has been done on reducing the energy consumption of the Internet of Things. Among these researches [12] Studied a solution, which verifies the power and integrity of the blocks. [13] to implement an architecture for increasing the capacity of wireless body sensor network technologies using the polarization method. [14] performed an application of a cognitive solution. The solution adopted is based on the adaptation of IEEE 802.15.6 [15]. Used two cognitive radio architectures based on the reduction of electromagnetic interference. [16] proposed a simplified human body system for detecting radio channels. This proposed solution aims to reduce energy consumption through several methods. [17] To propose a solution which is based on Ultra Wide Band technology, it is a radio modulation technique which is based on the transmission of pulses of very short duration. [18] Presented an architecture that relies on the management of IoT flows. This proposal emphasizes the centralization of flows, so that the nodes send all their data to the base. [19] Set up a description on the CRBSN for the management of medical files. A description of the wireless body network device systems has been provided. Thus, they studied the MAC methods used for energy efficiency. [19] Set up a description of Wireless Sensor Networks (WSN) for the management of medical records. A description of the wireless body network device systems has been provided. Thus, they studied the methods used for energy efficiency. To solve the problems described above as well as to reduce energy consumption and facilitate communication between different nodes, we have proposed a distributed architecture with a routing algorithm. The contributions of this work are summarized as follows: (1) We offer two architectures, the first with centralized communication and the second with distributed communication. In order to demonstrate the role of flow distribution compared to the traditional solution.

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(2) In addition, we have implemented our solutions in the medical field, precisely in the human body. (3) The proposed protocol aims to route the data sent by the nodes in order to determine the shortest path. The rest of the article is presented as follows: the model and architecture of the system and the proposed new architecture are presented in Sect. 2. Section 3 presents the new routing algorithm. In Sect. 4, a general architecture of the system is proposed. Finally, Sect. 5.

3 Network Architecture 3.1 Architecture Design The first network architecture is a traditional solution (Fig. 2). This solution distributes the nodes centrally on the architecture to collect data, and each of the nodes collects and will transmit the data to the base. Thus, this solution is a single hop topology: which will allow the nodes sent the data collected to the base. The base transmits what has been received via a wireless network. It is necessary to take into account the number of nodes used and the distance between the nodes and the base to establish this solution. The overload of the requests of the nodes towards the base can lead to collisions on the one hand. On the other hand, the distances between the nodes and the base are variable. Therefore, several nodes can be far from the base. Therefore, it is difficult to provide data appropriately to the base directly.

Fig. 2. Centralized collection

Fig. 3. Distributed collection

In Fig. 3, a new architecture is designed. The role of our solution is to tie the nodes together to avoid overloading the base and to avoid collision domains. Send data collects this in a hierarchical fashion where each node sends this data to the nearest node to the base.

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3.2 Architecture Implementation In the architecture of Fig. 4, several sensors are distributed evenly over the human body for data collection. Each of the sensors collects a set of information and transfers it to the base. The Nodes are fixed on the human body, named (N1, N2…). The base outside the body functions as an information collection point that receives information from node N1. Thus, it has a monitoring role. Due to the mobility of the nodes in the body, the distances between the sensors (N1, N2…) and the base change, where several nodes can be located at a great distance or else at a short distance, which makes the dimension of distances our main problem to be solved. Thus, several sensors can have a better channel quality compared to the base than the others can. Example, in our case, the first node will transmit these data to the node closest to it, the latter will transmit the data of the first node1 and these data specific to the base. This way we were able to establish a single connection with as much information as possible. The problem with transmitting data between nodes is that a single bit sent can sometimes consume as much power as the execution of a thousand instructions by the processor. In order to reduce energy consumption, we have used as multi-hop technology so that each node can serve as an intermediary (the routing role) for other nodes, selforganizing to build a route through which the traffic passes messages. The new network architecture can significantly displace the process of returning sensors to the base compared to traditional communication. Thus, new architectures could reduce delays and conflicts between sensors, which could lead to better energy savings.

Fig. 4. Distributed collection of nodes in the human body.

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4 Data Routing Algorithm 4.1 Proposed Routing Formulation In our network architecture, we try to get from one node to another, it is obvious to look for the shortest path to reach the node, that is to say the nodes having the shortest distance [20, 21]. If the number of possible paths between the starting node and the destination node is low, it will suffice to calculate the distances of the paths between the nodes by adding the length of the links, which compose it, and to directly compare the lengths obtained. However, a solution becomes impractical if the number of nodes is very large [22, 23]. A vertex x is said to be visited if at least one path from node A to x has been evaluated the provisional values of a visited vertex of m(x), p(x). M(x) is the minimum value and P(x) the predecessor [24, 25]. A vertex is said to be calculated if it is visited and if we know that its m(x), p(x) values are final. Initially, of course, the vertex A is calculated with m (A) = 0, p(A) undefined since A is the starting point of the paths, and each successor x of A is visited, Then M (x) = v(A, x), p(x) = A.

(1)

The principle of exploration from the best consists in finding, among the visited vertices not yet calculated, a vertex whose value m(x) is minimum. We can then show that, for such a vertex, the provisional values m(x), p(x) are final. This demonstration explicitly uses the fact that the values are non-negative. We therefore mark x as calculated and we extend the exploration by examining each of the successors of x: each successor y not yet visited becomes visited, With M(y) = m(x) + v(y, x), p(y) = x

(2)

While, for each successor there already visited, we update M(y) = min(m(y), m(x) + v(x, y))

(3)

P(y) = p(x) if m(y) is updated

(4)

and

M(y) = min(m(y), m(x) + v(x, y)) and p(y) = p(x) if m(y) is updated. The exploration stops when all the visited vertices are calculated (the unvisited vertices are inaccessible, we can consider that they have a value of m (x) = ∞). 4.2 Application of the Protocol In this section, the model of the links between the nodes and the base in Fig. 4 is provided with the Dijkstra Algorithm, in order to determine a shorter path to get to the point of arrival (the base).

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In Fig. 5, each node is represented by letters for example node A, node B. the flows are determined by distances. For example, the distance between node A and B in the human body where they are placed 85 cm. These measurements are taken randomly to determine the path of the nodes. The algorithm takes as input a directed graph weighted by positive reals and a source vertex. This involves progressively building a subgraph in which the various vertices are classified in increasing order of their minimum distance from the starting vertex. The distance corresponds to the sum of the weights of the borrowed arcs in Fig. 5. Initially, we consider that the distances from each vertex to the starting vertex are infinite, except for the starting vertex for which the distance is zero. The starting subgraph is the empty set. During each iteration, we choose outside the subgraph a vertex of minimum distance and we add it to the Table 1 subgraph. Then, we update the distances of the neighboring vertices of the added one. The update is carried out as follows: the new distance from the neighboring vertex is the minimum between the existing distance and that obtained by adding the weight of the arc between neighboring vertex and vertex added to the distance from the added vertex. We continue in this way until the summits are exhausted.

Fig. 5. Routing algorithm initial state.

Each step in Table 1 corresponds to a row. A line gives the current distances of the vertices from the starting vertex. A column gives the evolution of the distances of a given vertex from the starting vertex during the algorithm. The distance from a chosen vertex is underlined. Updated distances are crossed out if they are greater than distances already calculated.

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Table 1 gives not only the minimum distance between the nodes, but also the backward path (J - H - C - A) to go from A to J as well as all the minimum distances from node A to the other nodes arranged by Ascending. The result of routing the nodes is shown in the shortest path from A to J (Fig. 6): A→C→H →J

(5)

Fig. 6. Final state of the routing algorithm.

5 Conclusion In this article, a new architecture solution with a node routing algorithm is proposed taking into account the nature of the data collected. The proposed algorithm is based on

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the IEEE 802.15.6 CSMA policy and Dijkstra’s theorem. The objective of the proposed solution can be summarized as follows: first, reduce the retransmission process and reduce collisions by using a distributed architecture. Second, decrease the query overhead on the base, and third, apply Dijkstra’s algorithm to determine the shortest path. The proposed solution made it possible to obtain a better quality of transmission of critical data. In addition, it was able to significantly reduce the probability of failure and power consumption.

References 1. Weiser, M.: The computer for the 21st century. Sci. Am. 265(3), 94–104 (1991) 2. Gope, P., Hwang, T.: Untraceable sensor movement in distributed IoT infrastructure. IEEE Sens. J. 15(9), 5340–5348 (2015) 3. Gope, P., Hwang, T.: A realistic lightweight authentication protocol preserving strong anonymity for securing RFID system. Comput. Secur. 55, 271–280 (2015) 4. Liao, Y., Leeson, M., Higgins, M., Bai, C.: Analysis of in-to-out wireless body area network systems: towards QoS-aware health internet of things applications. Electronics 5(4), 38 (2016) 5. Fan, Y.J., Yin, Y.H., Xu, L.D., Zeng, Y., Wu, F.: IoT-based smart rehabilitation system. IEEE Trans. Ind. Inf. 10, 1568–1577 (2014) 6. Yang, J., Geller, B., Arbi, T.: Récepteur Multi-normes pour les Réseaux de Capteurs de l’IoT médical. Internet des objets 17(1) (2017). https://doi.org/10.21494/iste.op.2017.0136 7. Moosavi, H., Bui, F.M.: Optimal relay selection and power control with quality-of-service provisioning in wireless body area networks. IEEE Trans. Wirel. Commun. 15(8), 5497–5510 (2016) 8. Chen, X., Ma, M., Liu, A.: Dynamic power management and adaptive packet size selection for IoT in e-healthcare. Comput. Electr. Eng. 65, 357–375 (2018) 9. Alkhayyat, A., Gazi, O., Sadkhan, S.B.: The role of delay and connectivity in throughput reduction of cooperative decentralized wireless networks. Math. Probl. Eng. 2015, 1–10 (2015). Article ID 294016 10. Alkhayyat, A.: Joint next-hop/relay selection for distributive multihop cooperative networks. Discrete Dyn. Nat. Soc. 2015, 1–10 (2015). Article ID 613168 11. Moutaib, M., Fattah, M., Farhaoui, Y.: Internet of things: energy consumption and data storage. Procedia Comput. Sci. 175, 609–614 (2020) 12. Nazir, M., Sabah, A.: Cooperative cognitive WBSN: from game theory to population dynamism. In: 2011 3rd International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Singapore, pp. 1–6 (2011) 13. Jingling, F., Wei, L., Yang, L.: Performance enhancement of wireless body area network system combined with cognitive radio. In: 2010 International Conference on Communications and Mobile Computing, Singapore, vol. 3, pp. 313–317 (2010) 14. Chavez-Santiago, R., Nolan, K.E., Holland, O., et al.: Cognitive radio for medical body area networks using ultra wideband. IEEE Wirel. Commun. 19(4), 74–81 (2012) 15. Syed, A.R., Yau, K.-L.A.: On cognitive radio-based wireless body area networks for medical applications. In: 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Singapore, pp. 51–57 (2013) 16. Januszkiewicz, L.: Simplified human body models for interference analysis in the cognitive radio for medical body area networks. In: 2014 8th International Symposium on Medical Information and Communication Technology (ISMICT), Singapore, pp. 1–5 (2014) 17. Shaikh, A.Z., Tamil, L.: Cognitive radio enabled telemedicine system. Wirel. Pers. Commun. 83(1), 765–778 (2015)

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18. Mottola, L., Picco, G.P.: Programming wireless sensor networks: Fundamental concepts and state of the art. ACM Comput. Surv. 43(3), 1–51 (2011) 19. Sodagari, S., Bozorgchami, B., Aghvami, H.: Technologies and challenges for cognitive radio enabled medical wireless body area networks. IEEE Access 6, 29567–29586 (2018) 20. Tarik, A., Farhaoui, Y.: Recommender system for orientation student. In: Farhaoui, Y. (ed.) Big Data and Networks Technologies, pp. 367–370. Springer, Cham (2020). https://doi.org/ 10.1007/978-3-030-23672-4_27 21. Fattah, M.M., et al.: Multi band OFDM alliance power line communication system. Procedia Comput. Sci. 151, 1034–1039 (2019) 22. Boumaiz, M., et al.: Energy harvesting based WBANs: EH optimization methods. Procedia Comput. Sci. 151, 1040–1045 (2019). https://doi.org/10.1016/j.procs.2019.04.147 23. Daghouj, D., Fattah, M., Mazer, S., Balboul, Y., El Bekkali, M.: UWB waveform for automotive short range radar. Int. J. Eng. Appl. (IREA) 8(4), 158 (2020) 24. Maroua, A., Mohammed, F.: Characterization of ultra wide band indoor propagation. In: 2019 7th Mediterranean Congress of Telecommunications (CMT), pp. 1–4 (2019). https://doi.org/ 10.1109/CMT.2019.8931367 25. Moutaib, M., Ahajjam, T., Fattah, M., Farhaoui, Y., Aghoutane, B., el Bekkali, M.: Optimization of the energy consumption of connected objects. Int. J. Interact. Mob. Technol. (iJIM) 15(24), 176–190 (2021). https://doi.org/10.3991/ijim.v15i24.26985

Query Processing in IoT Based on Spatial and Temporal Information Chaimae Kanzouai1(B) , Abderrahim Zannou2 , El Habib Nfaoui2 , and Abdelhak Boulaalam1 1 LISA Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

{chaimae.kanzouai,abdelhak.boulaalam}@usmba.ac.ma

2 LISAC Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

{abderrahim.zannou,elhabib.nfaoui}@usmba.ac.ma

Abstract. As the Internet of Things (IoT) advances, the relevance of modeling and analyzing IoT data has expanded significantly. Because of the huge number of smart objects and the large scale of the network, classic query processing approaches aren’t always applicable, as processing large amounts of data collected in real-time from a diversity of IoT devices remains a challenge. We propose a novel approach for query processing in this study, where the queries are provided by end-users to locate the effective edge devices inexpensively way. We suggest a predictive model based on a query’s geographical and temporal information to search the data/service from the most potential devices. Results have demonstrated a high level of performance in terms of accuracy and recall, as well as, the proposed methodology can reach the destinations within a brief time frame, which speeds up the search process across a huge number of devices. Keywords: Internet of Thing (IoT) · Multi-layer perceptron · Spatial query processing

1 Introduction Internet of Things is changing the way commodities and industry activities are carried out regularly. The integration of sensors, lightweight processing, and the rise of many wireless systems on IoT infrastructures, platforms allow humans to engage in relation to their physical surroundings in a more comprehensive way [1–5, 21]. IoT intends to play a significant role in a variety of application fields like Smart cities [6], smart homes [7], industrial automation [8], medical car [9], intelligent transportation [10], resource management [11], and energy management [12], in smart home applications for example, depending on user-defined settings smart sensors and actuators perform a variety of monitoring and control activities such as air-conditioning monitoring, heating management, and re alarms. Data aggregation is a critical function in IoT systems and applications as it aims to transfer data points created by a group of dispersed sensor nodes into the receiving node [13]. The amounts of sensory data gathered from the real environment in a common IoT © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 269–276, 2022. https://doi.org/10.1007/978-3-031-02447-4_28

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implementation such as smart cities are large and diversified [14]. It is challenging to process a significant volume of real-time data from a wide variety of IoT devices for instance Wireless sensor networks (WSNs) are commonly used in IoT environments for monitoring and collecting data in a specific geographic area [15–19]. Devices in lossy networks are often power-constrained, with restricted calculation capability and storage capacity. Additionally, because each device has a varied capacity, if one node makes more requests over another, the lifetime of the lossy network will be reduced. The problem is figuring out how to get to the nodes quickly and distribute the request with the appropriate executor to save time and resources [20]. To remedy the issues mentioned above, we propose a prediction model based on neural networks in order to predict the most relevant area and find the effective edge devices while using the query’s geographical and temporal information to search the data/service from the most potential devices. Our objective is to speed up the duration of the research, which allows the network’s lifetime to be as long as possible. The following is how the rest of the paper is organized: The study is divided into four sections: Sect. 2 presents related work, Sect. 3 contains the suggested addition, Sect. 4 presents simulation results and discussion, and Sect. 5 finishes the paper.

2 Related Work The approach proposed by Zannou et al. [21] suggests a method for discovering and selecting services in the IoT. It takes into account the following factors: accuracy, length of the path, time of execution, energy consumed by the nodes, and network lifespan. Firstly, an edge server performs the discovery process, using a multi-layer perceptron to predict the relevant nodes suitable to a given request using semantic clustering. Then, these nodes themselves perform the selection process, which employs Ant Colony Optimizer to pick the most appropriate node. For both phases of discovery and selection, experimental results indicate high accuracy and longer network lifetime, as well as limited time for both phases. However, using a multi-layer perceptron of three layers decreases the execution time in the discovery phase, but it is not efficient for a large data set and decreases the accuracy. X. Li et al. [22] looked at query processing in an IoT sensing network, where different types of smart things in a particular sub-region must cooperate and collaborate for environmental monitoring and possible event detection. Traditional query processing mechanisms, which rely on such a centralized index graph, may not be applicable. To address this problem, the authors proposed a multi attribute aggregation query mechanism for edge computing in which an energy efficient IR tree is built to handle queries in singular edge nets, an edge node mapping graph was built to ease query handling for the marginal intelligent objects in the congested edge nets. Experimental assessment results show that this approach outperforms its competitors in terms of reducing network traffic and energy consumption. To handle complicated queries in current IoT systems and to overcome the problem of a huge quantity of sensory collected data the authors of [23] proposed an edgesupported IoT data surveillance system (EDMS). Data collected is gathered from a variety of sources and saved in some kind of a distributed fashion on servers linked

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to a distant cloud in EDMS. A query is converted into a collection of cloud-based data processing services, such like data aggregation, top-k query processing, image processing, etc. The edge servers have cached these data processing resources. When a query is sent to the cloud, it generates a query strategy and distributes jobs to the edge server. Edge servers can process a large amount of sensory data and complicated queries in a way that is distributed because they have more computing and storage resources. EDMS’ distributed query processing will allow the best use of edge server resources while lowering computing and transmission costs. As high data rate devices are becoming commonplace in IoT and massive volumes of data need to be transmitted to the users at the end, renting platforms from the commercial cloud is both expensive and inefficient. Thus, Zhao et al. [24] proposed ETC-IoT an architecture that uses peripheral nodes to manage data transfer created by massively distributed IoT assets delivered to the cloud core, reducing the traffic load on the central cloud in IoT. In a case study, it has been demonstrated that the suggested model of architecture would greatly reduce the bandwidth usage when used for real-time video streaming. Furthermore, this study has revealed a number of concerns that should be dealt with, in order to incorporate the proposed ETC-IoT architecture. In skewness distribution using multi attributes sensors, authors in [25] investigated the challenge of constructing an energy aware index tree in order to allow multi area attributes aggregating query. This technique groups multiple attribute sensor nodes to such a hierarchical tree known as the MFSI which is built by combining neighboring sub-regions with the most prevalent multi attributes clusters. This technique guarantees that the MFSI tree’s higher-level sub-regions have fewer empty spaces. The multi area attributes aggregation queries are effectively conducted in the network using this tree architecture. Along with the query recombination depending on query area and query attribute overlap, they have investigated dividing query attributes to alleviate the load on the head nodes and increase network lifespan. Their multiple query aggregation approach using MFSI tree may considerably reduce energy consumption and enhance network lifespan, according to extensive research based on simulations and tests.

3 Proposed Methodology In this section, we propose a novel contribution for query processing in IoT, where the query is generated by users or end devices to find efficient and low-cost edge devices. We rely on the spatial and temporal information of a query to start searching for data/services from most candidate devices. 3.1 Contribution Overview To clearly define the proposed solution, Fig. 1 shows the main communications that occur between the end-user/end device, the base station, the edge server, and the edge nodes. As shown, the query is generated from any device to be processed by the edge nodes within the network, and then the edge server receives the query and processes it. Then the query is resubmitted to a given zone based on the prediction model.

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Fig. 1. Proposed solution overview

The network is subdivided into a set of regions, where each region can contain several edge devices. In addition, there are connections between edge devices in the same region and different regions. In the proposed scenario, we assume that the edge nodes manage smart objects such as sensors, actuators, RFIDs, etc. These devices send their data to the nearby edge devices, which are responsible for storing and processing the data for the end-users. Thus, the edge server should find the most suitable region where their edge devices can process the query with low latency and high accuracy. 3.2 Spatial and Temporal Information of Source Query The query data contains information about the source of the query, the identification protocol of the destination network, the requested service and task, and other information. From this query, the edge server must find the most relevant region that has edge nodes capable of processing it. In this contribution, we study the effects of spatial and temporal information to efficiently answer the query. We consider the region where the queries are processed, such that all these queries pass through the base station. In particular, the edge server is responsible for processing the query before it is executed by the edge devices and is responsible for sending the response to the query source. The spatial information of a query is the coordinate of the device from which the query is generated. We rely on this property because there are many queries that are directly related to devices. For example, there are devices that are installed in a hospital, from this we can predict that queries generated from these devices have a high probability of being answered by the peripheral devices that are related to the smart hospital. In this case, the search for a service will be initiated by these devices. The temporal information of a request is when a device generates a request. We rely on this property because there are devices that generate requests at specific times, so we

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can notice a relationship between the requested service/device group and the time when requests are generated. 3.3 Predicting the Edge Devices for a Query Processing in IoT In this contribution, we assume that there is an edge server (or collaborative edge servers) that manages the edge devices and is responsible for the communication that occurs at the base station, as shown in Fig. 1 the edge server processes previous queries that have been or will be executed by the edge devices. We form a multi-layer perceptron network for the queries processed by the edge devices, where the processed queries are the spatial and temporal information. In addition, we assign the area identifier and the source device identifier to the processed query as shown in Fig. 2.

Fig. 2. Example of processed query

We proposed the MLP shown in Fig. 3, consisting of three layers: the input layer, the hidden layer, and the output layer. We choose a hidden layer in the MLP, which is preferable [26] because the execution time for the training data and the recognition phase are shorter. In particular, in the recognition phase, the proposed MLP is preferred. In the recognition phase, when the query is received by the edge server, it predicts the most relevant zone based on the MLP predictive model, the edge server forwards the query to an edge server up to the provider device. On the other hand, if the provider’s device is found in the current zone, the query will be forwarded to the neighboring zone.

Fig. 3. A one hidden layer Multi-Layer Perceptron

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4 Simulation Results and Discussion To evaluate the proposed solution, we used a set of real data. We collected the datasets from KDD CUP 99 [27]. In addition, 60000 and 20000 dataset queries for the training and test datasets, respectively. In addition, every query contains six attributes that present spatial and temporal information. We consider 10 areas in the domain of interest, where for each area we assigned 10000 queries and 2000 queries for the training phase and recognition phase respectively. The goal is to find the top device for each query in real-time. To measure the different metrics: accuracy, precision, recall, F1 score as shown in Table 1. The results obtained are between 80% and 82% for the recognition phase, which is acceptable. In addition, the results mean that the search space can be minimized, where the search starts with the most candidates that can execute the queries. Table 1. The metrics evaluation of the proposed methodology. Testing data of 60000 requests Accuracy (%)

80,9

Precision (%)

82,5

Recall (%)

81,4

F1 score (%)

80,5

To study the effect of this solution in terms of execution time, as shown in Fig. 4, we measure the total execution time required in the recognition phase, which is performed in real-time. The obtained results show a high performance, where the recognition time

Fig. 4. The time it takes to recognize a request based on the amounts of request

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function is considered as a natural logarithmic function. As a result, the proposed methodology can reach the destinations in a short period of time, which can speed up the search process in a large number of IoT devices.

5 Conclusion As our society becomes increasingly digitalized, IoT applications will become more prevalent. Therefore, we need approaches that enable low-cost data capture, collection, and processing. In this study, we suggested architecture to find edge devices for each real-time query. We suggested the MLP model to predict the most relevant region to process the query based on the spatial and temporal information to efficiently fulfill the request. The results obtained are between 80% and 82% in the recognition phase, which is acceptable. Furthermore, the results show that the search space would be minimized, where the search starts with the potential devices that can handle the query. Furthermore, the results show high performance in terms of execution time. As a result, the proposed methodology can reach the destinations in a limited period of time that can speed up the search process in a large number of IoT devices.

References 1. Babun, L., Denney, Z.K., Celik, B., McDaniel, A.P., Uluagac, S.: A survey on IoT platforms: communication, security, and privacy perspectives. Comput. Netw. 192, 108040 (2021) 2. Zannou, A., Boulaalam, A., Nfaoui, E.H.: A multi-layer architecture for services management in IoT. In: Ahmed, M.B., Boudhir, A.A. (eds.) Innovations in Smart Cities and Applications, pp. 324–334. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74500-8_30 3. Zannou, A., Boulaalam, A., Nfaoui, E.H.: System service provider–customer for IoT (SSPCIoT). In: Bhateja, V., Satapathy, S.C., Satori, H. (eds.) Embedded Systems and Artificial Intelligence. AISC, vol. 1076, pp. 731–739. Springer, Singapore (2020). https://doi.org/10. 1007/978-981-15-0947-6_70 4. Zannou, A., Boulaalam, A., Nfaoui, E.H.: A node capability classification in internet of things. In: 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, pp. 1–6 (2020). https://doi.org/10.1109/ISCV49265.2020.9204024 5. Zannou, A., Boulaalam, A., Nfaoui, E.H.: Path length optimization in heterogeneous network for internet of things. In: 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), Kenitra, Morocco, pp. 1–4 (2020). https:// doi.org/10.1109/ICE-COCS50124.2020.9314437 6. Gea, T., Paradells, J., Lamarca, M., Roldan, D.: Smart cities as an application of Internet of Things: experiences and lessons learnt in Barcelona. In: Proceedings of the International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 552– 557 (2013) 7. Crandall, A.S., Cook, D.J.: Smart home in a box: a large scale smart home deployment. In: Proceedings of the International Conference on Intelligent Environments, pp. 169–178 (2012) 8. Ungurean, I., Gaitan, N.-C., Gaitan, V.G.: An IoT architecture for things from industrial environment. In: Proceeding of the International Conference on Communications (COMM), pp. 1–4 (2014) 9. Michaelsen, K., Sanders, J.L., Zimmer, S.M., Bump, G.M.: Overcoming patient barriers to discussing physician hand hygiene: do patients prefer electronic reminders to other methods? Infect. Control Hosp. Epidemiol. 34(9), 929–934 (2013)

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Toward an IoT-Based System to Ensure Product Identification of Moroccan Handicrafts Youssef Aounzou1(B) , Fahd Kalloubi2 , and Abdelhak Boulaalam1 1 LSI Laboratory, Sidi Mohamed Ben Abdellah University, ENSA, Fez, Morocco

{youssef.aounzou,abdelhak.boulaalam}@usmba.ac.ma

2 LTI Laboratory, Chouaib Doukkali University, ENSA, El Jadida, Morocco

[email protected]

Abstract. With the rapid growth of the global economy, craft manufacturing activities have known significant progress in recent decades. Consequently, the handmade industry is booming thanks to a rise in demand for artisan products. However, customers such as tourists need more scientific and practical guidance to be sure that the handmade product is original. Throughout this paper, we will introduce a targeted solution for products identification based on the Internet of Things to monitor products in their lifecycle. Also, our system aims to protect from counterfeiting the artistic and brand values of traditional handicrafts handed down from generation to generation. To prove the effectiveness of our proposal we implemented a prototype that allows product identification of Moroccan handicraft products using labeling strategies and scientific methods. Keywords: IoT · Crafts system · Identification product · NFC · RFID

1 Introduction The Moroccan handicraft industry is rapidly developing and well-known throughout the world for its originality and authenticity, it also has a significant impact on the Moroccan economy. According to the paper [1], it contributes to 8.6% of the gross domestic product indicator and employs 2.4 million of total workers as published by the Moroccan Ministry of Tourism and Handicrafts [2]. However, with the growing number of imitation products in the handicraft domain and the lack of a method that verifies the originality of these products, consumers still find it difficult to distinguish between original products and falsified ones [3]. For these reasons, a decrease in the rate of handicrafts purchases in the Moroccan market has been noticed. As consequence, the global economy has been impacted remarkably. As a result, product identification becomes a requirement in the craft sector to avoid counterfeiting. Thus, ensuring the originality of products through a combination of different technologies as described in reference [4]. The main idea is to integrate suitable tracking and tracing solutions for protecting handmade products quality. And provide an operational guideline that can boost the handicraft production process. For this reason, an intelligent environment is needed to allow users to interact with products intuitively without human intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 277–284, 2022. https://doi.org/10.1007/978-3-031-02447-4_29

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Our contribution is the design of a smart system based on the product identification techniques in the internet of things paradigm to track Moroccan handicrafts and transfer their data via a short-range wireless connection technology like a Near-Field Communication (NFC). The remainder of this article is organized as follows: Sect. 2 will make a literature review of targeted subjects followed by a presentation and discussion of related work. Section 3 present our proposed system. At last, we conclude our work and address the future work.

2 Literature Review and Related Work 2.1 The Internet of Things (IoT) In the field of information technology, the internet of things (IoT) has become a growing innovation that aims to bridge the gap between the digital and physical worlds using a connection method to ensure that all resources and services are always available. According to Atzori et al. [5], IoT technology is identified as a paradigm that is founded on the convergence of different visions: ‘Things’, ‘Semantic’ and ‘Internet’ visions. Each one is based on a set of technologies. Things vision focused on how to identify and integrate the objects in the paradigm using a set of technologies and concepts like NFC, RFID, and smart items. Internet vision ensures the connectivity in the paradigm between devices via IP protocol and other tools. And the semantic vision uses semantic technologies in managing data and storing the exchanged information. Typically, the internet of things collects all types of simple and complex data from multiple entities using identification sensors that are physically attached to the physical object. Generally, the simple data may be collected using only one sensor, while complex and sophisticated data requires the use of many sensors in the collection. As also, sensors can communicate and send data to a cloud using network protocols to allow the system to process the current data in real-time. In terms of application, this approach is implemented in several fields. The paper [6] describes a case study in which IoT technology is used to link all devices and transfer data in libraries utilizing RFID technology. Every object in the library is equipped with an RFID tag that stores bibliographic data, transaction data, and digital visualization. Collection circulation, overdue, and penalties may all be linked by connecting the library card with RFID tags. As a result, libraries can use IoT technology to alert consumers about missing materials and collect fines digitally. Regarding the use of sensors in the Internet of Things approach and their applications, cultural institutions have used electronic tags for controlling stock [7], authenticity [8], and improving the visiting experiences inside museums [9]. RFID and the global positioning system (GPS) are major technologies that have been frequently employed in the paradigm of IoT to ensure the surveillance, reporting, and traceability of products. RFID technology is identified as an innovative short-range wireless communication system with high speed and limited capacity that is known for its adaptability, comfort, security, and ease of use. As well, it is used in several areas such as the product identification field.

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RFID-NFC technology operates in three modes: reader/writer, card emulation, and peer-to-peer modes as detailed in the paper [10]. The card emulation mode works like a smart card, it allows users to perform a variety of transactions like purchasing, controlling access, and getting tickets. For the reader/writer mode, it allows the NFC devices to read and retrieve data from tags linked in posters or smart screens for future use. And the third peer-to-peer mode provides connectivity between two NFC-enabled devices for data exchange and file sharing. In the proposed porotype we will use NTAG213 NFC sensors and contactless smart card reader/writer of type ACR122U NFC as shown in Fig. 1. This type of chip has a read-write lock function that can be repeatedly edited or read-only which ensures quick access to a specific object’s digital data and the interactivity between the system components.

Fig. 1. Equipment used in the system

2.2 Product Identification Product identification is defined as a set of labeling categories that includes a range of services such as product traceability and brand protection. By these later, you can identify and track a product during its lifecycle to guarantee authenticity value. In recent years, several significant contributions have been made to deal with product identification in several areas. Among them, we mention the reference [11] that focuses on the libraries and museums fields, the QR codes have been used to access product information, to help shoppers in their purchases, and accomplish their organizational objectives. Also in the same domain, the paper [12] presents a potential strategy for securing art items in museums using the internet of things and NFC-enabled detectors, this paper aims to ensure the long-term conservation of the art items’ exposure to environmental circumstances via an online record. In the field of food production, the paper [13] presents research on confirming the ‘Halal certification’ of products in Malaysia, employing RFID technology as an effective way to identify and validate quality in place of SMS and barcode technologies. Also, RFID wireless product identification technology is adopted in the food supply chain management as detailed in [14]. Thus, giving the possibility to trace and control products from manufacturers to distributors in real-time.

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Generally, product identification is ensured by different techniques depending on consumer needs, budget, and technical constraints. These techniques are classified into three main categories: Barcode, QR code, and RFID technology, each one is used in its area of expertise to meet the individual demands of consumers. More details are provided in the following paragraphs: Barcode is a representation of numeric or alphanumeric data in the form of a symbol made of bars and spaces. It was developed in 1994 to allow faster reading of complexstructured 2D barcodes to control the stocks, scan the process, and export data to the database. This technique has been used for its advantages in terms of cost, and versatility. But the drawback of barcodes lies in the simplicity to reproduce them, therefore they can be easily imitated by a copier. Regarding the QR code technique, it is identified as an ISO standard that can contain information in both dimensions vertical and horizontal as detailed in the paper [15], it can be encoded on several types of data, it can be also read and understood by mobile devices with the ability to send messages externally. However, the communication in this technique is unidirectional between the QR code and the reader which makes the communication passive. The RFID-NFC technology is used to ensure wireless identification by using an RFID tag attached to the object, and an RFID reader that reads the RFID tags. It is a practical technique widely adopted in the market due to its simple implementation and its low cost. In addition, it guarantees the interactivity between the sensor and the user in the case of active sensors. The possibility to combine the RFID-NFC technology with smartphones constitutes another major aspect that contributes to the growth of its applicability as detailed in the paper [16]. Also, the RFID-NFC approach was compared with other solutions such as barcodes in the papers [17, 18]. It has been observed that the NFC provides various advantages, such as distinctive item identification, increased data storage capacity, and a high capacity for reading/writing data from tags. For the aforementioned reasons, we have chosen this technique in our system to identify handicrafts products. 2.3 Labeling Strategy Approach In parallel, labeling strategy becomes an attractive alternative to prove products authenticity. In this context, especially in the field of handicrafts, the Moroccan ministry of tourism and handicrafts proposed a series of standard labels under the subject of “Moroccan Handmade” [19], to help purchasers in verifying the authenticity of their purchases. These labels serve as an official certification that the handcrafted products conform with a set of requirements defined by a usage guideline that ensures a certain standard of quality. Figure 2 presents the labeling strategy, which was focused on five categories of labels: ‘Premium Quality label’, ‘Certified Quality label’, ‘Madmoun label’, ‘Responsible Craftsman label’, and ‘Regional Product label’. The process of attending one of these labels is based on three levels of commitments, each level is determined by different requirements, that are added to encourage various actors of the craft sector to progress through a valorization of their handmade products. Since the launch of the labeling strategy by the ministry of tourism and handicrafts in 2013, 566 production units in 12 regions of Morocco were branded according to the department of Craft [20].

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Fig. 2. Labeling strategy for Moroccan handicrafts

When the manufacturing unit obtains one of these labels, they are permitted to make the brand mark shown in Fig. 2 on all their own produced items to provide originality.

3 System Design 3.1 System Components To verify the credibility and the identity of produced handmade items that comply with the standard labeling requirements, we present an IoT-based system that uses the national label of handicraft in digital format to ensure the originality and the traceability of items as shown in Fig. 3. In this subsection, the three layers that form our system will be discussed. The physical layer focuses on the acquisition and gathering of data from a variety of sources such as sensors fixed on the products and from the product lifecycle using RFID technology. Once the data has been collected, it will be transferred to the cloud layer for further analysis and processing. The cloud server layer is used to examine the data flow received from the physical and application layers via real-time processing to collect useful information that will be used by different system actors. The application layer allows the consumers to exchange with the different layers of the system and obtain available information and access to the different proposed services about handicraft products. It gives them also the possibility to verify the authenticity of items via a mobile device equipped with an RFID reader. Overall, the system offers various services, including brand protection through digital verification, supply chain control via digital tracking and authentication features, and the ability to create a customer relationship based on personal interactions and exclusive experiences. This set of services is provided through a sequence of activities and interactions that occur during system operations, such as:

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– The interaction between users and sensors, and data processing and analysis. – Real-time data gathering from tags and products lifecycle. – Monitoring track of products during signs of progress from manufacturing to consumption.

Physical layer

Cloud server layer

Applicaon layer

End User

Fig. 3. System overview

3.2 Tracking Handicraft Product The goal of traceability is to improve product security by optimizing the planning process, distribution, logistics systems, and procedures [21]. Generally, traceability is maintained throughout the lifecycle of products to guarantee that it progresses through all stages. Figure 4 illustrates how traceability is ensured in the craft industry at all production stages, from manufacturing to consumption. This allows us to have all handicraft items’ details and make sure that they are authentic. 3.3 Prototype Implementation To implement the proposed system, we started with the development of a prototype, which consists of a mobile application that allows users to view all details about products and check their identification using RFID technology. The application collects data from the tags that are attached to the products, and then sends a request to the server to retrieve information about the products, the production unit, their status, and other information. In our case, we used active and passive sensors to offer interactivity between all system components.

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Fig. 4. The lifecycle of a handmade product from manufacturing to customer

4 Conclusion This paper has proposed a simple prototype of a system that guarantees the identification and authenticity of products in the areas of an artisanal ecosystem by using the internet of things technology (IoT). This later has become an established part of life and has allowed us to introduce the usage of different labels of Handicraft proposed by the minister of tourism in digital format. The next investigation will be concentrated primarily on data analysis retrieved from interactions between system layers to build a credible identity and a high level of authenticity. Moreover, we aim to develop an appropriate method to prevent our tags from eventual attacks. Acknowledgment. This work was supported by the National Center for Scientific and Technical Research of Morocco identified by the following number: Alkhawarizmi/2020/28.

References 1. El Amrani, I., Saka, A., Matta, N., Chahdi, T.O.: A methodology for building knowledge memory within the handicraft sector. Int. J. Knowl. Manage. 15(3), 45–65 (2019). https://doi. org/10.4018/IJKM.2019070103 2. Moroccan Ministry of Tourism and Handicrafts: Ministry of Tourism and Handicrafts 2022 (2022). https://mtataes.gov.ma/fr/artisanat/. Accessed 08 Jan 2022 3. Bellamine, W., Afiri, M.: The Impact of Globalization on Small Scale Artisans in Azrou, Morocco. Submitted to Faculty of Worcester Polytechnic Institute (2007) 4. Chouiraf, F., Chafi, A.: Lean manufacturing for handicraft production. In: 2018 4th International Conference on Optimization and Applications (ICOA) (2018) 5. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010). https://doi.org/10.1016/j.comnet.2010.05.010

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6. Kaba, A., Ramaiah, C.K.: The internet of things: opportunities and challenges for libraries. Libraries at University of Nebraska-Lincoln (February 2020) 7. Giuliano, R., Mazzenga, F., Petracca, M., Vatalaro, F.: Application of radio frequency identification for museum environment. In: Proceedings of the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2013, June 2016, pp. 190–195 (2016). https://doi.org/10.1109/WETICE.2013.86 8. Mostarda, L., Dong, C., Dulay, N.: Place and time authentication of cultural assets. In: Karabulut, Y., Mitchell, J., Herrmann, P., Jensen, C.D. (eds.) IFIPTM 2008. ITIFIP, vol. 263, pp. 279–294. Springer, Boston, MA (2008). https://doi.org/10.1007/978-0-387-094281_18 9. Ghiani, G., Paternò, F., Santoro, C., Spano, L.D.: UbiCicero: a location-aware, multi-device museum guide. Interact. Comput. 21, 288–303 (2009). https://doi.org/10.1016/j.intcom.2009. 06.001 10. Vagdevi, P., Nagaraj, D., Prasad, G.V.: Home: IOT based home automation using NFC. In: Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017, pp. 861–865 (2017). https://doi.org/10.1109/I-SMAC.2017.8058301 11. Schultz, M.K.: A case study on the appropriateness of using quick response (QR) codes in libraries and museums. Libr. Inf. Sci. Res. 35, 207–215 (2013). https://doi.org/10.1016/j.lisr. 2013.03.002 12. Steinberg, M.D., Kimbriel, C.S., d’Hont, L.S.: Autonomous near-field communication (NFC) sensors for long-term preventive care of fine art objects. Sens. Actuators A Phys. 285, 456–467 (2019). https://doi.org/10.1016/j.sna.2018.11.045 13. Khosravi, M., Karbasi, M., Shah, A., Brohi, I.A., Ali, N.I.: An adoption of halal food recognition system using mobile radio frequency ıdentification (RFID) and near field communication (NFC). In: Proceedings of the 6th International Conference on Information and Communication Technology for the Muslim World, ICT4M 2016, November, pp. 70–75 (2017). https:// doi.org/10.1109/ICT4M.2016.74 14. Li, D., Kehoe, D., Drake, P.: Dynamic planning with a wireless product identification technology in food supply chains. Int. J. Adv. Manuf. Technol. 30(9–10), 938–944 (2006). https:// doi.org/10.1007/s00170-005-0066-1 15. Várallyai, L.: From barcode to QR code applications. J. Agric. Inf. 3(2), 9–17 (2013). https:// doi.org/10.17700/jai.2012.3.2.92 16. Benyó, B., Vilmos, A., Fördos, G., Sódor, B., Kovács, L.: The StoLPan view of the NFC ecosystem. In: 2009 Wireless Telecommunications Symposium, WTS 2009, May (2009). https://doi.org/10.1109/WTS.2009.5068969 17. Asif, Z., Mandviwalla, M.: Integrating the supply chain with RFID: a technical and business analysis. Commun. Assoc. Inf. Syst. 15, 393–426 (2005). https://doi.org/10.17705/1cais. 01524 18. Wamba, S.F.: Achieving supply chain integration using RFID technology: the case of emerging intelligent B-to-B e-commerce processes in a living laboratory. Bus. Process Manage. J. 18(1), 58–81 (2012). https://doi.org/10.1108/14637151211215019 19. Health Ministry: Labeling and certification 2021 (2021). https://mtataes.gov.ma/fr/artisanat/ qualite-et-innovation/labellisation/. Accessed 24 Nov 2021 20. Ministry of Handicraft: The national artisanal label of Morocco. National artisanal label of Morocco (2022). https://label.artisanat.gov.ma/consomateur?lng=fr. Accessed 22 Nov 2021 21. Musa, A., Gunasekaran, A., Yusuf, Y.: Supply chain product visibility: methods, systems and impacts. Exp. Syst. Appl. 41(1), 176–194 (2014). https://doi.org/10.1016/j.eswa.2013.07.020

A New Dual Band Antenna with Improvement Performances for the Internet of Things Applications Youssef Mouzouna1(B) , Hanane Nasraoui2 , Jamal El Aoufi2 , and Ahmed Mouhsen3 1 Laboratory LRI, National High School for Electricity and Mechanics ENSEM,

Hassan 2 University, Casablanca, Morocco [email protected] 2 Laboratory of Aeronautical Telecommunication, Mohammed VI, International Academy of Civil Aviation, Casablanca, Morocco 3 Laboratory IMII, Faculty of Science and Technical Hassan 1 University Settat, Settat, Morocco

Abstract. In the Internet of Things (IoT) age, thousands of objects will communicate with each other at the same time using multiple frequency bands. In this context, the demand for efficient antennas will increase, as they are the main part of these communication systems. These antennas must be small and operate in several frequency bands to be used in numerous applications at the same time. In this work, simple planar resonators and defected ground structure are proposed for designing a new miniature antenna for Internet of Things applications, this antenna functions two operating bands of 2.45 GHz for WLAN (Wi-Fi, Bluetooth, ZigBee, Z-wave)/IIoT/WBAN, MBAN and 5.8 GHz for WiFi. In the first section, this antenna is studied, designed, optimized, and simulated on FR-4 Substrate characterized with a dielectric thickness of 1.6 mm and a relative permittivity of 4.4; In terms of matching, bandwidth, radiation pattern, and gain, better simulation results are obtained. The CST microwave studio software is used to generate all simulation results. In the second section, the proposed antenna is fabricated on FR4 substrate commercial, measured, and compared with simulation results for both bands, a good matching is found for these bands. In the last section, the proposed antenna is compared with those published in previous works. As a result of this comparison, this antenna provides a new high level of performance in terms of matching, bandwidth, gain, radiation pattern, cost, and size. Keywords: Internet of Things · RFID · Antenna · Gain · Return loss

1 Introduction According to IEEE, the Internet of Things (IoT) is a framework in which all things on the Internet have a representation and a presence. More precisely, the Internet of Things promises to provide new applications and services. bridging the physical and virtual worlds, where Machine-to-Machine (M2M) communication serves as the foundation for interactions between Things and Cloud applications [1]. An IoT system’s function © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 285–294, 2022. https://doi.org/10.1007/978-3-031-02447-4_30

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is to monitor its surroundings, to enable and assist, or to automate a response to changes in the system’s environment [2]. It helps humans to efficiently solve a variety of modern society’s difficulties by gathering and analyzing extensive information about events and environments with the help of billions of connected devices, making human existence safer, healthier, more productive, and more comfortable. Simultaneously, it would open up significant business potential for a variety of vertical industries and social sectors [3]. It can be used in a wide range of industries, including smart homes and cities (e.g. security, heating and lighting control, waste management system) as well as healthcare, agriculture and manufacturing [4]. IoT is therefore seen as one of the main technologies for improving global living standards, economic growth, and job availability [5]. To achieve good communication, IoT antennas must be small, cost-effective, and energy efficient in order to operate in the various bands for WLAN (IEEE 802.11 a/b/g/n), GSM (800 MHz, 850 MHz, and 1.9 GHz), Zigbee (IEEE 802.15.4), WiMAX (IEEE 802.16), and so on [6]. Table 1 shows a variety of communication technologies and frequency bands that have been identified for IoT applications [7, 8]. Table1. Technologies communication and their frequency bands used in IoT Technology

Frequency

IoT Application

Bluetooth

2.4 GHz

ZigBee

915 MHz, 2.4 GHz

Usage General (Smart Home, Smart Building, WSN….)

Z-wave

2.4 GHz

Wi-Fi

2.4 GHz, 5 GHz and 5.8 GHz

Wireless HART

2.4 GHz

ISA 100.11a

2.4 GHz

MBAN

2360–2400 MHz

WBAN

2.4 GHz

WAIC

4200–4400 MHz

Industrial IoT Medical Avionics

As a result, small antennas that can operate in several bands and over a wide range of frequencies are critical for future IoT wireless communication systems. In addition, Planar Monopole antennas are widely used in the wireless mobile communication. Their expanding use in mobile communication systems has inspired popular research effort in dual-frequency band antennas due to their simple fabrication technique, low cost, and acceptable radiation properties [9]. Many antennas for the Internet of Things application are proposed in the literature, and some of them are presented here, in [10] for IoT wireless communications applications, a quad-band printed antenna is proposed, in [11] Bashir et al. have developed a new multiband LP-RFID reader antenna based on a new topology. A Bandpass Filtering Antenna with reconfigurable design wad devlopped for IoT Applications by Vamsee K. Allam in [12], in [7] a new design of a reconfigurable antenna operating in WLAN, C-Band,Wi-Max, bands for IoT applications and is designed. In [13] A new design of a dual ultra wideband CPW-Fed Printed Antenna

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was achieved for the Internet of Things, in [14] Yifan Mao and all built a tiny dual-band monopole antenna with a defected ground plane for the Internet of Things, H. Raad created for a Flexible IoT Wireless System an array of UWB Antenna in [15]. In [16] IoT and ISM Band Applications miniature antenna are being developed using patch and microstrip arrays, in [17] a Wireless Antenna application using Reconfigurable Inkjet Printing on Paper Substrate, in [18] for 2.4 GHz ISM-Band Internet of Things (IoT) Applications an enhanced bandwidth antenna was created with meander line. The goal of this work is to design, analyze and fabricate the proposed microstrip resonators integrated into the patch antenna and simple shape of DGS integrated inground plane to make this antenna covering the two bands frequencies for Internet of Things applications. This antenna operates at 2.45 GHz for WLAN (Bluetooth ZigBee, Z-wave, Wi-Fi)/RFID/IIoT/MBAN, WABN for medical Internet Of Things applications, and operates at 5.8 GHZ for Wi-Fi in the unlicensed 5 GHz bands. This work covers firstly the study of microstrip monopole patch antenna based on fundamentals of the cylindrical monopole antenna. Secondly, the parametric study includes the investigation of various techniques for optimizing the various antenna parameters in order to achieve the best results and performance. This antenna is designed and simulated using CST Microwave Studio simulation software. The proposed antenna is fabricated and measured in the final section to validate the design study in the simulation.

2 Study, Simulation and Design of the Antenna The antenna proposed in this paper as shown in Fig. 1 is developed on a thick FR4 substrate with a thickness of 1.6 mm with a single simple resonator placed on the substrate and a rectangular-shaped structure is introduced in the ground plane. The design of this antenna is based on cylindrical monopole antenna basics where the resonance frequency that is determined in function of the Lc, Rc, and p parameters expressed of the single resonator and structure of the round plan dimensions is expressed in Eq. (1) [9]. The Lc and Rc are expressed in Eqs. (2) and (3), present respectively high and radius equivalent of the cylindrical monopole antenna. The proposed antenna is optimized and simulated using a CST simulator for obtaining excellent performances for both the 2.45 GHz and 5.8 GHz commercial bands. The proposed antenna is fed by a standard 50  microstrip feed line, and various parameters with optimized values are listed in Table 2. Figure 2(a) and 2(b) show the reflection coefficient simulation results for the optimized parameters m and f, respectively, while Fig. 3 illustrates the final result of the reflection coefficient of the proposed antenna. The Figs. 4 and 5 show 2D radiation pattern in E and H planes at 2.45 GHz and 5.8 GHz, respectively, while Fig. 6 illustrate the gain in both frequency bands. 7, 2 Lc + Rc + P

(1)

Lc = a + c + f + 2e

(2)

Freq =

Rc =

e(a + c + f + m + 4e) 2.π(a + c + f + 2e)

(3)

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P = (d − e − Wg)

(4)

Fig. 1. Illustration of the design of the proposed dual-band antenna, (a) patch and (b) ground plane

Table 2. Parameters values of the designed antenna Parameter

L

W

Wg

a

b

Value (mm)

40

30

14

17.32

13

Parameter

C

d

e

f

m

Value(mm)

5.16

21.16

3.16

4.13

1

Figure 2(a) and 2(b) presents the simulated results of the reflection coefficient by modifying the m and f parameters, respectively. After Fig. 2(a), the s11 vs frequency curve goes towards a higher frequency of the band frequency of 5.8 GHz as the value of m is increased, while the in-band frequency of 2.45 GHz, it does not affect. After Fig. 2(b), it can be seen that the f parameter affects both band frequencies, when we

Fig. 2. Simulated results of the reflection coefficient, (a) based on the optimization of the m parameter, and (b) based on the optimization of the f parameter.

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increase the value of the f the S11 shifts towards a lower frequency. Although the m and f parameters have optimized for many values, the two resonance frequencies of the IoT and RFID systems have not exactly obtained at their values (2.45 and 5.8 GHz). Therefore, we can conclude that the value of m and f parameters that are optimum and fixed for another parameter optimization are m = 1 and f = 4.6. After optimization of the two parameters (m and f) of the resonator element and fixing of their optimum values, the third parameter called b modified to have a reflection coefficient with strong matching at the two resonance frequencies of the IoT and RFID bands. After changing the b parameter for various values, a partial ground plane with a rectangular edge is employed to boost the antenna’s bandwidth. Figure 3 illustrate the final result of the reflection coefficient of the proposed antenna, where good matching has been achieved with −22.32 dB at 2.45 GHz, and −21,181 dB at 5.8 GHz. Besides, the bandwidth is 510 MHz for the band frequencies of the center frequency 2.45 GHz, and 795 MHz for the band frequencies of the center frequency 5.8 GHz.

Fig. 3. Simulation results of the reflection coefficient at 2.45 GHz and 5.8 GHz

Figures 4 and 5 show the proposed antenna’s 2D radiation pattern in the E-plane and H-plane at 2.45 GHz and 5.8 GHz, respectively as can be observed the antenna has bi-directional patterns and an opening angle of 88° and 51° at both frequencies 2.45 GHz for 5.8 GHz respectively.

Fig. 4. The antenna’s simulated radiation pattern at 2.45 GHz in the (a) H-plane and (b) E-plane

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Fig. 5. The antenna’s simulated radiation pattern at 5.8 GHz in the (a) H-plane and (b) E-plane

Figure 6 shows the simulated gain for both bands’ frequencies. After this figure, it is well observed that the gain value is over 2 dBi for both bands with 2.14 dBi at 2.45 GHz, and 4.5 dBi at 5.8 GHz.

Fig. 6. Simulated results of the gain for the two bands operating.

3 Fabrication of the Proposed Antenna and Measurement Results The proposed antenna, which was designed, optimized, and simulated, was manufactured on a commercial FR4 substrate with a high dielectric constant of 1.6 mm and a permittivity dielectric constant of 4.4, and validated using a Vectorial Network Analyzer (VNA). The prototype of the constructed antenna is shown in Fig. 7, and the operation of measuring the reflection coefficient using the KEYSIGHT Network Analyzer is shown in Fig. 8.

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Fig. 7. Photograph of the antenna that was fabricated

Fig. 8. Photograph of the proposed antenna captured during the reflection coefficient measurement using the KEYSIGHT Network Analyzer

The measure of the reflection coefficient of the antenna that was manufactured is depicted in Fig. 9. As is shown, that it is less than −15 dB at 2.45 GHz, and −25 dB at 5.8 GHz. The fabrication tolerance is responsible for the minor frequency shift between the measured and simulated results.

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Fig. 9. Comparison between the measured reflection coefficient and the simulated one.

The proposed antenna in this paper is compared to the studies indicated in Table 3 that have recently been published. In terms of matching, radiation pattern, gain, number of bandwidths, and size, we can conclude that the suggested antenna provides excellent results. Table 3. The proposed antenna and other research papers comparison Reference

Dimension (mm2 )

Frequency bands (GHz)

S11 (dB)

Gain (dB)

F1

F2

F1

F2

F1

F2

[15]

71 * 94

2.45

5.8

−11

−18

2.2

2.31

[16]

75 * 40

2.45

5.8

−16

−14

6.3

5.16

[17]

51 * 43

2.45

5.8

−25

−12

2.4

4.1

[18]

38 * 35

2.45

5.8

−16

−29

1.5

2.4

Proposed antenna

40 * 30

2.45

5.8

−24

−33

2.14

4.5

4 Conclusions Recent breakthroughs in Internet of Things have enabled the growth and the demand for efficient antennas. Building on this further, the aim of the current research was to propose the new compact dual-band antenna based on cylindrical monopole antenna basics and defected ground structure. In the present work, the antenna is optimized, simulated, and fabricated on a thick FR4 substrate, and measured using Vectorial Network Analyzer. The obtained results in terms of the matching, pattern radiation, and gain are appropriate to fulfill the requirement of RFID and Internet of Things applications where 280 MHz

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of bandwidth at 2.45 GHz and 515 MHz at 5.8 GHz. Besides, this antenna can play an important role in a performance perspective as simple structure, low cost, and small size in which this antenna will be easy integrated into modern devices. Those characteristics could be one of the prime concerns of the Internet of Things.

References 1. Iqbal, M.A., Hussain, S., Xing, H., Imran, M.A.: Internet of Things: Fundamentals, Design, and Applications. IEEE Press, Wiley (2021) 2. Ande, R., Adebisi, B., Hammoudeh, M., Saleem, J.: Internet of things: evolution and technologies from a security perspective. Sustain. Cities Soc. 54, 101728 (2019). https://doi.org/ 10.1016/j.scs.2019.101728 3. Kafle, V.P., Fukushima, Y., Harai, H.: Internet of things standardization in ITU and prospective networking technologies. IEEE Commun. Mag. 54(9), 43–49 (2016). https://doi.org/10.1109/ MCOM.2016.7565271 4. Shin, S., Kwon, T.: Two-factor authenticated key agreement supporting unlinkability in 5Gintegrated wireless sensor networks. IEEE Access 6, 11229–11241 (2018) 5. Vukobratovic, D., et al.: CONDENSE: a reconfigurable knowledge acquisition architecture for future 5G IoT. IEEE Access 4, 3360–3378 (2016) 6. Awais, Q., Chattha, H.T., Jamil, M., Jin, Y., Tahir, F.A., Rehman, M.: A novel dual ultrawideband CPW-Fed printed antenna for Internet of Things (IoT) applications. Wirel. Commun. Mob. Comput. 2018, 2179571 (2018). https://doi.org/10.1155/2018/2179571 7. Singh, P.P., Goswami, P.K., Sharma, S.K., Goswami, G.: Frequency reconfigurable multiband antenna for IoT applications in WLAN, Wi-Max, and C-Band. Progr. Electrom. Res. 102, 149–162 (2020) 8. Allam, V.K., Madhav, B.T.P., Anilkumar, T., Maloji, S.: A novel reconfigurable band-pass filtering antenna for IoT communication applications. Progr. Electrom. Res. C 96, 13–26 (2019) 9. Pandey, A., Mishra, R.: Compact dual band monopole antenna for RFID and WLAN applications. Mater. Today: Proc. 5, 403–407 (2018) 10. Mohamed, H.A., Sultan, K.S.: Quad band monopole antenna for IoT applications. In: International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting IEEE (2018) 11. Bashir, U., Jha, K.R., Mishra, G., Singh, G., Sharma, S.K.: Octahedron-shaped linearly polarized antenna for multi standar services including RFID and IOT. IEEE Trans. Antennas Propag. 65(7), 3364–3373 (2017) 12. Allam, V.K., Madhav, B.T.P., Anilkumar, T., Maloji, S.: A novel reconfigurable bandpass filtering antenna for IoT communication applications. Progr. Electrom. Res. C 96, 13–26 (2019) 13. Awais, Q., Chattha, H.T., Jamil, M., Jin, Y., Tahir, F.A., Rehman, M.U.: A novel dual ultrawideband CPW-Fed printed antenna for Internet of Things (IoT) applications. Wirel. Commun. Mobile Comput. 2018, 1–9 (2018) 14. Mao, Y., Guo, S., Chen, M.: Compact dual-band monopole antenna with defected ground plane for Internet of things. IET Microw. Anten. Propagat. 12(8), 1332–1338 (2018) 15. Raad, H.: An UWB antenna array for flexible IoT wireless systems. Progr. Electrom. Res. 162, 109–121 (2018) 16. Olan-Nuñez, K.N., Murphy-Arteaga, R.S., Colín-Beltrán, E.: Miniature patch and slot microstrip arrays for IoT and ISM band applications. IEEE Access 8, 102846–102854 (2020). https://doi.org/10.1109/ACCESS.2020.2998739

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17. Abutarboush, H.F., Shamim, A.: A reconfigurable inkjet-printed antenna on paper substrate for wireless applications. IEEE Antennas Wirel. Propag. Lett. 17(9), 1648–1651 (2018). https://doi.org/10.1109/LAWP.2018.2861386 18. Shahidul Islam, M., Islam, M.T., Ullah, M.A., Kok Beng, G., Amin, N., Misran, N.: A modified meander line microstrip patch antenna with enhanced bandwidth for 2.4 GHz ISM-band Internet of Things (IoT) applications. IEEE Access 7, 127850–127861 (2019). https://doi. org/10.1109/ACCESS.2019.2940049

Target Classification Algorithm Based on Characteristics of UWB Signals Dounia Daghouj1(B) , Mohammed Fattah2 , M. Abdellaoui1 , S. Mazer1 , Y. Balboul1 , and M. El Bekkali1 1 Sidi Mohamed Ben Abdellah University, Fez, Morocco

[email protected] 2 Moulay Ismail University, Meknes, Morocco

Abstract. Road traffic involves many different objects such as trees, pedestrians, cyclists, vehicles which complicates the signal processing phase and requires a complex algorithm to classify the obstacles. The objective of this work is to develop an efficient and simple algorithm to classify targets present in the urban environment at low cost using the characteristics of the received pulses and based on the correlation properties while respecting the real-time constraint. This paper presents a classification algorithm for a UWB radar system allowing the identification of target types detected in a road environment based on the correlation of the received signal with signals stored in a database. Keywords: Classification algorithm · Obstacle · UWB · Radar · Pulse

1 Introduction Today’s cars have gotten very smart, and drivers have received driving assistance. This assistance is based on developing systems capable of providing useful information in real-time to help drivers react in time to avoid road accidents. Despite technological advances, millions of people have died, and more than 50 million are injured on the road worldwide each year. Most of these road fatalities are user-related. As a result, vehicle manufacturers are increasingly demanding intelligent transportation systems (ITS) [1, 2] to reduce traffic accidents. With the considerable improvement of computer systems and telecommunication technologies, vehicles can communicate with each other and the transportation infrastructure to save lives and avoid collisions in the road environment [3]. Concerning road safety improvement, this research work focuses on presenting a classification algorithm of road obstacles allowing the development of an accident prevention system in urban areas.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 295–302, 2022. https://doi.org/10.1007/978-3-031-02447-4_31

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Road traffic involves many different objects [4], such as trees, street furniture, pedestrians, cyclists, and vehicles. Several works have been carried out to classify these obstacles to develop radar systems capable of detecting and classifying the obstacles encountered in an urban environment. The authors of [5] present a work in which data from vision and radar sensors are used to classify objects in the field of view of the vehicle, and the radar sensor performs the relative detection distance. In [6], the authors propose a low-cost, real-time approach to identify road obstacles using large-scale vehicle trajectory data and heterogeneous detection data from the road environment. the authors of [7] propose a classification method for automotive applications using deep learning with radar reflection. In [8], the authors developed a classification algorithm based on the extraction of distributional features and a multivariate Gaussian distribution model (MGD). In [9], a new deep learning-based sensor fusion framework, called “RVNet,” is proposed to efficiently combine monocular cameras and long-range radar for obstacle detection and classification. Most of the works dealing with this problem are based on the fusion of radar with another type of sensor, or they propose complex algorithms. This research work presents an algorithm for the classification of road obstacles simply and efficiently, based on the principle of correlation of the received signal with a reference signal called “Template” [10] to help the driver make the best decision and make the right reaction in time. In the first part of this paper, a road obstacle classification algorithm for the UWB radar is presented, and then a classification approach using real data from different driving scenarios (wooden pallet, car, metal plate, and highway barrier) is discussed.

2 Obstacle Classification Algorithm Urban areas have many different obstacles such as trees, street furniture, pedestrians, cyclists, and vehicles. Several studies have been conducted to develop radar systems capable of identifying and classifying the types of obstacles encountered in an urban environment [11–13]. However, most of these systems consist of a radar combined with another perception sensor such as a laser rangefinder camera, lidars [14, 15], which increases their complexity and cost. In this section, an obstacle classification algorithm is presented. This algorithm is dedicated to automatically identifying road obstacles in, a reliable and inexpensive way. It is based on the principle of correlation of the received signal with a database containing all the signatures of the obstacles present in a road scene [16] to help the driver make the best decision to react in time. The following figure (Fig. 1) shows that the proposed target classification algorithm generates a 0.2 ns wide unicycle pulse at transmission. After the phase of coherent reception of the captured signal, this signal is correlated with the signatures of the different obstacles recorded in the database to identify the type of the detected target [17, 18].

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Fig. 1. Obstacle classification algorithm

Classification is an important phase for driver assistance systems in urban environments. It allows identifying the types of detected obstacles to classifying them according to their degree of danger. To automatically classify the targets detected in a vehicular environment using a short-range UWB radar [19], the principle of correlating the received signal with the signals reflected by the different obstacles in an urban environment and recorded in the database remains the best solution in terms of simplicity, accuracy, and cost.

3 Results and Discussion To facilitate the ranking and have reliable and consistent results, a database is generated to record a group of obstacles to be used for the final ranking. In this work, the signals used are the models of the most common obstacles that can be seen on the road, and the database is generated from their signature data. The signatures used are samples from the University of Valenciennes and HainautCambrésis (UVHC) [20], and they are signals reflected from a car, a metal plate, a highway guardrail, and a wooden plate. Figures 2, 3, 4, and 5 present the signatures of the different obstacles exploited in this work.

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Fig. 2. Signature of a car

Fig. 3. Signature of a wooden plate

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Fig. 4. Signature of a metal plate

Fig. 5. Signature of a highway guardrail

As shown previously, the classification algorithm is based on the correlation between the signals reflected by the target detected in the radar space and the signatures recorded in our database to identify their natures. The following figures (Figs. 6 and 7) show the correlation between the signal received and the different signatures of the database. Case 1: The signal received is a signature from a car

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Fig. 6. Autocorrelation of a car and correlation of a car and a wooden plate

Case 2: The signal received is a signature of a metal plate

Fig. 7. Autocorrelation of a metal plate and correlation between a metal plate and a highway guardrail.

The simulation results (Figs. 6 and 7) illustrate that the maximum correlation peaks show the degree of resemblance between the received signal and the signals in the database. This algorithm being addressed to the urban domain, its capacity is tested by exploiting real signatures such as a vehicle, a metallic plate, a highway guardrail, and a wooden plate. According to the different simulations performed, it turns out that this short-range UWB radar system using the classification algorithm based on the correlation of the signals captured in the urban environment with the signatures recorded in the database gives

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better results translated by the correlation peak in each classification case. It allows identifying the detected obstacle types with high accuracy even in bad weather conditions. This algorithm has been tested for the same kind of targets with different target-radar distances, and it has proven its classification ability even for the same obstacle [21, 22], which makes it more reliable and effective for the critical environment such as road environments. In addition, the applied automation function can announce the result with great speed, which helps the driver react in time. This algorithm is adopted for the classification of obstacles present in its database. Since the road environment is very rich in obstacles, it does not seem easy to apply this solution when the signature of a target does not exist in the database.

4 Conclusion In this paper, the problem of obstacle classification has been addressed by proposing an obstacle classification algorithm in the road environment. The algorithm is based on the correlation of the received signal with signatures already stored in the database to determine the nature of the detected signal in a dynamic environment by analyzing the maximum peak of correlation translating the similarity between the correlated signals. The validity of this method is tested using signatures generated during real scenarios such as a car, a metal plate, a highway barrier, and a wooden plate. The simulation results show the ability of this algorithm to identify obstacles in real-time with better accuracy and reliability.

References 1. Zichichi, M., Ferretti, S., D’angelo, G.: A framework based on distributed ledger technologies for data management and services in intelligent transportation systems. IEEE Access 8, 100384–100402 (2020). https://doi.org/10.1109/ACCESS.2020.2998012 2. Adu-Gyamfi, Y.O., Asare, S.K., Sharma, A., Titus, T.: Automated vehicle recognition with deep convolutional neural networks. Transp. Res. Rec. 2645, 113–122 (2017) 3. Fattah, M., et al.: Multi band OFDM alliance power line communication system. Procedia Comput. Sci. 151, 1034–1039 (2019) 4. Hosaka, A., Mizutani, H.: Improvement of traffic safety by road-vehicle cooperative smart cruise systems. IATSS Res. 24(2), 34–42 (2000) 5. Jha, H., Lodhi, V., Chakravarty, D.: Object detection and identification using vision and radar data fusion system for ground-based navigation. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 590–593 (2019). https://doi.org/10. 1109/SPIN.2019.8711717 6. Chen, L., et al.: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 4 (2017) 7. Ulrich, M., Gläser, C., Timm, F.: DeepReflecs: deep learning for automotive object classification with radar reflections. In: 2021 IEEE Radar Conference (RadarConf21), pp. 1–6 (2021). https://doi.org/10.1109/RadarConf2147009.2021.9455334 8. Xiao, Y., Daniel, L., Gashinova, M.: Feature-based classification for image segmentation in automotive radar based on statistical distribution analysis. In: 2020 IEEE Radar Conference (RadarConf20), pp. 1–6 (2020). https://doi.org/10.1109/RadarConf2043947.2020.9266596

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9. John, V., Mita, S.: RVNet: deep sensor fusion of monocular camera and radar for image-based obstacle detection in challenging environments. In: Lee, C., Su, Z., Sugimoto, A. (eds.) PSIVT 2019. LNCS, vol. 11854, pp. 351–364. Springer, Cham (2019). https://doi.org/10.1007/9783-030-34879-3_27 10. Daghouj, D., Fattah, M., Mazer, S., Balboul, Y., El Bekkali, M.: UWB waveform for automotive short range radar. Int. J. Eng. Appl. 8(4), 158 (2020). https://doi.org/10.15866/irea.v8i4. 18997 11. Daghouj, D., Mazer, S., et al.: Modeling of an obstacle detection chain in a vehicular environment. In: 2019 7th Mediterranean Congress of Telecommunications (CMT), Fès, pp. 1–4 (2019). https://doi.org/10.1109/CMT.2019.89313808 12. Song, W., Yang, Y., Fu, M., Qiu, F., Wang, M.: Real-time obstacles detection and status classification for collision warning in a vehicle active safety system. IEEE Trans. Intell. Transp. Syst. 19(3), 758–773 (2018). https://doi.org/10.1109/TITS.2017.2700628 13. Bers, K., Schulz, K.R., Armbruster, W.: Laser radar system for obstacle avoidance. In: Proceedings of the SPIE 5958, Lasers and Applications, 59581J, 11 October 2005. https://doi. org/10.1117/12.626082 14. Bertozzi, M., Bombini, L., Cerri, P., Medici, P., Antonello, P.C., Miglietta, M.: Obstacle detection and classification fusing radar and vision. IEEE Intell. Veh. Symp. 2008, 608–613 (2008). https://doi.org/10.1109/IVS.2008.4621304 15. Wang, Z., Miao, X., Huang, Z., Luo, H.: Research of target detection and classification techniques using millimeter-wave radar and vision sensors. Remote Sens. 13, 1064 (2021). https://doi.org/10.3390/rs13061064 16. Abdellaoui, M., Daghouj, D., Fattah, M., Balboul, Y., Mazer, S., El Bekkali, M.: Artificial intelligence ach for target classification: a state of the art. Adv. Sci. Technol. Eng. Syst. J. 5(4), 445–456 (2020) 17. Abdellaoui, M., Fattah, M.: Characterization of ultra wide band indoor propagation. In: 2019 7th Mediterranean Congress of Telecommunications (CMT), Fès, pp. 1–4 (2019). https://doi. org/10.1109/CMT.2019.8931367 18. Daghouj, D., et al.: UWB coherent receiver performance in a vehicular channel. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1996–2001 (2020) 19. Mimouna, A., Khalifa, A.B., Alouani, I., Amara, N.E.B., Rivenq, A., Taleb-Ahmed, A.: Entropy-based ultra-wide band radar signals segmentation for multi obstacle detection. IEEE Sens. J. 21(6), 8142–8149 (2021). https://doi.org/10.1109/JSEN.2021.3050054 20. Sakkila, L., et al.: Methods of target recognition for UWB radar. In: 2010 IEEE Intelligent Vehicles Symposium, San Diego, pp. 949–954 (2010). https://doi.org/10.1109/IVS.2010.554 7962 21. Daghouj, D., et al.: Automatic target recognition based on the features of UWB radar signals. Int. J. Eng. Appl. 9(6), 310 (2021) 22. Daghouj, D., et al.: Automatic target detection and localization using ultra-wideband radar. Int. J. Electr. Comput. Eng. 12(2), 1695–1702 (2022). https://doi.org/10.11591/ijece.v12i2

Forward Error Correction for Routing Protocols in WSN: A Comparative Performance Analysis Ikram Daanoune(B)

and Abdennaceur Baghdad

EEA & TI Laboratory, Faculty of Sciences and Techniques, Hassan II University, BP 146, 20650 Mohammedia, Morocco [email protected]

Abstract. Wireless Sensor Network (WSN) has been extensively investigated by the research community. The WSN serves to monitor and supervise an area through many wireless nodes. The issue with these networks is that they are highly energyconstrained since they are powered by a small battery that may not be rechargeable or renewable in some environments. Consequently, optimizing energy consumption and increasing lifetime is the most critical design factor for WSNs. In this regard, several routing protocols are proposed in the literature to conserve energy consumption and extend the network lifetime. However, these protocols cannot guarantee communication reliability. Therefore, both energy consumption and link reliability present the main challenge in WSN applications. Forward Error Correction (FEC) is a technique used to ensure link reliability. Due to the energy constraint of WSN, it is necessary to find suitable techniques that guarantee energy efficiency with reliability links. Furthermore, the choice of an adaptive FEC with routing protocols is very critical. The principal objective of this paper is to compare block FEC codes in terms of BER and decoding energy consumption to obtain the efficient FEC code for routing techniques in WSN. Simulation results show that the LDPC code with BPSK under the AWGN channel is the best for the LEACH routing protocol. Keywords: WSN · FEC · BER · RS · BCH · LDPC · Routing protocol

1 Introduction During the past decades, there have been several research efforts on WSN. The WSN consists of a group of dedicated nodes deployed in a coverage area to monitor the environmental conditions of the space and organize the collected data to send it to a base station. WSN is applied in many applications such as health, military, security, and so forth. Nodes in WSN are generally operated by small batteries, which their replacement is a complicated and difficult process in certain applications due to geographical location and other criteria. Consequently, WSNs are energy constraints and limited power capability. It is necessary to find appropriate techniques to conserve energy in WSN. In this regard, numerous researches are proposed in the literature, which proposed various conservation techniques to optimize energy consumption and increase the lifetime of WSN, such as [1–5]. Among these techniques, the most used are routing protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 303–311, 2022. https://doi.org/10.1007/978-3-031-02447-4_32

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In WSN, each sensor node communicates with other nodes via wireless communication. Therefore, the data transmitted by nodes are susceptible to corruption by errors in the wireless channel. Consequently, it is necessary to provide a proper Error Control Coding (ECC) to reduce the Bit Error Rate (BER) and to reach a reliable and consistent transmission of data. In reception, if a data packet arrives corrupted, the data may be discarded and the node waits for a new transmission. Nonetheless, there is a dissipation of energy. Therefore, one of the key challenges in deploying WSNs is to enhance the network lifetime and energy efficiency with maintaining reliability. In this context, many kinds of research have been conducted on the analysis of ECCs in WSN and wireless multimedia networks, such as [6–8]. The principal purpose of this paper is to evaluate the BER performances of block error correction codes: RS, BCH, and LDPC to deduce which of them will be efficient for hierarchical routing protocols. The modulation technique used in the radio communication system is the BPSK with the AWGN channel. This paper is constructed as follows: the previous works concerning error correction schemes and the performance of RS, BCH, and LDPC are conferred in Sect. 2. Section 3 presents simulations and results. Section 4 presents a discussion and future research. Finally, we present the concluding points of this work in Sect. 5.

2 Related Works Since any radio signal is affected by channel noise, link reliability for any radio communication chain is important. For this reason, the ECC must be applied at the link layer to enhance the reliability of WSN. ECC ensures transmission reliability, also can help in conserving the power of the nodes by providing a coding gain that results in a lower signal-to-noise ratio to achieve the same BER as an uncoded scheme. So, ECC presents also an energy-saving approach. In recent years, several types of research have been conducted on the analysis of ECC mechanisms in WSN and wireless multimedia networks. In [6], the authors provide a comprehensive analysis of packet coding schemes used in Industrial-WSNs (IWSNs). They have interested in studying the transmission of short block length messages and investigating channel coding to meet reliability and low latency requirements in IWSN. In [7], experimental results show that LDPC codes are a good applicant for WSN. EzZazi et al. in [8] look at the performance analysis of ECCs with BPSK modulation and AWGN channel. They concluded that RS codes are suitable for WSN because it has achieved better BER performance than other codes for short-code length. Gil et al. [9] proposed an efficient scheme using FEC to increase the reliability of communication in solar-powered WSNs. Authors in [10] have proposed an adaptive LDPC code using SPA decoding, which purposes to reduce the energy consumption of WSN. The LDPC code rate used in this paper is varied among 3/4, 1/2, and 1/3. Islam in [11] evaluated the error characteristics of the wireless channel using the node “mote”. Simulations results provide that the RS (31,21) adapts to both BER and energy consumption. However, none of these researches are directly applicable to any of the conservation techniques for WSN, especially for energy-efficient routing protocols. The ECCs are grouped mainly in two categories: the ARQ (Automatic Repeat Request), and FEC. The ARQ codes use retransmission of transmitted data in case of

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receiving a corrupted data packet. To avoid retransmission costs by using ARQ, Wang et al. [12] are proposed FEC techniques. These channel coding types add at the transmitter a redundancy, which permits both detection and correction of a certain number of errors at the receiver. So, the FEC schemes can ensure great reliability of data communication. In a very noisy channel (the error rate is higher), the FEC schemes will be better than ARQ because of in this case the number of retransmissions in ARQ will be higher. Additionally, when the channel is very noisy and the number of retransmissions is increased, the power consumption will increase. Furthermore, the ARQ technique is not suitable for WSN applications with frequent errors, which require more energy. Accordingly, the ECC scheme that is more candidate for WSN application is FEC. The FEC type is classified into two classes: convolutional codes, such as turbo code, and block codes that also is divided into two sub-classes: non-binary codes like RS, and binary codes, such as Hamming, Golay, BCH, LDPC, etc. In this paper, the discussion of ECCs codes is limited to FEC codes and especially on block codes. As obtained in [13], the ARQ is not suitable for WSN. 2.1 Bose Chaudhuri Hocquenghem (BCH) BCH codes are conveniently depicted in the language of finite field theory. They are cyclic block codes that have found various applications due to the advantage of their algebraic structure, which simplifies and facilitates hardware implementation and reduces computational complexity. These codes can have binary or non-binary elements. With low treatment latency, flexible choices in code rate and packet size, BCH codes are employed in flash memory and optical communication systems. BCH code is an important subclass of cyclic codes and has been applied in IWSNs for different scenarios [14]. 2.2 Reed Solomon (RS) Irving Reed and Gus Solomon have invented RS codes in 1960 [15]. RS codes have a wide range of applications in digital communications and data storage systems. Even though these codes were proposed before BCH codes, they can be treated as a subgroup of BCH codes (non-binary BCH). RS code is presented as RS (N, K, t) over a GF (2m ) with m-bit symbols. It means that the transmitter transmits the N symbol codeword, where K is information symbols of m bits each and 2t = N − K presents the number of parity symbols. Therefore, an RS (N, K, t) code is adopted to encode m bits symbol within blocks including N = 2m − 1. Where m ≥ 3 and 0 < K < N < 2m + 2. The error-correction capability in RS code is donated by t = (N − K)/2. 2.3 Low-Density Parity-Check (LDPC) LDPC codes were developed by Gallager [16] and enhanced by Mackay and Neal [17]. LDPC is designated by the following characteristics: higher overall performance and lower decoding complexity in comparison to Turbo codes when the block size is large;

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excellent flexibility; simple description and resulting in the orifical verifiability; parallel capability, which makes hardware implementation easier; high throughput, which guarantees high-speed decoding. LDPC codes are iteratively decoded as Turbo codes and also suffer from BER/PER degradation when applied to short packet communications [6]. LDPC codes are considered linear blocks with high error correction capability. They are also known as Gallager codes, defined by sparse parity-check matrices using bipartite graphs called Tanner graphs. Figure 1 presents an example of a parity check matrix and its tanner graph.

Fig. 1. H matrix and its tanner graph.

3 Simulation and Results In this study, we have evaluated different block FEC performances. Simulations were performed with the MATLAB simulator. These simulations allow us to analyze the performance in terms of BER of RS, BCH, and LDPC schemes in WSN with the BPSK modulation technique under the AWGN channel model. Figure 2(a) presents a comparison between RS and BCH for the same codeword length and different code rate R (scenario 1). Figure 2(b) shows a comparison between RS and BCH for the same (N, K) (scenario 2).

Fig. 2. (a) Comparison between RS and BCH for the same N and different R (scenario 1), (b) for the same (N, K) (scenario 2).

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From these results, we observe that the RS codes have better performances in terms of BER than the BCH codes for all cases, and the improvement becomes larger with increased Eb/No. In addition, the BER performances of both RS and BCH become better when the code rate decreases. It means that the RS and BCH codes outperform only when sending small data and more parity checks. The code rate, error-correcting capability, and decoding energy consumption of RS and BCH codes for these scenarios are highlighted in Table 1. Table 1. Code rate, error-correcting capability, and decoding energy consumption for different scenarios. Scenarios

Codes

R

t

% Of t

Edec

For the same N and different R

RS (31,15) BCH (31,16) RS (31,25) BCH (31,26)

0.4839 0.5161 0.8065 0.8387

8 3 3 1

0.2581 0.0968 0.0968 0.0323

0.043680 0.014280 0.014280 0.004480

For the same R and different t

RS (63,57) BCH (63,57) RS (63,45) BCH (63,45) RS (63,39) BCH (63,39)

0.9048 0.9048 0.7143 0.7143 0.6190 0.6190

3 1 9 3 12 4

0.0476 0.0159 0.1429 0.0476 0.1905 0.0635

0.027720 0.008960 0.090720 0.027720 0.126000 0.037520

From the result in Table 1, we notice that the BCH codes consume less than the RS codes. Although the less consumption energy of BCH, it corrects fewer errors than RS for the same codeword. For example, the BCH (31,16) corrects only 3 errors when the RS (31,15) can correct 8 error symbols. So, the RS can correct 25.81% of codeword while the BCH (31,16) can correct just 9.68%. Consequently, the RS can correct more than BCH by 64.56%. Figure 3 shows the power consumed by the decoder of RS and BCH codes.

Fig. 3. Diagram of decoding energy consumption of RS and BCH codes.

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Figure 4 presents a comparison of performances of LDPC compared to RS and BCH codes.

Fig. 4. Comparison between LDPC, RS, and BCH codes.

From these results, it is clear that the LDPC code significantly outperforms other candidate codes RS and BCH by providing a good coding gain. At BER = 10−3 , the LDPC has a coding gain of 2.5 dB while the RS and BCH have only 1.2 dB. Figure 5(a) shows the performance of LDPC (204, K) with different code rates. From these curves, we can constate that the LDPC codes have better performance when the code rate increase.

Fig. 5. (a) LDPC with different code rates, (b) LDPC with different iterations.

With code rate R = 1/2, we compared LDPC (204,102) for different iterations as shown in Fig. 5(b). From these results, we remark that the LDPC performances increase with the increased number of iterations.

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Therefore, we implement the LDPC and RS code with the LEACH routing protocol to compare their BER performance and conclude which code is appropriate for the LEACH protocol. Figure 6 illustrates the performance of LEACH with RS and with LDPC in terms of BER.

Fig. 6. BER performance of LEACH with RS and LDPC.

It can be seen from Fig. 6 that the LDPC outperforms the RS in terms of the BER. For example, at BER of 10−2 , the LDPC has a coding gain of 4 dB while the RS can reach only 1.5 dB. These results confirm that the LDPC code is more suitable for the LEACH routing protocol than the RS code.

4 Discussion and Future Work From Fig. 2 and the table above, we concluded that the RS code can increase communication reliability with lower code rates. Nonetheless, it consumes more energy for decoding. The BCH consumes less energy for decoding compared to RS. But it has limited correctional power. We conclude that the reliability of the communication and the energy consumption are in a trade-off relationship. Since the WSN applications are energy constraints and require reliability, RS and BCH codes are not suitable for WSN. Results in Figs. 4 and 5 show that the LDPC code increases communication reliability by providing a good coding gain. In addition, LDPC has better performance with a higher code rate and with more decoding iterations. Figure 6 indicates that the LDPC code is more apt for LEACH than the RS code. Consequently, the LDPC scheme has the best performance, whether it can fit the routing protocols in WSNs. In future work, we will apply the LDPC code with certain recently routing protocols to obtain an efficient routing protocol that optimizes energy consumption while maintaining also communication reliability.

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5 Conclusion Using the FEC schemes can permit a system to provide a good coding gain than an uncoded system for the same BER. Nonetheless, the WSN is energy-limited. Therefore, the choice of an adaptive FEC in WSN applications is very critical. In this study, we have investigated the performance of block FEC codes. Decoding energy consumption for candidate codes is given in this paper. After the consideration of BER performance, encoding and decoding complexity, and decoding energy consumption of FEC, we have concluded that the BCH codes are suitable for WSNs in terms of energy consumption, but it has limited error correction capability. On the other side, the RS has a higher error correction capability. However, it uses a longer parity-check that requires higher energy consumption at the decoder. After comparing RS, BCH, and LDPC codes, simulation results show that LDPC with code rate 1/2 outperforms other discussed codes by giving a good coding gain. Additionally, LDPC has better performance even if the code rate increase. Also, it is characterized by a high error correction capability. That means that the system with LDPC can forward important data with less parity check and more reliability, which decreases energy consumption. Finally, we implement LDPC and RS codes with LEACH protocol to verify which code will be applicable with LEACH. Consequently, results show that LEACH with LDPC gives better performance compared to LEACH with RS. So, LDPC code can be a candidate for implementation with routing protocols to obtain an efficient routing protocol with communication reliability.

References 1. Daanoune, I., Baghdad, A., Ballouk, A.: An enhanced energy-efficient routing protocol for wireless sensor network. Int. J. Electr. Comput. Eng. IJECE 10, 5462–5469 (2020) 2. Hasan, M.Z., Al-Rizzo, H., Günay, M.: Lifetime maximization by partitioning approach in wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2017(1), 1–18 (2017) 3. Kacimi, R., Dhaou, R., Beylot, A.-L.: Load balancing techniques for lifetime maximizing in wireless sensor networks. Ad Hoc Netw. 11, 2172–2186 (2013) 4. Daanoune, I., Baghdad, A., Ballouk, A.: Improved LEACH protocol for increasing the lifetime of WSNs. Int. J. Electr. Comput. Eng. IJECE 11, 3106–3113 (2021) 5. Daanoune, I., Abdennaceur, B., Ballouk, A.: A comprehensive survey on LEACH-based clustering routing protocols in wireless sensor networks. Ad Hoc Netw. 114, 102409 (2021) 6. Zhan, M., Pang, Z., Dzung, D., et al.: Channel coding for high-performance wireless control in critical applications: survey and analysis. IEEE Access 6, 29648–29664 (2018) 7. Pham, D.M.: On efficient design of LDPC decoders for wireless sensor networks. J. Netw. 9, 3207–3214 (2014) 8. Ez-Zazi, I., Arioua, M., Oualkadi, A., et al.: Performance analysis of efficient coding schemes for wireless sensor networks. In: 2015 Third International Workshop on RFID and Adaptive Wireless Sensor Networks (RAWSN), Agadir, Morocco, pp. 42–47. IEEE (2015) 9. Gil, G.W., Kang, M., Kim, Y., et al.: Efficient FEC scheme for solar-powered WSNs considering energy and link-quality. Energies 13, 3952 (2020) 10. Sasikala, T., Bhagyaveni, M.A., Senthil Kumar, V.J.: Cross layered adaptive rate optimized error control coding for WSN. Wirel. Netw. 22, 2071–2079 (2016) 11. Islam, M.R.: Error correction codes in wireless sensor network: an energy-aware approach. 7 (2010)

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12. Wang, C., Sklar, D., Johnson, D.: Forward Error-Correction Coding (2001) 13. Kang, M., Noh, D., Yoon, I.: Energy-aware control of error correction rate for solar-powered wireless sensor networks. Sensors 18, 2599 (2018) 14. Yitbarek, Y.H., Yu, K., Akerberg, J., et al.: Implementation and evaluation of error control schemes in industrial wireless sensor networks. In: 2014 IEEE International Conference on Industrial Technology (ICIT), Busan, South Korea, pp. 730–735. IEEE (2014) 15. Reed, I.S., Solomon, G.: Polynomial codes over certain finite fields. J. Soc. Ind. Appl. Math. 8, 300–304 (1960) 16. Gallager, R.: Low-density parity-check codes (1963). https://ieeexplore.ieee.org/document/ 1057683 17. MacKay, D.J.C., Neal, R.M.: Near Shannon limit performance of low-density parity-check codes. Electron. Lett. 32, 1645–1646 (1996)

Packet Delay Budget-Based Scheduling Approach for 5G Time Division Duplex Asmae Mamane1(B)

, Mohammed Fattah2 , Mohammed El Ghazi1 , and Moulhime El Bekkali1

1 Laboratory of Artificial Intelligence, Data Sciences and Emerging Systems, Sidi Mohammed

Ben Abdellah University, Fez, Morocco [email protected] 2 IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco

Abstract. The fifth generation of mobile networks offers a flexible architecture and allows for various use cases and scenarios to meet the diverse demands of end-users. The multiple applications necessitate different network criteria such as reduced latency, high reliability, enhanced data rates, and efficient spectrum usage. Hence, it is required to adopt innovative scheduling processes and approaches to efficiently allot radio resources to multiple users. Furthermore, the fifth generation of mobile networks extended the spectrum usage to mmWaves, necessitating the adoption of time division duplexing. The fifth generation of mobile networks (5G) intends to adopt flexible time division duplex (TDD), allowing autonomous uplink/downlink mode scheduling at each cell depending on local traffic to facilitate TDD switching in short intervals. However, fulfilling the 5QI requirements for TDD-based networks remains challenging and necessitates an appropriate scheduling scheme to make the optimal resource allocation decision. This work presents a new PDB-aware scheduling technique that considers the packet delay budget of each QCI to enhance the quality of service (QoS) of the 5G system. The simulation results demonstrate that our proposed algorithm accurately allocates radio resources following 5QI indications and achieves a cell throughput of 482 Mbps, surpassing state-of-the-art schedulers. Keywords: 5G · Scheduling algorithm · eMBB · URLLC · Resource allocation · Packet budget delay · QoS class identifier

1 Introduction The world is moving toward a fully connected society and smart systems. The emerging development of various applications and services necessitates many requirements and considerations to perform the end-user expected Quality of Experience (QoE), such as a high data rate, efficient resource allocation, and reliable communication [1, 2]. Hence, responding to the various requirements of each service remains challenging and confusing [2]. For these reasons, the 3rd Generation Partnership Project (3GPP) has set specifications for use cases and applications by assigning an indicator for the types of traffic requiring the same performance. The 5G QoS Indicator (5QI) describes the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 312–321, 2022. https://doi.org/10.1007/978-3-031-02447-4_33

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priority level, type of flow, amount of packet budget delay tolerated, packet error loss limits, and examples of associated applications for each flow [3]. On the other hand, dynamic time division duplexing (TDD) is the primary duplexing technology for 5G New Radio (NR), allowing flexible communication through the mmWaves deployed for 5G systems [4, 5]. As a result, given the limited time available for transmitting either in uplink (UL) or downlink (DL) [6], meeting the QoS requirements of different 5G traffic using a timedivision duplex and making the appropriate radio resource allocation decision remains challenging. In this context, this study proposes a novel scheduling scheme that is TDD-based and PDB-aware to achieve optimal allocation for each user while meeting the QoS requirements of the various flow types. We employ the Packet Delay Budget (PDB) in our new scheduling method to avoid packet expiration, consider the QoS requirements of every flow, and manage the limited transmission time forced by the time-duplexing mode. Furthermore, the comparison analysis shows that our scheduler solves the scheduling problem by assigning radio resource blocks (RBs) to each user on a per-user basis. Based on their packet delay budget and accurately satisfying their demands. The remainder of this work is outlined accordingly: The second section introduces the connected works. Section 3 contains the proposed scheduling scheme. Section 4 defines the research method adopted in this paper. Section 5 analyzes and explains the newly developed algorithm’s achievement. Section 6 summarizes the findings and reviews the future work.

2 Related Works Several approaches and schemes have been suggested to distribute radio resources according to the QoS requirements properly. According to the authors’ perspectives, the algorithms consider different factors and indicators to develop the most efficient scheduler that meets the QoS targets. The authors in [7] propose a resource allocation approach for Long Term Evolution (LTE) downlink relying on the distinction of QoS. The suggested scheduler considers two categories of QoS flows Non-Guaranteed Bit Rate (NGBR) and Guaranteed Bit Rate (GBR). To meet the QoS of real-time services, it allocates radio resources to GBR bearers before non-GBR bearers. It distributes resources for all GBR flows, or until GBR’s available resources are consumed, then it serves non-GBR services, which may cause starvation of resources for users transmitting non-GBR traffic. The technique [8] determines the users’ priorities based on the weight of each data bearer’s QoS class and the accumulated data rate of that bearer in the past. The GBR group then employs all the RBs until the desired bitrate is reached. If additional RBs are still available, the scheduler assigns the remaining RBs to non-GBR users using the Proportional Fair (PF) metric. A QoS-aware scheduler for GBR flows was proposed in [9]. The Priority Set Scheduler (PSS) defines users’ priorities based on their achieved bitrate and prioritizes users below their target bitrate. The remaining users are classified as having a lower priority. The first set of users is scheduled through the Blind Equal Throughput [10], while the proportional fair algorithm [11, 12] allocates resources to the second group.

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The research in [13] proposes a channel-aware scheduling algorithm based on the 3GPP-specified QoS class identifier (QCI) to meet GBR requirements by incorporating a delay-dependent factor based on a sigmoid function, head of a line delay, and packet budget delay. In contrast, non-GBR flows are scheduled to follow the PF metric. To satisfy service requirements, the mechanism proposed in [14] provides radio resources to LTE-A users depending on their QCI. Furthermore, based on their traffic load, it splits the connected devices into cell-centered and cell-edge users to maintain a minimal throughput for cell-edge users. The study described in [15] aims to offer equitable and efficient uplink scheduling to meet end-to-end latency limitations based on the uplink packet delay budget. However, the uplink packet delay budget used in this approach is not specified in the 3GPP standard. It was calculated based on the downlink delay experienced by consumers. Hence, it does not consider the 5QI criteria. Although these studies were valuable, they cannot use a dynamic parameter depending on the QoS of each flow that fulfills the user’s needs for the 5G time division duplex without non-GBR starvation. We proposed a PDB-aware scheduler for 5G use cases for these reasons.

3 Scheduling Algorithm Our proposed algorithm uses the packet delay budget and the priority level of each QCI according to table 6.1.7-A of the 3GPP technical report (TR 23.203) [16] to perform a PDB-aware scheduler. The Packet Delay Budget (PDB) establishes the maximum limit for the acceptable packet delay between the user equipment (UE) and the Policy and Charging Enforcement Function in milliseconds. On the other hand, the priority level depends on each QCI, where the highest priority value refers to the flow with the lowest priority. It is used to distinguish between the different service data flows of the same user or among users’ traffic. Hence, the priority level helps determine which critical users or services are in the network. Our new scheduling method, “PDB-aware scheduler,” bases its metric on the Packet Delay Budget (PDB) to avoid packet expiration and allow an appropriate resource assignment according to the QCI of each service flow. In this way, the user with the smallest packet delay budget value will acquire the maximal metric. Hence, it prioritizes the user with the shortest packet delay and assigns more RB than other flows. As a result, it reduces communication latency and avoids packet loss. However, the proposed scheduling scheme is developed to serve all the delaysensitive services for 5G use cases. Consequently, higher data rates and good fairness levels remain important requirements for 5G services. Our scheduling strategy employs the proportional fair metric to meet these goals, ensuring equity while increasing total throughput. The newly developed technique computes the metric wi,j, which correlates the i-th user with the j-th RB by multiplexing the PF metric by the inverse of the packet delay budget of each user, as shown in (1). wi,j =

ri,j Ri

×

1 PDBi

(1)

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The expressions of the instantaneous rate ri,j , and the average data rate Ri are further detailed in [17, 18]. The scheduling scheme proposed in this paper runs in five steps: 1. Initialization: We create the “QCI_MAP” table that maps each service to the appropriate QCI. 2. Step 1: We identify the corresponding QCIi for the UEi in the QCI_MAP table at the current slot. 3. Step 2: We look for the packet delay budget (PDBi ) associated with the QCIi in the QCI_table, a table created by the 3GPP to standardize the QCI value for fifthgeneration use cases. 4. Step 3: In this step, we calculate the metric that associates the user UEi with the RBj as presented in Eq. (1). 5. Step 4: This step ranks the UE according to metrics saved in a resource allocation matrix (UE, RB). Priority is given to the UEi having the highest metric wi,j . Then, for each RBj, we look at the UEi with the highest metric across users to assign it the RBj, as shown in Eq. (2).    RBj → UE argmax wi,j ; i = 0, 1, . . . , NUE

(2)

According to the 3GPP considerations, scheduling distinct service data flows should primarily depend on their PDB. If all UEs have sufficient radio channel quality and their data flows have the same PDB constraints, the scheduling decision depends on the QCI Priority level. Conforming to the specifications mentioned above and considering the case where two users have the same metric value, we changed Eqs. (2) to (3) to rank UEs according to their priority level by selecting the user with the lowest priority value.     (3) RBj → argmin UE_Priority argmax wi,j ; i = 0, 1, . . . , NUE Because we use TDD symbol-based scheduling, the steps from 1 to 4 are repeated for each symbol until the gNB’s available resource blocks are depleted. In the end, we get the resource allocation decision, providing the number and the identifier of the resource blocks to allocate to each user, considering the QoS constraints defined in the metric.

4 Research Method The studied network comprises a single macrocell, where a gNB provides Nrb radio resource blocks to serve Nu devices. The user equipment transmits and receives different services identified with their QCIs. For every symbol, the gNB has to make the right decision to allocate the available radio resources to the devices existing in the served cell. The gNB adopted in this scenario serves the UEs through a dynamic time-division duplex. Our gNB communicates through a 28 GHz frequency carrier, part of the frequency range 2 (FR2) that uses mmWaves. Using an FR2 enables us to use the maximum bandwidth possible for the 5G networks, which is 400 MHz, available only for 120 kHz

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subcarrier spacing. According to the 3GPP specification, this subcarrier spacing configuration enables 8 slots per subframe, for a total of 80 slots in each radio frame [19]. Each slot contains 14 OFDMA symbols. In addition, numerology 3 provides the lowest symbol duration supporting data transmission 8.33 µs. Hence, the 120 kHz SubCarrier Spacing (SCS) offers more flexibility to the dynamic TDD configuration, which is necessary for mmWaves usage. The DL-UL periodicity, the number of slots per frame, and the number of resource blocks are set according to the 120 kHz SCS adopted. As stated in section 38.213-11.1 [20], the DL-UL periodicity for numerology 3 is 0.625 ms. Besides, the combination of DL-UL periodicity and a subcarrier value defines the number of slots per period, which is 5 slots/period in our case. We configured the special slots (S) according to Table 11.1.1-1 in [20] to achieve the best performance through the compared algorithms. We chose the slot format n45 to support balanced load transmission, enabling similar throughput for uplink and downlink. Table 1 summarizes the scenario parameters set. Table 1. Simulation parameters Traffic type

Balanced load

Slot format pattern

n45

Number of slots

5

Number slots DL

2

Number slots UL

2

Number symbols DL

6

Number symbols UL

6

5 Results and Discussion To appropriately assess the performance of the scheduling algorithm described in this research, we assigned four distinct QCI values to four different users, as shown in Table 2. In other words, we carried out simulations of four use-cases to evaluate the scheduler’s behavior in each situation. We affected similar Channel Quality Indicator (CQI) values to evaluate only the impact of the scheduling algorithm on the network. Furthermore, we analyzed the simulation results of the suggested scheduling approach in terms of realized throughput and resource sharing among users to evaluate priority consideration when allocating radio resources and discuss the enhancement of the overall throughput compared to existing schedulers.

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Table 2. QCI_Map table UE

QCI

Priority level

PDB

Example of the flow type

UE1

1

2

100

Conversational voice

UE2

4

5

300

Non-conversational video (buffered streaming)

UE3

7

7

100

Interactive gaming

UE4

71

5.6

150

Live uplink streaming

5.1 Resource Sharing In our simulation scenario, we assumed that all users had similar channel quality and assigned equal CQI values to all of them. However, the best CQI scheduler considers only the channel status of the users. Hence, it allocates radio resources according to the transmitted data size. In Fig. 1, presenting the resource share for the downstream, the UE2 seems to have the most resources since it downloads streaming video packets. Besides, as given in Fig. 2, the Best CQI allocates more resources to UE4 in the uplink to allow the live uplink streaming without considering the 5QI specifications set by the 3GPP.

Fig. 1. Best CQI downlink resource share

Fig. 2. Best CQI uplink resource share

Figures 3 and 4 display the resource share of the proportional fair scheduler for downlink and uplink, respectively. The PF allocates radio resources to users, considering the fairness factor while ignoring the network’s flow type. The figures below show that all users had nearly the same number of resources in the overall simulation time for both streams.

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Fig. 3. PF downlink resource share

Fig. 4. PF uplink resource share

The proposed PDB-aware scheduler allocates radio resources depending on the 3GPPP specifications. Figures 5 and 6 reveal that it prioritizes the UE1 having the lowest PBD and the highest priority level, followed by the UE3, UE4, and UE2 respecting the QCI_mapping presented in Table 2. In addition, it ensures stability in the resource share between the users, unlike the PF and Best CQI.

Fig. 5. PDB-aware downlink resource share

Fig. 6. PDB-aware uplink resource share

5.2 Cell Throughput Figure 7 illustrates that our newly suggested scheduler surpasses the other studied schedulers regarding the overall cell throughput. Our PDB-aware approach attains 471.339 Mbps downstream, while the best CQI and the PF achieve 319.637 Mbps and 259.119 Mbps, respectively. Furthermore, the PDB-aware algorithm achieves a maximum data rate of 482.07 Mbps, surpassing the best CQI value of 327.408 Mbps and the PF overall uplink data rate 316.111 Mbps.

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Throughput performances in Mbps PDB -aware scheduler (proposed) PF

BCQI 0

100

200

300

BCQI

PF

overall cell throughput DL

319.637

259.119

overall cell throughput UL

327.408

316.111

overall cell throughput DL

400

500 600 PDB -aware scheduler (proposed) 471.339 482.07

overall cell throughput UL

Fig. 7. Overall cell throughput performance of the simulated schedulers

We conclude from the simulation results that our suggested PDB-aware scheduler fulfills the throughput requirements and respects the 5QI specifications.

6 Conclusion Scheduling algorithms are necessary for allocating limited radio resources to varied flows and use cases requiring different needs depending on the type of service. This work proposes a new PDB-aware scheduling technique based on time division duplexing that considers each QCI’s packet delay budget to meet the 5G system’s QoS requirements. Furthermore, we performed an analytical investigation consisting of four use cases to analyze the scheduler’s behavior in each circumstance to evaluate the effectiveness of the scheduling strategy presented in this research. Moreover, we examined the suggested scheduling technique’s outcomes regarding attained throughput and resource usage among users to assess priority consideration when allocating radio resources and discuss the improvement in overall throughput compared to conventional schedulers. The system model adopted for this paper incorporates time division duplex and frequency range 2, with a carrier frequency of 28 GHz. According to the simulation findings, the proposed method successfully distributes radio resources according to 5QI requirements and achieves higher cell throughput than state-of-the-art schedulers. In the future, we plan to improve our scheduling system to handle more complex scenarios and analyze existing challenges in the field that need further study to propose more efficient algorithms and innovative solutions.

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References 1. Chowdhury, M.Z., Shahjalal, M., Ahmed, S., Jang, Y.M.: 6G wireless communication systems: applications, requirements, technologies, challenges, and research directions. IEEE Open J. Commun. Soc. 1, 957–975 (2020). https://doi.org/10.1109/ojcoms.2020.3010270 2. Mamane, A., El Ghazi, M., Barb, G.R., Otesteanu, M.: 5G heterogeneous networks: an overview on radio resource management scheduling schemes. In: 7th Mediterranean Congress of Telecommunications 2019, CMT 2019, pp. 1–5. IEEE (2019) 3. George, B., Class, E.O.A.M.S.: 5G QoS: 5QI to IP DSCP Mapping (2020) 4. Es-Saqy, A., et al.: A 5G mm-wave compact voltage-controlled oscillator in 0.25 µm pHEMT technology. Int. J. Electr. Comput. Eng. 11, 1036–1042 (2021). https://doi.org/10.11591/ijece. v11i2.pp1036-1042 5. Es-Saqy, A., et al.: 28 GHz balanced pHEMT VCO with low phase noise and high output power performance for 5G mm-wave systems. Int. J. Electr. Comput. Eng. 10, 4623–4630 (2020). https://doi.org/10.11591/ijece.v10i5.pp4623-4630 6. Samidi, F.S., Radzi, N.A.M., Ahmad, W.S.H.M.W., Abdullah, F., Jamaludin, M.Z., Ismail, A.: 5G new radio: dynamic time division duplex radio resource management approaches. IEEE Access 9, 113850–113865 (2021). https://doi.org/10.1109/ACCESS.2021.3104277 7. Fu, W.H., Kong, Q.L., Tian, W.X., Wang, C., Ma, L.L.: A QoS-aware scheduling algorithm based on service type for LTE downlink. Appl. Mech. Mater. 347–350, 2468–2473 (2013). https://doi.org/10.4028/www.scientific.net/AMM.347-350.2468 8. Zaki, Y., Weerawardane, T., Görg, C., Timm-Giel, A.: Multi-QoS-aware fair scheduling for LTE. In: IEEE Vehicular Technology Conference (2011) 9. Monghal, G., Pedersen, K.I., Kovács, I.Z., Mogensen, P.E.: QoS oriented time and frequency domain packet schedulers for the UTRAN long term evolution. In: IEEE Vehicular Technology Conference, pp. 2532–2536 (2008) 10. Sudheep, S., Rebekka, B.: Proportional equal throughput scheduler-A very fair scheduling approach in LTE downlink. In: 2014 International Conference on Information Communication and Embedded Systems (ICICES 2014). Institute of Electrical and Electronics Engineers Inc. (2015) 11. Kim, K., Kim, H., Han, Y.: A proportionally fair scheduling algorithm with QoS and priority in 1xEV-DO (2002) 12. 3GPP. 3GPP TR 21.905 - Vocabulary for 3GPP Specifications (2011) 13. Ameigeiras, P., et al.: 3GPP QoS-based scheduling framework for LTE. EURASIP J. Wirel. Commun. Netw. 2016(1), 1–14 (2016). https://doi.org/10.1186/s13638-016-0565-9 14. Gatti, R.: Shivashankar: Improved resource allocation scheme for optimizing the performance of cell-edge users in LTE-A system. J. Ambient. Intell. Humaniz. Comput. 12, 811–819 (2021). https://doi.org/10.1007/s12652-020-02084-x 15. Samanta, A., Panigrahi, B., Rath, H.K., Shailendra, S.: On low latency uplink scheduling for cellular haptic communication to support tactile internet. Wireless Pers. Commun. 121(3), 1471–1488 (2021). https://doi.org/10.1007/s11277-021-08680-0 16. TSGS. TS 123 203 - V15.4.0 - Digital cellular telecommunications system (Phase 2+) (GSM); Universal Mobile Telecommunications System (UMTS); LTE; Policy and charging control architecture (3GPP TS 23.203 version 15.4.0 Release 15) (2018) 17. Sun, Z., Yin, C., Yue, G.: Reduced-complexity proportional fair scheduling for OFDMA systems. In: Proceedings of the International Conference on Communications, Circuits and Systems (ICCCAS 2006), vol. 2, pp. 1221–1225 (2006). https://doi.org/10.1109/ICCCAS. 2006.284866

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18. Mamane, A., Fattah, M., El Ghazi, M., Balboul, Y., El Bekkali, M., Mazer, S.: Proportional fair buffer scheduling algorithm for 5G enhanced mobile broadband. Int. J. Electr. Comput. Eng. 11, 4165 (2021). https://doi.org/10.11591/ijece.v11i5.pp4165-4173 19. Technical Specification. TS 138 211 - V16.3.0 - 5G; NR; Physical channels and modulation (3GPP TS 38.211 version 16.3.0 Release 16) (2020) 20. 3GPP. 138 213 - V15.8.0 - 5G; NR; Physical layer procedures for control (2020)

Mutual Coupling Reduction in Array Antenna Using a New EBG Structure Abdellah El Abdi(B) , Moussa El Ayachi, and Mohammed Rahmoun Applied Science Research Laboratory, National School of Applied Sciences, Mohamed I University, Box 669, 60 000 Oujda, Morocco [email protected], {m.elayachi,m.rahmoun}@ump.ac.ma

Abstract. The goal of this study is to use Electromagnetic Band Gap (EBG) structures to reduce reciprocal coupling between the radiating elements of an antenna array. Initially, the Mushroom-like EBG structure was used as a reference structure in this field. Second, in comparison to the Mushroom-like EBG structure, a new beneficial EBG structure was used. The proposed New EBG (NEBG) structure’s interest is demonstrated by a comparison of the outcomes obtained in each situation. Two separate simulators, HFSS and CST MWS were used to simulate all of the structures. The findings we achieved by the two methods are nearly identical for each structure. Keywords: Antenna array · Mutual coupling · EBG structure

1 Introduction Antennas array are widely used in various wireless communications applications, such as MIMO systems, radars, satellites [1–4] … etc. The major disadvantage of the antenna array is the problem of electromagnetic coupling between the neighboring radiating elements, and consequently the impact on the degradation of the radiation performance of the antenna [5–7]. To solve this problem, we employed EBG structures [8–12]. Based on an antenna array made up of two patches, we demonstrate the utility of this method. The coupling within this antenna was initially studied on a classical Ground Plane (GP). After that, the EBG structure was installed on the GP below the two patches to eliminate mutual coupling between the radiating elements. Initially, the EBG structure resembled that of a mushroom [13–15]. Second, a NEBG structure competing with the initial EBG structure was proposed [16].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 322–329, 2022. https://doi.org/10.1007/978-3-031-02447-4_34

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2 Antenna Array on Classical GP Figure 1 shows the geometry of the antenna studied on a classical GP (Perfect Electric Conductor). It’s an antenna made up of two rectangular patches of size 65 mm × 65 mm. These two patches are printed on a substrate of dielectric permittivity 2.2 and thickness 0.75 mm. The type of feed line is a coaxial cable. Between the substrate and the GP has placed an airbox of thickness 12.5 mm. The GP is 180 mm × 180 mm in size.

Fig. 1. Antenna array geometry on classical GP.

Figure 2 depicts the simulation results for the reflection coefficient S11 and transmission coefficient S21.

0 -5

S11-CST

Sij[dB]

-10

S21-CST

-15

S11-HFSS

-20

S21-HFSS

-25 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Freq[GHz]

Fig. 2. S11 and S21 of the antenna array on classical GP obtained through HFSS and CST.

The results obtained show that the two patches are adapted in the frequency band [1.84–2.18 GHz] in HFSS and [1.88–2.2 GHz] in CST. However, they are considerably coupled between them; S21 > −10 dB. After that, we aim to reduce this coupling by using EBG structures.

3 Antenna Array on Mushroom-Like EBG Structure 3.1 Mushroom-Like EBG Structure The shape of the Mushroom-like EBG structure is depicted in Fig. 3 [17]. This illustration depicts the Mushroom-like structure’s upper face as well as a view of its lateral face.

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This is a regular pattern of square metal patches printed on a dielectric permittivity 9.8 substrate. A metallic via through connects each patch to the GP.

Fig. 3. The geometry of the Mushroom-like EBG structure.

Table 1 lists the structure’s ideal parameters. The EBG parameter represents the frequency band within which the EBG structure operates. Following that, this structure should be inserted into the antenna array’s GP. Table 1. The Mushroom-like EBG Structure’s parameters. Parameter

Value

Permittivity

9,8

h

3,175 mm

g

0,45 mm

w

11,55 mm

EBG

[1,925 to 2,225] GHz

3.2 Antenna Array with Mushroom-Like EBG Structure Inserted Figure 4 illustrates the geometry of the antenna array on the Mushroom-like EBG structure.

Fig. 4. Antenna array geometry on a Mushroom-like EBG structure.

Figure 5 shows the simulation results for the reflection coefficient S11 and the transmission coefficient S21.

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0

Sij[dB]

-10

S11-CST

-20

S21-CST

-30

S11-HFSS

-40

S21-HFSS

-50 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Freq[GHz]

Fig. 5. S11 and S21 of the antenna array on Mushroom-like EBG structure obtained through HFSS and CST.

The results demonstrate that by incorporating the Mushroom-like EBG structure, the coupling between the two radiating elements was reduced; S21 < −10 dB on the bandwidth [2.04–2.24 GHz] in HFSS and [1.97–2.19 GHz] in CST, relatively shifted in comparison with the initial bandwidth.

4 Antenna Array on NEBG Structure 4.1 NEBG Structure Figure 6 shows the geometry of the NEBG structure developed [16]. It’s made up of two periodic arrays of metallic square patches printed on both sides of a dielectric permittivity substrate with a dielectric permittivity of 2.2. The two arrays are separated by a quarter of the diagonal distance between one of the structure’s cells [18–20].

Fig. 6. One cell of the NEBG structure’s geometry.

Table 2 summarizes the optimal parameters of this new structure. The bandgap width of the NEBG structure is 0.45 GHz, which is significantly greater than the bandgap width of the Mushroom-like EBG structure.

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Value

Permittivity

2,2

h

4,575 mm

g

0,5 mm

w

11,5 mm

EBG

[2, 65] GHz

4.2 Antenna Array with NEBG Structure Inserted The antenna array geometry on the NEBG structure is illustrated in Fig. 7.

Fig. 7. Antenna array geometry on the NEBG structure.

The simulation results of the reflection coefficient S11 and the transmission coefficient S21 are shown in Fig. 8.

0

Sij[dB]

-10 -20

S11-CST

-30

S21-CST

-40

S11-HFSS

-50

S21-HFSS

-60 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Freq[GHz]

Fig. 8. S11 and S21 of the antenna array on the NEBG structure obtained through HFSS and CST.

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The results reveal that adding the NEBG structure reduced the coupling between the antenna’s two radiating elements; S21 < −10 dB on the bandwidth [1.98–2.26 GHz] in HFSS and [1.95–2.3 GHz] in CST, slightly shifted in comparison with the initial bandwidth. Tables 3 and 4 summarize all of the results obtained so that we can compare the performances of the various structures. Table 3. Comparison between the results obtained with HFSS and CST. Bandwidth HFSS

Coupling CST

Antenna array on [1.84–2.18 GHz] [1.88–2.2 GHz] classical GP

HFSS

CST

−10 dB < S21 < −10 dB < S21 < −5 dB −5 dB

Antenna array on [2.04–2.24 GHz] [1.97–2.19 GHz] S21 < −15 dB Mushroom-like EBG structure

S21 < −10 dB

S21 < −16 dB

S21 < −16 dB

Antenna array on [1.98–2.26 GHz] [1.95–2.3 GHz] the NEBG structure

Table 4. Comparison between Mushroom-like EBG structure and NEBG structure. Antenna array on Mushroomlike EBG structure

Antenna array on the NEBG structure

Complexity of the EBG structure

Complicated (presence of vias)

Uncomplicated (absence of vias)

Cost of the EBG structure

Expensive

Cheap

Weight of the EBG structure

Important

Low

Simulation time and need for calculation resources

Important

Low

When compared to the EBG type Mushroom-like structure, the proposed NEBG structure displays interesting performance; The two EBG structures allow approximately the same reduction in the coupling between the radiating elements of the antenna, whereas the proposed NEBG structure has advantages in terms of simplicity of the structure, absence of vias, cost less expensive, a low weight, and in terms of calculation time and computing resources needed during simulations. Conclusion In this work, we have examined the problem of the mutual coupling between the radiating elements of an antenna array composed of two patches. By exploiting two EBG structures as a solution to this problem, this coupling has been considerably reduced.

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Mushroom-like is the first EBG structure to be used. The second is a NEBG structure that has been presented as a solution to the Mushroom-like EBG structure’s drawbacks. A comparison of the collected findings reveals that the proposed EBG structure outperforms the Mushroom-like structure. Indeed, the NEBG framework has been developed by avoiding the usage of metallic vias, which are present in the Mushroom-like structure, and instead employing a low permittivity substrate, which is less expensive and easier to install than a high permittivity substrate.

References 1. Elwi, T.A., Noori, M., Al-Naiemy, Y., Yahiea, E.S.: Conformal antenna array for MIMO applications. J. Electromagn. Anal. Appl. 6, 43–50 (2014) 2. Elwi, T.A.: A miniaturized folded antenna array for MIMO applications. Wireless Pers. Commun. 98(2), 1871–1883 (2017). https://doi.org/10.1007/s11277-017-4950-4 3. Anjaneyulu, G., Varma, J.S.: Design and simulation of multi band microstrip antenna array for satellite applications. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 140–143 (2018) 4. Mosalanejad, M., Ocket, I., Soens, C., Vandenbosch, G.A.E.: Wideband compact comb-line antenna array for 79 GHz automotive radar applications. IEEE Antennas Wirel. Propag. Lett. 17(9), 1580–1583 (2018) 5. Bait-Suwailam, M.M., Siddiqui, O.F., Ramahi, O.M.: Mutual coupling reduction between microstrip patch antennas using slotted-complementary split-ring resonators. IEEE Antennas Wirel. Propag. Lett. 9, 876–878 (2010) 6. Balanis, C.A.: Antenna Theory, Analysis and Design, 2nd edn. Wiley (1997) 7. Ayman Ayd Saad, R., Hesham Mohamed, A.: Printed millimeter-wave MIMO-based slot antenna arrays for 5G networks. AEU – Int. J. Electr. Commun. 99, 59–69 (2019) 8. Bahare, M., Afsahi, M.: Mutual coupling reduction and gain enhancement in patch array antenna using a planar compact electromagnetic bandgap structure. IET Microw. Antennas Propag. 11(12), 1719–1725 (2017) 9. Yang, F., Rahmat-Samii, Y.: Electromagnetic Band Gap Structures in Antenna Engineering. Cambridge University Press, Cambridge (2004) 10. Ripin, N., Awang, R.A., Sulaiman, A.A., Baba, N.H., Subahir, S.: Rectangular microstrip patch antenna with EBG structure. In: IEEE Student Conference on Research and Development (SCOReD), pp. 266–271 (2012) 11. Jaglan, N., Gupta, S.D.: Design and analysis of performance enhanced microstrip patch antenna with EBG substrate. Int. J. Microw. Opt. Technol. 10(2), 79–88 (2015) 12. Gao, G., Hu, B., Wang, S., Yang, C.: Wearable circular ring slot antenna with EBG structure for wireless body area network. IEEE Antennas Wirel. Propag. Lett. 17(3), 434–437 (2018) 13. Coulombe, M., Farzaneh Koodiani, S., Caloz, C.: Compact elongated mushroom (EM)EBG structure for enhancement of patch antenna array performances. IEEE Trans. Antennas Propag. 58(4), 1076–1086 (2010) 14. Jin, H., Zhang, L., Yang, X., Cheng, P., Li, E., Zhang, Y.: A novel heatsink with mushroomtype EBG structure for EMI radiation suppression. In: IEEE International Symposium on Electromagnetic Compatibility 2018. IEEE Asia-Pacific Symposium on Electromagnetic Compatibility (EMC/APEMC), pp. 772–775 (2018) 15. Jaglan, N., Gupta, S.D., Kanaujia, B.K., Srivastava, S.: Band notched UWB circular monopole antenna with inductance enhanced modified mushroom EBG structures. Wirel. Netw. 24(2), 383–393 (2016)

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16. Elayachi, M., Brachat, P., Ribero, J-M.: Miniaturization of printed antennas by using the EBG structures (ISAP2007), Niigata, Japan, 20–24 August 2007 (2007) 17. Sievenpiper, D., Zhang, L., Broas, R.F.J., Alexópolous, N.G., Yablanovitch, E.: High impedance electromagnetic surfaces with a forbidden frequency band. IEEE Trans. Microw. Theory Tech. 47(11), 2059–2074 (1999) 18. Elayachi, M., Brachat, P., Ribero, J.M.: Novel EBG structure for antenna miniaturization. In: The Second European Conference on Antennas and Propagation (EuCAP 2007), Edinburgh, 2007, pp. 1–4. IET Conference Publications (2007) 19. Elayachi, M., Ribero, J.M., Brachat, P.: Planar low profile and gain enhancement of printed antennas using EBG structures. In: 2008 IEEE Antennas and Propagation Society International Symposium, San Diego, CA, 2008, pp. 1–4. IEEE Conference Publications (2008) 20. El Ayachi, M., Brachat, P., Rahmoun, M.: New electromagnetic band gap structure for planar low profile antenna with wide bandwidth and high gain. Int. J. Commun. Antenna Propagat. 8(5), 385–389 (2018)

Web-Based Techniques and E-learning

Predicting Student Success in a Scholarship Program A Comparative Study of Classification Learning Models Majjate Hajar1(B) , Jeghal Adil1,2 , Yahyaouy Ali1 , and Alaoui Zidani Khalid1 1 LISAC Laboratory, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah

University, 30030 Fez, Morocco [email protected] 2 National School of Applied Sciences Fes (ENSA), Sidi Mohamed Ben Abdellah University, 30030 Fez, Morocco

Abstract. To be eligible in a scholarship program, the majority of Universities and academic organizations require applicant students to hold a high academic performance, that’s why students often have to show proof of their achievement and their excellence in multiple school subjects by presenting their school marks. Therefore, the selection of the right admitted student was never an easy task because selecting the wrong student will have a bad impact on the reputation of the institution. In this paper, we propose a Machine Learning method to build a predictive model, where we analyze and compare the performance of eight Machine-learning algorithms (Decision Tree, Random Forest, Extra Tree Classification, Gradient Boosting, Ada Boosting, K-Neighbors, Support Vector Classifier, and Gaussian Naive Bayes). This comparison will reveal the algorithm giving the best accuracy score on predicting student admission on our dummy model of scholarship program based on their scores and levels in different topics. In a second part, we explore the technique of Feature Importance to reveal and analyze The Most and the least important features that boost the admission chance. The sample consisted of 200 high school students who had been involved in this study by filling up an online questionnaire. These are our powerful tools to gain the suitable educational dataset to build our predictive model, which showed a high performance according to previous studies in predicting students’ chance of admission in terms of various performance metrics for binary classification. Keywords: Predicting admission · Ada Boosting · K-Neighbors · Random Forest · Decision trees algorithm · Linear regression · Support Vector Classifier · Gaussian Naive Bayes

1 Introduction In recent years, Data mining techniques have made a significant contribution to improve the learning process and making teaching methods more intuitive and flexible with today’s learner needs. Data mining application is not limited to the educational field, it covers a wide variety of data analysis procedures with roots in many domains [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 333–341, 2022. https://doi.org/10.1007/978-3-031-02447-4_35

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The application of Data mining techniques in the educational field gives birth to the educational data mining (EDM) discipline. A discipline that assembles a variety of tools and techniques used to explore and analyze the huge amount of information coming from various educational environments such as the big data collected from e-learning platforms. It allows us to analyze this data and find hidden patterns and information [2] to serve them as a powerful tool in the improvement of learning processes. The EDM aims to track students’ behaviors on the way to improve their performance, develop the quality of the teaching process, and support decision making in the educational institutions, which is considered a crucial procedure because managerial decisions contribute to the success or failure of an organization [3]. Under the context of supporting decision-making in the educational area, in this research we focused on studying different students’ profiles and predicting their chance of success in a scholarship program. Because, student selection is an important procedure to any educational institution, which aims to collect a homogeneous group of students with common skills and talents, for the common purpose of creating a successful scholar program as well as for its professional reputation. The student selection process enhances the quality of education and supports academic excellence at all levels. The student admission policy is a very difficult mission, expects from students specific skills and grades, which vary according to every institutions’ needs. However, with the use of the sophisticated tools of educational data mining the manipulation of students’ information, the classification of their profiles, and the prediction of their chance of success becomes an easy task. Therefore, this research is conducted in the first place to prove the importance of EDM techniques in supporting decision-making by measuring and predicting the chance of a student in getting admission in a scholarship program. In this way, this study starts by using the latest exploratory data analysis tools existing in the panda’s library to visualize and explore student’s data, to reach the stage of: • Predicting whether a student will be admitted or not in our dummy model of a scholarship program. • Studying the most important factors affecting admission chances for a student from our target population. • Removing the least important features and reclassifying student data to build a performant decision model. • Making a performance comparison between different Machine learning algorithms (Decision Tree, Random Forest, Extra Tree Classification, Gradient Boosting, Ada Boosting, K-Neighbors, Support Vector Classifier, Gaussian Naive Bayes) to show which one performs better in this study.

2 Related Works On our way to building this study, we found many researchers share with us the same interest in using educational data mining techniques to analyze and predict student performance and success. We found especially two significant works discussing the rule of Machine learning algorithms in supporting Decision Making in educational institutions. Acharya, M. S., Armaan, A., Antony, A. S. [4] studied some predictors on the way to guide students to choose a suitable university based on past admissions, they developed

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four models including linear regression, vector support, decision tree, and random forest on the way to find out which model gives the best prediction. Abdullah Mengash, H. [5] Proposed four Machine-learning models: Artificial Neural Network, Decision Tree, Support Vector Machine, and Naive Bayes where the ANN model was able to reach a performance accuracy level of 79.22% to Support Decision Making in KSA University Admission Systems.

3 Methodology 3.1 Data Collection In the first stage of this study, we collected 200 Baccalaureate students’ records from different public high schools in two regions across the kingdom of Morocco: Fes-Meknes and Tanger-Tetouan-al-Houceima. The dataset contains data previously collected from students’ responses on an online form. 3.2 Exploratory Data Analysis Analysis of Variables We have used the option of pandas profiling from the panda’s library, to visualize and analyze the content of the dataset, as we can see, we have four Categorical variables and six Numerical variables (Table 1). Table 1. Variables Variable name

Type

Predictor/response

Genre

Categorical

Predictor

Fil

Categorical

Predictor

Redouble

Categorical

Predictor

Math

Numeric

Predictor

Francais

Numeric

Predictor

Physique

Numeric

Predictor

Info

Numeric

Predictor

Arabe

Numeric

Predictor

Anglais

Categorical

Predictor

Admission

Numeric

Response

In this experiment, we have selected the variable “Admission” as the Response variable because it is the variable of interest in this study while the other variables are predictors that affect the response variable (Fig. 1).

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Fig. 1. Gender percentage

Fig. 2. Field of studies

We notice that we have 56.28% of girls and 43.72% of boys of the total enrolled students (Fig. 2). We also have five courses of studies: SM: mathematic science, STE: Science and electrical technology, LT: letter and human science, SVT: the science of life and earth, and PC: physical science. Relation Between Variables We have noted that male math students often have a higher admission rate compared to other students, while male of human science students has the lowest admission rates. Therefore, at this stage, we can say that the course of study has an important impact on students’ performance (Figs. 3 and 4). We note from the distribution of the Admission variable, that the admission rate has an increasing and solid relationship with the results obtained in the five school subjects. Since high admission rates are often placed on the same scale of good marks.

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Fig. 3. The relation between study courses and admission

Fig. 4. The relation between Admission and the five school subjects

3.3 Model, Training, and Testing In this study, our first objective is to predict whether a student will be admitted to the scholarship program or not. Therefore, we notice that we have a binary classification problem.

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def Decision(Admission): if (Admission >= 0.50):return 'admitted' else: return 'not admitted' df["Decision"] = df.apply(lambda x Decision(x["Admission"]),axis=1)

We added a column in the dataset we called “Decision”, which assumes that a student is admitted if he obtains an admission rate of more than 50% and not admitted if he obtains an admission rate of less than 50%. Therefore, the variable Decision will be our target variable (response). Our target variable has only two possibilities either ‘admitted’ or ‘not admitted’. We tested eight classification algorithms on the way to choose the best model with the best accuracy score. The tested algorithms are: Decision Tree Classifier The Decision tree is a supervised learning algorithm used to solve regression and classification problems [6], presented similar to a flow chart [7]. A decision tree starts from a root node, and proceeds to split the source set into subsets, based on a feature value, to generate subtrees [8], it is a simple and powerful algorithm, widely used in decision making. Random Forest Classifier and Extra Tree Classification The Random forest Classifier combines multiple decision trees algorithm; it retrieves the prediction value from every tree and selects the best prediction score using voting. It gives also a good result in calculating the feature importance. On the other side, the Extra Trees Classifier method builds a collection of decision trees, its works the same way as the Random Forest but differs on the way of extracting the optimal split of the tree, with a variance in the execution time. Gradient Boosting Classification and Ada Boosting Classification Gradient Boosting is a robust machine-learning algorithm based on the technique of Boosting; this technique consists of training sequentially several underfitting models (mostly a decision tree) where each model corrects and optimizes the performance of the previous model to finally form a strong model. Adaptive Boosting algorithm or (AdaBoost) is an earlier version of Gradient Boosting, compared to gradient boosting, AdaBoost works by increasing the weight of the weakly predicted instances of each iterate and decreasing the weight of the well-predicted instance; the final prediction score recovers the total weights, instead of gradient boosting which work on optimizing the Loss Function during iterations. K-Neighbors Classification The K-nearest neighbor classifier is a simple and powerful supervised learning algorithm. The main idea of the KNN Neighbors is measuring the distance such as the Euclidean

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distance by calculating the distance function, between the tested data and all the training data to find the closest neighbors on the way to put it in the suitable Class. Support Vector Machine Support Vector Machine is a collection of classification and regression methods used in machine learning, the main idea of this Classifier is creating the optimal line to separate the existing data into homogeneous classes; the dividing line in this task is called a hyperplane. Gaussian Naive Bayes The Naïve Bayes classifier is a fast and easy classification method based on applying the probabilistic theory of Bayes. It is a collection of algorithms that considers each feature as an equal and independent variable. This Classifier can be an efficient tool to solve many classification problems.

4 Results and Discussion 4.1 Comparative Analysis of Models

Table 2. Comparative table Classification algorithm

Accuracy score

RandomForestClassifier

0.925%

DecisionTreeClassifier

0.875%

Gradient Boosting Classification

0.925%

Ada Boosting Classification

0.975%

Extra Tree Classification

0.925%

K-Neighbors Classification

0.95%

Support Vector Classification

0.97%

Gaussian Naive Bayes

0.95%

After testing the eight Algorithms, we can see that the Ada Boosting Classification algorithm gives the best accuracy score with a total of 0.975%, to prove that AdaBoost is qualified to give the best performance on binary classification problems. Without ignoring the high percentage given by the other Classification models, the Support Vector Classifier with an Accuracy Score of 0.97%, followed by The Gaussian Naive Bayes, K-Neighbors Classifier with 0.95% (Table 2). Generally, all the models give a good Accuracy score making just a small difference.

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4.2 The Most Important Features vs the Least Important Features Feature Importance is one of the most important concepts in machine learning; it presents the most important features contributing to our prediction variable. So after choosing two feature importance properties of two Classification models: Ada boost Classifier and Random Forest Classifier, we notice that the Field of study and redouble (student repeating years) does not seem to affect much the chance of being admitted in the scholarship program, optionally we can remove those least important features and reclassify our data (Fig. 5).

Fig. 5. Features importance

5 Conclusion Throughout this paper, we have conducted a comparative study of the most powerful Classification Algorithm on machine learning; we have explored various classifiers to solve a serious problem in the educational field. It is the problem of deciding the admission of new students in a scholarship program or a limited class within a university. We have experimented with our sample with a lot of algorithms and methods selecting the most important features helping students to have more chance in getting admission. In addition, solving the problem of getting admission or not by converting our continuous target variable on a binary one to explore such a useful tool to solve this type of Binary Classification Problem. All the selected models showed a high performance in predicting students’ chance of admission in terms of various performances for binary classification.

References 1. Bunge, J.A.: Data Mining. Kimberly Kempf-Leonard (2005). https://doi.org/10.1016/B978012373642-0.50007-7 2. Zori´c, A.B.: Benefits of Educational Data Mining, vol. 6. Marketing, J. O. (ed.) (2020). https:// doi.org/10.18775/jibrm.1849-8558.2015.61.3002

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3. Kaniati Amalia, A.K.: Leadership in education: decision-making in education. In: Advances in Social Science, Education and Humanities Research (2020) 4. Acharya, M.S., Armaan, A., Antony, A.S.: A comparison of regression models for prediction of graduate admissions. In: International Conference on Computational Intelligence in Data Science (ICCIDS), pp. 1–5 (2019) 5. Abdullah Mengash, H.: Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access 8, 55462–55470 (2020) 6. Chauhan, N.S.: Decision Tree Algorithm, Explained (2020). Retrieved from kdnuggets: https:// www.kdnuggets.com/ 7. Ranjit Panigrahi, S.B.: Classification and analysis of Facebook metrics dataset using supervised classifiers. In: Social Network Analytics. Academic Press (2019) 8. Du, C.J., Sun, D.-W.: Object classification method. In: Food Refrigeration and Computerised Food Technology. University College Dublin, National University of Ireland, Dublin 2, Ireland (2008). https://doi.org/10.1016/B978-012373642-0.50007-7

Software Architecture for Personalized Learning Systems: A Systematic Literature Review-Camera Ready Version Maida Ahtesham, Ramsha Khan(B) , and Ayesha Zulfiqar Computer Science and Software Engineering Department, Jinnah University for Women, Karachi, Pakistan [email protected]

Abstract. In recent times e-learning systems have emerged as an essential asset for all institutions and learning processes. As in this era, a wider range of people and institutions are moving towards the personal e-learning system for the feasibility in the education area. Personalization of learning systems holds a significant advantage over traditional methodologies. Such learning systems are known for offering a learning solution for large classes of diverse backgrounds, attitudes, and learning needs. Various concepts and studies have been discussed regarding personalized learning models and frameworks. Educational systems seek to benefit traditional learning environments by personalized learning styles and foster effective, active, and satisfactory learning. This research evaluates software architectures for personalized learning systems that have been discussed in literary studies in the last 10 years (2010–2020). In particular, the research identifies main areas that include, i) how traditional learning’s lack in offering current learning needs, and ii) how personalized learning platforms could be a solution. To obtain this purpose systematic literature review is done to evaluate proposed Personalized learning platforms and what results they have yielded in educational settings. Keywords: Adaptive learning · Personalized learning · Adaptive learning architecture

1 Introduction 1.1 Background In a learning context, it is believed that each learner is unique and hence they vary in their ways of learning. The variations exist in motivations, learning styles knowledgeability, and diverse cultures. Traditional learning is unable to fulfill the requirements based on such variations [1]. In contrast to this, E-learning platforms have proved to persist the potential to serve the learning according to learner’s preferences. The concept of E-learning has been evolved as the modern-day needs to fulfill distance education. E-learning management systems enable an unprecedented level of accessibility to educational resources and several other services including qualified instructors anywhere at © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 342–350, 2022. https://doi.org/10.1007/978-3-031-02447-4_36

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any time. The technological boom has increased the research and development area of this domain. The undergoing advancements in the educational world have provided institutions an opportunity to adjust these technologies and advancements according to their own needs and perceive their advantages [2]. To achieve the expected learning outcomes and advantages E-learning can be integrated to enable learners to have their learning scenarios. It can provide learning personalization based on individual characteristics. The personalized e-learning architectures are necessary for the production of innovative e-learning systems [2]. Such as traditional LMS system terms that offer similar services and features to all its users. But, the several proposed architectures of personalized learning systems aim to provide a personalized approach to E-learning’s, profiling, content adaptation and annotation, and many other features. This paper evaluates many such personalized learning systems and how it has overcome the issues of traditional learning. To do this, research studies conducted in the span of 10 years i.e. 2010–2020 have been searched and sorted using inclusion and exclusion criteria to understand the driving factors of personalized learning systems and what qualities they possessed over the traditional learning management systems. 1.2 Personalized Education The educational domain does not work on the approach called “one size fits for all”. This idea has provided the spark to the personalized learning concept. There might be a significant difference in the learning objectives, instructional approaches, and instructional content including the sequencing in the 21st-century skills, personalization equates with customization or digital personalization which frames the learning experiences [3]. Many researchers have made attempts to develop architectures and frameworks that can improve the learning experience and provide a personalized learning environment. As several personalized learning architectures identify the role of the instructor as the separate individual that is part of the diagnostic module of the learning system and is responsible for students grading and other instructive functions. The research area of personalized learning has broadened over several years to develop personalized learning models that fulfill modern-day learning needs. This research has come across several models and architecture that aims to provide personalized learning experiences [1] and web-based framework for user-centered evaluation [2] and many other related components, frameworks, and architectures has been proposed that meets the learning styles, background knowledge, and performance of personalized learning environment. It enables the selection and customization of learning content and activities and the sequencing of learning resources to readily suit the needs of particular learners. Schmidt and Petco [3] claimed that the literature on personalized learning shows that it is a multilayered construct. According to the researcher, [4] the personalized learning architecture proposed by Chin et al. [5] Offers learning materials that are closer to learners learning styles to make the learning’s more easy and natural. In the past ten years, advances in educational technology have been closely related to personalized learning [6]. This paper evaluates several architectural patterns of the personalized learning systems presented in literature and their quality attributes which is the reason behind providing preference over traditional learning systems.

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2 Methodology The research area of personalized learning is extremely vast and several studies have been identified between 2010 and 2020. The study aims to conduct a systematic literature review and evaluate the studies that have discussed the frameworks or architectures of personalized learning systems. In this respect, there are several sources for the research articles that can be referred to collect the relative studies including IEEE, Google Scholar, Science Direct, ACM digital library, Springer, and many more. In the systematic literature review, it is important to find the relative studies and efficient results. In this aspect, keywords search plays an important role to hit the target studies. The process of keyword determination is done using the combination of AND and OR operators. The AND operator aims to connect two aspects whereas the OR operator sets the selection of terminology or term. There are also several possible sections of the research article where the search can be conducted including abstract, Title, or full text. The researcher has to hit the correct target to find the appropriate results. 2.1 Research Questions What is the existing software architecture pattern for e-learning systems? Layer pattern, client-server pattern. Is current software architecture ready to cope with the current needs of a personalized learning system? No, the current systems lack in fulfilling all the requirements. What are the important driving quality attributes for personalized e-learning architectures found in literature? Scalability, interoperability, modifiability. 2.2 Search Process This study has utilized multiple search databases to find the relative studies. The search process is conducted by trying alternative search queries and getting the appropriate results, which are refined later to select studies that can fulfill the objectives of personalized learning. The sources are as follows: 1. 2. 3. 4. 5.

ACM Digital Library (https://dl.acm.org/) IEEE Explore (https://ieeexplore.ieee.org/Xplore/home.jsp) JSTOR (https://www.jstor.org/) Science Direct (https://www.sciencedirect.com/) Springer (https://www.springer.com/in)

2.3 Search Query To conduct the systematic literature review, the following databases are traversed to find the studies that have done contributions in the personalized learning domain. To do this, keyword search has been done by trying alternative combinations of operators AND, OR. Some of the search queries are as follows: 1. Tite: (Software architecture AND adaptive AND personalized Learning) “filtre”: Publication Date: (12/01/2010 TO 12/31/2020), NOT Virtual- Content: truie

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2. Tite: (“Document Tite”: software architecture for personnalise Learning) 3. AND (“Document Tite”: software architecture) AND (“Document Tite”: architecture personnalise Learning) filterpublication date: 2011–2021 4. Software architecture for personnalise Learning system or adaptive Learning and publishing year from 2010 to 2020 5. Software Architecture AND (Personnalise OR Adaptive) AND e-learning and publishing year from 2010 to 2021 6. Software Architecture AND Personalised AND Learning publishing year from 2010 to 2021

3 Data Extraction The number of papers goes through for the reason that first degree is 1616 Papers. The second degree its miles filtered into 127 papers created on inclusion and exclusion. Also, at the last level a selection of 22 papers that directly linked with the studies Inquiries to discover the content material and dialogue so one can Solution research questions? The overall consequences can be seen in the table underneath (Table 1). Table 1. Examine results for personalized learning’s Search results for personalized learning’s Database

Research found re-results

Observed results

Final results

ACM digital library

129

30

14

IEEE Explore

334

09

01

JSTOR

70

20

7

SCIENCE DIRECT

83

74

08

SPRINGER

1000

68

26

Total

1616

208

50

4 Results The cause of accomplishing literature observed in this looking research is to perceive the general standards of studying Framework of personalized eLearning concerning software architecture First, the criteria you need to understand include the common components that shape the idea for determining the personalization thing. Second, Identify the schemes or equipment used to decide to get to know the route to match the dreams and desires of the learner idea used. The Subject matters supplied in this research are (1) the tendency of “decided on studies” including the search and year of publication, Kind (journal/conference), (2) the tendency of the use of Components decide the personalization of gaining knowledge of, and (3) the methods used to decide the learning

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track to software architecture. Constructed on the literature overview, thing troubles, and the goal of making a customized studying environment. In the meantime, every learner is a unique and complex man or woman. Involving mental and pedagogical disciplines. There were three important points on which the researchers focus to maintain personalized learning with the software architecture 1) modeling learners and their demonstration to provide engaging learning experiences. 2) Designing adaptive support. 3) Constructing standards-based models to handle interoperability and portability. The most architecture pattern used in current e-learning system researchers has found is layered and client-server pattern although the current system needs attention on quality attributes like scalability, interoperability, etc. The articles had been coded and issues derived based on the following rubric: the system used, the aim of the study, target institution, content material, and elements of adaptation. We read research papers from them few which are mainly related to personalized learning with the architecture they are mentioned in the Table 2.

5 Discussion This paper has incorporated the systematic literature review to create an understanding of personalized learning architectures and what benefits does it hold over the traditional learning management systems. The literature studies found have identified the potential loopholes found in current e-learning architectures that are unable to meet modern learning’s requirements. There are many existing E-learning systems such as Blackboard, Moodle, ski, and Tutor. Though these learning systems have been widely used in institutions they lack in providing comprehensive functions as the system structure is fixed and is not customizable. And hence it is not able to support all the teaching and learning modes. Moreover, these existing software systems are not able to scale and are difficult to integrate with other functional modules. The following problems occur due to inefficient design and development of software systems. Including this, software systems work well when the nonfunctional factors are also considered to be important such as adaptability, re-usability, transferability, and reliability. Software architectures consider such quality attributes of software systems. The literature has explored several existing architecture patterns that are applied in personalized learning systems. It has been observed that most of the learning management systems are developed using layered and Service-oriented architecture patterns. The most prominent attributes that have been found in the personalized learning architecture are their scaling and efficient adoption capabilities. They can assist the individual needs of learners and teaching staff. It is mound so effectively to fit the needs of each individual. In this respect, several personalized learning architectures have been proposed by the researcher, which when practically implemented, would be able to carry out personalized learning effectively.

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Table 2. Search result of personalized learning-1 Database

Results for initial Main journals listed search on personalized learning

ACM digital library 129

Adaptive Behavior Animals, Animates, Software Agents, Robots, Adaptive Systems (44) Advances in Artificial Neural Systems (1) Biological Cybernetics (1) Education and Information Technologies (1) Genetic Programming and Evolvable Machines (1) IEEE Transactions on Software Engineering (1) International Journal of Adaptive Control and Signal Processing (1) International Journal of Web-Based Learning and Teaching Technologies (1)

Main conference listed

SEAMS’19: Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (4) SEAMS’20: Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (4) SEAMS’18: Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems (3) COMPSAC’15: Proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference - Volume 03 (2) SEAMS’16: Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (2) (continued)

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Database

Results for initial Main journals listed search on personalized learning

JSTOR

70

Journal of Educational Technology & Society (11) British Journal of Educational Studies (2) British Educational Research Journal (2) Learning and Teaching: The International Journal of Higher Education in the Social Sciences (2) Educational Technology Research and Development (6) European Journal of Education(2)

Springer

1000

Education and Information Technologies (22) User Modeling and User-Adapted Interaction (11) Journal of Internet Services and Applications (1) Neural Computing and Applications (2) Vietnam Journal of Computer Science (1) Multimedia Tools and Applications (7) Applied Intelligence (2) Multimedia Tools and Applications (7) International Journal of Educational Technology in Higher Education(4)

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(continued)

Software Architecture for Personalized Learning Systems

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Table 2. (continued) Database

Results for initial Main journals listed search on personalized learning

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IEEE

334

International Conference on Intelligent Agent & Multi-Agent Systems International Conference on Advanced Learning Technologies IEEE Transactions on Learning Technologies 7th International Conference on Computer Science & Education (ICCSE) IEEE Transactions on Learning Technologies International Conference on Computer Science and Service System (CSSS) 17th International Conference on Advanced Learning Technologies (ICALT)

IEEE Access

6 Conclusion and Future Work This research evaluates software architectures for personalized learning systems. Personalization of learning systems holds a significant advantage over traditional methodologies. The study aims to conduct a systematic literature review and evaluate the studies that have discussed the frameworks or architectures of personalized learning systems that have been discussed in literary studies in the last 10 years (2010–2020). This research has come across several models and architecture to provide personalized learning experiences such as architecture for an adaptive personalized learning environment for content recommendation and web-based framework for user-centered evaluation and many other related components, frameworks, and architectures that have been proposed that meets the learning styles, background knowledge, and performance. The search process is done by first visiting the source specified for database literature and entering the possible search query to find the results. In the next step the in-depth traversing is done, where the titles and abstracts of the relative studies are traversed to filter the results under the research objective of this study. In the third stage, the complete

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paper is evaluated to match the preference and objectives of the study. The first degree is 1616 Papers, within the second degree, its miles are filtered into 127 papers based on inclusion and exclusion. Also, at the last level Selected 22 papers that associated with the studies. The researcher has to hit the correct target to find the appropriate results. In the future, we want to focus in detail work on the Issue of trustfully associated with privacy that gives value to personalization, so any mechanism to take trust and privacy also needs to consider personalization. Personalization e-learning system mostly uses web-based recommendation but the knowledge base is further needed to be considered, future can be applying on different and big dataset and more personal, social and participatory frameworks have to be adopted. In recent times there are fresh possibilities for constructing interoperable personalized getting-to-know answers that remember a much broader variety of statistics coming from numerous learner situations and interaction functions. Personalized learning software structures cover an extensive spectrum of technological frameworks, interplay fashions, and academic models. As a result, it’s miles regularly tough to have a not unusual vocabulary between users and technical designers of personalized studying software systems.

References 1. Wan, S., Niu, Z.: A learner-oriented learning recommendation approach based on mixed concept mapping and immune algorithm. Knowl.-Based Syst. (2016). https://doi.org/10.1016/j. knosys.2016.03.022 2. Jando, E., Hidayanto, A.N., Prabowo, H., Warnars, H.L.H.S.: Personalized e-learning model: a systematic literature review (2018). https://doi.org/10.1109/ICIMTech.2017.8273544 3. Santos, O.C., Kravcik, M., Boticario, J.G.: Preface to special issue on user modelling to support personalization in enhanced educational settings. Int. J. Artif. Intell. Educ. 26(3), 809–820 (2016). https://doi.org/10.1007/s40593-016-0114-z 4. Erümit, A.K., Çetin, ˙I.: Design framework of adaptive intelligent tutoring systems. Educ. Inf. Technol. 25(5), 4477–4500 (2020). https://doi.org/10.1007/s10639-020-10182-8 5. Ismail, H., Belkhouche, B.: A reusable software architecture for personalized learning systems (2019). https://doi.org/10.1109/INNOVATIONS.2018.8605997 6. Limongelli, C., Miola, A., Sciarrone, F., Temperini, M.: Supporting teachers to retrieve and select learning objects for personalized courses in the Moodle-LS environment (2012). https:// doi.org/10.1109/ICALT.2012.110

A Modeling Learner Approach for Detecting Learning Styles in Adaptive E Learning Systems Ibtissam Azzi1 , Loubna Laaouina2 , Adil Jeghal3 , Abdelhay Radouane4(B) , Ali Yahyaouy5 , and Hamid Tairi5 1 Department of Informatics, Centre Régional des métiers de l’éducation et de Formation de

l’oriental, Annexe de Nador, Nador, Morocco [email protected] 2 LISA, National School of Applied Sciences, University of Sidi Mohamed Ben Abdellah, Fez, Morocco [email protected] 3 LISAC, National School of Applied Sciences, University of Sidi Mohamed Ben Abdellah, Fez, Morocco [email protected] 4 Department of Informatics, CRMEF, Centre Régional des métiers de l’éducation et de Formation, Fez, Morocco [email protected] 5 LISAC, Department of Informatics Faculty of Science Dhar-Mahraz, University of Sidi Mohamed Ben Abdellah, P.B 1796, Atlas-Fez, Morocco [email protected], [email protected]

Abstract. In adaptive E Learning systems, learner properties are often modeled to provide information about their preferences and their learning style. Thus, the learning style model is the most used personalization parameter in the modeling of learners. The major problem is how this model can be used to provide efficient learner modeling. In this paper, we are studying this important research topic to create a learner model that can facilitate the detection of learning style. The basic idea is to introduce into the proposed model a new field of information concerning the motivation about each dimension of the learning style model considered. To this end, the dimensions of Felder’s and Silverman’s learning styles model are considered. The motivation rate corresponding to each dimension is measured and then stored in the model built to allow immediate detection of the learning style by simply consulting the field associated with the motivation rate and without resorting to treatments dedicated to the detection of styles nor the use of classification techniques. The proposed modeling approach exploits the benefits of existing standards to be able to reuse other models, which makes it possible to add the proposed new information field, namely the field associated with the motivation rate. To represent and store the profiles of the learners the XML standard is used. Keywords: Adaptive e-learning · Modeling learners · Learning styles · Felder and Silverman learning styles model · Motivation measure · Detecting learning styles · XML standard

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 351–360, 2022. https://doi.org/10.1007/978-3-031-02447-4_37

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1 Introduction Learner modeling is the key element in any E-learning system design so the way of representing the learner remains a major problem in the research work. Indeed, the modeling of the learner aims at structuring the information that concerns him so that the system uses them on the one hand to improve the interaction between the learner and the system. On the other hand to provide means of automatic manipulation that can be used either for the interoperability of learner profiles or for the adaptation of learning. According to Koch [1], learner modeling aims to adapt the content of learning according to the needs of the learner, to make it easier for learners in the phase of choosing an educational content, to adapt and to set up a navigation interface for each learner, helping learners in collaborative work and finally helping teachers to have a clearer idea about the level of learners. However, the quality and quantity of information that learner profiles must contain depends closely on the purpose of the use of modeling. Thus, a learner profile may contain the learner’s personal information, characteristics and properties. These properties are stored in the learner model after assignment of corresponding values. These values may be final or may change. In terms of design of models, we find in the literature several standards of learner models proposed to solve different research problems and to meet needs in the design phase of E Learning systems. Thus, for the PAPI learner standard [2], the aim is to propose a standard for learner modeling in E learning systems in order to facilitate communication between cooperative systems and allow teachers to build teaching resources adapted to Learner models defined according to this standard. On the other hand, for the IMS-LIP (Instructional Management System Learner Information Package) standard that was developed first to allow interoperability between IMS compliant systems, then became a standard for several E Learning systems [3]. The aim of this standard is to standardize the representation of learners as well as to use the profiles built from this model in the learning personalization phase. However, to meet learning needs in terms of learning style, the use of learning style models allows to identify all the ways in which each learner prefers to learn. Furthermore, in these models we suppose that each person has a distinctive way to perform learning, to organize and process information [4]. Then, from existing studies in the literature, it is established that among the learner models that describe the learning style, the Felder and Silverman model [5] turns out to be the most used model. For this model, learning styles are classified into four dimensions which expresses different aspects of learning. Thus, depending on how learners perceive the information, receive the information, process the information and in the end understand information, they can be modeled as ‘sensory’ or ‘intuitive’, ‘visually’ or ‘verbally’, “active” or “reflective” and “sequential” or “global” respectively. Thus, the learning style models can be integrated into adaptive learning systems to reach different perspectives, and the detection of learning styles is the main perspective, according to research studies [6–9] and [10]. Our work is part of learning style detection and involves presenting a learner modeling approach that can provide a way to detect learning styles by introducing into the proposed model a new information field about motivation for each dimension of the Felder and Silverman learning styles model. This allows an immediate detection of the learning style by simply consulting the field associated with the motivation rate and

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without resorting to treatments dedicated to the detection of styles nor the use of classification techniques. The following sections describe the remainder of the paper. So, in the next section we will present the related works for learning style detection in adaptive E-Learning systems. Section 3 describes the approach used to detect the learning style. Section 4 presents the proposed model. Finally, Sect. 5 concludes the paper.

2 Related Works In E Learning systems, knowledge of different styles can offer adaptability so as to optimize the learning process and improve efficiency. Thus, several studies have been conducted on the detection of learning styles and recently, the most proposed approaches are centered on artificial intelligence techniques to solve the problem of style detection using techniques such as fuzzy logic, neural networks, tree decision and Bayesian network. According to Abdullah et al. [11], the FSLSM questionnaire index is used to predict the learning styles and then the classification of learning style is ensured by the Naive-Bayes Tree technique. However, this proposition has not considered the learner’s online behavior. For the same reason, the algorithm developed by Latham et al. [12] has not considered the learning styles in adaptive e-learning context since it considers only questionnaire data of FSLSM. But, according to their approach the Artificial Neural Network technique is combined with the Multi-Layer Perceptron in order to classify the learner’s learning styles. In a research conducted by Ahmed and Badii [13] the learning styles are integrated into Educational Hypermedia Systems. The importance of this integration is clarified by mentioning various techniques, namely Artificial Neural Network, Bayesian Network and Decision Tree etc. in order to classify learners’ online data. In other research conducted by Deborah et al. [14], the Fuzzy rules are introduced in order to handle the uncertainty problem in the learning style predictions process. In this research, the authors have considered many parameters such as image area and mouse movement to represent the fuzzy rules. This research reveals that in web environments the FSLSM is suitable for learning style identification. However, only some learner categories of FSLSM are considered and not all categories. Based on the above cited limitations and the fact that it is difficult to compare the performance of different approaches as mentioned in Feldman et al. [15], the problem of detecting the learning styles is still opened.

3 A Proposed Approach to Detect the Learning Style Our approach proposes a learner model that can provide a way to detect learning styles. An information field about motivation for each dimension of the Felder and Silverman style model is introduced into the proposed model. In that new field the motivation rate corresponding to each dimension of the FSLSM is stored. For both new and current learner, the system provides a learning situation which includes learning objects involving one of the learning styles corresponding to one of the dimensions of the FSLSM. Then the motivation rate in relation with the situation is measured via a dedicated questionnaire. The other dimensions will be considered in the next situations that the learner will perform, thus going through all the dimensions, the system will have the complete

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information concerning the learning style and then will be able to predict the learner’s learning style. The mapping or the labeling of learning objects (LOs) as per FSLSM [16] is shown by the following table (Table 1): Table 1. Labeling of LOs as per FSLSM

3.1 The Main Contributions of the Proposed Approach The main contributions of this learner modeling approach concern two major aspects involved in the detection of styles. Firstly, the use of index learning style (ILS) is confronted with some misuses as mentioning in the analysis given by [17]. So, by using the ILS the scoring methods that counts response are closely related to the learning style independently of the subject of learning. For example, if a student comes out as a sensing learner on the ILS, it does not mean that he should avoid science at all costs. In our approach, we are interested in detecting style in relation with the subject of learning. For this purpose, for each learning subject, we measure the motivation concerning the way of learning [18] and [19]. This measurement is done by a dedicated questionnaire. The instructor has the choice to set a motivation threshold. Secondly, the model we are proposing allows immediate detection of the learning style by simply consulting the field associated with the motivation rate and without resorting to treatments dedicated to the detection of styles nor the use of classification techniques. Thirdly, with this approach we can make the learning styles detection as a dynamic one by applying the procedure of detection if necessary (if the instructor wants to adjust the learning style).

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3.2 The Methodology of Styles Detection of Our Approach The methodology’s steps of styles detection of the proposed approach are shown in the following figure (Fig. 1):

Questionnaire Learners Motivation Rate motivation measure Data acquisition Learner model Detecting learning styles Fig. 1. Steps of the proposed methodology

4 A Proposed Learner Model In the present work we aim to develop a new learner model based on existing standards. Indeed, the various existing standards offer the possibility of using existing models, whether to make changes or to improve them. Thus, on the basis of the analysis made we intend to take advantage of the existing standards to try to add other fields to the existing models. In this sense, we will in particular introduce the field of information which reflects the motivation of the learners in relation to the different learning styles of the FSLSM. 4.1 Standards Used to Create the Profiles of Our Learner Model The standards we are adopting to create our model are: Firstly, the IMS Learner Information Package [3]. It enables the storage of information about learners for processing, maintenance and interoperability in different learning management systems. This standard is defined in an XML structure and includes the following categories: The identification category which contains information about the learner that includes personal data (name, age, gender, address, phone, email, demographic information, etc.). The qualification category which contains information regarding the official training accreditations of the learner, such as licenses and diplomas obtained. The affiliation category stores information about the organizations with which the learner has been associated. Information to be included in this category includes data on the organization in question, the role of the learner in that organization and the duration of such affiliation. The goal category

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which contains information about the different educational goals of the learner. This category may optionally include information to track the learner’s progress in achieving these goals. The skills category which contains information describing the skills and the different experiences acquired by the learner. These skills may possibly be associated with elements described in the qualification or activity categories, indicating that they are the results of said certifications or activities. The interest category which describes the hobby activities of the learner. The activity category which contains information about the different activities related to the training of the learner. The accessibility category which describes the preferences, needs and disabilities of the learner. The transcript category which contains information regarding the content of the learner’s training. The security category which contains information regarding the security of learner data, such as passwords and access rights. The relationship category indicates the relationships between the elements defined in the different categories. Indeed, in many cases, some elements of different categories are linked. Thus, a certain educational activity defined in an activity category (for example, completing a vocational training), can be linked to obtaining an official certificate described in a qualification category and involve the acquisition of a series of skills described in a skills category. Secondly, the PAPI Learner standard [2] is introduced specially to consider the learner data treatment executed by the systems. In this sense, the PAPI standard considers many types of learner information such as Learner Contact Information, Learner Relations Information, etc. Thirdly, the FOAF standard [20]. It consists on the connection between people and objects in one side and semantic information on the other using a web platform. Accordingly, distinguishes four categories of properties to describe a profile: Properties defining the person’s identity: These properties give person identity information such the first name, the age, the gender and the according person image. Properties describing person coordinates: It give the contact information such the Web page, the mail… Person web activities properties: They are the online account, the topic interest and the publications. Relationships: these properties define relationships linking directly or indirectly a person to another or to a group, to a project or to an organization. And finally, the Jean Daubias Model is also adopted. This model has been proposed in order to promote the reuse of existing models. Indeed, according to the author, the learner profiles concern different subjects for several learners. It can also come from the analysis of activities undertaken remotely or not. Activities can correspond to various levels and disciplines. The profiles are created at the request of certain initiators, then constituted by different creators so that they can be used by different recipients. According to the author also, the profiles are differentiated according to: the type of information, the nature of the information, the time taken to carry out the activities as well as the evaluation of these activities to give it a note. Other dimensions resulting from the differentiation are also proposed by the author, namely: internal representation, external representation, visualization, the evolution of the learner’s knowledge, the different standards used, the platforms, the devices and the storage format. In short, it is about twenty dimensions. 4.2 Structure of the Proposed Model Our model contains seven main categories and aims to assure providing information about administration, identification, security, accessibility, diplomas, training and the

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environment. So, this model also permits the reuse of the created profiles by other systems which guarantees the portability. To represent and store the profiles of the learners the XML standard is used. The structure of the proposed model is represented by the following figure (Fig. 2):

Fig. 2. The proposed structure of our model

4.3 Structure of the Training Category We are interesting about the motivation field so we describe in this study the training category which contain the subcategory where motivation about the FSLSM dimensions will stored. The description omits the other categories. The structure of the training category is represented by Fig. 3. In this category all information regarding training are almost gathered essentially the motivation about the FSLSM dimensions. To do so, subcategories are created, namely the subcategory concerning the learning objective (goal), the date subcategory, which designates the start date and the end date of the training which makes it possible to have an idea on the training duration, we then move on to the relationship subcategory which represents the collaborative work of the learner (group project, etc.), the assessment subcategory is used to assess a learner, to indicate his grades, his classification, his progress in the training process (notes are taken into consideration in this subcategory) and finally in this subcategory, we also indicate the motivation of the learner concerning each dimension of the FSLSM. (This information is subdivided into eight sub-categories, each containing the learner’s motivation rate for each dimension of the FSLSM). For the event subcategory, it serves to present the various events in which the learner will participate, while the publication subcategory is maintained optional.

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goals start date end group relaon

project other date

assessment

note

motivation Active

evolution

motivation Reflective

ranking

motivation Sensing

Training

FSLSM motivation

motivation Intuitive motivation Visual motivation Verbal motivation Sequential motivation Global

name evenement

date lieu

publicaon Fig. 3. Structure of the training category

4.4 Portability Purpose In general, the information contained in a profile comes from several systems which requires their conversion. This is the case in our work. So, to transform the external profiles our system provides the opportunity to view profiles already created in order to re-edit them while respecting the representation adopted by our model. The following

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figure shows the handling system that offers visualization and transformation to assure portability of our model (Fig. 4):

Fig. 4. Transformation of the external profiles

5 Conclusion After studying of the existent work on both learner modeling and learning style detection, we describe our new approach which consists of creating a learner model that can facilitate the detection of learning style models. The basic idea is to introduce into the proposed model a new field of information concerning the motivation about each dimension of the learning style model considered. To this end, the dimensions of Felder’s and Silverman’s learning styles model are considered. The motivation rate corresponding to each dimension is measured and then stored in the model built to allow immediate detection of the learning style by simply consulting the field associated with the motivation rate and without resorting to treatments dedicated to the detection of styles nor the use of classification techniques. So, the use of index learning style is also avoided by this approach. The proposed modeling approach exploits the benefits of existing standards to be able to reuse other models, which makes it possible to add the proposed new information field, namely the field associated with the motivation rate. To represent and store the profiles of the learners the XML standard is used. The next stage of our work consists of using another procedure of detection to provide an automatic method of detecting the learning style using the data stored in our learner model.

References 1. de Koch, N.P.: Software engineering for adaptive hypermedia systems-reference model, modeling techniques and development process (2001)

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2. Farance, F.: Draft standard for learning technology. Public and private information (PAPI) for learners (PAPI Learner), Version 6.0, Technical Report. Institute of Electrical and Electronics Engineers, Inc. (2000). http://ltsc.ieee.org/wg2/papi_learner_07_main 3. IMS Global Learning Consortium: IMS Learner Information Packaging Information Model Specification version 1.0. IMS Global Learning Consortium (2001) 4. Jones, D., Mungai, D.: Technology-enabled teaching for maximum learning. Int. J. Learn. 10, 3491–3501 (2003) 5. Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988) 6. Aslan, B.G., Öztürk, Ö., Inceoglu, M.M.: Effect of Bayesian student modeling on academic achievement in foreign language teaching (university level English preparatory school example). Educ. Sci. Theory Pract. 14(3), 1160–1168 (2014) 7. Chang, Y.C., Kao, W.Y., Chu, C.P., Chiu, C.H.: A learning style classification mechanism for e-learning. Comput. Educ. 53(2), 273–285 (2009) 8. Özpolat, E., Akar, G.B.: Automatic detection of learning styles for an e-learning system. Comput. Educ. 53(2), 355–367 (2009) 9. García, P., Amandi, A., Schiaffino, S., Campo, M.: Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comput. Educ. 49(3), 794–808 (2007) 10. Azzi, I., Jeghal, A., Radouane, A., Yahyaouy, A., Tairi, H.: A robust classification to predict learning styles in adaptive e-learning systems. Educ. Inf. Technol. 25(1), 437–448 (2019). https://doi.org/10.1007/s10639-019-09956-6 11. Abdullah, M., Daffa, W.H., Bashmail, R.M., Alzahrani, M., Sadik, M.: The impact of learning styles on learner’s performance in e-learning environment. Int. J. Adv. Comput. Sci. Appl. 6(9), 24–31 (2015) 12. Latham, A., Crockett, K., Mclean, D.: Profiling student learning styles with multilayer perceptron neural networks. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp. 2510–2515 (2013) 13. Al-Azawei, A., Badii, A.: State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss). Int. J. Comput. Sci. Inf. Technol. 6(3), 1–10 (2014) 14. Deborah, L., Sathiyaseelan, R., Audithan, S., Vijayakumar, P.: Fuzzy-logic based learning style prediction in e-learning using web interface information. Sadhana 40(2), 379–394 (2015). https://doi.org/10.1007/s12046-015-0334-1 15. Feldman, J., Monteserin, A., Amandi, A.: Automatic detection of learning styles: state of the art. Artif. Intell. Rev. 44, 157–186 (2015). https://doi.org/10.1007/s10462-014-9422-6 16. Villaverde, J.E., Godoy, D., Amandi, A.: Learning styles’ recognition in e-learning environments with feed-forward neural networks. J. Comput. Assist. Learn. 22(3), 197–206 (2006) 17. Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005) 18. Jeghal, A., Oughdir, L., Tairi, H., Radouane, A.: Approach for using learner satisfaction to evaluate the learning adaptation policy. Int. J. Distance Educ. Technol. 14(4), 1–12 (2016) 19. Azzi, I., Jeghal, A., Radouane, A., Tairi, H.: Personalized e learning systems based on automatic approach. In: International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, pp. 1–6 (2019) 20. Dumbill, E.: XML watch: finding friends with XML and RDF: the friend-of-a-friend vocabulary can make it easier to manage online communities (2002) 21. Jean Daubias, S., Eyssautier-Bavay, C.: Aider l’enseignant pour le suivi des compétences des apprenants. In: Environnements Informatiques pour l’Apprentissage Humain (EIAH 2005), Montpellier, France, pp. 353–358 (2005)

A Hybrid Recommender System for Pedagogical Resources Yassamina Mediani1(B) , Mohamed Gharzouli1 , and Chahrazed Mediani2 1 MISC Laboratory, Abdelhamid Mehri-Constantine 2 University, Constantine, Algeria

[email protected] 2 LRSD Laboratory, Ferhat Abbas Setif-1-University, 19000 Setif, Algeria

Abstract. The present paper proposes an e-learning system that combines popularity and collaborative filtering techniques to recommend pedagogical resources. A recommender system helps users get a correct and personalized decision by applying several recommendation methods such as content-based, collaborative filtering, and other hybrid approaches. However, predicting a relevant resource with a specific context, like pedagogical content, becomes a challenge. In our work, we propose a model to ameliorate the traditional collaborative filtering technique by (i) using the Singular Value Decomposition (SVD) to tackle the problem of scalability and data sparsity; (ii) extracting the most popular resources that the user does not interact with before to resolve the cold start problem; and (iii) combining the results of popularity and SVD factorization methods to improve the recommendation accuracy that evaluated by applying the recall, precision and f1-score of each approach. The comparison shows that the obtained results exhibit an encouraging performance of our model. Keywords: Recommender system · E-learning · Collaborative filtering · Popularity · SVD

1 Introduction A recommender system aims to predict new items such as movies, music, articles to a particular user inferred by items that he has consumed previously [1]. In several organizations such as Amazon, Netflix, and Facebook, recommender systems have become interesting and important. However, predicting relevant information resources with specific axes such as pedagogical documents becomes a challenge. Online pedagogical resources are included in e-learning systems and used to involve educational characteristics to build an intelligent education environment [2]. There are many approaches such as (i) content-based recommender system [3], which recommends to users similar resources based on their previous interactions; (ii) collaborative filtering [4], which recommends resources to the user based on their predictions by collecting their preferences and rates that can be explicit or implicit [5]; and (iii) hybrid approaches [6] that combine two or more different methods to improve the recommendation.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 361–371, 2022. https://doi.org/10.1007/978-3-031-02447-4_38

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In this context, collaborative filtering algorithms have been applied more widely in recommender systems than content-based algorithms because they need user preferences and feedback only instead of the actual content description [7]. The massive amount of data and sparse rating due to the data sparsity issue, in which compared to the unrated items, the rated ones are very small [8]. However, to improve the quality of recommendation that suffers from data sparsity, scalability issues that affect the system’s performance [1], and the ability to recommend new items to the users, we need to find solutions that can enhance these problems and perform better than the previous technologies. Many research works have suggested that Singular Value Decomposition (SVD) [9] can generate best results than traditional collaborative filtering. In this paper, we have combined the popularity and SVD methods to improve recommendations’ precision and generate a more performant approach than the traditional collaborative filtering techniques do. This paper is organized as follows. In the next section, we present some existing works related to hybrid recommender systems. Section 3 discusses the data collection and preprocessing. Section 4 presents our approach to recommend pedagogical resources, and Sect. 5 contains the experimental evaluation procedure and discussion of the results. The conclusion and future research works are given in the last section.

2 Related Work Including and developing recommender systems becomes an important research topic. Therefore, much research has been done regarding this field. Some of the notable works are as follows: Recommender systems aim to generate suggestions for new items or predict the usefulness of items for a given user [10]. Collaborative filtering is one of the methods of recommendation that produce high-quality suggestions, but the massive volume of user-items data degrades this quality. To fill in the missing rating values and make the user-item rating matrix dense, SVD cannot use an explicit rating in the collaborative filtering because the user does not rate all items [11]. In the work of [8], SVD technology reduces the dimensionality of recommender system databases and gives better results than a traditional collaborative filtering algorithm by using the denser MovieLens dataset. However, SVD is more expensive than other methods [12]. The proposed work in [12] presented a Social Popularity-based SVD++ Recommender System. This work is based on latent factor models by integrating popularity factor using the social interactions as implicit feedback to improve the accuracy, which was tested on a MovieLens dataset and showed promising results. Among the works that have been conducted to generate recommendations to support the learners in their learning, we can cite the work of [2], which improves and develops educational approaches by analyzing users’ behavior and activities within their system. Compared with e-commerce recommender systems resources, e-learning systems have characteristics that can be affected by users’ interactions. However, e-learning platforms are very delicate especially, with the vast number of the learner’s characteristics and the diversity of learning objects, which require a precise recommender system [13]. Whereas in [2], and to better analyze the student characteristics, the authors applied the context data and implicit feedback in order to improve basic hybrid systems. In [14], and to treat

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the limitation of effective methods in E-learning, the authors propose a recommendation framework for e-learning that can learn from large-scale data. Their proposition is based on the k-Nearest Neighbor (KNN) method to train their model to guarantee accuracy. Then, it can recommend new items that have unknown similarities.

3 Data Collection and Preprocessing In this section, we have used two data sets, shared_resources.csv and users_interactions.csv (Table 1), containing information about pedagogical resources. Table 1. Data description. Heading level

Example

Font size and style

Description

Contains information about the shared articles such as Date, URL, Title, Text (content), Article language, and some information about the author

Contains logs of user interactions for example, the “ContentId”, “EventType”, “PersonId”

Number of samples

3128

72312

For the preprocessing operation, we take in the following actions: • The dataset shared_resources contains the “eventType” attribute with two possible values, “CONTENT SHARED” if the article is available or “CONTENT REMOVED” if the article is unavailable. We take only the content that is shared (available). • The dataset User_ineraction contains the “eventType” attribute, which is composed of some values such as view when the user opens the article, like, comment, follow (to be notified on any new comment in the article), and the bookmark, which simplify the return in the future to the article. • Data munging: we associate the different interaction types of users (view, like, bookmark, follow, and comment) with a weight. We sort them by the higher interest of the user. • Transformation: users can do several interactions with a resource (view a resource many times or like and comment on the same resource). To model a user’s interest, we should aggregate all user interactions weight to the given resource. • Preference: is a ranking formula of a collection of items that the user is interested in. We can define it as follow: Pr = {user u, item i, Weight u,i }. • Features engineering: we selected only the useful feature by ignoring the irrelevant ones. In addition, we removed the duplication of articles when the title was repeated many times and the interactions with the same article by the same type of interaction to train the model well. • Data splitting: we use the holdout method in which we split the data sets to 80% for training and the remaining 20% of the data for testing. Noting that all of our evaluation metrics were computed using the test set.

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4 A Recommender System for Pedagogical Resources We have first implemented the popularity approach and collaborative filtering in the proposed solution. Then, we have combined both of these approaches to generate a hybrid model for pedagogical resources recommendation. In this section, we will describe the followed process. 4.1 Popularity-Based Recommender System A popularity-based recommender system proposes to users the most popular pedagogical resources that they have not seen before and based on a given metric. The capacity of interactions provided by many users regarding those items indicates that popularity feedback can powerfully affect the behavior of users. Our algorithm used in this module consists of these steps: • Propose a score (Weight) to rate the resources based on the user/item interaction (see Table 2). • For every user/resources interaction, aggregate the different interaction weights regarding a resource • Collect all the interaction weights regarding each resource. • Sort descending the weight of all resources and give the top results. Table 2. Associated interaction weights Interaction

Weight

VIEW

1

LIKE

2

COMMENT

3

FOLLOW

5

BOOKMARK

4

4.2 Collaborative Filtering Recommender System We have used collaborative filtering to recommend the pedagogical resources for a particular user that he does not interact with yet. In order to calculate the similarity between users and resources, we have chosen the matrix factorization of the utility matrix to handle the scalability and sparsity issues. The utility matrix is the users-items matrix An,m where n represents the number of users, m is the number of items, and Aij is calculated as follow: Primly, we calculate the sum of user-item interactions: k wi (1) sumij = i=1

where:

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• k is the number of interactions. • Wi represents the weight given to each interaction. After, we have transformed the result obtained from (1) by applying the logarithm function f(x) = Log(x + 1). The obtained result is the element Aij of the matrix A for every user i and item j. Then, we applied the SVD technique of the matrix A: Application of SVD. SVD is a matrix decomposition method, which reduces a matrix to its component. Formally, SVD decomposes a matrix A into the product of three matrices as given below [12]: T An∗m = Un∗r .r∗r .Vr∗m

(2)

where: • An∗m is the utility matrix of the users-items matrix where n represents the number of users and m is the number of resources. • Un∗r is a left singular orthogonal matrix representing the relation between users and latent factors • r∗r is a diagonal matrix of the original matrix A (with positive real values) describing the strength of each latent factor. T is a right singular orthogonal matrix, which indicates the similarity between • Vr∗m resources and latent factors. SVD is used to decrease the utility matrix A dimension by extracting its latent factors that represent the characteristics of the items. Also, it is employed to map each user and item into latent space to facilitate the representation of user/items relationships [9]. A vector Xi represents the item and, a vector Yu represents the user. The expected rating by a user to an item can be given as [15]: rˆui ≈ XiT .Yu

(3)



Here, r ui is a form of factorization in singular SVD. In order to obtain the appropriate rating prediction, SVD minimizes the difference between the product of the vectors and the expected rating as given below [15]: Min(X , Y )

2   r u,i − XiT .Yu 

(4)

(u,i)∈K 

where: this function takes the value of 1 if r ui exists and 0 otherwise. In order to make the model train well and avoid overfitting, the term of regularization is added to the function where λ is the regularization parameter. The equation can be written as [15]: Min(X , Y )

  (u,i)∈K



r u,i − XiT .Yu

2

  + λ Xi 2 + Yu 2

(5)

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Then, the algorithm uses the bias term to reduce the error between the actual value and the predicted one. The final equation after adding the regularization term and bias of user-item (u, i) can be given as [15]: 2   rˆu,i − XiT .Yu − μ − ri − ru min(X , Y , ri , ru ) (u,i)∈K

  + λ Xi 2 + Yu 2 + ri2 + ru2

(6)

where: • μ: is the average rating of all the items. • ri : is the average rating of item i. • ru : is the average rating given by user u minus μ. As a result, SVD eliminates all the missing values. Then the Collaborative filtering algorithm uses the result matrix to recommend to users the top k items that they have not seen before and based on their preferences. 4.3 The Proposition of a Hybrid-Recommender System A hybrid recommender system is introduced to improve the recommendation, which combines at least two principal techniques that are accurate, differently [16]. The main idea of a hybrid system is to fill the weakness of one approach with the strengths of the others [16]. The implementation of the Hybrid recommender system requires specifying the recommendation strategy (switched, mixed, and weighted). Our algorithm combines two approaches (popularity and SVD based collaborative filtering (CF_SVD)) independently by using the weighting strategy. Then, each approach generates a list of recommendations list. Next, these generated lists of recommendations are joined together (intersected or united). The popularity algorithm generates a list of popular resources that the user does not interact with before. While the CF_SVD algorithm is used to solve the sparsity and scalability issues of the recommender system. The hybrid of them aims to ameliorate the recommendations given by these two algorithms.

5 Experiment In this section, we will analyze our approach, which is a hybrid recommender system of pedagogical resources using the interactions between users and resources. We have assigned a weight value for each interaction type to calculate the degree of relevance resources to a given user. First, we have implemented the popularity-based recommender system and the SVD-based collaborative filtering (CF_SVD) presented in the precedent section. Then, we have combined these two algorithms. This approach gives, as a result, a list of sorted resources deemed relevant to the user. The results of the hybrid algorithm are compared to those of CF_SVD and the popularity-based recommender system. To evaluate the quality of these three approaches, we have used the metrics that we will present in the following sub-section.

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5.1 Evaluation Metrics The recommender system gives in output a list of pedagogical resources of all users that they have not interacted with before. In this case, we need to evaluate our list of recommendations by using evaluation metrics like recall, precision, f1-score, and accuracy metric. These metrics are averaged over the pedagogical resources and users. In our approach, we have used recall, precision, and f1_score to evaluate the quality of the proposed recommendations. The top-N recommendation is used to rank the Nrelevant resources for each user. In our case, the relevant resources are those the user interacted with, making N = 80. For a given user u, the resource R can be relevant RRV or not relevant NRV, recommended RRD, or not recommended NRD. The recall, precision, and the f1-score of N first recommended resources are respectively defined as [17]: recall u @N =

card (RRV u @N ∩ RRDu @N ) card (RRV u @N )

precisionu @N =

card (RRV u @N ∩ RRDu @N ) card (RRDu @N )

f 1 − scoreu @N = 2 ∗

recall u @N ∗ precisionu @N recall u @N + precisionu @N

(7) (8) (9)

To calculate the final evaluation values of our recommender system, we calculate the average over the number of users. These metrics obtained are given by:  recall u @N global − recall@N = u (10) nbr_users  precisionu @N global − precision@N = u (11) nbr_users  f 1 − scoreu @N global − f 1 − score@N = u (12) nbr_users

5.2 Results and Discussion In this section, we compared the performance of CF_SVD, popularity, and the hybrid algorithm. The result of the comparison between these three algorithms is presented in (Table 3).

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We have evaluated the quality of our approach by using the holdout cross-validation. We split our data into 80% of the train set and 20% of the test set. To evaluate the performance of the hybrid algorithm, which is the combination of popularity and CF_SVD algorithms, we have used recall, precision, and f1-score at the top N recommended resources. The overall results of precision, recall, and f1-score of each algorithm represented in (Table 3) show that the hybrid algorithm could find more relevant resources than popularity and CF_SVD when we consider the precision and recall metrics (see Fig. 1 and Fig. 2). Otherwise, the average between these two metrics defined by f1_score shows that the hybrid model performs more than the others do (see Fig. 3). Table 3. Test results of popularity, CF_SVD, and Hybrid algorithms using top@N evaluation metrics; R: recall, P: precision, and F1_s: f1_score.

Popularity

CF_SVD

Hybrid

@5

@10

@20

R

0,280116

0,420792

0,606023

P

0,022243

0,033413

0,048121

F1_s

0,41649

0,353598

0,329055

R

0,319101

0,461015

0,624381

P

0,025839

0,03733

0,050559

F1_s

0,414399

0,360923

0,339131

R

0,312294

0,470091

0,655941

P

0,0376

0,056599

0,078975

F1_s

0,418857

0,38473

0,367366

Fig. 1. Recall score for popularity, CF_SVD, and hybrid algorithms.

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The results above show that recall@20 is better than recall@10 and recall@5, respectively. Moreover, the same result for precision metric, the f1-score@5 is better than f1-score@10 and f1-score@20, respectively.

Fig. 2. The precision score for popularity, CF_SVD, and hybrid algorithms.

Fig. 3. F1_score for popularity, CF_SVD, and hybrid algorithms.

6 Conclusion This paper proposes a hybrid recommender system for pedagogical resources, which combines both popularity and SVD-based collaborative filtering. We aim to propose an algorithm that performs better than each of its components taken alone does through

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this work. We have used three evaluation metrics to evaluate these algorithms: recall, precision, and f1-score. The results show that the hybrid algorithm performs better than others when using recall, precision, and f1-score metrics. As a perspective, we try to find other models and methods that ameliorate the hybrid algorithm for further research aspects.

References 1. Adomavicius, B.G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 634–749 (2005) 2. Ansari, M.H., Moradi, M., NikRah, O., Kambakhsh, K.M.: CodERS: a hybrid recommender system for an e-learning system. In: 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS), pp. 1–5. IEEE (2016) 3. Javed, U., Shaukat, K., Hameed, I.A., Iqbal, F., Alam, T.M., Luo, S.: A review of contentbased and context-based recommendation systems. Int. J. Emerg. Technol. Learn. (iJET) 16(3), 274–306 (2021) 4. Rajendran, D.P.D., Sundarraj, R.P.: Using topic models with browsing history in hybrid collaborative filtering recommender system: experiments with user ratings. Int. J. Inf. Manag. Data Insights 1(2), 100027 (2021) 5. Zhao, Q., Harper, F.M., Adomavicius,G., Konstan, J.A.: Explicit or implicit feedback? Engagement or satisfaction: a field experiment on machine-learning-based recommender systems. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 1331–1340 (2018) 6. Walek, B., Fojtik, V.: A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst. Appl. 158, 113452 (2020) 7. Fazeli, S., Loni, B., Drachsler, H., Sloep, P.: Which recommender system can best fit social learning platforms? In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) EC-TEL 2014. LNCS, vol. 8719, pp. 84–97. Springer, Cham (2014). https://doi.org/10.1007/978-3319-11200-8_7 8. Guan, X., Li, C., Guan, Y.: Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems. IEEE Access 5, 27668–27678 (2017) 9. Yuan, X., Han, L., Qian, S., Xu, G., Yan, H.: Singular value decomposition based recommendation using imputed data. Knowl.-Based Syst. 163, 485–494 (2019) 10. Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_1 11. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system—a case study. In: ACM WebKDD 2000 (Web-mining for Ecommerce Workshop) (2000) 12. Kumar, R., Verma, B.K., Rastogi, S.S.: Social popularity based SVD++ recommender system. Int. J. Comput. Appl. 87(14) (2014) 13. Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-0120187-z 14. Wang, X., Zhang, Y., Yu, S., Liu, X., Yuan, Y., Wang, F.: E-learning recommendation framework based on deep learning. In: IEEE International Conference on Systems 2017, pp. 455–460. IEEE (2017)

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15. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009) 16. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi. org/10.1007/978-3-540-72079-9_12 17. Valcarce, D., Bellogín, A., Parapar, J., Castells, P.: Assessing ranking metrics in top-N recommendation. Inf. Retr. J. 23(4), 411–448 (2020). https://doi.org/10.1007/s10791-020-093 77-x

An Investigation of the Effect of Flipped-Jigsaw Learning Classroom on Primary Students’ Autonomy and Engagement in E-Learning Context and Their Perceptions of the Flipped-Jigsaw Learning Classroom Vahid Norouzi Larsari1(B) , Flora Keysan1 , and Radka Wildova2 1 Department of Pre-Primary and Primary Education, Faculty of Education, Charles University,

Prague, Czech Republic [email protected] 2 Faculty of Education, Charles University in Prague, Prague, Czech Republic [email protected]

Abstract. Due to the development of COVID-19, governments are forced to close educational schools to control the prevalence of this disease. In other words, COVID-19 leads to an important change in pedagogy and learning all over the world. The present study aimed to examine the impact of the flipped-jigsaw learning classroom on Primary students’ autonomy and engagement through two different approaches of teaching (i.e. Social Network and Face-to-face training) then, explore students’ perceptions and opinions toward the flipped-jigsaw learning approach during the prevalence of Covid-19. The present study is based on the quasi-experimental design which is conducted for 60 primary students who had studied in the first year of study in 2020–2021 in a primary school in Tehran. One group was educated in a traditional (face-to-face) class that was deprived of the Internet and the students performed their homework at their home, whereas the other group was educated utilizing flipped-jigsaw learning classroom through the social network. Before and after treatment, all the participants filled out the learner’s autonomy and engagement questionnaires which served as the pre and post-tests. The researchers utilized covariance analysis (ANCOVA) to answer the research questions. The results showed that the students’ level of autonomy and engagement in the experimental group increased compared to the performance of the students in the control group. Due to the COVID-19, educational institutions could utilize virtual learning, and the flipped-jigsaw learning classroom to improve students’ autonomy and engagement in the E-learning context. Keywords: Flipped-jigsaw learning · Social network · Learner autonomy · Engagement · COVID-19 · E-learning

1 Introduction Nowadays, COVID-19 is considered an infectious disease, which no one is protected from it [1, 2]. With the development of the COVID-19, governments are forced to lock © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 372–382, 2022. https://doi.org/10.1007/978-3-031-02447-4_39

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down educational schools, especially schools so that they could monitor the outbreak of this virus [3]. In other words, this pandemic has led to a fundamental change in the principles of models of pedagogy [2]. Covid-19 has caused a strong impact on education and learning among students around the world, and therefore education platform has changed tremendously around the world, which has forced all schools to switch to distance learning. In this context, most teachers plan to utilize the educational approaches which increase students’ autonomy and, consequently, promote their engagement. On the other hand, with the development of communication, Internet and social networks are considered as a challenge to establish creativity and innovation in teaching and learning [4]. Social networks are used for E-learning, especially virtual education which is one of the most important electronic learning methods [4]. With the main focus on the lockdown of educational schools during the Coronavirus disease, E-learning is extremely increasing [5]. According to the results of research, e-learning environments had a strong impact on students’ performance [6]. Flipped classroom, as a new approach, which is originated from progress in pedagogy. Flipped learning classroom is resulting from a mixture of traditional and digital performances [7]. In flipped learning classroom, activities outside the class, such as projects and homework are moved into the class and the tasks which are done in the classroom are changed out of the class [8]. Flipped classroom stimulates students to receive out-of-class instruction while indicating engagement and contribution within the class itself [9]. Some researchers found that flipped classrooms improved students’ responsibility, feeling of group belonging, interaction, and engagement [10]. Some studies have shown that flipped classrooms have a strong influence on students’ performances [22]. In contrast, other research has shown that the flipped classroom did not have any effect on students’ performances [11]. Working in the cooperative community and social contexts is essential for students to solve intricate issues [12]. One of the most important collaborative learning strategies is Jigsaw, in which students take part of the responsibility for activity in predefined communities. Each member of the group studies the given topic and then the members of the different collaborative groups who have taken responsibility for a similar part form a community and discuss the given task. Afterward, each member returns to his group and teaches the other members [13]. Compared to a competitive context, students’ motivation increases when they are engaged and working together for a common goal [14]. Rachmah viewed the jigsaw as a significant element of increasing students’ performances [15]. The COVID-19 has left various challenges in teaching-learning affairs. In this regard, teachers are making efforts to find proper ways of increasing students’ performances, thereby improving their performances. According to studies on this topic, E-learning [16, 17] and flipped classrooms [18, 19] are the most common methods of E-learning. Based on the merits and demerits of each of these methods, the current study examined flipped-jigsaw learning as a modern approach. Therefore, this study aimed to investigate the effect of the classroom with a jigsaw on the autonomy and engagement of primary students through two different teaching approaches (i.e., social network and face-to-face training) and to explore the students’ perceptions of flipped–jigsaw learning classroom. Therefore, the following research questions were investigated in this present study: (1) To what extent does the Flipped-jigsaw learning Classroom affect students’ autonomy?

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(2) To what extent does the Flipped-jigsaw learning Classroom affect students’ engagement? (3) What the students’ perceptions are of flipped–jigsaw learning classroom in the Iranian Primary school?

2 The Objectives of the Study The objectives of this study are as follows: (a) to investigate the impact of Flippedjigsaw learning classroom on Primary students’ autonomy, (b) to examine the impact of flipped-jigsaw learning classroom on Primary students’ engagement, and (c) to explore the students’ perceptions of flipped–jigsaw learning classroom.

3 Methods 3.1 Study Design and Setting The main purpose of this study was to investigate the impact of the flipped-jigsaw learning classroom on Primary students’ autonomy and engagement by using two different instructional methods (i.e. social network and face-to-face training) and then, explore students’ perceptions toward the flipped-jigsaw learning classroom; therefore, the design of the current research relied on a mixed method with a focus on critical analysis. 3.2 Participants 90 Iranian Primary male students’ sixth (6th) graders in a primary school, were selected by simple random sampling, participated in this study. The students were studying in a Primary school in Iran. The participants were male and sixth (6th) graders in a private primary school of Tehran. The age of the participants were around 12–13 years old. The researchers randomly assigned them into two groups of experimental and control groups, each of which consisted of 45 participants. In addition, 25 primary students were selected for an open-ended to investigate students’ perspectives to the flipped-jigsaw learning classroom. 3.3 Materials and Instrumentations 3.3.1 Student Engagement Questionnaire In order to measure the effectiveness of the flipped-jigsaw learning classroom in student engagement, the researchers adopted the “Classroom Engagement Inventory” by Wang, Bergin, and Bergin (2014). The questionnaire measures five specific elements of student engagement. The “Classroom Engagement Inventory” is composed of 24 items, which were developed for school- level engagement (Wang, Bergin & Bergin, 2014). The items of the questionnaire are based on a 5-point Likert scale with options of “Never”, “Rarely”, “Sometimes”, “Of- ten”, and “Always”. The questionnaire involved both positive and negative Likert-type items. Moreover, the questionnaire was validated by Sever (2014).

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According to this study, Cronbach Alpha’s internal consistency coefficient by deleting one item was 0.930. However, the internal consistency of the scale has been recalculated for this study and the Cronbach alpha coefficient of reliability has been found as .72 and this coefficient has been considered sufficient and acceptable. 3.3.2 Learner Autonomy This questionnaire was developed and revised by Zhang and Li’s. The questionnaire included 21 items in the front of two parts. The first part of the questionnaire included 11 questions with respect to the five-point Likert scale ranging from never to always. The second section of the questionnaire included 10 multiple choice item and the participants should chose items based on their view. 3.3.3 English Textbook The researchers used an English Book taught by Primary teacher for sixth graders in a Primary school classroom. The researchers also selected only four of the English units, consisting of 12 lessons, to be taught in both classes over a period of time. Each unit has different components of the language: “vocabulary, grammar, and pronunciation”. Each component has its own learning objectives. 3.4 Data Collection Procedures This study utilized the flipped-jigsaw learning classroom with sixth-grade primary students in English class. The researchers divided English classes into two groups for the purpose of this study. Each class consisted of 30 students. One class was selected for flipped-jigsaw learning classroom, while another class was selected for traditional (face-to-face) classroom teaching. Before the treatment two questionnaires as a pre-test and post-test were utilized to measure the students’ autonomy and engagement before and after the major instruction. The tests were piloted to ensure their reliability. The researchers selected 30 individuals to measure the reliability of questionnaires. In the control group, the instructor informed the students of some important aspects such as research procedures and etc. Then, the instructor provided the students with student’s autonomy and engagement questionnaires as a pre-test before the intervention. The aim of the pre-test is to evaluate the prior knowledge of the students in the two classes before the teaching English course. The control group was run by non-flipped instruction. In fact, it was instructed in a traditional way that teachers taught the material inside the class and students did their homework and assignments outside the classroom. The students in the traditional (face-to-face) class were deprived of the Internet (Social Network) and the students were taught in the classroom rather than out of the class- room. Before teaching each unit, the researchers provided background knowledge for the students and after teaching each unit, the students were required to answer some questions related to the text. In the traditional classroom, the course was taught by the instructor in the class, and the instructor also presented the materials and practices in Power Point formats. The students in the traditional (face-to-face) class were often listened and took notes. In the classroom, there were some practices and discussions about the book content, for which

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there were often no sufficient time to do them; therefore, the students did their homework at their home. This procedure continued till the last session. The whole treatment lasted 10 sessions of 45 min once a week. In contrast, the experimental group was run by flipped-jigsaw learning. The researchers provided the students with some explanations about the researcher procedure and informed them of their electronic informed consent forms. Before intervention, the researchers provided the students with student’s autonomy and engagement questionnaires as a pre-test like traditional (face-to-face) group. In the experimental group, the researchers utilized flipped-jigsaw learning for 10 sessions of 45 min in a week. To this end, the researchers provided a video clip of the intended content of English course along with some questions through the Shad Platform (Elearning management system) prior to each education session. Then, the students could prepare themselves by watching the video clip or reviewing other resources such as the electronic English book during the week. In each session, the researchers divided the students into groups of five or six. Each member of the group was assigned a specific content of English course. They were required to study the whole material, with a closer look at the assigned topic. It is worth mentioning that the researchers also selected only four of the English units consisted of 12 lessons to be taught in both classes over period. Each Unit has different components of language: “Vocabulary, Grammar, and Pronunciation”. Each subsection has its own learning objectives. After the end of the 10 sessions, the instructor provided the students with student’s autonomy and engagement questionnaires as a posttest after the intervention. Finally, among all the participants in the experimental group, 25 were selected for an in-depth semi-structured interview to find their perception of the flipped- jigsaw learning classroom.

4 Data Analysis Data analysis was conducted based on the data collected in the questionnaires. In the first phase, a repeated measures of ANCOVA was conducted for the quantitative research question. In the second phase, descriptive analysis was conducted to explore students’ perceptions of flipped-jigsaw learning Classroom.

5 Results Hypothesis (1): Flipped-Jigsaw learning classroom increased primary students’ autonomy. According to the first research question, to understand students’ level of autonomy before and after the flipped Jigsaw learning classroom, a learner autonomy questionnaire as pre-test and post-test was given to both groups of students in the experimental and control groups. A repeated measures of ANCOVA was conducted to analyze the pre-test and post-test of the experimental and control groups to find if there was any significant difference between the students’ autonomy in both groups. Descriptive statistics for the pre-test and post-test scores obtained by the experimental and control groups are given in Table 1:

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Table 1. Descriptive statistics for pre and post-tests scores of autonomies Groups

Pretest

Posttest M

t

Sig

M

SD

SD

Control

66.16

10.91

76.16

11.16

−6.22

0.00

Experimental

69.40

7.80

87.92

12.65

−13.54

0.00

The results of the matched t-test analyses showed that for each group there was a statistically significant progress in the mean scores of learner autonomy from the pre-test to the post-test. The table shows that there was also a significant increase in the mean scores for the autonomy in the control group (t = −6.22, p < 0.05). Similarly, the change in the mean scores of students’ autonomy in the experimental group was statistically significant (t = −13.54, p < 0.05). As the results show, the mean score of the control group was 66.16 at the pre-test and increased to 76.16 at the post-test, which makes it clear that the control group experienced a significant increase in learner autonomy. There was also a significant advance in the mean scores of the experimental group on the learner autonomy pretest from 69.40 to 87.92 on the post-test. This difference was also statistically significant (Table 2). Table 2. ANCOVA results for autonomy Scores Source

Type III Sum of Squares

df

Mean square

F

Sig.

Partial eta squared

Corrected model

2954.114a

2

1477.057

34.880

.000

.636

Intercept

1045.745

1

1045.745

24.695

.000

.382

Pre-autonomy

1508.454

1

1508.454

35.621

.000

.471

Group

940.302

1

940.302

22.205

.000

.357

Error

1693.886

40

42.347

Total

300875.000

43

Corrected total

4648.000

42

The results of the ANCOVA, which can be seen in Table 3, showed that a statistically significant difference was found between the experimental group and the control group in the results of the student autonomy posttest F(1, 40) = 22.20, p = 0.000, partial eta squared = 0.357). Hypothesis (2): Flipped-Jigsaw learning classroom has a strong effect on primary students’ engagement. To understand student engagement before and after the flipped jigsaw classroom, students in the experimental and control groups were administered a classroom engagement questionnaire as a pretest and posttest. A repeated measures of ANCOVA was

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conducted to analyze the pre-test and post-test of the experimental and control groups to determine if there was a significant difference between the autonomy of the students in both groups. The descriptive statistics for the pretest and posttest results of the experimental and control groups can be found in Table 3: Table 3. Descriptive statistics for pre and posttests scores of engagements Groups

Pretest M

Posttest SD

M

t

Sig

SD

Control

87.83

11.42

91.72

11.16

12.15

0.00

Experimental

85.12

11.17

99.92

12.65

13.24

0.00

The results of the matched t-test analyses showed that for each group there was a statistically significant advance in the mean scores of engagements from the pre-test to the post-test. As shown in the table, the mean score of engagement also increased significantly in the control group (t = 12.15, p < 0.05). Similarly, the change in the mean score of engagement in the experimental group was statistically significant (t = 13.24, p < 0.05). As the results show, the mean score of the control group was 87.83 at the pre-test and increased to 91.72 at the post-test, which clearly indicates that the control group experienced a significant improvement in their engagement. The experimental group also experienced significant progress, increasing from 85.12 on the pretest to 99.92 on the posttest. This difference was also statistically significant (Table 4). Table 4. ANCOVA results for autonomy scores Source

Type III sum of squares

df

Mean square

F

Sig.

Partial eta squared

Corrected model

2940.502a

2

1470.251

74.046

.000

.787

Intercept

1054.052

1

1054.052

53.085

.000

.570

Pre-autonomy

2237.209

1

2237.209

112.671

.000

.738

Group

1026.046

1

1026.046

51.674

.000

.564

Error

794.242

40

19.856

Total

404065.000

43

Corrected total

3734.744

42

The results of ANCOVA revealed that the two groups were significantly different in terms of their post-test scores of writing motivation, F (1, 40) = 51.67, p = 0.000, partial

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eta squared = 0.56). As the ANCOVA results in Table 5 show, flipped jigsaw enhanced student’s engagement of the experimental group significantly. Students’ Perceptions of Flipped-Jigsaw Learning Classroom The researchers asked the students to indicate the extent to which they agreed or dis greed on a scale to measure their perceptions towards the use of flipped- jigsaw learning classroom, through social platform. The results are shown in Table 5. Table 5. Students’ perceptions of flipped-jigsaw learning classroom Questions

Strongly agree

Agree

Disagree

Strongly disagree

1

Was the information in our flipped-jigsaw learning classroom helpful to the students by using social platform?

17%

70%

5%

8%

2

The utilization of Flipped-jigsaw learning via social platform caused me to communicate and get comments from the instructor

7%

80%

10%

3%

3

Flipped-jigsaw learning classroom was useful using my social platform

2%

85%

2%

11%

4

I discover it easy to interact with the instructor and other students on online activities using flipped-jigsaw learning classroom

5%

77%

13%

5%

5

I was informed of the program with my social platform in and outside the classroom

4%

79%

17%

14%

6

That is fine to utilize these activities as authentic-materials

7%

80%

3%

10%

Based on the information retrieved from students’ perceptions, 80% of participants agreed or strongly agreed that the flipped-jigsaw learning through social platform can provide them with the required information they needed, whereas 10% of them disagreed or strongly disagreed. Similarly, 85% of the participants agreed that using the flippedjigsaw learning classroom helped them to communicate with and get valued comments

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from the instructor while 13% disagreed. Another finding of the data (See Table 5) is that 85% of the participants accessed the class through their social platform and found it useful while some of them disagreed. In terms of ease of interaction (Table 5, item 4), 77% of the participants found it easy to communicate with their instructors and other students, while 13% disagreed. On the question of the usefulness of flipped-jigsaw learning classroom for engagement (See Table 5, item 5), 79% of participants agreed that the technology helped them to engage and be informed about the module inside and outside the classroom, while 17% disagreed. When asked if the use of the flippedjigsaw learning classroom should be utilized as authentic materials (See Table 5, item 6), 80% of participants agreed that the flipped-jigsaw learning classroom should be utilized throughout the programmed.

6 Discussion The purpose of this study was to examine the impact of the flipped-jigsaw learning classroom on Primary students’ autonomy and engagement by using two different instructional methods (i.e. social network and face-to-face training) and then, to explore students’ opinions of flipped-jigsaw learning classroom. The results of the study show that the flipped-jigsaw learning classroom had a greater influence over the students’ autonomy and engagement than face-to-face training approach. According to the report published by Dliss and Sukur, students who are passionate about social networking platforms are more likely to maintain their motivation during the COVID-19 pandemic than those who did not make a clear connection to virtual learning methods. The results of a research study clearly state that the Flipped-Jigsaw Learning Classroom improves student’s autonomy and engagement through effective factors such as readiness in terms of commencing the class, the ability to pay more attention, and the ability to share given assignments [20]. Alsancak and Ozdemir believe that other important elements to increase student’s autonomy and engagement include authentic and interactive tasks, stimulating of student activities, and providing instructional materials at all times of the day [9]. In another study, researchers found that jigsaw instruction was more likely to promote autonomy in nursing students because it was challenging and encouraged students to engage in discussion [21]. In the present study, the flipped-jigsaw method was more useful in improving students’ autonomy and engagement because it was student-centered, helped students prepare adequately before entering the classroom, and made them feel independent. Also, the instructor provided them a sense of responsibility and divided them into small groups, which improved with each other, which was engaging and demanding [22]. The results indicated that the incorporation of flipped and jigsaw methods improved students’ autonomy and engagement. Accordingly, there was a significant improvement in autonomy and engagement among primary students in the classroom with jigsaw learning, but not among students in the face-to-face approach [27]. Therefore, this flipped-jigsaw learning method was helpful in increasing students’ autonomy and engagement, which were suitable for their academic progress.

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7 Conclusion Due to the spread of Covid-19, schools have been forced to adopt e-learning and elearning methods. This pandemic issue has resulted in changing the teaching methods of the teachers, which can greatly affect the educational process of the students. In this context, teachers are trying to find some approaches to increase the autonomy of learners to enhance their engagement. The findings of this study showed that the classroom learning with a jigsaw was more effective in developing students’ autonomy and engagement compared to the face-to-face teaching approach. In other words, the researchers concluded that social networking has become a valuable element of online communication and e-learning. In this regard, students spend hours every day on virtual networks. In other words, social networks should be used for education of students as well as people who study in institutions and for whom it is more difficult to attend classes than in day schools.

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MRDFPD: Metadata Driven RDF Based Product Discovery Framework Saloni Gordhan Rakholiya, Gerard Deepak(B) , and A. Santhanavijayan Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India [email protected]

Abstract. In recent years, the number of people who use e-commerce has risen rapidly. Users can utilise a feature on shopping sites to search for items based on their descriptions. The inputs can be thought of as product discovery and recommendation queries. Product recommendation was previously made using syntactic methods, which were ineffective in categorising and discovering appropriate products solely based on keyword matching. Each online platform has a large number of products that must be organised into taxonomies in order to improve product discovery accuracy. A metadata-driven RDF-driven framework for product discovery and classification is proposed in this paper. Preprocessing and query upper ontology matching are included in the queries. The knowledge base that stores the common categories from e-commerce sites is held by the category upper ontology, a semantic model. The proposed framework takes into account the input queries as well as the number of user clicks. It uses logistic regression for product classification on the dataset and the Harris’ Hawks optimisation algorithm to compute semantic similarities to produce accurate recommendations and rankings. Keywords: Harris’ Hawks optimization · Logistic regression · RDF · Semantic similarities · Upper ontology

1 Introduction Product categorisation, also known as product classification, is the process of grouping things into categories. With the rise of online shopping, the requirement to offer relevant items on a user’s screen has proliferated, affecting sales on a wide scale. The product taxonomy should be improved to improve the user experience and permit more accurate and relevant searches amid the sea of items. Manually categorising the items is inefficient since the categories or labels used vary from person to person and are based on their logical reasoning. Users should be provided with the most relevant possibilities when searching with random terms in a more efficient approach. Ontologies [1] and knowledge descriptor models have played a vital role in modelling efficient, intelligent systems due to the provisioning of sufficient background knowledge. Semantically enabled recommendations [2] depend on the quality of ontologies modelled [3] along with stringent relevance computing mechanisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 383–393, 2022. https://doi.org/10.1007/978-3-031-02447-4_40

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There are two approaches for categorising products: lexicon-based methods and Machine Learning techniques. The categorization should enable easy information retrieval. However, the traditional keyword-based searching is highly inefficient, and a more robust method is required, which calls for semantic ways of information organization. When people read a phrase, they comprehend the context and the individual words. Product categorisation may be improved if robots could discern meaning from language. The term “semantic methods” refers to the process of determining the relationships between words in a text. Machine learning algorithms with semantics can improve product categorisation accuracy. After using techniques like Word Sense Disambiguation, semantic links between words are discovered as part of semantic feature extraction. Semantic extraction models are constructed on top of this to extract particular data from the knowledge base. One such way is generating knowledge graphs based on ontologies and using SPARQL queries [4] or similarity computation [5] to retrieve information as a part of knowledge extraction. Contribution: This paper presents a comprehensive framework for e-commerce product discovery and recommendation generation using semantic approaches. The system integrates user clicks, and user queries as a category upper ontology to create appropriate query phrases. The categorisation of the products is based on logistic regression. The World Wide Web’s generated RDFs are also taken into account. Semantic similarity computation is performed under Harris’ Hawks optimization algorithm to produce recommendations. Various indicators are used to assess the model’s improved performance. Organisation: The rest of the paper is laid out as follows. The Related Work is shown in Sect. 2. Section 3 depicts the Proposed System Architecture. The Implementation and Performance Evaluation are illustrated in Sect. 4. Section 6 brings the paper to a culmination.

2 Related Work Gerard Deepak et al. [1] provided a framework based on Ontologies for first aid prediction. Though this paper deals with IoT, it mainly concentrates on how the sensory inputs are integrated with other data in the cloud. Deepak Surya et al. [2] have put forward an efficient algorithm based on bagging classification and sunflower optimization to propound a sensor that generates data concisely. Lin similarity is applied to the pertinent query terms, and classification models are applied along with optimization to suggest apt sensors. Deepak Surya et al. [3] portray how to represent knowledge in an ontology and visualize the same using Web VOWL. It proposes a data representation method for climate change-based data. Bhutani et al. [4] proposed the semantic framework for facilitating product discovery where they create a knowledge graph and use SPARQL to make queries into the knowledgebase. An RDF store is used to store as well as retrieve data using semantic queries. Sejal D et al. [5] proposed a recommendation system using images, ACSIR, which implements the cosine similarity measure, considering visual and textual features integrated to infuse the differences between image descriptions given by different people. Jiang et al. [6] give a trust-based collaborative filtering recommendation algorithm, a modification of slope one algorithm to provide recommendations to users who are most likely to be liked. It comprises selecting trusted data, calculating the similarity between users, and

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integrating the computed user similarity with the revised algorithm’s weight factor. Giri et al. [7] came up with an ontology recommendation system, a semantic method that conserves the relations among the entities in the ontology while making a recommendation that is context-based. Domain-level web ontology language (OWL) ontologies are translated into RDFs by deriving transitional XML parse trees, and axioms between ontological concepts and individuals are retained. Wei et al. [8] proposed a collaborative filtering and deep-learning methods-based recommender system that resolves the cold start and the sparseness problem of collaborative filtering methods. Mohana et al. [9] proposed an ontology-based collaborative filtering method based on similarity index and nearest neighbours to generate ranking and top N recommendations for the end-user. Tarus et al. [10] gave a recommender system that is hybrid and knowledge-based, built with an ontology along with sequential pattern mining, where the ontology represents the domain knowledge. A recommendation system for web pages is given by Deepak Surya et al. [11], where random forest classification and ant colony optimization are employed on the knowledge base, and the standard dataset is classified to generate recommendations. Deepak Surya et al. [12] suggested a recommendation system to quicken web searching based on preprocessing based on query term frequency and sorting with respect to concept similarity. Density-based spatial clustering of applications with noise is applied to the processed terms to produce relevant searches. Wu et al. [13] put forward a recurrent neural network (RNNs) based recommendation system that learns by backpropagation through time. Dakhel et al. [14] provide an algorithm for collaborative filtering that uses K-means clustering incorporated with the neighbours’ voting to produce recommendations. The Harris’ Hawks optimization algorithm was given by Ali Asghar Heidari et al. [15], which is thoroughly studied to be incorporated in the proposed framework as a metaheuristic optimization algorithm. Alabool et al. [16] provide a comprehensive review of recent variants of Harris’ Hawks optimization algorithm. In [17–22], several models in correlation with the literature supporting this work have been demonstrated.

3 Proposed System Architecture Figure 1 depicts the recommended system framework for the E-commerce product recommendation system. A proposed E-commerce product suggestion is IntelliEComRec, a recommended hybrid semantic intelligence framework for proposing things from ecommerce websites. The IntelliEComRec approach uses the user’s input queries and clicks as inputs, which are preprocessed to produce query words and preprocessed terms from user clicks. They’re all stored in a hash table called acquired query words. The current user clicks are integrated as part of the query terms since the user clicks, and query words convey the user’s current, informative wants. Tokenization, lemmatization, stop-word removal, named entity recognition (NER), and word sense disambiguation (WSD) are all part of the preprocessing. The preprocessing approaches were implemented using Python’s Natural Language Toolkit (NLTK) framework. A bespoke blank space special tokenizer is utilized for tokenization. Wordnet Lemmatizer has been used to transform words to their base form, or root forms, for lemmatization. Regular expression-based stop-word removal is used to improve the

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Fig. 1. Proposed architecture for MRDFPD framework

model’s performance by leaving just the meaningful tokens after all the conventional stop-words have been removed. For this regular expression-based stop-words removal, a customized method was used, in which frequently occurring stop-words were sampled, and regex matching was performed over the words. Entities are named in NER using the NLTK library’s pre-trained entity chunker. To execute WSD and understand the context of the words, a specialized knowledge-based technique is employed. The query words (initial query term set) have been codified at the conclusion of the preprocessing stage, from which the query term upper ontology matching is performed. The upper category ontologies for numerous e-commerce-related categories are then combined into an upper one that is matched with the query term where the query word upper ontology occurs. Concept similarity has been used using a 0.5 threshold for this matching. Thus, at the end of the query term upper ontology matching, the initial query term set is merged with the upper category ontology set to obtain an enriched query word set, which is used for further mapping and final recommendation. The E-commerce dataset used in this study is the Myntra all product dataset, which has been customized to fit the suggested model. After getting characteristics from the query term matched upper ontology set, it is classified. For categorization, the dataset is subjected to logistic regression. This regression model was chosen because it is a reliable classifier. It is incredibly efficient to train, in addition to being simple to apply and comprehend. It is one of the most basic machine learning algorithms, and it just requires a small amount of processing power. Because the data in our system is dynamic, logistic regression also gives insight into correlations between characteristics and is flexible enough to adapt to reflect new data rapidly. Along with the reasons mentioned above, it is resistant to overfitting and thus seemed to be a suitable model for our classification, which proved to be true.

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The top 80% of the occurrences identified under each class are evaluated since the dataset is somewhat shallow and not overly huge. The query phrases and higher ontology matched set are used to illustrate each class. As a result, 80% of categorized cases are included in each class. Under metaheuristic optimization, which is the Harris’ Hawks optimization (HHO), semantic similarity is first computed from enriched query words using Jaccard similarity with a threshold of 0.75, normalized google distance (NGD) with a threshold of 0.75, Kullback-Leibler divergence (KL-divergence) with a threshold of 0.75, and Bregman divergence with a threshold of 0.25. The metaheuristic optimization (MHO) algorithm runs on initial sets generated by semantic similarity and divergence measures. And these initial sets are further optimized using an algorithm to yield the matching entity set. For enriched query words, a resource description framework(RDF) is generated, using OntoCollab as a tool, by using content from the world wide web, specifical content from fashion websites, fashion blogs, user-contributed blogs on fashion, as well as reviews from e-commerce websites based on products and product descriptions are used to generate RDF. Also, further between RDF, subject, or object, semantic similarity is computed between a matched entity set and generated RDF. RDF is a three-part structure, but only subject and object are considered, not the predicate. Again, semantic similarity is computed using Jaccard similarity, normalized google distance (NGD), Kullback-Leibler divergence (KL-divergence), and Bregman divergence under Harris’ Hawks optimization algorithm. Jaccard similarity is specified by Eq. (1), where J represents the Jaccard distance, P represents set 1, and set 2 is represented by Q. Normalized google distance is computed using Eq. (2), where the normalized google distance between the words p and q is represented using NGD(p, q). The term f(p) stands for the count of pages having the word p, f(q) indicates the count of pages having the word q, f(p, q) denotes the count of pages with both word p and q, and N represents the product of the total number of pages explored and the average number of words per page. Equation (3) gives the Kullback-Leibler divergence (KL divergence) between the two distributions, p and q. Considering a and b are vectors in Rn with components {a1 , …, an }, {b1 , …, bn }, Eq. (4) gives the Bregman divergence. J(A, B) = P ∩ Q ∨

P ∪ Q∨

max{logf (p), logf (q)} − logf (p, q) logN − min{logf (p), logf (q)} n p(xi ) DKL = p(xi ).log i=1 q(xi ) n  Bf = f (ai ) − f (bi ) − (ai − bi )f (bi )

NGD(p, q) =

i=1

(1) (2) (3) (4)

Only Jaccard similarity will be taken into account for all matched sets in the end, and values will be saved in a hash-map. The key will be the suggested item, with the Jaccard similarity value as the value. E-ranking generates a rating based on Jaccard similarity value and recommends it to the user until no further user clicks are required. If user clicks occur again, the process is repeated recursively using only user clicks until no more user clicks occur. There is no need to re-generate the recommendation if there are no user clicks.

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Harris’ Hawks Optimization Algorithm is a gradient-free metaheuristic optimization algorithm with time-varying exploitation and exploration stages. It is a swarmbased algorithm and is known for its excellent performance, good results as well as the flexibility of its structure. It is a bio-inspired or could be known to be nature-inspired optimization algorithm, crafted based on the cooperative behavior of the bay winged hawks, Harris’ Hawks, whose nature is to surprise pounce. Surprise pounce is a strategy of many of these birds to attack their prey from different directions, surprisingly pouncing upon it. The algorithm has multiple striking features, including an escaping energy parameter that holds a dynamic and randomized time-varying behavior that helps explore and exploit the Harris’ Hawks algorithm better. These features help in transitioning smoothly between the two stages. Thus, the optimization consists of 3 major stages: the exploration stage, the transitioning from the exploration stage to the exploitation stage, and the exploitation stage, performed in the same order. The exploration phase of the algorithm indicates the hawks’ positioning to explore its prey. It involves searching for the prey based on positions of real members, as well as based on perch on the random tree (Prand ), as given from Eqs. (5), (6), and (7). Variables q and ri for i being an element in {1, 2, 3, 4} represents random numbers in set (0, 1). Pi (t + 1) represents the hawks’ updated position in the following iteration. Prand (t) Represents the present position of the hawks. Pprey (t) represents positions of the prey. Pm gives the average of the hawks positions. Z gives the measure of the difference between upper and lower bounds.  Prand (t)− r1 ∨ Prand (t) − 2r  2 P(t)∨, ∧q ≥ 0.5 (5) Pi(t + 1) = Pprey (t) − Pm − Z , ∧q < 0.5 n Pi (t) Pm = i=1 (6) N Z = r3 LB + r4 (UB − LB)

(7)

The next phase is based on the transition from exploring to exploiting. This depends on escaping energy (E), as formulated in Eq. (8), where E0 denotes initial prey energy, which randomly changes between −1 and 1. E = 2E0 (1 −

t ) T

(8)

The exploitation phase is depicted so that it shows how hawks attack their prey. The four chasing techniques are hard besiege, soft besiege followed by intensifying fast dives, and hard besiege accompanied by intensifying rapid dives. The escaping energy is used to switch between these options.

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4 Implementation and Performance Analysis The proposed RDF infused product discovery framework using intelligence semantics is implemented on a processed version of the “All products 2019 dataset” from Myntra.com. It consists of 376840 records having fields ranging from product link and size to colours, type, etc. The dataset is cleaned thoroughly after examining records that could be erroneous. Some attributes are added by synthetically generating user data for implementing the architecture. The framework is implemented in Google Colaboratory on an i7 processor and 16 GB RAM with a 64-bit operating system. It is implemented using Python3 and various libraries as required in the proposed architecture. The performance of the proposed meta-data-driven RDF-based product discovery framework(MRDFPD) is measured using average accuracy, average precision, average recall, average F-measure, false discovery rate (FDR), and normalized discounted cumulative gain (nDCG). Average precision, average recall, average accuracy, and average F-measure compute the relevance of the inferred results. FDR quantifies the number of false discoveries captured by the system, and nDCG or normalized discounted cumulative gain measures the diversity of the results. The three baseline models are SFFPD [4], ACSIR [5] and TCF [6]. K-means clustering and cosine similarity, Fuzzy C-means with Jaccard similarity, and RNN are also used as experimentation variations. It is evident from the Table 1 that the suggeted MRDFPD framework performs better than the other baseline models with 94.48% average precision, 97.32% average recall, 95.9% average accuracy, 95.88% average F-measure, FDR of 0.05, and nDCG of 0.95. Table 1. Performance comparision between the proposed MRDFPD framework with other approaches Search technique

Average precision %

Average recall %

Average accuracy %

Average F-measure %

FDR

nDCG

SFFPD

82.63

84.89

83.76

83.74

0.17

0.74

ACSIR

84.32

86.18

85.25

85.24

0.16

0.81

TCF

87.18

89.42

88.30

88.29

0.13

0.71

K-means clustering and cosine similarity

81.27

83.79

82.53

82.51

0.19

0.73

Fuzzy C-means 82.12 with Jaccard similarity

86.32

84.22

84.17

0.18

0.71

RNN

91.44

93.68

92.56

92.55

0.09

0.74

MRDFPD

94.48

97.32

95.90

95.88

0.05

0.95

SFFPD produces low precision and recall, mainly because they use static ontologies obtained through SPARQL queries. The results float around the average values but could

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be improved. Due to static ontologies having auxiliary knowledge but lacking relevance computation scheme, they always demand particular and verified ontologies, which is impossible in all cases thus needs alternate ways. TCF, as mentioned earlier, refers to a trust-based collaborative filtering algorithm. Trust is for improving security aspects, whereas collaborative filtering is for the relevance aspect of results yielded. The model doesn’t perform exceptionally well and has disadvantages mainly because the items have to be rated. There can always exist a bias when items are not rated, and thus, this model requires improvements. SFFPD and TCF did not incorporate new terms, topic modelling, hidden topic discovery, linguistic semantic indexing, and even sufficient auxiliary knowledge. These factors resulted in low nDCG values of 0.74 and 0.71 for SFFPD and TCF, respectively. To address the semantic gap, ACSIR employs cosine similarity metrics that take into account both visual and textual data. If used, visual features don’t provide all of the information about the product. Products may seem physically similar yet have different branding, necessitating the establishment of a category and branding. This could be improved in terms of a more in-place classification. K-means clustering with cosine similarity is based on a clustering method that is computationally intensive and may fail with a big dataset. The model operates admirably and yields excellent results. When the data is analyzed, it is discovered that lesser precision, accuracy, recall, Fmeasure, and FDR indicate a need for topic modeling and auxiliary knowledge addition that needs to be improved. The computing cost of the Fuzzy C-means with the Jaccard similarity model is high, but Jaccard similarity is substantially better than cosine similarity. Although this architecture does not contain auxiliary knowledge, several enhancements can be developed. RNN is a deep learning technique that uses recurrent neural network layers to automatically extract features. The model is complicated, computationally intensive, and occasionally over-fitted. As a result, the findings are sparse and less diversified, and diversity is not taken into account. The MRDFPD framework uses logistic regression along with RDF generation, metadata generation, and metadata classification. Metadata-driven RDF generation takes place. The upper ontology gives categorical matching, thereby ensuring it is well accomplished. Knowledge density is high when metadata and RDF are used. The logistic regression algorithm for the classification of metadata is relatively not expensive. Semantic similarity computation is done using Jaccard similarity, normalized google distance, KL-divergence, and Bregman divergence, and they ensure that the relevance computation is very high. The Harris’ Hawks algorithm (HHO) for MHO computation makes sure the initial set is refined, and a very seasoned final recommendation status is yielded. The density of auxiliary knowledge supplied takes care of diversity, making the nDCG very high. Moderating multiple approaches ensures recall, precision, accuracy, F-measure, FDR, and nDCG are much better. From the graph in Fig. 2, it is evident that the proposed MRDFPD framework gives the highest precision with respect to number of recommendations.

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Fig. 2. Precision% vs. number of recommendations graph

5 Conclusion The MRDFPD framework, an effective technique for product discovery and recommendation, is presented in this study. It uses a model that includes both input searches and user clicks to provide efficient results. After preprocessing, higher ontology matching yields a query phrase. The query phrases’ characteristics are extracted, and the dataset is submitted to logistic regression classification. Under MHO, semantic similarity is calculated using enriched query terms and the top 80% of categorized instances, followed by query term-based RDFs and matching entities from the preceding computation. The computed similarity is used to rank and recommend the products to the user. The high accuracy, precision, F-measure, recall, and nDCG, as well as the low FDR, demonstrate that this is a dynamic framework that satisfies the demands for delivering the most suitable suggestions. The framework enables the integration of descriptive metadata about the product with auxiliary knowledge and dynamic customer behavior. Using logistic regression and assessing semantic similarity using Harris’ Hawks optimization technique, the efficacy of MRDFPD is increased.

References 1. Deepak, G., Kumar, D.N., Surya, D., Razak, K., Venugopal, K.R.: An ontology-based semantic approach for first aid prediction in aviation services incorporating RBFNN Over a cloud

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OntoKIQE: An Ontology Infused Approach for Knowledge Integrated Query Expansion Using Semantic Intelligence Vignesh Mohanavelu1 , Gerard Deepak2(B) , and A. Santhanavijayan2 1 Department of Electrical and Electronics Engineering, National Institute of Technology

Tiruchirappalli, Tiruchirappalli, India 2 Department of Computer Science Engineering, National Institute of Technology

Tiruchirappalli, Tiruchirappalli, India [email protected]

Abstract. As the Web 2.0 moves towards the Web 3.0 there is an increasing need for simple and efficient information retrieval techniques. Yielding the users’ required search intent has proved to be an exhaustive and tedious task from just using Mutual Information model (NPMI) and the Pielou’s Evenness Index (PEI), along with Domain Ontology based query term enrichment and Knowledge enrichment of the term pool with the help of Wikidata API. The user input terms, resulting in unreliable and quavering search results. To solve this issue efficiently, we deploy semantically compliant automatic query expansion techniques. The proposed OntoKIQE model is composed of three primary statistical base models namely Bose Einstein-1, Normalized Pointwise Accuracy and Recall percentages of 94.18 and 92.84 respectively have been yielded by the proposed model, being a substantial improvement over the baselined models by enriching the query terms with auxiliary background knowledge and generating ontologies. Keywords: Knowledge driven · Ontology · Pileous evenness index · Query expansion · Semantic intelligence

1 Introduction In the past, Information Retrieval from the Web was widely handled by search engines, fetching results exclusively from query keywords, often constricting the search results to a narrow spectrum or providing irrelevant documents as a response. Traditionally, in keyword-based information retrieval techniques, the documents or indexes that contain query terms are retrieved and ordered to their decreasing order of similarity to the query. Since usually, queries are short (around two to three words) and the ambiguity of natural languages, these matches tend to be incomplete or wrong. Another reason could be the fact that queries are often provided incompletely, making it harder for the system to process and ‘understand’ it. As the World Wide Web expands faster than ever before, yielding the required search queries have proved to be an exhaustive and tedious task using traditional IR methods such as NLP algorithms. However, after the arrival of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 394–405, 2022. https://doi.org/10.1007/978-3-031-02447-4_41

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Semantics, and the move towards the era of the World Wide Web 3.0, search engines and databases have been progressively revamped to solve the issue of low precision and information recall using automatic Query Expansion (AQE). Query Expansion aims to solve this issue by reformulating adding and re-weighing query terms using various ML and AI techniques to improve retrieval performance. In a QE process, the native user query is expanded by adding synonyms, hypernyms, and hyponyms of the query ‘words’ using an already existing vocabulary or lexical resource e.g., WordNet, or a user’s profile/topic of interest, or a local or relevance feedback. Local feedback methods rely on the fact that relevant documents in the database could contain words or terms that could be useful to reformulate a query. Though several ML based QE models based on distribution statistics such as Bose Einstein statistics have already been proposed and implemented, there is a strong need for semantically compliant QE techniques to further reduce lexical inconsistencies of document retrieval. The Proximity Relevance Model [2] focuses more on the nature of the query terms that make them more semantic, rather than their position within a document such as in the PRT model. Query Expansion techniques are generally either based on the analysis of an already existing analysis of a document collection or an implication from dictionary-based methods. For this paper, an Ontology based, query expansion technique is proposed with auxiliary background knowledge generation using bagging algorithm to modify the primary query. A knowledge-based query expansion is essential for a semantic Web. Implementation of knowledge discovery applications requires writing of complex database search requests for IR, maintaining the structural complexity of the databases and the semantic relationships between the data in them. To address the mentioned constraints, there has been a heavy focus on incorporating auxiliary knowledge representation techniques in query generation aided with the help of ontological models. Motivation: There is an increasing need for a semantically driven query expansion techniques to cater to the progressive expansion towards Web 3.0. Though traditional query expansion techniques have existed for a while, machine learning assisted Query Expansion techniques such as knowledge representation, take us to the next step towards the goal of the ‘Semantic Web’. The following study aims to provide an insight towards a less meticulous implementation of knowledge-based applications with the help of Ontological Models. Contribution: An efficient ontologically driven, semantically compliant query expansion model has been proposed. The proposed model, OntoKIQE combines three statistical models (BO1, NPMI, PEI) using bagging algorithms. Additionally, the term sets are enriched with auxiliary knowledge with the help of dynamically generated ontologies and SemantoSim is used to rank the enriched term sets based on their semantic similarity and consequently recommended to the user in a bigram or trigram sequence. The model achieves a higher precision recall and accuracy percentage without being computationally intensive. Organization: Following the introduction, the second section describes Related Work. Section 3 profiles the Proposed System Architecture. Results and Performance Analysis

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in comparison with baselined models are shown in Sect. 4. Section 5 finally brings the paper to a conclusion.

2 Related Work A lot of research work has been put reported on the domain of Information Retrieval (IR) and Query Expansion (QE) over the last decade. So far, several automatic query expansion models have been proposed that generate terms and rank them to the efficiency of the IR model by yielding a better query with the help of user feedback [3]. HK Azad et al. [4] have surveyed the development of query expansion techniques in information retrieval from 1960 to 2017 and the introduction of automatic query expansion techniques. S Kuzi et al. [5] have presented several QE techniques based on Word2Vec’s word embedding and integrating queries with an effective pseudo relevance feedback model. Ge Gao et al. [6] have proposed a semantic search engine prototype in regard to retrieving Building Information Modelling (BIM) technology by combining the ontologies and local context analysis (LCA). Pedronette et al. [7] suggested a general approach for context-based image retrieval in which query ranking methods have been developed which aims to exploit the ‘contextual information’ which depends on the similarity of top k lists retrieved by efficient indexing structures. Newly developed optimisation techniques such as swarm intelligence algorithms have been incorporated in query expansion techniques proposed by Ilyes Khennak and Drias [8] where Accelerated Particle Swarm Optimisation (APSO) technique is used to efficiently used in the query expansion application of a medical dataset. Various AI incorporated, neural based models have been incorporated which require highly trained data, Li et al. [9] has explored additional methods of incorporating neural networks into code search and automatic query expansion. A recently proposed pseudo relevance feedback model by I Rasheed et al.[10] combines traditional term selection methods like Kullback-Leibler divergence [11] to select the most appropriate term and uses aggregation methods of boosting algorithm to modify the final query and is primarily focused on a more vernacular language-based information retrieval. QuantQueryEXP is another AQE model proposed by I S Kaushik et al. [12] which implements the Deutsch-Jozsa [13] algorithm for query expansion regarding quantum computing and cryptography applications. Focus on semantics in query expansion being the need of the hour, recent revelations on leveraging semantic resources in diversifying query expansion [14] has helped develop more nuanced results for automatic query expansion. Yunzhi et al. [15] presents an QE model based on constructing hepatitis ontology, including synonym hypernym/hyponym expansion of similar words, and applying semantic similarity calculation to weigh the similarity between the retrieved terms. Devi et al. [16] has proposed an ontology-based QE model, SIRSD for semantic IR of terms related to the sports domain using Wordnet and related domain ontologies. In [17–26] several models in support of the proposed literature have been discussed.

3 Proposed System Architecture The following section describes the various methodologies involved in the proposed model architecture and shown in Fig. 1. The User query was first standardised through

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Fig. 1. Proposed system architecture

Fig. 2. Enriched terms pool with dynamic ontology generation

Query Pre-Processing that involves tokenization, stemming and removal of stop words, and lemmatization through which redundant words are eliminated. Furthermore, the query goes through Named Entity Recognition (NER) where the query was categorized into entities. The Query terms after pre-processing comprises the Initial terms Pool. The candidate terms were weighted, and its semantic similarity was computed using three models, Bose Einstein-1[27], Normalized Pointwise mutual Information model (NPMI)[28] and the Pielou’s evenness index (PEI)[29] to expand the initial query terms pool. Bose Einstein 1(BO1) model: The Bose-Einstein statistical approach for weighing terms is given by Eq. (1), incorporated in the Bose Einstein 1 (BO1) model. w(t) =

n d ∈n

tf (t, d ) × log2

(1 + Pc ) + log2 (1 + Pc ) Pc

(1)

where we(t) is the weight of the term  ‘t’ and tf is the term frequency of the query in top-ranked documents. Pc equals F N where F represents frequency of the query term in the collection and N represents the number of documents in the collection [27]. The threshold is set to 0.8 to filter out the weighty relations for the current use case. Normalized Pointwise mutual Information model (NPMI): Given to random pair of variables x and y, the mutual information and the semantic heterogeneity between them is the calculated using Pointwise Mutual Information [30] measure proposed by Church

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and Hanks. The PMI measure is symmetric and can take a positive or a negative value and is depicted by Eq. (2) where if x and y are independent, the value is zero [31].   p(x, y) (2) pmi = log p(x)p(y) Pointwise mutual information is normalized between the bounds ‘[−1, +1]’ where the left bound [−1] indicates x and y never occurring together, ‘[0]’ for independent variables and [1] for co- occurrence. Equation (3) gives the normalised PMI values. npmi =

pmi(x, y) log(p(x, y))

(3)

where log(p(x, y)) is the joint probability. Currently, the threshold is set to 0.5 to filter out the weighty relations. Pieliou’s Evenness Index (PEI): Originally introduced to measure biological diversity among species is another model incorporated here for measuring the semantic similarity between the terms pool It is deployed here to improve the intradomain diversity among the concepts and entities within a particular domain and is normalized within the ranges [0, 1] where 0 represents no evenness and 1 representing complete evenness. The threshold here is set as 0.75. The candidate terms subjected to the B01, NPMI and PEI models are then further weighted by using the bagging methods. The multiple terms compiled from the three term sets TS1 TS2 AND TS3 obtained from the above-mentioned expansion models, comprise the intermediate term pool as presented in Fig. 2. The term pool has been subjected to be enriched with auxiliary knowledge and to aid this process, OntoCollab [32] is used for construction of knowledge bases using ontology modelling. The dynamically generated Ontologies are further enriched by comparing the latent knowledge from the framework with knowledge bases available online. The OntoKIQE model has used implemented Wikidata API as the latent knowledge base. The Cognitive gap between the real-world knowledge and knowledge from the framework has been reduced greatly in the process increasing the auxiliary knowledge of the term pool. The terms from the enriched terms pool along with the top 25% of the weighted instances from the initial terms pool was measured for semantic compliance and are ranked using the SemantoSim measure [33]. SemantoSim is a semantic ranking measure derived from the NPMI measure and the semantic similarity within two terms is given by Eq. (4)   pmi(x, y) + p(x, y)log p(x, y)   (4) SemamtoSim(x, y) =  p(x) · p(y) + log(p(y, x) where p(x, y) gives the probability of the variable term x and its co-occurrence with term y. p(x) and p(y) are the probabilities of xandy respectively. The ranked entities in the terms pool are then returned as a query result in a bigram, trigram or a n-gram sequence back to the user for user feedback. The process is repeated until the desired query is returned. The Proposed OntoKIQE Algorithm is depicted as Algorithm 1.

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Algorithm 1: Proposed OntoKIQE Algorithm Input: The user input seed query q containing the terms related to the documents to be fetched Output: A semantically compliant terms pool containing weighted terms pool relevant to the search query in a bigram, trigram or an N-gram sequence. begin Step 1: The query q is pre-processed in which tokenization is performed along with lemmatization and the removal of stop words to obtain a set of query words – initial terms pool. Step 2: for x,y,z in terms pool: x in Bose-Einstein 1 model y in NPMI model z in Pielou’s EI model Step 3: Generate Term Sets: If (x > 0.8): include in TS1 If (y > 0.5): include in TS2 If (x > 0.75): include in TS3 Step 4: Generate Aggregated Termset, Aggr = return top 25% classified instances Step 5: From Aggregated Termset: generate Intermediate Terms pool Step 6: while (Intermediate_term.next() != 0): OntoCollab(): generate Ontologies Step 7: Enrich Intermediate Terms Pool with auxiliary knowledge, modelling ontologies with ‘Wikidata API’ Step 8: qf = SemantoSim (x top 25% classified instances, y enriched Intermediate Terms Pool) Q = qf.rank(SemantoSim) Step 9: recommend the processed query, Q as a bigram or trigram sequence accordingly for user feedback end

4 Results and Performance Evaluation The OntoKIQE model has been baselined with four distinct models: KLDBQT [34], IGBQT [35], User Profiles + Folksonomy [36], and Fuzzy C-Means Clustering + LSI [37]. The performance evaluation metrics for the model are Average Precision and Recall, Accuracy, FDR, F-Measure, and Discounted Cumulative Gain (nDCG). The average Precision percentage of the model is calculated by Eq. (5) while Eq. (6) measures the Average Recall of the same. The accuracy of the model with regards to precision and

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recall is computed as shown in Eq. (7). Equation (8) and Eq. (9) assesses the model’s F-Measure and False Discovery Rate (FDR). Equation (10) calculates the nDCG of the proposed model where the DCG is given by Eq. (11). F-Measure or the F-Score is the weighted harmonic mean of the test’s precision and recall characteristics. The metrics: Precision, Recall, Accuracy and F-Measure compute the relevance of the query results yielded. Whereas FDR quantifies the number of false positives yielded by the model. Finally, nDCG measures the diversity in the results which is furnished by the proposed OntoKIQE model. Precision % = Recall % =

Retrieved ∩ Relevant . Retrieved

(5)

Retrieved ∩ Relevant . Relevant

(6)

Precision + Recall . 2

(7)

Accuracy % =

F − Measure % =

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

(8)

False Discovery Rate = 1 − Precision nDCG = DCG =

α

(9)

DCGα . IDCGα

(10)

Rel i . + 1)

(11)

i=1 log(i

Table 1. Comparison of performance of the proposed OntoKIQE with other approaches Query expansion model

Average precision %

Average recall %

Accuracy %

F-Measure

FDR

n-DCG

KLDBQT [34]

88.48

85.17

86.82

86.79

0.12

0.88

IGBQT [35]

86.17

82.16

84.16

84.08

0.14

0.86

User Profiles+ Folksonomy [36]

87.15

84.89

86.02

86.00

0.13

0.87

Fuzzy C-means 88.91 Clustering+ LSI [37]

84.81

86.72

86.65

0.11

0.83

OntoKIQE

92.84

93.51

93.50

0.06

0.94

94.18

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Table 1 Illustrates the performance metrics of the OntoKIQE model with baselined query expansion models for an approximate of 7224 queries. OntoKIQE is an approach for query expansion which has been evaluated for an approximate of 7224 queries whose ground truth has already been collected. The baseline models namely KLDBQT, IGBQT, User Profiles+ Folksonomy, and Fuzzy C-Means Clustering + LSI were evaluated at exactly similar testing conditions and code environment as the OntoKIQE model, and the results of the performance metrics are depicted and tabulated in Table 1. It is evident that the proposed OntoKIQE model and has the highest percentages of Average Precision, Average Recall, Accuracy, F-measure and nDCG of 94.18% 92.84%, 93.51%, 93.50% and 0.94 respectively. It has the lowest false discovery rate of 0.06. A higher nDCG yields a higher and a higher F-measure (obtained from precision-recall analysis) yields in a higher relevance between the results. A low FDR ensures a higher number of true positives obtained. The KLDBQT model uses the Kullback-Liebler divergence for the computation of weighted similarity of the query’s terms. The IGBQT model is based on the information gain (IG) for its computation. From Table 1, its observed that these standalone models produce a lower precision-recall performance, deeming to be insufficient in real world use cases. To increase the relevance of the search results, a combination of these standalone models are implemented. The User profiles + Folksonomy model generated which contain the user’s past annotation information to fetch queries. Folksonomy details from Folksonomy websites are used to enrich the generated User Profiles with topical weight, enhanced word embeddings including the similarities between the terms inside the pool and the query term [26]. Despite the introduction of multiple query expansion techniques (Folksonomy) we observe a very marginal improvement over the standalone models since the incorporation of user profiles rendering it fairly user centric and user driven, making the model susceptible to a higher margin of error. The fourth baseline model involves Latent Semantic Indexing (LSI) for topic modelling of query terms incorporated with Fuzzy C-means Clustering [27]. C-means Clustering is a soft-clustering method where the data points, in our case, the topic is allotted a specific probability score for which it belongs to a cluster. Fuzzy C-means clustering enforces a complex computational time on the system since it requires the categorical information to be converted into a non-categorical quantitative measure. A standalone LSI approach would not be sufficient to obtain acceptable performance metrics. Observing Table 1, the Fuzzy C-means Clustering + LSI model observes a moderate improvement over the other baseline models but as a result, suffers from a slower computational time due to its complexity. The proposed OntoKIQE primarily incorporates three standalone models, Bose Einstein-1, Normalized Pointwise mutual Information model (NPMI) and the Pielou’s evenness index (PEI), aggregated using decision tree bagging techniques to produce the top 25% of the classified query term instances. The results from the three standalone models are also enriched with auxiliary knowledge from dynamically generated Ontologies with the help of Wikidata API into a separate terms pool which are finally ranked with the generated classified instances using the SemantoSim measure to return the final query to the user.It is evident that the proposed OntoKIQE model and has the highest percentages of precision, recall, accuracy, F-measure and nDCG of 94.18% 92.84%, 93.51%,

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Fig. 3. Precision percentage vs number of recommendations for the baselined models with the proposed model

93.50% and 0.94 respectively. It has the lowest false discovery rate of 0.06. A higher nDCG yields a higher and a higher F-measure (obtained from precision-recall analysis) yields in a higher relevance between the results. A low FDR ensures a higher number of true positives obtained. The use of the three standalone models pipelined along with a relatively efficient ranking measure, SemantoSim, and the auxiliary background knowledge generation provides a much higher semantic similarity and an efficient relevance computation which is pretty evident from the results of the performance analysis. The model is a complete, integrated, intelligent query expansion approach and the bigram, trigram and n-gram sequenced retrieval ensures that a wide range of semantically compliant query results are returned for user feedback. If the precision recall standards aren’t met, the computation runs again with user feedback until the desired results are returned. Figure 3 illustrates the Precision Vs Number of Recommendations of query expansion models in discussion in steps of 5 recommendation from 10 to 50. The OntoKIQE model has a precision of 97.64% for 10 numbers, 96.54% for 20 numbers, 94.32% for 30 numbers of 93.21% for 40 numbers and 91.14% for 50 numbers respectively which are a significantly higher precision values compared to the other baselined models. From Fig. 3 results, it is observed that the O model yields a higher precision percentage for the number of recommendations from 10 to 50. The primary reason for the significant improvement in efficiency can be correlated due to the use of Domain Ontology based query term and knowledge enrichment with the help of Wikidata API, along with the aggregation of pre-existing standalone models, i.e., Bose Einstein-1, Normalized Pointwise mutual Information model (NPMI) and the Pielou’s evenness index using bagging algorithms. The further ranking of the term sets with the efficient SemantoSim measure along with the bigram, trigram and n-gram sequencing further adds to the overall efficiency of the model.

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5 Conclusions The performance of an Information Retrieval system is significantly in proportion to the efficacy and speed of a retrieved query. Thus, it is essential to introduce query expansion methods to refine the relevance of the retrieved documents. The paper has explored various query expansion methods and its relevance towards the World Wide Web 3.0. An efficient, ontology driven query expansion model has been proposed which effectively combines three statistical models namely, Bose Einstein-1, Normalized Pointwise mutual Information model (NPMI) and the Pielou’s evenness index (PEI). The proposed OntoKIQE model is composed of Domain Ontology based query term enrichment and Auxiliary Knowledge enrichment of the term with the help of Wikidata API, while the similarity index between the query terms is ranked using SemantoSim. The combination of these techniques help the model yield a much higher precision-recall percentages as well as higher accuracy and F-measure with a low FDR without being computationally intensive. The OntoKIQE model has achieved Accuracy and Recall percentages of 94.18 and 92.84 with an nDCG of 0.96 and an FDR as low as 0.06.

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MetaBlog: A Metadata Driven Semantics Aware Approach for Blog Tagging Harsh Shaw1 and Gerard Deepak2(B) 1 Department of Computer Science and Engineering, SRM Institute of Science and Technology,

Chennai, India 2 Department of Computer Science and Engineering, National Institute of Technology,

Tiruchirappalli, India [email protected]

Abstract. Blogs on the internet are very popular and due to their popularity and knowledge filled contents on various topic, today internet has a lot of blogs on various sites, hence if a person wants to look for a particular blog it becomes quite a task for him/her to get the relevant blog out of internet searches. To solve this problem several approaches such as TextRank and TF-IDF, User Selection combined with knowledge flow, labels and PageRank, clustering etc. were proposed in the past. The proposed MetaBlog approach involves knowledge centric approach infused with machine learning paradigm such as random forest where a semantic similarity is computed between the RDF generated values and Random Forest top 25% selection and a final tag is finalized. The proposed MetaBlog yields an overall Accuracy of 93.20%, with a low False Discovery Rate of 0.09. Keywords: Metadata · Random Forest · RDF · Semantic Web · Tag recommendation

1 Introduction Nowadays anything is searchable on the World Wide Web, there are millions of data, records, blogging pages etc. available for either reference or educating people about a certain topic, among them, blogging has gained popularity in recent times. A blog is an online journal or an informational website that displays information about a certain topic (e.g., cricket, politics etc.), with the latest blogs, appear first, it is a platform where people share their views on an individual subject, blogs are very profitable hence it attracts more and more writers, with so many platforms and writers we can expect a huge number of blogs flooding the World Wide Web. Searching a particular blog or finding out the best-suited blog for one can be a tedious task, hence we require a method of tagging blog which will improve searching, looking up for a particular topic. Blogs tagging should be done based on their impact, subject, indexed, social causes hence the tagging method should be efficient enough to value all these metrics. The Semantic Web is an extension of the World Wide Web where data is linked on the Web this creates machine-readable data, to increase their computation and make it © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 406–414, 2022. https://doi.org/10.1007/978-3-031-02447-4_42

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easy for a task involving data on the Web it is implemented using RDF, OWL, SKOS etc., conventional tagging mechanism does not cater to the need, hence a better semantically driven, ontological focused tagging of blogs is the need of the hour. Ontology is defining a category or relationship of an object to subject, data, etc., various methods are used for tagging systems such as TextRank, TF-IDF, etc. Motivation: There was a need for semantic-based approaches since the present era is of Web 3.0, owing to the explosion of data on the World Wide Web, non-semantic approaches or classical approaches become insufficient and do not yield effective results when the density of data is very high so for a highly dense cohesive environment like the Semantic Web there is a need for the semantically inclined tagging mechanism to improve the retrieval of content, entities. Contribution: An RDF generation has been incorporated for the unique categories obtained from the dataset, a Metadata based approach has been proposed for semantically enriched tag recommendation, random forest is used for classification then knowledge is provided by incorporating the entities from LOD cloud, Google Knowledge Graph API, Wikidata for enriching the tag density approach and then the semantic similarity is measured using NPMI, Jaccard, ANOVA concept cosine similarity and the percent of precision, accuracy, F-measure is increased compared to the baseline approaches. Organization: The remaining part of the paper is organized as follows. Section 2 describes the Related Work. Section 3 describes the Proposed Architecture. The Implementation and Performance Evaluation is depicted by Sects. 4 and 5 respectively. The paper is concluded in Sect. 6.

2 Related Work Tang et al., [1] proposed two methods to extract tags from blogs using TF-IDF based Tags Extraction in which TF (Term frequency) is the number of occurrence in a document which is normalized and IDF is the inverse document frequency which is ratio of number of all document and document with the related term TextRank based algorithm using cooccurrence of words implemented by graph to extract keyword from documents.Hayes et al., [2] showed an algorithm for Feature selection using PageRank Algorithm, to calculate the importance of webpages, where multi label graph based feature selection (MGFS), uses Correlation Distance Matrix (CDM). MGFS creates weighted graph calculating significance of each vertex is estimated, implementing PageRank algorithm. User’s historical preference-based Architecture was proposed by Zhang et al., [3] where users were clustered with respect to their preferences to micro blog topic. Furthermore, knowledge flow different class were recommended, and updated with user selections to improve the topic recommendation. Veeramachaneni et al., [4] used content-based clustering, where he observed that frequently occurring tags in each cluster served as good meta labels for cluster concept. Rohit et al., [5] uses Recommendation system which is improved using sentiment analysis. Recommendation system uses classification of blogs, it uses parameters such as title of blog, blog images, tagger of the blogs etc. It further uses POS (Part of Speech) text analyzer for classification of text. Hashemi et al., Sriharee et al., [6] proposed an ontology-based tagging mechanism, where the auto tagging methodology consisted of

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two processes classification and tag selection process. Classification used semantic analysis including term weight matrix and cosine similarity, whereas right ontological tag was the focus for tag selection process. Jung et al., [7] proposed a novel Architecture called blog context overlay system, Kim et al., [8] proposed an approach known as CDIP for blog tagging based on content reuse, Brooks et al., [9] did an analysis of effectiveness of tagging of blogs. In [10–21] several models in support of the proposed literature have been depicted.

3 Proposed Architecture The Proposed Architecture blog tag has been depicted in Fig. 1 the blog data has to go through preprocessing which includes tokenization, lemmatization, stop words removal and named entity recognized and obtain unique categories from data, and also the named entity. The architecture is divided into three phases, first phase includes the preprocessing of the dataset, second phase includes the RDF generation with entity enrichment, third phase is Metadata generation and its classification.

Fig. 1. Proposed system architecture

An RDF gives a subject, predicate, and object association (SPO), predicate is dropped and only the subject and object is taken as categories, further from the generated RDF graph, the entity enrichment happens in 3 phases, first LOD (linked open data cloud), Wikidata, Google Knowledge Graph API, the LOD is accessed via API and relevant

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entities are loaded, further to which relevant content from Wikidata is accessed to add more entities, further to populate the entity subgraph from Google Knowledge Graph API is also loaded for all the matching and relevant entities for the subject as well as the object of the RDF of the unique categories. The reason for the entity enrichment is just to increase the knowledge centric and to reduce the cognitive gap between the real world and the proposed MetaBlog model. Metadata is important as it increases the knowledge and it is 3 folds higher than the entity enrichment, it accesses the substructure of knowledge and also ensures none of the entities are missed out. The limiting data is exponentially large, so because of this the entire Metadata cannot be used, the Metadata classification takes place using a Random Forest algorithm, inputs are dataset categories, based on the category. Random forest is widely known algorithm used in machine learning for classification and regression it is an ensemble learning method, which means it uses multiple learning algorithm to predict outputs. Training algorithm for Random Forest uses bootstrap aggregating or bagging for Eqs. (1) and (2). X = x1.....xn

(1)

Y = y1.....yn

(2)

Process repeatedly selects a sample random in nature for replacement for the training set, fits the tree to this sample. After training, predictions for the unseen samples from the test set say x ‘can be made by taking average prediction from the individual regression trees on x‘as in Eq. (3). 1 fb(X ) B B

f =

(3)

b=1

or for the classification of tree a majority of vote is taken. Semantic similarity is measured using the triadic similarity measurement scheme using distinct similarity measures, mainly the normalized pointwise mutual information measure (NPMI), concept similarity with ANOVA and Jaccard similarity. The reason for using is mainly to increase the relevance between the individual term, which is recommended by the framework, also to increase the tag which are conceptualized from the proposed framework recommendation, which should be highly relevant and does not deviate from the essence of tag, and impact that the tag would create. PMI is the measure of association, which is used in statistics and information theory, MI (Mutual Information) refers to the average but PMI (Pointwise Mutual Information) refers to a single event. In normalized pointwise mutual information, the values are normalized between [−1, + 1], −1 for event which will never occur together, 0 if they are independent, + 1 for complete co-occurrence as presented in [22]; NPMI is depicted in Eq. (4). NPMI =

PMI h

(4)

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Jaccard is a similarity measure used in recommendation systems to compute similarity between two user or products, it calculates similarity using sets, it compares similarity using Eq. (5). J (X , Y ) =

|X ∩ Y | |X ∪ Y |

(5)

4 Implementation The proposed framework was implemented using python language, anaconda as IDE, OWL ontologies were converted into RDF, RDF were generated using OntoCollab. OntoCollab was used for automatic generation of ontology Google Knowledge Graph API, and LOD Cloud were accessed via API whereas the Wikidata was accessed via agent.Dataset used in the proposed framework and also with the baseline approaches is https://data.world/opm/open-government-blog, Open Government Blog in the office of Personal Management, we have 10 rows in the dataset and 11 columns, with Blog, title, URL, author, tags, description, thumbnail, Image, medium square, published and short tile as the columns.

Algorithm 1: Proposed MetaBlog Algorithm Input: Blogs with URL, Open-Government-Blog dataset Output: Output will be highly relevant tags proposed for a particular blog using proposed framework Step 1: Data is preprocessed using, Lowercasing, Stemming, Lemmatization, Stop word removal Step 2: D.Tokenize(); D.lemmatize(); ds.WordNet.Lemmatize(); ds.Wordnet.Tokenize(); ds.WordNet.StopWordElimination(); ds.TolowerCase(); ds.NER(); Step 3: Ts=ds.Tokenize(); Ts.Lemmatize(); Step 4: if(ls.Ts==StopWord) Eliminate Ts.current() endif Step 5: RDF generation using LOD CloudWikidata Google Knowledge Graph API Step 6: HashSet C= Ts.current(); Step 7:obtain unique categories Metadata Generation classify using random forest obtain top 25 % Step 8: Compute similarity score using Jaccard, NPMI, ANOVA concept if (ss ≤ 0.25) HashMap = Term, SS Step 9: finalize Tags

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As shown in Algorithm 1, we see that the input used for the framework are blogs with URL, from our dataset, output that is expected is a finalized tag for a particular blog post the initial phase of algorithm consist of data preprocessing, this includes lowering, stemming, lemmatization etc. which are generally done in natural language processing as a general requirement before starting. In the second phase the RDF generation takes place for knowledge enrichment where Google Knowledge Graph API, LOD Cloud, Wikidata are used to get more entries via API calling or agents.

5 Performance Evaluation The proposed metablog tagger is baseline with 4 different approaches TextRank, TFIDF [1], Labels + PageRank [2], Knowledge Flow + User Selection [3], Precision, Recall, Accuracy, F-Measure, FDR (false discovery Rate) are used as the metrics, which is depicted in Eqs. (6), (7), (8), (9), (10) respectively. Precision = Recall =

Retrieved ∩ Relevent Retrieve

(6)

Retrieved ∩ Relevent Relevant

(7)

Precision + Recall 2

(8)

Accuracy =

F − Measure =

2.Precision.Recall (Precision + Recall)

FDR = 1 − Precision

(9) (10)

From Table 1 it is highly indicative that the proposed approach gives the highest value for, Precision, Recall, Accuracy, and Table 2. shows F-Measure and lowest value for the FDR with the baseline approaches, The TextRank [1] gives a low Average precision, Recall, Accuracy, F-Measure and High FDR compared to the proposed framework, this is due to the reason that, the ranking is based on individual terms of text namely the keyword or the terms which is considered of high importance, also since it is a standalone method used it yield a low value and high FDR (False Discovery Rate). For [1], the precision is 73.28%, Recall is 77.54%, Accuracy is 75.36%, F-Measure is 77.30% and FDR is 0.28 the reason is again it deals with the rarity and frequency of the term appearing in the corpus.TF-IDF [1] is again a standalone methodology which depends on the frequency of the terms appearing in the corpus hence it has low Precision, Recall, Accuracy, F-Measure and high FDR. Clearly [2] Methodology has a better precision of 78.74%, Recall 81.14%, Accuracy 79.94%, F-Measure 79.92% and FDR 0.22 than TextRank [1], TF-IDF [1], since it uses labels and moreover when PageRank is used its a scheme where hierarchical relationship is taken into consideration. Knowledge Flow + User Selection [3] is used it is clear that it has Precision, Recall, Accuracy, F-Measure higher than TextRank [1], TF-IDF [1], Labels with PageRank [2] and Low FDR compared to other methodology (88.14%, 91.18%,89.66%, 89.63%,

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Approach

Average precision %

Average recall %

Average accuracy %

TextRank [1]

71.32

74.15

72.735

TF-IDF [1]

73.28

77.45

75.36

Labels + PageRank [2]

78.74

81.14

79.94

Knowledge flow + User selection [3]

88.14

91.18

89.66

Proposed MetaBlog

91.57

94.84

93.21

Table 2. F-Measure and FDR comparison Approach

F-Measure %

FDR

TextRank [1]

72.70

0.28

TF-IDF [1]

75.30

0.27

labels + PageRank [2]

79.92

0.22

Knowledge flow + User selection [3]

89.63

0.12

Proposed MetaBlog

93.17

0.09

Fig. 2. Recall Vs number of recommendations graph

0.12 respectively) because Knowledge centric scheme tends to increase the overall Precision, Recall, Accuracy, F-Measure and decrease the FDR value. There is a scope of improvement which is being proposed in MetaBlog has Precision 91.57%,Recall 94.84%,Accuracy 93.205%,F-Measure 93.17%, and FDR of 0.09 lowest among all the

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other approaches, this is mainly due to the reason that Propose MetaBlog methodology is semantically aligned knowledge centric approach infused with machine learning paradigm, so the use of Random Forest for classification and selection of only the top 25% of entities which are already classified decreases the computation load and also induces the clarity in framework also the use of LOD Cloud, Wikidata, Knowledge API increase the cognitive knowledge from the real world and increases the number density of entities, therefore making the approach appealing, Metadata increase the amount of knowledge exponentially also a very good regulatory measures such as ANOVA cosine similarity, Jaccard, NPMI ensures that highly relevant tags are proposed. From Fig. 2 it can be seen that the number of Recommendation vs Recall for the Proposed Metablog is maximum as compared to the baseline approaches due to its unique knowledge centric infused approach for tagging.

6 Conclusion The proposed MetaBlog methodology which uses semantically rich knowledge entities via Google Knowledge Graph API, WikiData, LOD Cloud, combined with Metadata classified with Random Forest and computing a semantic similarity between the two has shown that the MetaBlog tagging system has better performance than the baseline approaches clearly, where the classical method of using TextRank and TF-IDF was outperformed by a considerable difference, hence an knowledge centric approach infused with machine learning paradigm (Random forest)and a semantic similarity score is generated which produces final tags for the blog, we achieve an Recall of 94.84%,precision of 91.57%,accuracy of 93.205% and FDR of 0.09.

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9. Brooks, C.H., Montanez, N.: An analysis of the effectiveness of tagging in blogs. AAAI Spring Symposium: Comput. Approach. Anal. Weblogs 6, 9–14 (2006) 10. Deepak, G., Gulzar, Z., Leema, A.A.: An intelligent system for modeling and evaluation of domain ontologies for Crystallography as a prospective domain with a focus on their retrieval. Comput. Electric. Eng. 96, 107604 (2021) 11. Roopak, N., Deepak, G.: OntoKnowNHS: Ontology Driven Knowledge Centric Novel Hybridised Semantic Scheme for Image Recommendation Using Knowledge Graph. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M.A. (eds.) KGSWC 2021. CCIS, vol. 1459, pp. 138–152. Springer, Cham (2021). https://doi.org/10.1007/978-3030-91305-2_11 12. Ojha, R., Deepak, G.: Metadata Driven Semantically Aware Medical Query Expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M.A. (eds.) KGSWC 2021. CCIS, vol. 1459, pp. 223–233. Springer, Cham (2021). https://doi.org/10.1007/978-3030-91305-2_17 13. Yethindra, D. N., Deepak, G.: A Semantic approach for fashion recommendation using logistic regression and ontologies. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), (pp. 1–6). IEEE September 2021 14. Adithya, V., Deepak, G.: HBlogRec: a hybridized cognitive knowledge scheme for blog recommendation infusing XGBoosting and semantic intelligence. In: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), (pp. 1–6). IEEE July 2021 15. Surya, D., Deepak, G., Santhanavijayan, A.: KSTAR: A knowledge based approach for socially relevant term aggregation for web page recommendation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 555–564. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-73882-2_50 16. Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In: 2021 7th International Conference on Web Research (ICWR), (pp. 232–237). IEEE May 2021 17. Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 791–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72 18. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019) 19. Roopak, N., Deepak, G.: KnowGen: a knowledge generation approach for tag recommendation using ontology and honey bee algorithm. In: Musleh Al-Sartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds.) EAMMIS 2021. LNNS, vol. 239, pp. 345–357. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_33 20. Deepak, G., Santhanavijayan, A.: UQSCM-RFD: A query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Comput. Appli. 34, 651–675 (2021) 21. Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft. Comput. 25(13), 8337–8355 (2021). https://doi.org/10.1007/s00500-021-057 56-8 22. Kumar, N., Deepak, G., Santhanavijayan, A.: A novel semantic approach for intelligent response generation using emotion detection incorporating npmi measure. Proc. Comput, Sci. 167, 571–579 (2020)

An Extended Framework for Semantic Interoperability in PaaS and IaaS Multi-cloud Karima Benhssayen(B) and Ahmed Ettalbi IMS Team, ADMIR Laboratory, ENSIAS, Rabat IT Center, Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected]

Abstract. Cloud computing is currently a widely used technology, helping enterprises focus on their value-added, rather than spending time and money acquiring and managing software or hardware solutions. Unfortunately, it suffers from the lock-in problem, which leads cloud customers to use specific solutions from a single cloud provider. On the other hand, the multi-cloud strategy is becoming the trend these days, as it remains the best way to maximize the benefits of cloud computing technology. Consequently, communication between solutions of different cloud providers is necessary to ease access and use of services in multi-cloud environments. Hence, even with efforts to standardize semantic interoperability in multi-cloud computing, this problem remains difficult due to the different representation models used by cloud providers. In this paper, we propose an extended version of a framework presented in a previous work dealing with semantic interoperability in a multi-cloud environment. This extended framework is based on five new components, which improves our previous framework and covers, in addition to the IaaS service model, the PaaS one. The main objective is the retrieval of IaaS resources and PaaS services easily. Keywords: Framework · Semantic interoperability · Multi-cloud environment

1 Introduction Cloud computing is a ubiquitous technology where consumers can buy services in a pay-as-you-go manner. According to the report published by Rightscale Cloud Industry Research Team [1], 94% of respondents use cloud technology, within 786 technical professionals surveyed about their adoption of cloud. In addition, 84% of them have a multi-cloud strategy, combining both private and public clouds, or using different clouds of the same category (private or public) from different cloud providers. Despite this, the real concurrence between cloud providers leads them to publish their proprietary API, challenging consumers who intend to switch to another cloud provider or use different cloud services from different cloud providers in parallel. Consequently, the success of using a multi-cloud strategy is affected by the vendor lock-in problem [2], which makes the semantic interoperability between cloud services more challenging.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 415–424, 2022. https://doi.org/10.1007/978-3-031-02447-4_43

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To deal with the semantic interoperability problem, several standardization approaches were proposed, especially in the PaaS and IaaS service models, such as Tosca [3] and CIMI [4]. In contrast, the SaaS service model is controlled by cloud providers. As a result, cloud services are represented in different ways, leading to the same problem: how to semantically communicate these cloud services. To this end, we propose in this article an extended version of our previous framework [5] targeting, in addition to the IaaS service model, the PaaS one. The essential objective of the proposed framework is to semantically interconnect cloud services IaaS and PaaS offered by different cloud providers, even if different standard representation models are used. The rest of this paper is organized as follows: Section 2 presents the problematic and the related work. In Sect. 3, we describe the architecture of our proposed framework and we highlight the main interactions between components of this framework. We conclude in Sect. 4 and we present our perspectives and future work.

2 Problem Definition and Related Work 2.1 Problematic and Existing Standards In addition to IaaS, the PaaS layer is another prominent service model in cloud computing, where hardware and software tools (PaaS Offerings) are delivered to be used by software developers in a pay-as-you-go manner. Unfortunately, software developers are confronted with several problems when using PaaS services. First of all, PaaS offerings that exist in the PaaS market are heterogeneous, due to the underlying technologies used, including libraries and programming languages. Consequently, the migration of a software developer’s application from one PaaS provider to another is difficult, because of the need to redevelop the application to adapt it to the targeted PaaS offering. Secondly, cloud providers use their proprietary APIs, locking their customers to them, even if the underlying technologies used by cloud providers are the same. As a result, the semantic interoperability between different PaaS offerings seems to be impossible until the standardization of API’s representation. On the other hand, several standardization efforts have been proposed dealing with the interoperability problem in a multi-cloud environment, especially in the IaaS and PaaS service models. However, each standard uses its proprietary representation model, which means that the semantic interoperability between two cloud providers using two standards will again be impossible. The following Table 1 summarizes existing standardization efforts dealing with the interoperability problem in the PaaS and IaaS service models. 2.2 Framework-Based Existing Solutions As stated before, several research efforts have been proposed dealing with the semantic interoperability problem in a multi-cloud environment, especially at the IaaS and PaaS levels. Among these solutions, we can find ontologies, brokers, projects, and frameworks. For this last category, which is our focus in this article, some proposed frameworks target the semantic conflicts that reside in the IaaS layer.

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Table 1. Existing standards targeting the semantic interoperability in IaaS and PaaS service models Semantic Interoperability Standard

IaaS

PaaS

Description

UCI

✓

✓

The Unified Cloud Interface is an open and standardized API, trying to unify cloud APIs, platforms, or infrastructure using one abstraction layer. (Code available at [6])

TOSCA

✓

✓

Is an open standard that enhances the portability and interoperability of cloud services. It defines a topology for the description of cloud applications, including their components, relationships, dependencies, requirements, and capabilities

CIMI

✓

OCCI

✓

IEEE P2302

✓

The Cloud Infrastructure Management Interface (CIMI) is an open standard API, developed by the DMTF organization. It models the basic elements of the IaaS service model (networks, storage, and machines), and describes the protocol for managing interactions between IaaS providers and consumers of IaaS services ✓

The Open Cloud Computing Interface (OCCI) is a protocol and Restful API developed by the Open Grid Forum. It was initially proposed to solve the semantic interoperability problem in the IaaS service model but was then extended to cover the PaaS and the SaaS ones [7] Is a standard for intercloud interoperability and federation, based on the NIST reference architecture and developed by the IEEE community [8]

In our previous work, we have proposed a semantic interoperability framework, that ensures the discovery and fast retrieval of IaaS resources in a multi-cloud environment. Yongsiriwit et al. [9] proposed a framework helping cloud resources represented using the following standards: TOSCA, CIMI, and OCCI semantically interoperate by proposing ontologies specific to each standard using the OWL ontology. Also, Di Martino et al. proposed two frameworks. The first one [10] is a scalable architecture relying on the IEEE P2302 Standard, and using semantic web technologies (OWL, SPARQL) to ensure the information exchange of cloud resources. The second one [11] is a multi-layer ontology architecture that semantically models cloud resources and services.

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For the PaaS layer, we can find the three-dimensional framework PSIF [12], which aims to capture any semantic interoperability conflict in the PaaS layer, and the FCloud framework [13, 14], which is based on the OCCI Standard and on formal models that describe mathematically cloud provider’s APIs using the Alloy formal language. In addition, the intPaaS [15] framework, which is based on the OWL-S ontology language and a multi-agent system, promotes service description and automatic discovery, respectively, and lastly, the framework proposed by Androcec et al. [16], which consists of annotating APIs’ operations, including their inputs and outputs, using SAWSDL and cross PaaS concepts from their developed ontology using the ontology development methodology 101 [17]. In addition, the Cloud4SOA project [18] proposed a framework for the matchmaking of cloud PaaS offerings sharing the same technology, based on developers’ requirements. Also, the mOSAIC project, aiming mainly at solving interoperability and portability issues in a multi-cloud environment, proposed a framework based on the management, governance, and security of cloud resources and services in the IaaS and PaaS service models.

3 The Proposed Framework The proposed solution is an extension of our previous work, whereby we have tried to address the semantic interoperability problem between IaaS resources even if different representation models are used (standard representation model or proprietary API). Therefore, the framework also addresses the semantic interoperability problem between PaaS services of different PaaS providers. Consequently, PaaS consumers of multi-cloud service offerings can find the service offering they are looking for using our framework, and communication between different service offerings will be possible. As illustrated in the general architecture of the framework (see Fig. 1), the “IaaS/PaaS Resource Request Listener” gets requests from cloud consumers looking for specific resources. Then, it verifies the “Resource Database” containing RDF triples, linking concepts of cloud providers’ ontologies to standards’ ones. If the requested resources are found, the “IaaS/PaaS Resource Request Listener” component sends resources to the cloud consumer, else, it calls the “IaaS/PaaS Resource Requestor Component”. This last’s principal role is the gathering of available XML files, from different cloud providers, containing cloud IaaS or PaaS resources. The objective is to send these resources to the “Resource Persistency Component”, which in turn, creates the OWL version of XML files, before sending them to the “Resource Mapping Component” permitting the matching between IaaS/PaaS cloud OWL concepts and standard ones. The resulting RDF triples are sent to the “Resource Database”, and related resources are sent to the cloud consumer.

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Fig. 1. The general architecture of the framework

We call resource each entity identified by URI (IaaS resource or PaaS service). The main components of our framework are described in the sections below: 3.1 Extended Components Three components, previously introduced in our previous work, are extended to take into account, in addition to IaaS resources, the PaaS ones. These components are IaaS/PaaS Resource Request Listener, IaaS/PaaS Resource Requestor Component, and Resource Persistency Component. 3.2 Resource Mapping Component The Resource Mapping Component previously presented as a single component is now developed to more automate the mapping process, which is considered the prominent operation, permitting the matching between IaaS/PaaS resources of available cloud providers and standards’ concepts of stored standards’ ontologies. The Resource Mapping Component is composed of the following components: Ontology Receiver Component. The main tasks of this component are described as below:

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Step 1. It receives the OWL files related to XML responses from the Resource Persistency Component. Step 2. For each received OWL file, it consults the black list containing ontologies previously verified by the component and which do not comply with the OWL specifications. Step 3. If the OWL file exists in the black list, it ignores it and continues with step 2, else it verifies the conformity of the OWL ontology with the OWL specifications. Step 4. If the OWL file is valid, it sends it to the Ontology Analyzer Component, else it stores it in the aforementioned black list. For the validation step, there are several owl validators as presented in Table 2. Table 2. OWL validators OWL validator

Description

Owl Manchester validator Is a web-based validation tool, developed under the OWL API project [19]. It accepts ontologies written in different formats (RDF/XML, OWL/XML, OWL Functional Syntax, Manchester OWL Syntax, OBO Syntax, or KRSS Syntax) and validates the syntactic format according to one of the existing OWL sublanguages (OWL2, OWL2 DL, OWL2 El, OWL2 QL or OWL2 RL) Triple checker

Is developed under the Graphite PHP Linked Data Library [20]. It is an open-source online PHP-based tool that checks the syntactic RDF/OWL format of a given URI/URL ontology. A web service version of the Triple Checker tool is available at [21]

W3C RDF validator

Is an online W3C tool [22] that allows the validation of RDF/XML documents. While the OWL ontology language is an extension of the RDF one, OWL documents could be validated by the tool, using their RDF/XML syntax

While we intend to develop a concrete implementation of our framework, the usage of web services will be the primitive criterion in the choice of the syntactic validation tool to be used, because of its standard response format (generally XML). Consequently, the Triple Checker is the only tool that has a web service to access on. For the Owl Manchester validator, there was an attempt to create a web service in the proposed RESTFul OWL API by [23], which proposed a web service to access the majority of OWL Java-based API functionalities, including the OWL syntactic validation one. Therefore, the documentation and the web service are not accessible. Ontology Analyzer Component. As we expect that our proposed framework could be used either in the IaaS service model or in the PaaS one, it is important to exactly match cloud consumers’ requests with the right ontology file (IaaS or PaaS ontology). Consequently, the Ontology Analyser Component is integrated before the mapping process,

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and it permits the classification of the verified OWL ontologies according to their service model, IaaS or PaaS (Fig. 2). OWL file Ontology Analyzer Component

OWL file matching the requested service model

Type of request (IaaS/PaaS) Fig. 2. The general architecture of the Ontology Analyzer Component

Archive Component. The Archive Component’s primary role is the archiving of the OWL CP file if there are no correspondences with available Standards’ ontologies. It essentially performs the following tasks: Task 1. It verifies if the owl file is archived after being called by the Ontology Mapping Component. Task 2. It gets information about the ontology file to archive from the Ontology Mapping Component, and then stores them in the Resource Database as the following triple:

Standards’ Ontology Receiver Component. It requests available standards’ ontologies from the Knowledge Base Component at the beginning of each mapping process, according to the type of requested resources (IaaS or PaaS). then, it stores the received standard’s ontologies locally to ease and go faster with the mapping process. Ontology Mapping Component. It maps each cloud provider’s OWL file to Standards’ ontologies following the sequence diagram presented in Fig. 3. For each cloud provider ontology (CPO), the Ontology Mapping Component calls the Archive Component to check if the CPO is previously archived, and so, verified by the former but with no correspondences found with any of the available standard ontologies (STOs). In the opposite situation (CPO not archived), the CPO is compared with each STO. The objective is to find correspondences between them, and then map concepts from CPO and STO if found. In this case, resulting RDF triples are stored in the resource Database following the syntax: is equivalent to . If no correspondences are found between CPO and all available STOs, the Ontology Mapping Component calls again the Archive component that archives the cloud provider ontology in the database.

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Fig. 3. Sequence diagram of the Resource Mapping Component

4 Conclusion Adopting the multi-cloud environment as a prominent solution is a new and significant strategy being adhered to these days by enterprises. The main goal is to maximize benefits from cloud computing technology. However, this leads to a very important question: how to make communication between cloud services of different cloud providers easy and possible even with existing challenges like proprietary API and lock-in problems? These challenges have been addressed in the research and industrial fields and many solutions have been proposed. Unfortunately, these last proposed different representation models, leading again to the famous question: how can we make semantic interoperability in a multi-cloud environment possible? In this article, we have presented an extension of our previous solution dealing with semantic interoperability problems in the IaaS and PaaS service models. This proposed solution is a framework having as objective the ease of communication in a multi-cloud environment even if different representation models are used. The great advantage is the discovery and fast retrieval of IaaS resources and PaaS services.

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For our future work, we intend to further develop the Resource Mapping Component and to propose an ontology that consolidates concepts from the PaaS and IaaS service models. This ontology will be used in the mapping process to easily match concepts of different standards with cloud providers’ ones.

References 1. “RightScale 2019 State of the Cloud Report from Flexera,” Flexera (2019). http://resources.fle xera.com/web/media/documents/rightscale-2019-state-of-the-cloud-report-from-flexera.pdf 2. Toivonen, M.: Cloud provider interoperability and customer lock-in. In: Proceedings of the Seminar (No. 58312107), pp. 14–19 (2013) 3. Rutkowski, M., Lauwers, C., Noshpitz, C., Curescu, C. (eds.): TOSCA Simple Profile in YAML Version 1.3. OASIS Standard, p. 372, February 2020 4. Cloud Infrastructure Management Interface (CIMI) Model and RESTful HTTP-based Protocol, DMTF DSP0263 (2016). http://www.dmtf.org/documents/cloud/cloud-infrastructuremanagement-interface-cimi-model-and-restful-http-based-protoc-5 5. Benhssayen, K., Ettalbi, A.: Semantic interoperability framework for IAAS resources in multi-cloud environment. Int. J. Comput. Sci. Netw. Secur. 21(2), 1–8 (2021). https://doi.org/ 10.22937/IJCSNS.2021.21.2.1 6. Reuven, C.: Unified cloud interface project. http://code.google.com/archive/p/unifiedcloud/ 7. Nyren, R., Edmonds, A., Papaspyrou, A., Metsch, T., Parak, B.: Open cloud computing interface – core. Open Grid Forum, OCCI-WG, Specification Document, p. 18 (2016) 8. Bernstein, D., Deepak, V., Chang, R.: Draft standard for intercloud interoperability and federation (SIIF). In: IEEE P2303 (2014) 9. Yongsiriwit, K., Sellami, M., Gaaloul, W.: A semantic framework supporting cloud resource descriptions interoperability. In: IEEE 9th International Conference on Cloud Computing (CLOUD), pp. 585–592 (2016) 10. Martino, B.D., et al.: Towards an ontology-based intercloud resource catalogue–the IEEE P2302 intercloud approach for a semantic resource exchange. In: IEEE International Conference on Cloud Engineering, pp. 458–464 (2015) 11. Martino, B.D., Cretella, G., Esposito, A., Carta, G.: An OWL ontology to support cloud portability and interoperability. Int. J. Web Grid Serv. 11(3), 303–326 (2015) 12. Loutas, N., Kamateri, E., Tarabanis, K.: A semantic interoperability framework for cloud platform as a service. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science, Athens, Greece, November 2011, pp. 280–287 (2011). https://doi. org/10.1109/CloudCom.2011.45 13. Challita, S., Zalila, F., Merle, P.: Specifying semantic interoperability between heterogeneous cloud resources with the FCLOUDS formal language. In: IEEE 11th International Conference on Cloud Computing (CLOUD), p. 8 (2018) 14. Challita, S., Paraiso, F., Merle, P.: Towards formal-based semantic interoperability in multiclouds: the FCLOUDS framework. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, CA, USA, June 2017, pp. 710–713 (2017). https://doi.org/ 10.1109/CLOUD.2017.98 15. Hoare, S., Helian, N., Baddoo, N.: A semantic-agent framework for PaaS interoperability. In: 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, July 2016, pp. 788–793 (2016). https://doi.org/10.1109/UIC-ATC-ScalCom-CBD Com-IoP-SmartWorld.2016.0126

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16. Androcec, D., Vrcek, N., Küngas, P.: Service-level interoperability issues of platform as a service. In: 2015 IEEE World Congress on Services, pp. 349–356 (2015) 17. Filho, H.P.P.: Ontology development 101: a guide to creating your first ontology. Technical report SMI-2001-0880, Stanford Medical Informatics, p. 28 (2001) 18. Kamateri, E., Loutas, N., Zeginis, D., Ahtes, J., D’Andria, F., Bocconi, S., Gouvas, P., Ledakis, G., Ravagli, F., Lobunets, O., Tarabanis, K.: Cloud4soa: a semantic-interoperability paas solution for multi-cloud platform management and portability. In: Lau, K.-K., Lamersdorf, W., Pimentel, E. (eds.) ESOCC 2013. LNCS, vol. 8135, pp. 64–78. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40651-5_6 19. Horridge, M., Bechhofer, S.: The OWL API: a Java API for OWL ontologies. Semant. Web 2(1), 11–21 (2011). https://doi.org/10.3233/SW-2011-0025 20. Graphite PHP Linked Data Library. http://graphite.ecs.soton.ac.uk/ 21. RDF Triple-Checker. http://graphite.ecs.soton.ac.uk/checker/ 22. W3C RDF Validator. http://www.w3.org/RDF/Validator/ 23. Dirsumilli, R., Mossakowski, T.: RESTful encapsulation of OWL API. In: Proceedings of the 5th International Conference on Data Management Technologies and Applications, Lisbon, Portugal, pp. 150–157 (2016). https://doi.org/10.5220/0005987201500157

Image and Information Processing

Design of a Security System Based on Raspberry Pi with Motion Detection Zaidan Didi1(B)

, Ikram El Azami1

, and El Mahdi Boumait2

1 Computer Science Research Laboratory (LaRI), Faculty of Sciences, Ibn Tofail University,

Kénitra, Morocco {Zaidan.didi,ikram.elazami}@uit.ac.ma 2 Smart Systems Laboratory, ENSIAS, Mohammed V University, Rabat, Morocco [email protected]

Abstract. Currently, the integration of IoT technology in various fields is very widely used, however, data security remains the essential point to be monitored especially in companies, and also in homes. To control and overcome securityrelated problems, we adopted Internet of Things technology based on a Raspberry pi4 as the main data processing element in this study. In this paper, we present a simple, efficient, and very reliable study for the monitoring of a video stream coming from a camera installed on a Raspberry pi4 which constitutes the essential element in our project. To reproduce this realization, we did not use a motion sensor, but we took advantage of the algorithm advantages of the Motion software integrated into the free operating system MotionEyeOs on a Raspberry pi4 to trigger motion detection by causing a beep to draw attention. On the other hand, our study was implemented without noticed difficulty, and with a great level of performance and stability which shows that our realization of the Video Stream Surveillance System is successful. Keywords: IoT · Raspberry pi4 · Motion detection · MotionEyeOs · Camera module

1 Introduction In recent years, the use of IoT technology in everyday life has experienced a great revolution [1, 2], In this axis, interesting studies aim at solving problems related to security in companies and homes through the analysis of video streams recorded on storage media [3, 4]. Interesting achievements have ensured high security through the integration of network cameras and IoT technology [5–7]. Other recent studies have successfully detected movement by integrating a Raspberry pi as a processing element and a passive infrared (RIP) sensor [8–10]. In this paper, we have proposed a system for monitoring a video stream coming from a Webcam to configure and install on a Raspberry pi4 model B 4 GB, menu of a free operating system MotionEyeOs [11], in which we installed the Motion software to study consecutive images, this technique, based on a very

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 427–434, 2022. https://doi.org/10.1007/978-3-031-02447-4_44

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powerful algorithm, makes it possible to have the number of distinct pixels according to a threshold determined in advance to trigger motion detection [12]. Finally, our system is capable of sending a notification to the administrator via Radiofrequency technology [13], or by GSM technology [14].

2 Materials and Methods 2.1 Materials In this embodiment, the physical design consists of the following elements. Raspberry pi4 Model B – 4 GB. Among the advantages of this Raspberry pi4, we have noted the presence of a very high-performance 64-bit Quad-Core processor [15], with the possibility of dual display and a maximum resolution of around 4K, this Raspberry Pi 4 also integrates two micro-HDMI ports and a Gigabit Ethernet network port, a 2.4/5.0 GHz band wireless network, Bluetooth 5.0, USB 3.0. See Fig. 1.

Fig. 1. Raspberry pi 4 model B - 4GB.

Surveillance Cameras. In this part, we have used two types of cameras to monitor the video stream and make comparisons to select the best method to detect motion. Figure 2 shows a Full HD 1080p USB Webcam with a built-in microphone that is mounted in the front of our house, while Fig. 3 shows a 3.6 mm IR 1080P Camera Module for Raspberry Pi.

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Fig. 2. Webcam USB full HD 1080p.

Fig. 3. Camera Module 3.6 mm IR 1080P for Raspberry Pi.

The hardware part also includes a SanDisk microSDXC Ultra 64 GB memory card “see Fig. 4” in which we have installed the open-source MotionEyeOs distribution. And finally, a stable 5V supply to power the Raspberry Pi, (see Fig. 5).

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Fig. 4. SanDisk microSDXC Ultra 64 GB memory card.

Fig. 5. Micro USB power supply for Raspberry Pi.

2.2 Methods In this part, we will start by installing the MotionEyeOs distribution on the SanDisk microSDXC Ultra memory card, to carry out this step we downloaded the image from the MotionEyeOs distribution then we used the free software BalenaEtcher which is developed by Electron (framework) to burn the image previously downloaded to the memory card (see Fig. 6).

Fig. 6. BalenaEtcher, burn the MotionEyeOs image to the memory card.

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We noted here that MotionEyeOs is an open-source Linux-based distribution, which is particularly well-founded to make the Raspberry Pi like a connected camera. Figure 7 shows the logo of MotionEyeOs.

Fig. 7. MotionEyeOs distribution logo.

The main operating principle of this realization is based on a very powerful trick, since it can make a comparison between the number of pixels of the different consecutive images according to a threshold determined in advance [16], this is the principle for triggering motion detection in this study, with a configuration that is certainly fundamental and easy to learn “system configuration and camera configuration”. Finally, we will specify the paths of the software resources. • BalenaEtcher software to ensure efficient burning on our SanDisk microSDXC Ultra, a free download from the site: https://www.balena.io/etcher/ • The open-source distribution MotionEyeOs, a free download from the site: https://git hub.com/ccrisan/motioneyeos/releases.

3 Results and Discussion The results obtained in this study which aims at security by using a Raspberry Pi4 as the main processing element as well as the MotionEyeOs distribution are perfectly acceptable, the beeps which signal the detection of a movement have an acceptable accuracy with an increasingly short delay, Fig. 8 and Fig. 9 show captures recorded during motion detection. We noted that the MotionEyeOS video servers located by the port scanner with a name that must start with “meye-”, in our study we used the hostname command to change the name of Raspberry pi4: #hostname meye-12345678.

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Fig. 8. Webcam settings.

Fig. 9. Preview of motion detection.

A simple comparison between the different approaches aimed at motion detection, we found that our study is very simple to set up, it is efficient and reliable with a note to increase the storage space and activate the sending of SMS or Email notifications. While studies that integrate sensors as an element of motion detection such as the passive infrared sensor (RIP) [9, 17], these studies have shown a lot of precision for the movements of small objects, as well as the speed of beep notifications. Other very important advantages of using sensors are energy-saving and ease of use in IoT projects. The following table, (see Table 1) describes a brief comparison between the two approaches.

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Table 1. Comparison of studies. Methods

Advantages

Inconvenient

Motion detection by • Simple to set up integrating algorithms to Speed of notification study successive images [18]

• Saturation (storage) • Gourmet (Energy)

Motion detection by integrating passive infrared (RIP) sensors [9]

• Sensitivity to temperature variations

• Easy integration into IoT projects • Invisible to the human eye • Insensitive to noise and vibrations • Very flexible • Very fast notification

4 Conclusion In this study, we have developed a simple and efficient method based on the new Internet of Things technology, with the integration of a Raspberry pi4 model B 4 GB with the open-source distribution MotionEyeOs, this approach performed a simple comparison between the number of pixels of successive images to detect the movements of the objects. Finally, comparing this study with other works that point to the same objectives, we believe that our study was carried out efficiently and without any problems detected, except for some rare planting related to data flows. In the perspectives of the next study, we integrate the sending of notifications by SMS via a messaging server.

References 1. Rekha, S., Thirupathi, L., Renikunta, S., Gangula, R.: Study of security issues and solutions in Internet of Things (IoT). Mater. Today Proc. (2021). https://doi.org/10.1016/j.matpr.2021. 07.295 2. Neeli, J., Patil, S.: Insight to security paradigm, research trend & statistics in Internet of Things (IoT). Glob. Transit. Proc. 2(1), 84–90 (2021). https://doi.org/10.1016/j.gltp.2021.01.012 3. Ammi, M., Alarabi, S., Benkhelifa, E.: Customized blockchain-based architecture for secure smart home for lightweight IoT. Inf. Process. Manag. 58(3) (2021). https://doi.org/10.1016/ j.ipm.2020.102482 4. Delgosha, M.S., Hajiheydari, N., Talafidaryani, M.: Discovering IoT implications in business and management: a computational thematic analysis. Technovation (2021). https://doi.org/ 10.1016/j.technovation.2021.102236 5. Wu, K., Lagesse, B.: Detecting hidden webcams with delay-tolerant similarity of simultaneous observation. Pervasive Mob. Comput. 65 (2020). https://doi.org/10.1016/j.pmcj.2020.101154 6. Abas, K., Obraczka, K., Miller, L.: Solar-powered, wireless smart camera network: an IoT solution for outdoor video monitoring. Comput. Commun. 118, 217–233 (2018). https://doi. org/10.1016/j.comcom.2018.01.007

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7. Coquin, D., Boukezzoula, R., Benoit, A., Nguyen, T.L.: Assistance via IoT networking cameras and evidence theory for 3D object instance recognition: application for the NAO humanoid robot. Internet Things 9 (2020). https://doi.org/10.1016/j.IoT.2019.100128 8. Gu, Z.: Home smart motion system assisted by multi-sensor. Microprocess. Microsyst. 80 (2021). https://doi.org/10.1016/j.micpro.2020.103591 9. Verma, M., Kaler, R.S., Singh, M.: Sensitivity enhancement of Passive Infrared (PIR) sensor for motion detection. Optik 244 (2021). https://doi.org/10.1016/j.ijleo.2021.167503 10. Surantha, N., Wicaksono, W.R.: Design of smart home security system using object recognition and PIR sensor. Procedia Comput. Sci. 135, 465–472 (2018). https://doi.org/10.1016/j. procs.2018.08.198 11. Stolojescu-Crisan, C., Crisan, C., Butunoi, B.-P.: Access control and surveillance in a smart home. High Confid. Comput., 100036 (2021). https://doi.org/10.1016/j.hcc.2021.100036 12. Anandhalli, M., Baligar, V.P.: A novel approach in real-time vehicle detection and tracking using Raspberry Pi. Alex. Eng. J. 57(3), 1597–1607 (2018). https://doi.org/10.1016/j.aej. 2017.06.008 13. Didi, Z., El Azami, I.: IoT design and realization of a supervision device for photovoltaic panels using an approach based on radiofrequency technology. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 365–375. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-73882-2_34 14. Santhosh, C., et al.: IoT based smart energy meter using GSM. Mater. Today Proc. 46, Part 9, 4122–4124 (2021). https://doi.org/10.1016/j.matpr.2021.02.641 15. Chakraborty, A., Banerjee, A.: Modular and parallel VLSI architecture of multi-dimensional quad-core GA co-processor for real time image/video processing. Microprocess. Microsyst. 65, 180–195 (2019). https://doi.org/10.1016/j.micpro.2019.02.002 16. Ali, R., Chuah, J.H., Talip, M.S.A., Mokhtar, N., Shoaib, M.A.: Automatic pixel-level crack segmentation in images using fully convolutional neural network based on residual blocks and pixel local weights. Eng. Appl. Artif. Intell. 104 (2021). https://doi.org/10.1016/j.eng appai.2021.104391 17. Sasi, G.: Motion detection using Passive Infrared Sensor using IoT. J. Phys. Conf. Ser. 1717, 012067 (2021). https://doi.org/10.1088/1742-6596/1717/1/012067 18. Li, X., Zheng, H.: Target detection algorithm for dance moving images based on sensor and motion capture data. Microprocess. Microsyst. 81 (2021). https://doi.org/10.1016/j.micpro. 2020.103743

Robust Face Recognition Under Advanced Occlusion Proposal of an Approach Based on Skin Detection and Eigenfaces Faouzia Ennaama1(B) , Khalid Benhida1 , and Sara Ennaama2 1 Cady Ayyad University, Marrakesh, Morocco

[email protected] 2 Abdelmalek Essaadi, Tetouan, Morocco

Abstract. Facial occlusion is a critical problem in many face recognition applications. It complicates the process of automatic face recognition because many factors such as occluded facial region, shape occlusion, occluded region color, and occlusion position are variable. Existing face recognition approaches that deal with occlusion issues focus mainly on classic facial accessories. In this paper, we consider occlusions types well studied in the literature (sunglasses, neck warmer, beard, hair, etc.) as well as other occlusions, which are not studied extensively. We assess the Eigenface method in the presence of occlusions and we develop an original optimal approach of simple and more robust facial recognition allowing operating the Eigenfaces method even for occluded faces in more advanced conditions. For this, we have combined skin detection and the Eigenface method. We validated our method on several facial occlusions using FEI database containing several types of faces. Keywords: Face recognition · PCA · Eigenfaces · Skin modelling · Skin-color classifier

1 Introduction Face recognition is an attractive area of research that has evolved considerably in recent years. It exploits many disciplines, in particular, image processing, pattern recognition, machine learning, visual perception, psychophysics, and neuroscience. This biometric technique is a task that humans do habitually and naturally in their daily lives. Compared to other biometric methods, face recognition has several advantages: It is natural, nonintrusive (does not require special image sensors, as is the case for fingerprint recognition or iris), easily collectible, and allows simultaneous recognition of faces [1]. However, pose variations (side view, face) [2], facial expressions [3], aging [3], illumination [4], image resolution, occlusions [5] and other factors may cause the failure of the proper functioning of the face recognition systems. Facial occlusions, due for example to sunglasses, hats, scarves, beards, etc., can significantly affect the performance of any facial recognition system. The presence of facial occlusions is quite common in real-world applications, especially when individuals do © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 435–445, 2022. https://doi.org/10.1007/978-3-031-02447-4_45

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not cooperate with the system. Although a lot of research has been devoted to recognizing faces under changes in facial expressions, lighting, and image degradation, the problems caused by occlusions are generally little addressed. Since partial occlusions can alter the appearance of an original face image, they can dramatically alter the performance of face recognition systems based on holistic representations [6, 7] (since face representations are largely modified as a result). Indeed, controlling partial occlusion is a crucial issue to achieve robust facial recognition. Most of the work [8–10], and [11] focus on the search for characteristics or tolerant classifiers to reduce the partial occlusions effect of facial representation. However, information from occluded parts can still adversely affect recognition performance. The Eigenfaces [6] method is one of the first useful face recognition techniques. It is based on the Principal Component Analysis (PCA). Even though the Eigenfaces technique is extremely famous, simple, and gives satisfactory outcomes in controlled conditions; it has specific impediments as a result of changes in lighting, angle, occlusions, and distance as mentioned in some references from 2011 and 2015 [12, 13]. Indeed, basic methods using subspaces for face recognition such as PCA and LDA [7] work on the global facial characteristics and depend on the pixel values. In face recognition, lighting and occlusion problems become more complex, when we try to solve them using these subspace methods [8]. In this paper, we propose a new method based on skin detection and the Eigenfaces method for face recognition under occlusions. In the second section, we present the Eigenfaces method. In the third section, we assess the functioning of the Eigenfaces method under occlusions. In the fourth section, we present our proposed approach to handle occlusions. In the fifth section, we validate our approach with the FEI database. The conclusions are given in the last section.

2 Eigenfaces Method Turk and Pentland presented the Eigenfaces method in 1991 [6], and it is a classic and widely used face recognition approach. The basic idea behind Eigenfaces is to find a component space with a reduced aspect, which is then used for recognition. The basic steps of the Eigenfaces approach according to Turk and Pentland [6] are the following: a) Collect face images I1 , I2 , . . . , IM (training images). The face images must have the same size N × N . b) Transform training images of RGB colorspace into grayscale. c) Convert each face image into a set of vectors  = {Γ1 , Γ2 , . . . , ΓM } , each vector has N 2 × 1 as dimension. d) Find the average face Ψ : =

M 1  i M i=1

where M: number of images, Γ i : image vector.

(1)

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e) Subtract the average face from the faces in the training images. Φi = Γ i − Ψ

{i = 1, 2, . . . , M }

(2)

A = [Φ1 , Φ2 , . . . , ΦM ](N 2 × M matrix) f) Calculate the covariance matrix C. C=

M 1  ΦiΦ T = AAT (N2 × N2 matrix) M

(3)

i=1

AT the transpose matrix of A = [Φ1 , Φ2 , . . . , ΦM ] g) Obtain the eigenvectors ei and eigenvalues λi of the covariance matrix C. h) Calculate eigenfaces and select K best eigenvectors. ωK = eT (Γi − Ψ )

(4)

K = 1, . . . , M  is the number of eT ’s eigenvectors (Eigenfaces) selected. i) Compute “Weight Vectors”. TK = [ω1 , ω2 , . . . , ωK ]

(5)

j) Compare any two weight vectors by an Euclidean distance: ε2 =  − k 2

(6)

where Ω k is a vector identifying the K th face class. A face image is indexed as “recognized” when εk < θ ε . If not the face image is classified as “unrecognized”. 2.1 Limitations of the Eigenfaces Method Under Facial Occlusions In order to test the Eigenfaces method under facial occlusions, we used the Brazilian FEI face database [14]. It comprises pictures captured from 200 people, framing a sum of 2800 face images. For our review, we have chosen two face images of ten men and ten ladies of various ages, for a sum of 40 face images. After modifying the images from the database by adding accessories such as neck warmer, veil, cap, etc. (See Fig. 1), we selected three occluded face images for the test, then we applied the algorithm of the Eigenfaces method for facial recognition described in the second section. The Eigenfaces approach was implemented via MATLAB 2015b software installed on a computer with an Intel i5 processor at 2.50 GHz frequency, and a 4 GB RAM. The comparison is made by calculating the Euclidean distance between the weight vector of the occluded test image and all the weight vectors of the face images of the database.

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Fig. 1. Examples of modified face images from the FEI database.

Table 1. Facial recognition Results of occluded images by the Eigenfaces method. Original image number

Subject image number 2

Subject image number 21

Subject image number 25

Recognition results

Euclidean distance

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Table 1 presents our evaluation results concerning face recognition under some types of occlusions by the Eigenface method. The first column presents the original image classification in the database. The second column displays; firstly the test image and secondly facial recognition result. The third column shows the Euclidean distance result of the occluded test image and all the face images in the data set; the first number X presents the image found classification and the second number Y indicates the Euclidean distance value. In this section, we have implemented and tested the Eigenfaces method on different types of images of faces with occlusions and we have confirmed the limitations of this method in the presence of this problem. The results of this study can be explained by the fact that the Eigenfaces method processes the whole image and aims to compare the pixels without taking into account the occluded and non-occluded parts. For this reason, we have sought methods in the literature which make it possible to reinforce the Eigenfaces method with special regard to facial occlusions. We have found in some research work [15, 16] of facial recognition that Skin detection is robust in the presence of occlusions.

3 Proposal of a New Approach for Face Recognition Under Occlusions In general, occlusions, as we have already seen in the introduction, involve two types of difficulties for the facial recognition system. First, the discriminating characteristics are distorted and, the intra-class variations are greater. Second, occlusions types in practical scenarios are unpredictable, also the location, size, and shape of the occlusions are unknown. The intuitive idea of facial recognition to recognize occluded part images is to detect occlusions and then identify those images from the non-occluded part. Based on this idea, we propose a new approach based on detecting skin color and then using the Eigenfaces method for facial recognition. The Eigenfaces method is known for processing grayscale images. Much research on face recognition has focused on the use of grayscale images. With the increase in computing power and the increasing availability of color images, it makes sense to develop approaches to integrate color information into the recognition process. Color is the most prevalent primitive method for separating pixels from the skin of non-skin pixels in an image. The main reason behind choosing this descriptor can be explained by the fact that, in a color space, the range of skin colors is located in a narrow band as shown by the works [17, 18], and [19]. Furthermore, color is relevant information and has robustness concerning occultation problems, geometrical transformations, or changes in scales. From this principle, skin detection is currently considered as a preliminary process in several techniques such as face detection [19], face tracking [20], face localization [20], gesture analysis [21], etc. Skin detection in an image consists of separating skin pixels from non-skin pixels [22]. Color information is used as a feature to identify the individual’s face in an image and separate it from the background [23]. One of the methods used for skin detection is to define explicitly through a set of rules the boundaries that delimit the area of the skin in a certain color space.

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In this paper, we consider occlusions types that have been well studied in the literature (sunglasses, neck warmer, beard, hair, etc.) as well as other occlusions that are not studied extensively. Our goal is to develop an original optimal algorithm of facial recognition allowing to make function the Eigenfaces method even for occluded faces in more advanced conditions. For this, we used the three recognition levels (occlusion detection, feature extraction, and recognition) and we combined the Eigenfaces with the RGB color space. As a first step, we start by processing the images in the database with the RGB color space. We detect and localize the non-occluded region in the face image by the color detector. Then, we just extract the skin region and store it again in the database. The algorithm that we have implemented to extract the components R, G, and B from the occluded image is based on the following equations [23]. The Pixel is defined as skin if: Rule1: R > 95 AND G > 40 AND B > 20 AND

(7)

Rule2: Max (R, G, B) − Min(R, G, B) > 15 AND

(8)

Rule3: |R − G| > 15 AND

(9)

Rule4: R > G AND R > B

(10)

R, G, and B respectively represent the Red, Green, and Blue color components. The first rule indicates that the range of the R-value is from 96 to 255. The value range of G is 41 to 239 and the value range of B is 21 to 254 [24]. While the R-value is always greater than G and B, the second and the third rules are always positive values, which can be rewritten as follows: Rule2: R − min(G, B) > 15 AND

(11)

Rule3: R − G > 15

(12)

In the case of flashlight or (light) daylight: (R> B) AND (G> B)) OR ((R> 220) AND

(13)

(G> 210) AND (B> 170) AND(|(R − G) | ≤15))

(14)

In other words, if it is a skin tone pixel, it will be saved in white. If not, it will be recorded in black. Figure 2 clarifies the Skin detection process.

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Fig. 2. Skin detection process

Figure 2 presents an example of an occluded image from the FEI database [14] which we modified (adding a green veil). We applied the RGB color space algorithm and got a new image containing the skin region only. This binarized image called “binary mask” will then be processed by the Eigenfaces method.

4 Results and Analysis In this section, we evaluate and validate our approach, based on Skin detection and the Eigenfaces method, by occluding face images that we previously modified by adding artificial accessories (such as caps, sunglasses, hats, neck warmers, veils, etc.) (see Fig. 1). 4.1 New Approach Validation of Facial Recognition by Occluded Images After delimiting the non-occluded region of the image, i.e., separating the skin color pixels from those of non-skin and keeping only the skin region, we perform the Eigenfaces method for face recognition. Recalling that the Eigenfaces method makes it possible to extract the “weight vectors” from face images in the database. The facial recognition is finished by comparing the weight vector obtained from the occluded test image with all the weight vectors obtained from the data set images. This comparison is made through Euclidean distance. For our validation, we chose two facial images of ten men and ten women of different ages from the FEI database. Table 2. presents the results of our evaluation. Table 2 presents the obtained results by the Eigenfaces method alone and our approach. For each occluded test image, we calculated the Euclidean distance between the proposed test image and the face images of the database by each of the aforesaid methods, and we observed whether the minimum of the Euclidean distance corresponds to the person in question. It can be seen from the table above, that our approach finds the minimum of the Euclidean distances which corresponds correctly to the test face images with hidden parts, whereas the Eigenfaces method alone led to greater Euclidean distances and thus to errors in face detection. In this part, we tested and validated the proposed approach which combines skin detection and the Eigenfaces method under occlusions and we have obtained excellent results. Based on these results, we can say that we have achieved our goal of recognizing faces in the presence of different types of occlusions.

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Table 2. Comparative study between Eigenfaces method and our approach, column 1: number of the original image in the Database, column 2: Face Recognition using Eigenfaces method, column 3: Face Recognition using our approach. Original image number

Eigenfaces results

Skin detection and Eigenfaces results (Our approach)

Subject image number 2

Subject image number 21

Subject image number 25

(continued)

Robust Face Recognition Under Advanced Occlusion Proposal Table 2. (continued)

Subject image number 31

Subject image number 18

Subject image number 34

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5 Conclusion In this paper, we have dealt with one of the major problems encountered in face recognition. It’s about classical facial occlusions due, for example, to sunglasses, hat/cap, scarf, and beard, as well as occlusions in more advanced conditions that are not strongly studied in the literature. It is, therefore, a question of solving, on the one hand, the automatic extraction of the face and its characteristic regions, and on the other hand, face recognition. We first showed that the Eigenfaces method didn’t work correctly for occluded images, and we proposed an approach to reinforce this technique. Our method is based on detecting non-occluded facial areas by extracting the facial skin component and preserving them for later recognition. The skin component being considered is a grayscale image and therefore does not increase the size of the image stored and rendered. The skin color is used to detect and locate the face in the image and the classic Eigenfaces method is used for the extraction of characteristic vectors for face recognition. The results obtained by our approach show greater robustness of the Eigenfaces method combined with the skin detection compared to the Eigenfaces method considered alone. We evaluated and validated our approach on the FEI database characterized by a large variety of appearance and positions.

References 1. Jain, A.K., Li, S.Z.: Handbook of Face Recognition, vol. 1. Springer, New York (2011). https://doi.org/10.1007/978-0-85729-932-1 2. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001) 3. George, F.P., Shaikat, I.M., Ferdawoos, P.S., et al.: Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier. Int. J. Electr. Comput. Eng. 9(2), 2088–8708 (2019) 4. Samal, A., Iyengar, P.A.: Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recogn. 25(1), 65–77 (1992) 5. Zhang, T., et al.: Face recognition under varying illumination using gradientfaces. IEEE Trans. Image Process. 18(11), 2599–2606 (2009) 6. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosic. 3, 71–86 (1991) 7. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997) 8. Tan, X., et al.: Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble. IEEE Trans. Neural Netw. 16(4), 875–886 (2005) 9. Park, B.G., Lee, K.M., Lee, S.U.: Face recognition using face-ARG matching. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1982–1988 (2005) 10. Jia, H., Martinez, A.M.: Support vector machines in face recognition with occlusions, In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 136–141 (2009) 11. Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3642-15567-3_33

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12. Jaiswal, S.: Comparison between face recognition algorithm-eigenfaces, fisherfaces and elastic bunch graph matching. J. Glob. Res. Comput. Sci. 2(7), 187–193 (2011) 13. Hussain Shah, J., et al.: Robust face recognition technique under varying illumination. J. Appl. Res. Technol. 13(1), 97–105 (2015) 14. Thomaz, C.E.: FEI face database. FEI Face Database Available (2012) 15. Pham-Ngoc, P.T., Huynh, Q.L.: Robust face detection under challenges of rotation, pose and occlusion, Department of Biomedical Engineering, Faculty of Applied Science, Hochiminh University of Technology, Vietnam (2010) 16. Jaiswal, V., Sharma, V., Varma, S.: An implementation of novel genetic based clustering algorithm for color image segmentation. Telkomnika 17(2), 1461–1467 (2019) 17. Osman, M.Z., Maarof, M.A., Rohani, M.F., et al.: A multi-color based features from facial images for automatic ethnicity identification model. Indones. J. Electr. Eng. Comput. Sci. 18(3), 1383–1390 (2020) 18. Alksasbeh, M.Z., Al-omari, A.H., Alqaralleh, B.A.Y., et al.: Smart hand gestures recognition using K-NN based algorithm for video annotation purposes. Indones. J. Electr. Eng. Comput. Sci. 21(1), 242–252 (2021) 19. Singh, V., Aswani, D.: Face detection in hybrid color space using HBF-KNN. In: Tiwari, B., Tiwari, V., Das, K.C., Mishra, D.K., Bansal, J.C. (eds.) Proceedings of International Conference on Recent Advancement on Computer and Communication. LNNS, vol. 34, pp. 489–498. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8198-9_52 20. Al-Shehri, S.A.: A simple and novel method for skin detection and face locating and tracking. In: Masoodian, M., Jones, S., Rogers, B. (eds.) APCHI 2004. LNCS, vol. 3101, pp. 1–8. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27795-8_1 21. Jaiswal, V., Sharma, V., Varma, S.: MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation. Int. J. Electr. Comput. Eng. 10(1), 196 (2020) 22. Lumini, A., Nanni, L.: Fair comparison of skin detection approaches on publicly available datasets. Expert Syst. Appl. 160, 113677 (2020) 23. Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection, vol. 2. IEEE (2003) 24. Osman, G., Hitam, M.S., Ismail, M.N.: Enhanced skin colour classifier using RGB ratio model. arXiv preprint arXiv:1212.2692 (2012)

Moroccan Carpets Classification Based on SVM Classifier and ORB Features Hanae Moussaoui1(B)

, Nabil El Akkad2

, and Mohamed Benslimane3

1 FST of Fez, Engineering, Systems, and Applications Laboratory, ENSA of Fez,

Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] 2 LISA, Engineering, Systems, and Applications Laboratory, ENSA of Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] 3 LTI Laboratory, EST, Sidi Mohamed Ben Abdellah University, Fez, Morocco [email protected] Abstract. Moroccan carpets remain one of the major artisanal products that are very known worldwide. This encourages us to look forward to a system that gives people the ability to distinguish between the different types of Moroccan rugs. In this paper, we’ll be presenting a novel architecture for Moroccan carpet images classification. For this, we’ll first preprocess the images by applying the k-means clustering for segmentation. After that, we’ll use the ORB features detector that is going to help for sure by detecting the major features in each type of carpet. For this, we used to gather images from the web by uploading about 200 images for each type. The proposed model achieved an accuracy of 82%. Keywords: Image segmentation · Image classification · Support Vector Machine (SVM) · ORB

1 Introduction The machine learning wave is now taking control over many important areas, unfortunately, the tourism sector wasn’t included until now. Every day thousands of crafts pervade the Moroccan market, including the huge number of rugs with different designs. For this, the tourism sector should include an intelligent system that allows people and tourists from worldwide to identify the carpet they want to buy, without the need for expertise. After doing many types of research in the area; the literature didn’t provide previous works that give the ability to distinguish between the different types of Moroccan carpets. The proposed model is composed of four steps and currently working on two types of rugs which are “Rabat/Fez rugs” because they are so identical and have almost the same designs, and the “Amazigh rugs”. The first task we had to carry out is the data gathering that we accomplished by downloading 100 images for each type. After that comes the role of image segmentation which is an image processing pillar. Image segmentation occurs when we want to separate objects from the background depending on some properties of those objects. in this paper, we’ll be using k-means clustering which is one of the most important unsupervised machine learning algorithms. After © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 446–455, 2022. https://doi.org/10.1007/978-3-031-02447-4_46

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segmenting the images of our dataset, we’ll use ORB features extraction to extract the most significant features shown in the given image of carpet. A feature detector’s mean role is to detect key points, and a feature descriptor describes these key points which are regions of interest. The reason behind choosing Oriented Fast and Rotated Brief (ORB) is that it’s a combination of a detector and a descriptor. Finally, we’ll apply the SVM classifier to label the images into one of the two categories of carpets. In every image processing system, we find the strong presence of image segmentation [1, 2]. Image segmentation is the process of image partitioning, to separate and extract important objects and regions which are known as Regions Of Interest (ROI). The purpose of image segmentation is that these regions of interest should be meaningful to help us solve the vision problem we’re dealing with. In image segmentation [3–5] we try to group pixels in the image that have similar visual attributes or characteristics. In other words, we’re going to treat it as a clustering problem. At each pixel in the image, we have a feature vector that describes that pixel and that can be seen as a point in high dimensional feature space. in this feature space, we cluster points together, and those clusters are going to represent our meaningful regions or segments [1, 6, 7]. Image classification is the process of assigning a pixel to a class depending on many characteristics. It’s an important core task in computer vision. What happens is that the algorithm is going to take as input an image, so the algorithm needs to assign a category label to that input image. When we talk about image classification, we typically have some fixed set of category labels in mind that the algorithm is aware of, in our case we have two labels “Rabat/Fez rugs” and “Amazigh rugs”. As the algorithm performs image classification, what it needs to do is simply assign one of these two labels to the image we’ve given as an input. For humans this is a trivial task, but for the computer that’s not so easy. There are two types of image classification technics, we find the supervised classification when the algorithm is trained on images that are already labeled. For this type of classification, we find methods such as the support vector machine, decision trees, and the k-nearest neighbors. For the second type which is the unsupervised classification, the labeling is absent. The rest of the paper will be as follow, in the second part, we’ll be discussing the proposed method. In this section of the paper, we’ll be explaining in detail the first algorithm used which is k-means clustering [8, 9]. After that, we’ll put lights on the Oriented Fast and Rotated Brief detector and descriptor. Afterward, we’ll discuss the image classification method chosen which is the Support Vector Machine. Then comes the part when we’ll show the obtained results for some pictures. We’ll summarize with a conclusion.

2 Related Works The research we’ve made has revealed no pre-works related to carpet features detection or a system that helps make a difference between the different kinds of Moroccan rugs. The only available way nowadays is using the expertise of the Moroccan craftsman.

3 The Proposed Method The idea behind this proposed method is to engage the tourism sector in artificial intelligence innovation, and give to the tourists the possibility to identify a specific rug and

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its origins without even being an expert. As shown in Fig. 1, the method includes three main steps. The first thing after collecting the images is to apply k-means clustering to segment the image and group similar pixels in separated clusters. After that, we’ll use ORB as a feature detector and descriptor this helps a lot by defining the main features present in the carpet. Finally, we opt for the support vector machine SVM classifier to get a labeled image as an output.

Image gathering

Rabat/Fez carpet

Preprocessing

Amazigh carpet

K-means clustering

ORB features

SVM classifier

SVM classifier Fig. 1. The proposed method architecture

After collecting different images of carpets and constructing our database, we had to preprocess our data. For this, we resized and flattened all the images of the same size, so afterward we won’t have issues. 3.1 K-Means Clustering k-means clustering [8] is an unsupervised machine learning method that is used to group or cluster pixels within the image based on features or attributes, into k groups or partitions [9]. To apply k-means clustering segmentation we should go through four steps that are cited below: Step 1: Generate arbitrary the initial centroids or means {m1 , m2 , …, mk } of the k clusters in feature space.

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Step 2: For each pixel, Xj finds the nearest cluster mean mi to pixel’s feature fj and assigns pixel to cluster i. Within clusters: SSW (c, k) =

n    xi − cp(i) 2

(1)

i=1

Between clusters: SSB(c, k) =

k

(j=1)

nj ||cj − x||2

(2)

Step 3: Recompute the mean of each cluster using its assigned pixels. Step 4: If the changes in all k means are less than a threshold E, stop. Otherwise, go back to step 2 as shown in Fig. 2. 2 k n   j  j= (3) xi − cj  j=1

i=1

Input image

Generate centroids

Find the nearest cluster

Compute the mean for each cluster

If k-means changes < ɛ

No

Yes Input image

Fig. 2. K-means clustering flowchart

3.2 Oriented FAST and Rotated BRIEF (ORB) Features are the part of the information that describes the image. Features can be edges, corners, circles, ellipses, blobs, and so on. A feature detector [10, 11] detects the key points or points of interest in the image, while a feature descriptor describes these key points. The main purpose of using feature detectors and descriptors is for example to compare between images, for image registration or like in our case image classification. As a replacement to the patented SIFT and SIRF, ORB [12, 13] was developed at OpenCV labs. Besides being as efficient as SIFT and SURF, ORB is a free-to-use

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algorithm that works on keypoint matching. ORB has two algorithms involved which are the FAST detector and BRIEF descriptor. First of all, the FAST detector [13, 14] works by comparing the brightness of a given pixel A to its surrounding 16 pixels making a circle around the pixel A. the pixels in the circle are classified into three classes (brighter than A, darker than A or similar to A). in the case when we have more than 8 pixels lighter or darker than A, at that time it’ll be selected as a key point. ⎧ Ip→x ≤ Ip − t (darker) ⎨ d, (4) Sp→x = s, Ip − t < Ip→x < Ip + t (similar) ⎩ b, Ip + t ≤ Ip→x (brighter) when ‘I’ presents the intensity and t is the thresholding value. For the descriptor, it’s a vector that summarizes the local structure or the local surrounding of a key point, or any point. This means, by giving a pixel location to the algorithm, the descriptor should tell us how does this point looks like. Among the descriptors present nowadays we can list SIFT which uses the difference of Gaussians approach. In addition to different orientation histograms (HOG, GLOH). Binary features (BRIEF, BRISK) have different properties like rotational variance or scale. ORB uses the binary descriptor BRIEF [13], a popular technique for binary robust independent elementary features. It’s a 256 bytes descriptor, so we’ll have 256 different pairs. By selecting a patch around a key point and taking a local region around this point. After that, the BRIEF algorithm selects randomly pairs of pixels in the patch, then it compares all the pairs based on the intensity in the image.  1 if I(x) < I(y) b= (5) 0 Otherwise where x and y are pixel number 1 and pixel number 2. What comes after this, is concatenating a list of those bytes: b = 0001110… The final BRIEF formula looks exactly like this:  2i−1 b(I , xi , yi ) (6) fnd (I ) = 1≤i≤nd

where ‘n’ represents the binary feature vector length BRIEF is a very compact, easy to compute, easy to compare algorithm, and that’s what we want to have for our system. Unfortunately, it does have one disadvantage which is, in case we’re taking the image, we may accidentally take it in the wrong orientation. In this case, we’ll have a different descriptor. That’s something that is not that nice for a lot of 3d reconstruction tasks where there’s a platform moving through the environment and seeing objects from different viewpoints. As a result of this, the ORB features have been proposed, which can be seen as an extension of the BRIEF feature by adding a rotational component to deal with the different rotations that the camera may generate. To fix the orientation issue, we compute the image moments that are defined as follow: mpq =

 x,y

xp yq I (x, y)

(7)

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where ‘p’ and ‘q’, are the indices of the moment that we have With these moments we can describe certain properties of our image, such as computing the center of mass of the image:  m10 m01 (8) , C= m00 m00 We can also compute the orientation: θ = atan2(m01 , m10 )

(9)

Using this, we’ll be computing the main orientation of the local figure and simply perform a rotation around that point or the center of mass. Also, to compute a kind of transformed variance of our sample points. So, we’ll get a new pair: s = T (C, θ )s

(10)

where ‘s’ is the position where we are, which is the transformation matrix computed from the center of mass C and the orientation. In this case, we can rotate the image plane and still get the same pairs even if we’ll be rotating the camera. 3.3 Support Vector Machine (SVM) Support vector machine is a machine learning algorithm that aims to help us to separate or classify [15] or segment data into n number of classes. Support vector machines [16, 17] are widely used not just in computer vision, but in many different fields. SVM’s objective is to find a hyperplane that classifies our data which can be written as follow: w1 f1 + w2 f2 + b = 0 In vector form, it can be written as shown below:

 f [w1 w2 ] 1 + b = 0 f2 wT f + b = 0

(11)

(12) (13)

where ‘w’ is the coefficient vector, ‘f’ is the feature vector, and ‘b’ is a scalar that corresponds to the intercept. Linear decision boundaries of the same form can be used irrespective of the dimensionality of the feature space. if it happens to be in n-dimensional feature space, then we’ll have our decision boundary like: (n − 1) − D hyperplane

(14)

the equation of a hyperplane in high dimensions is: w1 f1 + w2 f2 + . . . + wn fn + b = 0

(15)

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wT f + b = 0

(16)

To evaluate the decision boundary, we need to define what’s called a safe zone or a margin, which is the width that we can extend or thicken this boundary until we hit features on both sides. the point here is to choose the decision boundary that maximizes the margin, and that’s what the support vector machine does. The support vector machine [18] tries to find a decision boundary that has a safe zone with a maximum margin. To use the SVM classifier we need to follow these steps [19]: Step1: Let’s assume that we have a k given training images {I1 , I2 , …, Ik } and the corresponding Haar features {f1 , f2 , …, fk }. Step 2: k corresponding labels { λ1 , λ2 , . . . , λk }, where λj = +1 if ‘Ij ’ belongs to the first class and λj = −1 if ‘Ij ’ belongs to the second class. Step 3: For each training sample (fi , λi ): If λi = +1 : If λi = −1 :

wT fi + b ≥ ρ/2

(17)

wT fi + b ≤ −ρ/2

(18)

We can combine the two previous expressions into one formula which is:

λi wT fi + b ≥ ρ/2

(19)

If S is the set of support vectors we’re trying to find, then for every support vector s ∈ S:

λs wT fs + b = ρ/2 (20) Step 4: So now, we can classify new features based on the support vector machine that we’ve just computed. We have Haar feature f for an image window and SVM parameters w, b, ρ, S. Classification: We first compute the distance d = wT f + b  d ≥ ρ/2 the first class If: (21) d ≤ ρ/2 the second class

4 Results and Discussion The proposed method was tested on 200 images, 100 per class. To evaluate the performance of our model, we opted for the accuracy method that has achieved 82% in this carpet classification model that we’ve been discussing through this paper. Also, by using the classification report, we could get other classification techniques such as the precision, the recall, and the f1-score as shown in Table 1 below:

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Table 1. Classification report metrics results Precision

Recall

F1-Score

Rabat/Fez carpet

0.64%

0.88%

0.74%

Amazigh carpet

0.86%

0.60%

0.71%

Where: Accuracy =

TP + TN TP + FN + TN + FP

(22)

TP TP + FP

(23)

Precision = Recal = F1 =

TP TP + FN

2 ∗ precision ∗ recall precision + recall

(24) (25)

TP: True positive/TN: True negative/FP: False positive/FN: False negative Table 2 shows some of the images that we used for our study. As we can see, all the classified images using the proposed method have shown a satisfactory classification. By getting a massive database composed of different kinds of carpets, we’ll get much more good convergence. The second column in the table below presents the percentage of belonging to a class. Table 2. The obtained results using the proposed method Carpet image

Percentage of each class Rabat/Fez carpet: 91.56% Amazigh carpet: 8.43% Rabat/Fez carpet: 93.57% Amazigh carpet: 6.42% Rabat/Fez carpet: 34.03% Amazigh carpet: 66% Rabat/Fez carpet: 6.68% Amazigh carpet: 93.31% Rabat/Fez carpet: 85.89% Amazigh carpet: 14.10%

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5 Conclusion In this article, we’ve presented a novel method for Moroccan carpet classification. For the proposed model we’ve used the support vector machine classifier combined with the Oriented FAST and Rotated BRIEF(ORB) for features detection and description. The obtained results were so satisfying by having an accuracy of 82%. In the upcoming works, we’re planning to widespread our research to cover the majority of the Moroccan artisanal products.

References 1. Moussaoui, H., Benslimane, M., El Akkad, N.: Image segmentation approach based on hybridization between K-means and mask R-CNN. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds.) WITS 2020. LNEE, vol. 745, pp. 821–830. Springer, Singapore (2022). https://doi.org/10.1007/978-981-33-6893-4_74 2. Moussaoui, H., Benslimane, M., El Akkad, N.: A novel brain tumor detection approach based on fuzzy C-means and Marker Watershed algorithm. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 871–879. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-73882-2_79 3. Zaitoun, N.M., Aqel, M.J.: Survey on image segmentation techniques. Proc. Comput. Sci. 65, 797–806 (2015). https://doi.org/10.1016/j.procs.2015.09.027 4. Minaee, S., Boykov, Y.Y., Porikli, F., Plaza, A.J., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2021). https://doi.org/10.1109/TPAMI.2021.3059968 5. Khan, A., Srisha, R.: Image segmentation methods: a comparative study. Int. J. Soft Comput. Eng. 2231–2307 (2013) 6. Khan, A.M., Ravi, S.: Image segmentation methods: a comparative study. Int. J. Soft Comput. Eng. 3(4) (2013). ISSN:2231-2307 7. Varshney, S.S., Rajpal, N., Purwar, R.: Comparative study of image segmentation techniques and object matching using segmentation. In: 2009 Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS), pp. 1–6 (2009). https://doi.org/10. 1109/ICM2CS.2009.5397985 8. Na, S., Xumin, L., Yong, G.: Research on k-means clustering algorithm: an improved k-means clustering algorithm. In: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 63–67 (2010). https://doi.org/10.1109/IITSI.201 0.74 9. Pham, D.T., Dimov, S.S., Nguyen, C.D.: Selection of K in K-means clustering. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 219(1), 103–119 (2005). https://doi.org/10.1243/095440605 X8298 10. Awad, A.I., Hassaballah, M. (eds.): Image Feature Detectors and Descriptors. SCI, vol. 630. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28854-3 11. Trajkovic, M., Hedley, M.: Fast corner detection. Image Vis. Comput. 16(2), 75–87 (1998). https://doi.org/10.1016/S0262-8856(97)00056-5 12. Nain, N., Laxmi, V., Bhadviya, B., M, D.B., Ahmed, M.: Fast feature point detector. In: 2008 IEEE International Conference on Signal-Image Technology and Internet-Based Systems, pp. 301–306 (2008). https://doi.org/10.1109/SITIS.2008.97 13. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56

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14. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2564– 2571. https://doi.org/10.1109/ICCV.2011.6126544 15. Batley, S.: Classification in theory and practice, pp. 1–29 (2014). https://doi.org/10.1533/978 1780634661.1. ISBN 9781843347859 16. Chaganti, S.Y., Nanda, I., Pandi, K.R., Prudhvith, T.G.N.R.S.N., Kumar, N.: Image classification using SVM and CNN. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), pp. 1–5 (2020). https://doi.org/10.1109/ICCSEA 49143.2020.9132851 17. Tran, H., Thuy, N.T.: Image classification using support vector machine and artificial neural network. Int. J. Inf. Technol. Comput. Sci. 4(5), 32–38 (2012). https://doi.org/10.5815/ijitcs. 2012.05.05 18. Prasad, S.V.S., Satya Savithri, T., Murali Krishna, I.V.: Comparison of accuracy measures for RS image classification using SVM and ANN classifiers. Int. J. Electric. Comput. Eng. 7(3), 1180–1187 (2017). https://doi.org/10.11591/ijece.v7i3.pp1180-1187.ISSN: 2088-8708 19. Agrawal, S., Verma, N.K., Tamrakar, P., Sircar, P.: Content-based color image classification using SVM. In: 2011 Eighth International Conference on Information Technology: New Generations, pp. 1090–1094 (2011). https://doi.org/10.1109/ITNG.2011.202

Interactive Large-Scale Graph Visualization and Analysis for Social Networks Jamal Elhachmi(B) and Youssef Cheikhani High National School of Computer Science and Systems Analysis (ENSIAS), Mohammed V University in Rabat, Rabat, Morocco [email protected]

Abstract. Dramatic popularity and prevalence of online social networking services have involved an increase of members publicly who articulate mutual “friendship” relations. Therefore, people have shown a great need to exchange information, share data, and adopt innovations in social networking applications. Data exchange has become the backbone of the economy on a global scale. In this paper, we investigate the Interactive Large-Scale Graph Visualization and Analysis of Social Networks that can process large-scale data. We have adopted a concrete large-scale corporate social networking site to realize this work; however, the approach can be easily generalized to other Social Networks. The problem is first stated as clearly as possible, and the social network is formulated as a joint optimization problem of graph selection. Then we have proposed some low-complexity search algorithms. The simulation results prove that our approach achieves good performance with a much lower computational complexity. Keywords: Visualization · Analysis · Data · Graphs · Social networks

1 Introduction Social networks have been widely adopted by individuals and professional organizations, especially, in the last decade, and new opportunities are enabled for the learning, construction of wider relationships, and the management of personal and professional interests. Social networks refer to an interconnection system between people who are interested in exchanging information, sharing data, and coming together to manage personal and professional interests. According to recent studies, the analysis of social networks allows us to identify and understand complex phenomena such as social organization, the behavior of organizational and non-organizational groups, and distant collaboration [1]. Networks are one of the most promising modeling approaches. They are often used for modeling problems related to large-scale data management systems such as World Wide Web [2], computer networks [3], social networks [4], and Social Media Sites. These social networks are presented in the form of a social structure of social entities, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 456–465, 2022. https://doi.org/10.1007/978-3-031-02447-4_47

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such as individuals or organizations, connected by relations, and interacting with each other. The use of these models would involve three main steps: Constructing a large-scale dataset of the communications traceability and data exchanges between entities. Analyzing these data, to establish statistical models such as the degree of saturation, the centrality, and to better evaluate the characteristics of the environment. And finally, making decisions based on the analysis of these data. New related approaches for large visualizing social networks were developed. But the most of them were limited to the visualization and analysis of small-scale graphs [5], while the most recent approaches in large-scale visualization and graph analysis were limited to static studies to provide decisions on relatively large graphs. These techniques are subsequently emerged, like modern parallel computing models, multicore computers, and parallel programming models. They can be very useful for data analysis of largescale networks. In this paper, we propose a graph-based drawing technique to represent social networks, a visualization system for exploring such networks, and we use jointly a drawing method for clustered graph drawing and a vertices comparison method for network analysis. This paper is organized as follows. We begin by describing the main components of analysis and large-scale visualization of social networks as well as the related research areas in Sect. 2. This is followed by a description of the used graph-based drawing to represent social networks in Sect. 3. In Sect. 4, we analyze the performance of our proposed approach on real instances and several concrete examples, before concluding this paper in Sect. 5.

2 Related Work Many researchers have brought great interest to the visualization of social networks, especially within the social sciences, with two main themes: community detection and community analysis. Since the 1930s, most of these approaches have been used a basic representation based on the node-link notion as a tool for analyzing social relations. Both sociograms and sociomatrices have been proposed to represent the analyzed social networks. Traditional social network visualization tools have been proposed by different research communities. Related works can be found in the following sources, but not restricted to [6–12] and [13]. Sociogram representations have been exploited in many traditional social network visualization tools. For example, tools like UCINET/Netdraw and Krackplot [6] are two strong tools in providing advanced metrics that can offer basic node-link visualizations in the domain of social network analysis. While Visione [7] uses an advanced combination of a certain number of metrics and layout algorithms that offer à basic node-link visualizations in social networks. Whereas, the community visualization/HCI tools, like Vizster [8], SocialAction [9], are more advanced tools that allow interactive examination of visual representations [1]. Most of these traditional tools cannot view very large networks, especially dense networks. An attempt to develop a more advanced tool for social network analysis on large networks was proposed by Netminer and Pajek [10], in the form of two social network analysis tools like UCINET, which provide a variety of data input methods in the form of matrix and node-link visualization.

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Since the early 80s, schematics and reorderable matrices have been considered as a powerful analysis tool for dense networks, which have shown more efficiency over the node-link diagrams. They adapt more quickly and allow an efficient and rapid verification of the existence of a connection. They can also be used to identify clusters in the network [11]. On the downside matrix approach shows less efficiency in visualizing large-scale networks, especially to detect the links between several nodes. It is less intuitive compared to the node-link diagrams, which may explain why they are currently under-used in the analysis. Subsequently, interactive adjacency matrices were used for the visualization of medium-scale social networks and especially in [12], therefore, sophisticated hybrid approaches have been proposed in the literature, as in [13]. Matrix visualizations have a large use, in analyzing different types of networks, however, these methods have a great limitation of scalability. To overcome this limitation, one potential solution proposed in [14] consists of using a hierarchical grouping of nodes into clusters on multiple levels of scale and then rendering the aggregated network instead, the mechanism is repeated, to achieve a visualization structure that allows browsing the network at different levels [14]. Today, numerous techniques have been offered solutions to the inherent complexity in large-scale networks and applied to social network analysis and relevant aspects of social media and literary data analysis [15]. The greatest success of web services and social media is due to the great need to exchange information, which generates a huge flow of data in the network. In particular, big data technologies show great potential and good qualities in handling the flood of social media data [16], a new appropriate term has emerged: Social Big Data (BSD) or Big Social Data (BSD). It designates the combination between the big data and analysis techniques used in the social network data processing. Understanding, analyzing, and operating this social data presents a valuable mine of information for economic and social actors. It is a powerful means to build a useful database for the social relations studies, such as the prediction of relationships on social networks, and can influence social commerce [17]. Most of the research proposed and applied for social network analysis in the literature has rather focused on the social network topology or the linkage information between individuals. Graph theory was identified as the most intuitive way to represent relationships in the social network. It allows better representation and good storage of the data representing relational knowledge of interacting entities, and efficient access to this data. Analyzing and representing complex networks based on graph theory can provide significant insights into the community, and detect many phenomena of interaction in this community [18], behavior analysis, and other applications (e.g., node classification [19], clustering [20], and recommendations [21] and can therefore influence areas of great importance, such as the economic field. To convert the raw graphics data into a vector of data, with suitable dimensions and conserving the intrinsic graph properties, the authors in [22] have proposed a graph embedding technique. The used representation is considered as graphs representation learning, which uses machine learning techniques to conveniently perform own-stream

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tasks [23]. Most of these approaches work well in analyzing but show integration problems of relations from different applications. Graph theory has also offered two graph description models: a semantic description in the form of resource description frameworks (RDFs), and inference description in the form of labeled property graphs (LPGs) [24]. Knowledge graphs have been proposed in [25] to allow automatic integration of the data into the semantic basis and keep the already established concepts for social network analysis. The authors in [26] have subsequently proposed knowledge graph research techniques for social relationship applications. Another more important way of mining social relationships is the combination of the neural network with ontology can solve content and sentiment-related problems [27]. According to Kostenally [28], some tools and libraries including several tasks have been developed for computer analyzes of social networks, here, we can cite graph-tool (Peixoto 2014), (SNAP 2016) [29], and scikitnetwork [30] as several examples. Some of these tools provide some capabilities for visualization and exploratory analysis, For example, the JUNG library [31] and the tools such as Pajek [32], and NodeXL [33]. Zhou et al. [34] have described an approach for network analyses in other biological domains that have also been successfully applied to social networks. Within the framework of the VAST challenge, the same authors have developed a social multilayer networks analysis and visualization, e.g., MuxViz [35]. And various graph models for social networks have been proposed: temporal and dynamic graphs [36–39], group structures [36], and large-scale graphs [37]. Several frameworks for different models of complex real-world networks have also been developed, including multifaceted graph visualization, and multilayer network visualization [38]. In the same context, we should quote the proposed multilayer network approach in [28], which uses a social relationship representation model to address both conceptual graph and domain ontology for modeling, analyzing, and representing complex networks.

3 Drawing a Social Network Graph Social networks can be modeled as a relational graph whose nodes represent entities (represented by N), and the edges represent the links (interactions) among these entities (represented by L) or arrows (arcs) in cases of directed graphs [39]. Nodes and edges can designate a weight and/or a label. According to the type of network and the relationships, the nodes may represent people, protein, companies, web site, or anything. And the relationships might include any link. When the graph is directed, the relation between two nodes Ni and Nj is not the same as the relation between Nj and Ni . The graph is undirected when the order of the relation between Ni and Nj is unimportant [40]. To illustrate this, we will use the Facebook Combined networks dataset, this dataset consists of the “circles” (or list of friends) of Facebook users. The network can be represented by an undirected graph since the “friend” relation is symmetrical, consisting of 4039 nodes and 88234 relations (Fig. 1).

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Fig. 1. A Representation of the Facebook combined dataset using a graph

Drawing a graph for social network analysis tends, starting from a network of initial links, to explore gradually the subsequent links, by adding links in a given cluster according to the operational change of the operating environment. The problem is formalized as follows. We follow the formalization used in [40] and [41]. The network is usually modeled in a weighted graph and as a triplet G = (N, L, ω), where: – N = {N1 ,N2 ,…, Nn } is a set of n distinct nodes (actors). – L = {l1 ,l2 ,…, lp } all p available links (relations) that connect pairs of vertices. That is, L consists of some couples e = (Ni , Nj ) where Ni , Nj  N. ω(e): E → R + represents a weight that quantifies the relationship between two nodes depending on the context. For example, if the relation quantifies the interaction strength based on the email exchange between individuals, the weight quantifies the “number of messages exchanged” [41]. The graphs we observe are simple without parallel edges, undirected, and loop-free (no edge joins a vertex to itself). Note that the clustered graph is a triple G’ = (N, L, P) where N is a finite set of Nodes, L ⊆ N × N is a finite set of links and P is a partition over N. The elements of P correspond to the clusters in the graph. The following figure shows a structure example of clustered graphs –with three clusters (Fig. 2).

Fig. 2. Structure of dendrogram [41]

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4 Processing Techniques and Results Our graph visualization and analysis methods include a set of functionalities allowing the user to import and transform the data extracted from a relational database into a graph model. After that, the data will be stored within the graph databases. Then, these data will be retrieved to be visualized, explored, and analyzed. The pipeline of our approach consists of four main steps: Step 1: The first step is to identify the network. It allows us to create all the nodes and relationships, that model our data and to customize them by adding properties, labels, and types. Therefore, the user generates his graph diagram and saves it to proceed to the data mapping step. Step 2: In this step, each component of the graph is associated with a data file. This relative data file can be imported, and each field is subsequently associated with a property of the corresponding node. Submitting a form leads to the direct creation of the corresponding nodes in the current database instance. Step 3: After it has been fully collected and stored, it can be presented in graph form. Then it can be retrieved, viewed, and analyzed using the implemented algorithms. These algorithms are well described in the upcoming sections. The following figure presents the execution of certain layouts, algorithms, and measurements on the data graph that we have just created (Fig. 3).

Fig. 3. Execution of the Force-Layout and Circular-Layout

Step 4: The last and most important step consists of analyzing data, and therefore recommending the appropriate actions. This is where our application’s strong point lies: Firstly, it can help present and display data to support analysis. Secondly, it can interactively visualize dynamic large-scale social networks with millions of nodes and edges. With technical innovations:

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-By using an iterative search algorithm like Dijkstra, we can find the shortest existing path and calculate the cost of travel (Fig. 4).

Fig. 4. Execution of Dijkstra’s algorithm

– PageRank To detect the nodes with the highest influence over the network, we run the PageRank algorithm to obtain a graph presentation. The algorithm uses the PageRank score as a metric to determine the size and importance of a node. The importance of a given node N, depends not only on the number of incoming links but also on the quantifier of the associated nodes (N1, N2, … Nn). It is calculated using the following equation: PR(N ) = (1 − d ) + d (PR(N 1)/O(N 1) + . . . + PR(Nn)/O(Nn))

(1)

• The damping factor d is a parameter that can vary between 0 and 1, but it is generally fixed at the value 0.85. • O(N) is the number of outgoing relationships from node N (Fig. 5).

Fig. 5. Running PageRank algorithm

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– Communities Identification To identify communities, we use the Louvain method. It begins by finding a first small community through local optimization of the modularity, then the small communities are grouped into a node and the first step is repeated. The modularity presents the density and the quality of partitioning nodes of a graph, it is calculated using the following equation:    Si Sj  1  wij − δ ci , cj (2) M = ij 2S 2S where: • wij is the edge weight between nodes Ni and Nj ; • Si and Sj are the sums of the weights of the edges attached to Ni and Nj respectively, and S is the sum of all edges weights; • ci and cj are the communities of the nodes Ni and Nj , and δ is the Kronecker delta function. Each community is associated with color to facilitate their identification (Fig. 6).

Fig. 6. Execution of the Louvain method

5 Conclusion In this paper, we have proposed a new method for social network visualization and analysis. That method allows the automatic generation of graphs from a data table. It facilitates the data visualization and analysis task necessary to use graph-based systems. The described approach can automatically find the shortest existing path. Using the PageRank algorithm enables us to obtain a graph hierarchical presentation and detect the nodes with the highest influence and importance over the network. This graph-based exploration tool makes it possible to identify the different communities in a network and facilitates the recommendation of certain relationships according to the determining influence and the importance of the nodes. Our approach can detect similarities between several networks. It was designed for the social network analysis, but can be applied in other fields without the need for additional loads and adaptation.

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22. Agrawal, R., Arquam, M., Singh, A.: Community detection in networks using graph embedding. Proc. Comput. Sci. 173, 372–381 (2020) 23. Zhang, L., Yan, Q., Zhang, L.: A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on Airbnb. Decis. Supp. Syst. 133, 113288 (2020) 24. Wylot, M., Hauswirth, M., Cudre-Mauroux, P., Sakr, S.: RDF data storage and query processing schemes: a survey RDF data storage and query processing schemes. ACM Comput. Surv. 51(4), 1–36 (2018) 25. Amador-Dominguez, E., Serrano, E., Manrique, D., Hohenecker, P., Lukasiewicz, T.: An ontology-based deep learning approach for triple classification with out-of knowledge-base entities. Inf. Sci. 564, 85–102 (2021) 26. Chen, C., Cai, F., Hu, X., Zheng, J., Ling, Y., Chen, H.: An entity-graph based reasoning method for fact verification. Inf. Process. Manag. 58(3), 102472 (2021) 27. Ali, F.F., El-Sappagh, S.S., Kwak, D.D.: Fuzzy ontology and LSTM-based text mining: a transportation network monitoring system for assisting travel. Sens. Sens. (Basel, Switzerland) 19(2), 1–23 (2019) 28. Zhao, H., Liu, Y.: Understanding large-scale social relationship data by combining conceptual graphs and domain ontologies. Hindawi: Discrete Dynam. Nat. Soc. 2021, 2857611 (2021) 29. Leskovec, J., Sosic, R.: SNAP: a generalpurpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. 8(1) (2016) 30. Bonald, T., de Lara, N., Lutz, Q., Charpentier, B.: Scikit-network: graph analysis in python. J. Mach. Learn. Res. 21(185), 1–6 (2020) 31. O’Madadhain, J., Fisher, D., White, S., Smyth, P., Boey, Y.-B.: Analysis and visualization of network data using JUNG. Unpublished preprint (2005) 32. Batagelj, V., Mrvar, A.: Pajek — analysis and visualization of large networks. In: Mutzel, P., Jünger, M., Leipert, S. (eds.) GD 2001. LNCS, vol. 2265, pp. 477–478. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45848-4_54 33. Hansen, D.L., Shneiderman, B., Smith, M.A.: Analyzing Social Media Networks with NodeXL: Insights from a Connected World. Morgan Kaufmann (2011) 34. Zhou, H., Shaverdian, A.A., Jagadish, H.V., Michailidis, G.: Multiple step social structure analysis with Cytoscape. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, VAST 2009, pp. 263–264. IEEE (2009) 35. De Domenico, M., Porter, M.A., Arenas, A.: MuxViz: a tool for multilayer analysis and visualization of networks. J. Comp. Netw. 3(2), 159–176 (2015) 36. Vehlow, C., Beck, F., Weiskopf, D.: The state of the art in visualizing group structures in graphs. In: Proceedings of the Eurographics Conference on Visualization- STARs, EuroVis 2015. The Eurographics Association (2015) 37. von Landesberger, T., et al.: Visual analysis of large graphs: state of-the-art and future research challenges. Comput. Graph. Forum 30(6), 1719–1749 (2011) 38. McGee, F., Ghoniem, M., Melançon, G., Otjacques, B., Pinaud, B.: The state of the art in multilayer network visualization. Comput. Graph. Forum 38(6), 125–149 (2019) 39. Berry, M.J.A., Linoff, G.S.: Data Mining Techniques. For Marketing, Sales, and Customer Support. Wiley (1997) 40. Falkowski, T.: Community Analysis in Dynamic Social Networks. PhD thesis. GuerickeUniversität Magdeburg, Otto-von (2009) 41. Truong, Q.D., Truong, Q.B., Dkaki, T.: Graph methods for social network analysis. In: Vinh, P.C., Barolli, L. (eds.) ICTCC 2016. LNICSSITE, vol. 168, pp. 276–286. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46909-6_25

Toward a New Process for Candidate Key-Phrases Extraction Lahbib Ajallouda1(B) , Oumaima Hourrane2 , Ahmed Zellou1 , and El Habib Benlahmar2 1 SPM-ENSIAS Mohammed V University in Rabat, Rabat, Morocco [email protected], [email protected] 2 LTIM – FSBM Hassan II University in Casablanca, Casablanca, Morocco

Abstract. Key-phrases in a document are the terms that allow us to have or know an idea of its content without having to read it. They can be used in many Natural Language Processing (NLP) applications, such as text summarization, machine translation, and text classification. These phrases are selected from a set of terms in the document called candidate key-phrases. Thus, any flaws that may arise during the selection of candidate phrases may affect the automatic key-phrase extraction (AKE). Despite the importance of identifying candidate key-phrases in the AKE process, we found a very limited number of researchers interested in identifying their features in the document. In this paper, we will present the features that will allow the identification of candidate key-phrases, based on the study and analysis of the features of 60,000 key-phrases manually selected from five different datasets. To improve the performance of AKE approaches. Keywords: Natural Language Processing · Key-phrases · Candidate key-phrases · Automatic key-phrase extraction

1 Introduction The considerable volume of documents published every day creates great difficulty in processing them both during analysis and synthesis [1]. According to the authors of [2], Automatic Key-Phrase Extraction (AKE) is a solution to overcome this problem. For this, a set of automatic approaches has been proposed to extract key-phrases from a document. However, not all of these methods give impressive results [3]. The first obstacle encountered by these methods is to identify candidate key-phrases to select from those the key-phrases. The objective of this paper is to identify the features of candidate key-phrases in a document, through the study and analysis of the characteristics of 60,000 key-phrases extracted manually from 6,400 documents stored in five datasets used to evaluate the AKE approaches. In addition to reducing the time required to extract key-phrases, the importance of identifying candidate key-phrases is to eliminate irrelevant text units that AKE methods might choose as key-phrases [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 466–474, 2022. https://doi.org/10.1007/978-3-031-02447-4_48

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The rest of the paper is presented as follows. Section 2 presents the model of automatic extraction of candidate key-phrases from a document. We will present our empirical study in Sect. 3, while Sect. 4 discusses the results obtained in Sect. 3. Finally, we will present our conclusion and the research avenues we will focus on in the future in Sect. 5.

2 Candidate Key-Phrases Selection In this section, we will introduce the techniques used in the process of selecting candidate key-phrases in a document. The selection of these phrases went through several stages that we present in Fig. 1.

Fig. 1. Candidate key-phrases extraction process

2.1 Document Cleaning Most documents do not contain only text but can include other types of data, such as tables, figures, symbols, mathematical equations, etc. First of all, it is necessary to clean up the document by removing these elements and keeping only the textual data, as well as converting all characters to lowercase, replacing non-alphabetic characters with space, and translating foreign words in the text in the language studied. 2.2 Tokenization Is a technique used to break up text into words. Each word corresponds to a token. In the tokenization step, the cleaned text is converted into an array of words, to define the morpho-syntactic class of each token.

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2.3 Part-of-Speech Tagging (POST) The objective of the labeling phase is to recognize the morpho-syntactic class of each word in the document. The POST technique [5] is used to identify the morpho-syntactic class of a word. Table 1 shows some POS Tags used in English. Table 1. Part of speech tagging Part of speech

Tag

Adjective

JJ

Adverb

ADV

Conjunction

CON

Determiner

DT

Interjection

INT

Noun

NN

Preposition

P

Pronoun

PRO

Verb

V

Past participle

VBN

Gerund

VBG

According to the authors of [6], 56 models of POS tags were constructed by manual labeling of the training dataset. 2.4 Chunking A process that uses regular expressions to generate phrases from the text. Several AKE approaches have noted that key-phrases are noun phrases consisting of one or more words [7–9]. Thus, chunking is used to select noun phrases in the document. for example, “big data” is a noun phrase on the other hand “saw a big car” is not a noun phrase. Not specifying the candidate phrase features during the selection process gives us a large number of irrelevant phrases that affect the results of the AKE process.

3 Empirical Study The selection of candidate phrases remains one of the most important steps in the AKE process. In this section, we present an empirical study to uncover the most important characteristics of candidate phrases by analyzing over 60,000 key-phrases manually selected from 6,400 documents.

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3.1 Datasets More than 20 datasets are used to measure the performance of AKE methods. These collections include files containing key-phrases for each document, identified by the authors or publishers. Five datasets were selected for our study, Inspec [10] and WWW [11] representing short texts, SemEval2010 [12] and Krapivin2009 [13] representing long texts, and Pubmed [14], which consist of a set of documents belonging to the same domain. Table 2 presents the five datasets used. Table 2. Datasets used in the study Dataset

Type of doc

Docs

Tokens/doc

Key-phrase/doc

Inspec

Abstract

2000

128

10

Krapivin2009

Paper

2304

8040

6

PubMed

Paper

500

3992

5

SemEval2010

Paper

243

8332

15

WWW

Abstract

1330

84

5

The dataset we used enabled us to process over 6,400 documents containing 21,000,000 words. 3.2 Feature Analysis There are many features for analyzing key-phrases. In our study, we used four features namely N-gram, Min and max size of the key-phrase, Term frequency (TF), and the morpho-syntax of the key-phrase. N-gram is a sequence of N contiguous words, 2-gram (bigram) is a sequence of two words, and 3-gram (trigram) is a sequence of three words. Their use in the AKE process provides a very large number of candidate phrases, which creates many problems [10]. First, this method produces irrelevant phrases, second, the maximum value of N is unknown. for this, we performed a comprehensive review of all the key-phrases from Table 3. Key-phrases according to the number of their constituent words DataSet

KP

1-Gram

2-Gram

3-Gram

4-Gram

5-Gram

Inspec

36548

14314

19212

2344

554

124

Krapivin2009

15232

6688

7436

876

178

54

PubMed

6900

2415

3110

978

296

101

SemEval2010

4631

1714

2454

353

80

30

WWW

7106

3407

2770

670

197

62

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five datasets that contain over 60,000 key-phrases, to find the appropriate values of N for the candidate phrases, whether in a short text or long. Table 3 shows the results we obtained. From the results obtained, it can be seen that the number of words that make up a candidate phrase can reach 5 (5-gram). Thus, phrases containing more than 5 words can be excluded. Figure 2 presents the distribution of key-phrases by N-Gram. 40000 30000 20000 10000 0 1-Gram

2-Gram

3-Gram

4-Gram

5-Gram

Fig. 2. Distribution of key-phrases by N-Gram.

On the other hand, Fig. 3 shows that 98% of the key-phrases are written either in short texts or in long texts, in the form 1-gram or 2-gram or 3-gram. Therefore, this factor should be taken into account when selecting candidate phrases.

2%

98%

Fig. 3. The most frequently used key-phrase format

Min and Max Size. By examining the key-phrases in the five datasets, we found that the length of the phrases approved as key-phrases is between 4 to 60 characters (see Table 4), and therefore the phrases whose length did not fall within this range can be excluded. Term Frequency. Term Frequency (TF) refers to the number of times the term t occurs in document d. The term appears more often in long documents than in short documents. To reduce this effect, we used normalization by dividing the term frequency by the total number of terms in the document (Formula 1). tf (t, d ) =

Count of t in d Number of terms in d

(1)

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Table 4. Key-phrases size DataSet

Min

Max

Inspec

4

45

Krapivin2009

4

52

PubMed

6

59

SemEval2010

4

48

WWW

5

50

By examining the key-phrases in the five datasets, we found that the min TF of phrases approved as key-phrases is 2 × 10–2 for short documents, and 5 × 10–4 for long documents (Table 5). Therefore, candidate phrases having a TF lower than the TF min can be excluded. Table 5. Term frequency of key-phrase DataSet

TF Min

AVG TF

Inspec

2 × 10–2

3.5 × 10–2

Krapivin

5 × 10–4

10–3

PubMed

8 × 10–4

15 × 10–4

SemEval2010

10–3

3 × 10–3

WWW

7 × 10–2

10–1

Noun Phrase Pattern. All the phrases selected in the 5 datasets as key-phrases are noun phrases that consist of one or more words and do not include stop words. Therefore, the process of extracting candidate key-phrases must take this into account. The noun phrase can appear in many different patterns. Table 6 presents Some models of key-phrases in the studied datasets. The POST technique is used to associate words with the corresponding grammatical information. POST converts a phrase to a list of tuples (word, tag). It assigns parts of speech to each word, like a noun, verb, adjective, etc. According to our research on the five datasets, we also found key-phrases containing the past participle form of a verb (VBN) and the gerund form of a verb (VBG). This confirms what was proposed in [15], where it was considered VBN as an adjective and VBG as a noun and that the pattern which will allow selection of candidate phrases should be in the form (NN * | JJ * | VBN | VBG) * (NN * | VBG).

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POS pattern

Description

∗ +

Begins with an optional determinant DT, followed by zero or more JJ adjectives. followed by one or more NN|NNS names

+ ?

Begins with one or more nouns NN|NNS, followed by zero or one optional adjective JJ

* +

Begins with an optional past participle form of a verb (VBN), followed by one or more NN|NNS names

+ ?

Begins with one or more gerund forms of a verb (VBG), followed by zero or one optional adjective JJ

4 Discussion The results we obtained in Sect. 3, which focused on the analysis of over 60,000 keyphrases, showed that candidate phrases can be identified using four features, which are: 4.1 N-gram We have shown that most candidate phrases can contain a maximum of three words, with a few exceptions, but in general, candidate phrases cannot contain more than five words, and therefore all phrases which do not meet this condition must be excluded. 4.2 Phrase Size The length of the key-phrases in all the datasets we studied varies between four and sixty characters, so we recommend ignoring any phrases whose length is outside this range. 4.3 Term Frequency Our results showed that all key-phrases have a TF value of at least TF 2 × 10–2 for short documents and 5 × 10–4 for long documents. Therefore, we recommend excluding all phrases that have a TF value lower than these values, and in the case of a corpus, we recommend using TF-IDF instead of TF to get more accurate results. 4.4 Noun Phrase Pattern Our results showed that all the key-phrases are noun phrases without stop words, and therefore the regular expression proposed by the authors of [15] can be adopted to choose the candidate phrases. We also recommend excluding phrases that represent names of people or places as they do not represent key-phrases.

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4.5 Process Using the process of identifying candidate phrases shown in Fig. 1 is not enough to eliminate a set of irrelevant phrases. Therefore, we suggest modifying this process using the above four features. Figure 4 shows how to integrate these features into the process.

Fig. 4. Improved Candidate Key-phrases extraction process

Some approaches exploit other techniques to reduce the number of candidate keyphrases. The authors of [16] suggested eliminating n-grams that do not have a minimum frequency of occurrence. The authors of [17] use the concordance called phraseness, which measures the probability that a sequence of words can be considered as a phrase. The authors of [15] use heuristic inequality to remove outliers as shown in formula 2. C
0 ⎩ VI dI + V dV < 0

at MPP at left of MPP at right of MPP

(7)

(8)

The advantage of the IC algorithm is the ease of knowing the relative position of the MPP. The operating concept of the IC is presented in the flowchart of Fig. 3.

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Fig. 3. Flowchart of IC algorithm.

Firstly, the measurement of the PV array voltage and current is done if (8) at the left of MPP, the duty cycle is decreased (i.e., the voltage is augmented), else, the duty cycle is increased (i.e., the voltage is reduced). Then, there is nothing to do if (8) at MPP is met. Despite IC advantages, it has various problems, such as the presence of oscillation of steady-state and wrong decisions when the irradiation suddenly increases. As shown in Fig. 4, when the irradiance is 500 W/m2 , and the system is operating at load 1, the MPP point B is tracked in the P-V characteristic. After some time, when the irradiation raises to 1000 W/m2 , load 1 will conduct the system to operate at point C of the I-V curve, where its projection on the P-V curve is at point D. The IC algorithm evaluates the value of slope between points D and B, which is greater than zero. Consequently, it will increase the PV panel voltage by the duty cycle decrease. However, as shown in Fig. 4, the 1000 W/m2 MPP is at point E, and the slope between points E and D is less than zero, the voltage of the PV panel must decrease to attain point E, instead of increasing it, and thus moving the operating system point away from point E as the conventional IC algorithm does.

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Fig. 4. I-V and P-V characteristics of 1000 W/m2 and 500 W/m2 irradiations.

3.3 Proposed IC Algorithm As shown in Fig. 5, the proposed IC algorithm introduces an error uncertainty to solve the first problem of steady-state fluctuation according to the equation [18]: dP V (9) dV + I < 0.08

Fig. 5. Flowchart of proposed IC algorithm.

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Then, to solve the second problem of the conventional IC algorithm related to the sudden increase in solar irradiation, a condition for detecting the increase in irradiation is added and checked by the relation ΔP ∗ ΔV > 0 in order to avoid the wrong decision of step choice made by the conventional IC algorithm. The flowchart of Fig. 5 shows all these modifications in the boxed part with a blue dotted line.

4 Results and Discussion The simulation is performed by the MSX-60 PV panel using the MATLAB/Simulink platform. The parameters of the above-mentioned panel are presented in Table 2. This panel is coupled to a boost converter circuit, which contains an input capacitor Cin = 100 μF, an inductor L = 3 mH, an output capacitor Cout = 100 μF, and a switching frequency of 10 kHz. Figure 6 shows a simultaneous comparison between the pr posed IC algorithm and the conventional one to see the difference when irradiation increases. The irradiation increases in the first interval from 500 to 600 W/m2 at time t = 0, 3 s. Then, it continues to increase in the second interval from 600 to 800 W/m2 at time t = 0, 6 s. And similarly, at time t = 0.9 s in the third interval (800 to 1000 W/m2 ). As can be seen in Fig. 6, the conventional IC technique is characterized by strong oscillations in the steady-state, which leads to power losses. In this regard, the proposed IC technique allows minimizing as much as possible the oscillations in the same regime by admitting an error of less than 8%. The zoom carried out in the simulation of Fig. 6 during the following durations ([0, 06 s to 0, 13 s]; [0, 75 s to 0, 8 s]; [1 s to 1, 08 s]) clearly shows the effectiveness of this proposal. Furthermore, the conventional IC technique causes the power to diverge with a sudden increase in irradiation Fig. 6. For instance, when the irradiance varies in the first range, the maximum PV power is 36.8 W. However, the conventional IC algorithm has reversed the direction, and the PV power curve diverges at 34,5 W, and when the irradiation varies in the second range (the maximum PV power is 48,8 W), the PV power diverges from 48,8 W to 43,8 W. Finally, when the irradiation varies in the third range

Fig. 6. Simulation of algorithm proposed IC and conventional IC.

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(the maximum PV power is 60.3 W), the power diverges from 60,3 W to 56,71 W. Accordingly, the system needs a considerable amount of time to converge around the MPP, due to the bad decision-making of the conventional IC technique. The proposed technique, as shown in Fig. 6, identifies the quick variation of irradiance and makes the proper step decision. Hence, the proposed IC technique can rapidly converge to the new MPP with a response time of 0,02 s. quickly, where it needs only 0,0007 s to reach the MPP. For all these reasons, the modified IC technique is more accurate and faster than the conventional IC technique, which requires a longer time to attain the MPP.

5 Conclusion This paper presents a theoretical study based on simulations, which allows optimizing the operation of the conventional IC algorithm by proposing a modified algorithm, which increases the tracking efficiency of MPP when the irradiation suddenly increases. This algorithm also decreases the steady-state oscillations, which leads to a gain in energy and convergence speed with reasonable control under rapidly increasing irradiation. In our future work, we will focus on improving an algorithm that can cope with irradiation variations in both directions so that it can be implemented on an embedded board.

References 1. Mohanty, P., Muneer, T., Gago, E.J., Kotak, Y.: Solar radiation fundamentals and PV system components. In: Mohanty, P., Muneer, T., Kolhe, M. (eds.) Solar Photovoltaic System Applications. GET, pp. 7–47. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-146 63-8_2 2. Tamrakar, R., Gupta, A.: A review: extraction of solar cell modelling parameters. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 3(1), 55–60 (2015) 3. Lin, P., Cheng, S., Yeh, W., Chen, Z., Wu, L.: Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Sol. Energy 144, 594–603 (2017) 4. Jung, Y., So, J., Yu, G., Choi, J.: Improved perturbation and observation method (IP&O) of MPPT control for photovoltaic power systems. In: Conference Record of the Thirty-First IEEE Photovoltaic Specialists Conference, pp. 1788–1791, January 2005 5. Kwon, J.M., Kwon, B.H., Nam, K.H.: Three-phase photovoltaic system with three-level boosting MPPT control. IEEE Trans. Power Electron. 23(5), 2319–2327 (2008) 6. Chtita, S., Derouich, A., El Ghzizal, A., Motahhir, S.: An improved control strategy for charging solar batteries in off-grid photovoltaic systems. Sol. Energy 220, 927–941 (2021) 7. Amir, A., Selvaraj, J., Rahim, N.A.: Study of the MPP tracking algorithms: focusing the numerical method techniques. Renew. Sustain. Energy Rev. 62, 350–371 (2016) 8. Gupta, A., Chauhan, Y.K., Pachauri, R.K.: A comparative investigation of maximum power point tracking methods for solar PV system. Sol. Energy 136, 236–253 (2016) 9. Roy, C.P., Vijaybhaskar, D., Maity, T.: Modelling of fuzzy logic controller for variable-step MPPT in photovoltaic system. In: 2013 IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), pp. 341–346. IEEE, December 2013 10. Zhu, W., Shang, L., Li, P., Guo, H.: Modified hill climbing MPPT algorithm with reduced steady-state oscillation and improved tracking efficiency. J. Eng. 2018(17), 1878–1883 (2018)

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11. Jately, V., Azzopardi, B., Joshi, J., Sharma, A., Arora, S.: Experimental analysis of hillclimbing MPPT algorithms under low irradiance levels. Renew. Sustain. Energy Rev. 150, 111467 (2021) 12. Ahmed, J., Salam, Z.: An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions. IEEE Trans. Sustain. Energy 9(3), 1487–1496 (2018) 13. Motahhir, S., El Ghzizal, A., Sebti, S., Derouich, A.: Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. Int. J. Photoenergy 2018 (2018). Article ID 3286479 14. Shang, L., Guo, H., Zhu, W.: An improved MPPT control strategy based on incremental conductance algorithm. Prot. Control Mod. Power Syst. 5(1), 1–8 (2020). https://doi.org/10. 1186/s41601-020-00161-z 15. Villalva, M.G., Gazoli, J.R., Ruppert Filho, E.: Modeling and circuit-based simulation of photovoltaic arrays. In: 2009 Brazilian Power Electronics Conference, pp. 1244–1254. IEEE, September 2009 16. Chtita, S., Chaibi, Y., Derouich, A., Belkadid, J.: Modeling and simulation of a photovoltaic panel based on a triple junction cells for a nanosatellite. In: 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 1–6. IEEE, November 2018 17. Tey, K.S., Mekhilef, S.: Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Sol. Energy 101, 333–342 (2014) 18. Motahhir, S., Chalh, A., El Ghzizal, A., Derouich, A.: Development of a low-cost PV system using an improved INC algorithm and a PV panel Proteus model. J. Clean. Prod. 204, 355–365 (2018) 19. Loukriz, A., Haddadi, M., Messalti, S.: Simulation and experimental design of a new advanced variable step size incremental conductance MPPT algorithm for PV systems. ISA Trans. 62, 30–38 (2016)

Solar Radiation Time-Series Prediction Using Random Forest Algorithm-Based Feature Selection Approach Gaizen Soufiane(B) , Fadi Ouafia, and Abbou Ahmed Electrical Engineering Department, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco [email protected]

Abstract. With the fast expansion of the photovoltaic market, it is critical to accurately anticipate solar radiation. Since solar radiation is highly dependent on environmental conditions, unplanned fluctuations in solar output would raise operational costs. Moreover, the unpredictable output power will be a substantial impediment to introducing this output energy into the grid. This paper aims to develop a suitable and efficient solar power prediction approach that will help to decrease the errors associated with estimating future production. To exploit the unpredictable characteristics of solar power information, this approach is based on combining the Random Forest methodology with feature selection. The initial model takes input temperature, humidity, pressure, wind speed, and wind direction, and outputs solar radiation. The trustworthiness of the proposed models was proved by comparing statistical performance indicators such as MAPE, RMSE. Keywords: Solar power · Radiation · Random forest · MAPE · RMSE

1 Introduction Photovoltaic (PV) production is by nature discontinuous and random because it is related to weather disturbances [1]. This uncertainty causes constraints on fossil fuel generators and tends to increase their operating cycle, especially at low speed when their efficiency is relatively low. Short-term solar radiation prediction is a critical tool for guaranteeing a consistent and cost-effective supply of high-quality energy to consumers. Furthermore, it reduces the uncertainty of PV production by allowing network managers to engage or disengage additional means of production more effectively to adapt photovoltaic production changes and to react to extreme events. Predicting solar power is a multimodal time-series prediction issue that can be grouped into three categories: short-term, medium-term, and long-term forecasts. Shortterm solar power prediction provides a helpful estimate of solar power for the next hour up to the next several weeks, which is important for the sustainable and efficient operation of power [2–10]. Meanwhile, during the last years, the use of machine learning

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 659–668, 2022. https://doi.org/10.1007/978-3-031-02447-4_68

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approaches to anticipate solar power has gained traction in the scientific world [11]. It includes: SVM algorithm [12], DT algorithm [13], ANN algorithm [14] and RF algorithm [15]. This is one of the most often used strategies for forecasting time series [16, 17]. Benali.L [18] has used neural networks and random forest to forecast hourly solar irradiations for time horizons from h + 1 to h + 6 the results show that the most effective approach is the random forest method. Srivastava R. [19] has used random forest, CART, MARS, and M5 to perform 1-day-ahead to 6-day-ahead hourly solar radiation forecasting. Based on the findings, it was found that the random forest model produced the best outcomes for the current study. To estimate monthly irradiance, a multilevel multivariable empirical mode analysis combined with ant colony management and RF was utilized by Prasad R. [20]. The suggested model, along with comparator models, is spatially evaluated in three places. Based on assessment measures, the suggested technique beat models evaluated during the testing phase, indicating the potential for an accurate forecasting tool. Liu D. [21] has combined the K-means clustering algorithm with the RF algorithm to simulate PV generation in 3 areas. According to the findings, the proposed model has improved prediction accuracy and resiliency. Niu D. [22] developed a hybrid prediction model that incorporates RF, PSO, and BPNN. Empirical evaluations of PV power plants were performed to validate the effectiveness of the hybrid model, demonstrating that the suggested technique is a promising way for predicting the PV power output. Chiang P. [23] has proposed a new method that combines stationary wavelet transforms with random forest models. The suggested technique is resistant to varied forecast time horizons and has a lower prediction error, as demonstrated by practical findings utilizing sensor data from the on-campus microgrid. There are four portions to this work. The Random Forest algorithm is described in Sect. 2. The forecasting findings are explained in Sect. 3. The conclusion is presented in Sect. 4.

2 Description of Random Forest Algorithm 2.1 Data Collection The data utilized in this study comes from the National Aeronautics and Space administration’s webpage. The dataset is collected from January 1, 2017, to January 1, 2019. This data was transformed to an hourly average number. The signal would be smoother if these values were averaged, and the algorithms would be simpler to learn. We discovered that forecasting the data at an hourly rate is more accurate than forecasting data 30 min in advance. Outliers and incomplete values are eliminated from the solar radiation data. Therefore, we consider the following variables to be features of the machine learning algorithm: Date (or Length of Day), Time of day, Temperature, Pressure, Humidity, Wind Speed, and Direction. 2.2 Random Forest Algorithm Random forest is a machine learning technique developed by Leo Breiman and Adele Cutler [24] that mixes the output of numerous decision trees to produce a single outcome

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(Fig. 1). Its popularity is due to its ease of use and adaptability since it can handle both classification and regression issues. Random forest is based on the principle of bagging and uses the random bootstrap approach. The adjustment component employs the uniform sampling approach, and all prediction function weights are equivalent, allowing each prediction function to be calculated in parallel. The RF is not simple to over-fit, has exceptionally powerful anti-noise capability, and has an incredibly rapid computation speed thanks to randomized forests.

Fig. 1. Random forest technique

Assume the sample size is N at the start. The quantity of DT in the arbitrarily specified RF is k, with M being the sample feature dimension. The following are the detailed modeling steps: • Using bootstrap to create k DT from the actual data. • Choose m characteristics from the M dimension to serve as training for various decision trees. • The decision trees are not clipped and are allowed to develop to their full potential. • RF results are calculated by averaging the outcomes of each DT. 2.3 Evaluation Metrics To assess the effectiveness of the previously described forecast configurations, absolute percentage error (MAPE) and root mean square error (RMSE) is determined.

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The RMSE is the gap between the real and expected values of a variable. A lower RMSE shows higher predictability and is referred to as:   N    2 Xf − Xa (1) RMSE = 1 N i=1

The (MAPE), on the other hand, reflects the error-to-real-value ratio. N   Xf − Xa 1 MAPE = N |Xa | i=1

(2)

3 Results and Discussion The performance evaluators for the recommended approach are MAPE and RMSE. In this article, 80% of input is utilized to adapt the model, while the remaining 20% is used for assessment purposes. Figure 2 shows the basic correlation matrix, which is used to screen out unnecessary data and highlight the most significant characteristics in the collection.

Fig. 2. Pearson correlation map

Using the most correlated features, Figs. 3, 4, and 5 show Radiation as a function of Temperature, Humidity, and Pressure on various timeframes.

Solar Radiation Time-Series Prediction

Fig. 3. Radiation as a function of temperature on various timeframes

Fig. 4. Radiation as a function of pressure on various timeframes

Fig. 5. Radiation as a function of humidity on various timeframes

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The patterns seen in these figures are as follows: • Radiation throughput is related to higher temperatures: The observed behavior of radiation “tracking” temperature on the daily and weekly time frames, as well as a Pearson R-value of 0.63, supports the argument. • Humidity has a less substantial effect on radiation throughput: With a Pearson R-value of the magnitude of 0.12, humidity cannot be neglected. On the weekly timeframe, there is information proving a negative association between the two characteristics. • Pressure has a weak relationship with radiation, but it does have a strong relationship with temperature and humidity: Temperature, pressure, and humidity are all atmospheric factors, therefore it’s not surprising that they’re connected. • In this investigation, wind speed and direction are irrelevant: Windspeed and direction have a slight relationship with temperature, pressure, and radiation; however, we realize from engineering judgment that this is merely a correlation and not causality. Solar radiation also has a substantial relationship with the time of day, although this is self-evident. We include the time of day as a feature so that the algorithm can distinguish between day and night. The effectiveness of the proposed approach technique was compared to the linear regression method in this investigation. In Fig. 6, the effectiveness of the linear regression approach in the hourly solar power prediction was stated as 2.2 for the RMSE and 33.5% for the MAPE.

Fig. 6. Forecasted solar power data versus the original data using the linear regression model

Figure 7 illustrates the forecasted solar power data versus the real data using a Random Forest algorithm with feature selection. The RMSE and the MAPE values for the random forest algorithm are respectively 0.11 and 2.5%. Random forest regression is significantly more adaptable in this situation since the actual data connection isn’t even close to linear. There are still certain regions where the algorithm fails, but overall accuracy has increased substantially.

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Fig. 7. Forecasted solar power data versus the original data

3.1 Tuning the Algorithm In random forests, each tree in the ensemble is constructed using a sample selected with replacement from the training set. Furthermore, when dividing a node during tree building, the split that is picked is no longer the optimal split across all features. Rather, the optimal split among a randomly selected collection of attributes is picked. As a result of this unpredictability, the forest’s bias generally increases significantly, but owing to averaging, its variance also lowers, usually more than compensating for the increase in bias and producing an overall superior model. Regression-based on decision trees frequently produces low bias, high variance models, and is prone to overfitting. While the random forest technique is more resistant to bias and variation, it still has the risk of overfitting.

Fig. 8. Forecasted solar power data versus the original data using the tunned random forest algorithm

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Figure 8 illustrates the forecasted solar power data versus the real data using a tuned Random Forest algorithm with feature selection. The RMSE and the MAPE values for the random forest algorithm are respectively 0.08 and 1.6%. 3.2 Result Comparison For the random forest algorithm, Table 1 presents the hourly power prediction findings. In this table, while assessing the error values, the RMSE of 0.11 and the MAPE of 2.5% were found in the normal random forest algorithm. RMSE of 0.08 and MAPE of 1.6% where the random forest algorithm is tuned. RMSE of 2.2 and MAPE of 33.5% were found for linear regression configuration. Table 1. Results of the tested configuration’s solar power predictions Configuration

RMSE

MAPE

Linear regression

2.2

33.5

Random forest

0.11

2.5

Tuned Random Forest

0.08

1.6

Artificial Neural Network

0.1

3.1

Support vector machine

1.9

10.5

4 Conclusion Our research gives an evaluation of the Random Forest method for predicting solar energy. Forecasting precision is much improved when compared to linear regression and other techniques. According to our findings, fine-tuning the random forest algorithm’s parameters enhances forecast precision. This is seen by the MAPE number, which starts at 2.5% (standard random forest technique) and drops to 1.6% as the parameters are adjusted. The proposed study is designed to address the fundamental issue of PV energy forecasting.

References 1. Ahmed, A., Khalid, M.: A review on the selected applications of forecasting models in renewable power systems. Renew. Sustain. Energy Rev. 100, 9–21 (2019) 2. Lopes, F.M., Silva, H.G., Salgado, R., Cavaco, A., Canhoto, P., Collares-Pereira, M.: Shortterm forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal. Sol. Energy 170, 14–30 (2018) 3. Ueshima, M., Babasaki, T., Yuasa, K., Omura, I.: Examination of correction method of longterm solar radiation forecasts of numerical weather prediction. In: 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 113–117. IEEE, November 2019

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4. Miller, S.D., Rogers, M.A., Haynes, J.M., Sengupta, M., Heidinger, A.K.: Short-term solar irradiance forecasting via satellite/model coupling. Sol. Energy 168, 102–117 (2018) 5. Halabi, L.M., Mekhilef, S., Hossain, M.: Performance evaluation of hybrid adaptive neurofuzzy inference system models for predicting monthly global solar radiation. Appl. Energy 213, 247–261 (2018) 6. Hou, W., Xiao, J., Niu, L.Y.: Analysis of power generation capacity of photovoltaic power generation system in electric vehicle charging station. Electr. Eng. 4, 53–58 (2016) 7. Miao, S., Ning, G., Gu, Y., Yan, J., Ma, B.: Markov Chain model for solar farm generation and its application to generation performance evaluation. J. Clean. Prod. 186, 905–917 (2018) 8. Agoua, X.G., Girard, R., Kariniotakis, G.: Short-term spatio-temporal forecasting of photovoltaic power production. IEEE Trans. Sustain. Energy 9(2), 538–546 (2017) 9. Massidda, L., Marrocu, M.: Use of multilinear adaptive regression splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany. Sol. Energy 146, 141–149 (2017) 10. Zhang, B., Chan, J.C.C., Cross, J.L.: Stochastic volatility models with ARMA innovations: an application to G7 inflation forecasts. Int. J. Forecast. 36, 1318–1328 (2020) 11. Al-Hajj, R., Assi, A., Fouad, M.M.: Forecasting solar radiation strength using machine learning ensemble. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 184–188. IEEE, October 2018 12. Zhu, L., Wu, Q.H., Li, M.S., Jiang, L., Smith, J.S.: Support vector regression-based shortterm wind power prediction with false neighbors filtered. In: 2013 International Conference on Renewable Energy Research and Applications (ICRERA), pp. 740–744. IEEE, October 2013 13. Wang, J., Li, P., Ran, R., Che, Y., Zhou, Y.: A short-term photovoltaic power prediction model based on the gradient boost decision tree. Appl. Sci. 8(5), 689 (2018) 14. Almadhor, A.: Performance prediction of distributed PV generation systems using Artificial Neural Networks (ANN) and Mesh Networks. In: 2018 International Conference on Smart Grid (icSmartGrid), pp. 88–91. IEEE, December 2018 15. Rafati, A., Joorabian, M., Mashhour, E., Shaker, H.R.: High dimensional very short-term solar power forecasting based on a data-driven heuristic method. Energy 219, 119647 (2021) 16. Sowthily, C., Senthil Kumar, S., Brindha, M.: Detection and classification of faults in photovoltaic system using random forest algorithm. In: Bhateja, V., Peng, S.-L., Satapathy, S.C., Zhang, Y.-D. (eds.) Evolution in Computational Intelligence. AISC, vol. 1176, pp. 765–773. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5788-0_72 17. Massaoudi, M., Chihi, I., Sidhom, L., Trabelsi, M., Refaat, S.S., Oueslati, F.S.: Enhanced random forest model for robust short-term photovoltaic power forecasting using weather measurements. Energies 14(13), 3992 (2021) 18. Benali, L., Notton, G., Fouilloy, A., Voyant, C., Dizene, R.: Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew. Energy 132, 871–884 (2019) 19. Srivastava, R., Tiwari, A.N., Giri, V.K.: Solar radiation forecasting using MARS, CART, M5, and random forest model: a case study for India. Heliyon 5(10), e02692 (2019) 20. Prasad, R., Ali, M., Kwan, P., Khan, H.: Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Appl. Energy 236, 778–792 (2019) 21. Liu, D., Sun, K.: Random forest solar power forecast based on classification optimization. Energy 187, 115940 (2019) 22. Niu, D., Wang, K., Sun, L., Wu, J., Xu, X.: Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: a case study. Appl. Soft Comput. 93, 106389 (2020)

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23. Chiang, P.H., Chiluvuri, S.P.V., Dey, S., Nguyen, T.Q.: Forecasting of solar photovoltaic system power generation using wavelet decomposition and bias-compensated random forest. In: 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech), pp. 260–266. IEEE, March 2017 24. Cutler, A., Cutler, D.R., Stevens, J.R.: Random forests. In: Ensemble Machine Learning, pp. 157–175. Springer, Boston (2012). https://doi.org/10.1007/978-0-387-84858-7_15

Analysis of Wind Turbine Vibration Faults to Improve Predictive Maintenance Strategies Aziza Nana(B) , Lalouli Abderrezzak, Kamilia Mounich, and Aicha Wahabi Laboratory of Physical Sciences, Faculty of Sciences, Hassan II University, Casablanca, Morocco [email protected]

Abstract. The use of renewable energies is inevitable due to many facts: climate change, pollution, and the increasing cost of fossil fuels. Investment in natural resources and especially in wind energy help increase the production of electricity. Research is very active in this direction to optimize the efficiency of wind turbines and correct their defects. One of these problems is the phenomenon of vibration, which affects productivity and reduces efficiency. This paper presents different problems caused by vibrations in wind turbines. We will model and simulate the tower: the most critical part of the wind power system. The developed model uses finite element software. Then we will present the results that will help us define and apply a relevant predictive maintenance strategy. Keywords: Wind turbine · Vibration · Predictive maintenance

1 Introduction Wind energy has become a primary component of the green economy. The increasing installation of wind turbines requires monitoring to communicate and collect data. Predictive maintenance needs an intuitive database to plan, forecast, and optimize the system. Vibration analysis is necessary for predictive maintenance [1]. The development and the implementation of new mathematical and statistical approaches for predictive maintenance depend on vibration analysis results. The Strengths generated by the vibrations increase during the functioning of the wind turbine and can cause the premature shutdown of the entire system. We define modal characteristics of vibration phenomena by proper modes, vectors, and frequencies. It is relevant to know the causes and predict the consequences to characterize the anomalies and carry out the study of the dynamic behavior when vibrations appear on wind turbine [2]. The structure of this paper is as follows: Sect. 2 presents the different vibration problems in wind turbines. Section 3 presents the results of a vibration tower simulation. Finally, the conclusion is in Sect. 4. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 669–674, 2022. https://doi.org/10.1007/978-3-031-02447-4_69

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2 Vibration Issues Wind energy has become a real asset for world energy resources because of its inclusion in environmental protection and its low cost in terms of electricity production. The deterioration of wind turbines due to vibrations poses several problems, which affect productivity and maintenance costs hence the need for monitoring, diagnosis, and compliance with standards. Wind turbines generate enormous vibrations due to the high towers of turbines and the heavy wind. The blades [3] move at regular intervals [4], generating vibration frequencies that affect the gearbox [5]. If these frequencies are close or proportional, there is a risk that a resonance phenomenon will occur, which may reduce the lifetime of the wind turbine [6]. Vibration problems are due to unbalance, misalignment, shaft deterioration, wear, loosening, wind excitation, earthquakes, and waves (for offshore wind turbines) [7]. Figure 1 describes different components of the wind turbine:

Fig. 1. Components of wind turbine

The various issues of vibration can be summarized as follows [8]: – – – –

Excessive loads and tower/nacelle vibration Blade vibration Vibration in the drivetrain Operational noise due to vibration

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– Vibration control in the industry These factors reduce efficiency and require the implementation of a vibration control strategy. The maintenance of wind turbines has always been the subject of many studies based on vibration measurements and associated signal analysis to avoid shutdowns. Other available information (sensors, staff experience, etc.) is necessary to plan maintenance interventions. We can control wind turbine vibration by designing, detecting, and optimizing different predictive methods. The structure of the wind system exposes to external dynamic excitations such as waves (offshores), wind, and seismic loads. Excessive vibrations lead to structural fatigue damage and even cause disasters.

3 Simulation and Results 3.1 Equation of Motion in the Tower After studying the different problems caused by the vibrations of the wind turbine system, we will now focus on the most critical component (the tower): modeling and simulating it. The wind turbine tower is a beam of constant and circular sections. We define it by its two ends containing nodes. A node has three translational and three rotational displacements in space. Figure 2 presents the complete system with the dimensions used in our simulation. The equations of motion of the beam are [9]:     ∂ 2 ω(x, t) ∂ ∂ω(x, t) ∂2 EI + P(x) (x) ∂x2 ∂ 2x ∂x ∂x   ∂ ∂ ω(x, ¨ t) + mω(x, ¨ t) = f (x, t) (1) mr 2 (x) − ∂x ∂x2 f (x, t) = f0 (x)exp[iωt] EI

2¨ t) ∂ 2 ω(x, t) ∂ 4 ω(x, t) 2 ∂ ω(x, + P − mr + mω(x, ¨ t) = f0 (x)exp[iωt] 4 2 2 ∂x ∂x ∂x

W (ξ) = exp{λξ }

(2) (3) (4)

The finite element method help simplify the modeling and the resolution of the vibrations frequencies problem. To obtain vibration frequency periods of the tower structure, we use ABAQUS. For dynamic modal analysis, we apply some perturbation movement to show the displacement and acceleration of the tower. The curves reflect the influence of vibrations on the structure. The results of our simulation are below (Fig. 5):

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Fig. 2. Dimensions of the tower

Fig. 3. Deformation of the tower under vibration conditions

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Fig. 4. Acceleration of the tower movement caused by vibrations

Fig. 5. Displacement of the tower movement caused by vibrations

We visualize in Fig. 3 the deformation of the tower that reflects the negative impact on the wind structure. In Fig. 4, we notice that for a short duration of the vibratory movements, the presence of a peak of resonance amplitude varies between 4 and 5 m2 /s. These amplitudes are high at the beginning. Therefore, the amplitudes of vibration are prominent from the beginning of the perturbation. In curve 5, the displacement amplitudes are high. The displacements are inadmissible for the stability of the structure. A study on the balancing of the tower is necessary to attenuate the vibrations and the noises.

4 Conclusion Vibration analysis is one of the most used techniques for predictive maintenance of wind turbines. However, accurate identification of the presence of a vibration fault is difficult in practice, especially when the frequency is still in the early stages and the signal/noise

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ratio is low. Monitoring wind power system exposed to vibration effects requires an intelligent method to analyze the vibration data. In this manuscript, we simulate the tower submitted to disturbing vibrations to visualize the deformations, the level of displacement, and acceleration of those deformations according to the time. On the tower, the vibratory phenomenon can lead to instability or damage the whole wind system. It can produce rupture or fatigue, friction between the other components, damage of the bearings, a sound noise, etc. The critical speed accelerates this phenomenon. The dynamic study of the tower is essential to reduce the maintenance costs and to have an effective predictive strategy for maintenance.

References 1. Laayati, O., Bouzi, M., Chebak, A.: Smart energy management system: SCIM diagnosis and failure classification and prediction using energy consumption data. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 1377–1386. Springer, Cham (2021). https://doi. org/10.1007/978-3-030-73882-2_125 2. Kanyiki, T.: Simulation par la Méthode des Eléments Finis du Comportement Vibratoire d’un Rotor. Incert Fiabilité Systèmes Multiphysiques, vol. 2, no. 2, December 2018. https://www.openscience.fr/Simulation-by-the-Finite-Element-Method-of-the-Vib ratory-behaviour-of-a-Rotor. Accessed 9 Jan 2022 3. Zhao, X., Weiss, G.: Suppression of the vibrations of wind turbine towers. IMA J. Math. Control Inf. 28(3), 377–389 (2011) 4. Staino, A., Basu, B., Nielsen, S.R.K.: Actuator control of edgewise vibrations in wind turbine blades. J. Sound Vib. 331(6), 1233–1256 (2012) 5. Yang, S., Li, W., Wang, C.: The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network. In: 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, pp. 1327–1330. IEEE (2008). http://ieeexplore.ieee.org/document/ 4580221/. Accessed 31 May 2021 6. Yildirim, T., Ghayesh, M.H., Li, W., Alici, G.: A review on performance enhancement techniques for ambient vibration energy harvesters. Renew. Sustain Energy Rev. 71, 435–449 (2017) 7. Saavedra, R.C., Samanta, B.: Noise and vibration issues of wind turbines and their impact – a review. Wind Eng. 39(6), 693–702 (2015) 8. Xie, F., Aly, A.-M.: Structural control and vibration issues in wind turbines: a review. Eng. Struct. 210, 110087 (2020) 9. Adhikari, S., Bhattacharya, S.: Dynamic analysis of wind turbine towers on flexible foundations. Shock Vib. 19(1), 37–56 (2012)

Integral Sliding Mode Control for DFIG Based Wind Energy Conversion System Using Ant Colony Optimization Algorithm Hasnae Elouatouat1(B) , Ahmed Essadki1 , Tamou Nasser2 , and Hamid Chojaa3 1 ERERA, Research Center in Sciences and Technologies of Engineering and Health (STIS),

Higher National School of Arts and Crafts (ENSAM), Mohammed V University in Rabat, Rabat, Morocco [email protected] 2 Embedded Systems Engineering Department, Higher National School of Computer Science and Systems Analysis (ENSIAS), Mohammed V University in Rabat, Rabat, Morocco 3 Industrial Technologies and Services Laboratory, Higher School of Technology (EST), Sidi Mohamed Ben Abdellah University in Fez, Fez, Morocco

Abstract. This paper presents a robust control method for controlling the powers of a wind turbine based on a doubly fed induction generator (DFIG) using an integral sliding mode control (ISMC). The sliding gain of the proposed controller is optimized through an artificial intelligence algorithm called ant colony optimization (ACO). The proposed controller is compared to the conventional PI controller in which the ACO is also used to tune the kp and ki gains (ACO_PI). The simulation results indicate that the proposed ACO_ISMC controller has high accuracy, fast response time, and minimal integral time absolute error (ITAE) compared to the ACO_PI controller. Keywords: Doubly fed induction generator (DFIG) · Integral sliding mode control (ISMC) · Ant colony optimization algorithm (ACO)

1 Introduction The industrial revolution has made energy the principal power of production and development for human civilization. On the one hand, the natural reserves of fossil fuels are depleted. On the other hand, the combustion of fossil fuels contributes significantly to the environmental problems related to global warming. In these contexts, several countries have oriented towards renewable energy solutions including, wind and solar energy. Currently, wind energy conversion systems are of great interest in many developed countries [1]. Due to their advantages, doubly-fed induction generators (DFIGs) are currently the leading type of wind energy conversion system [2]. It has important features such as long life, rigid structure, and lower cost of the power converters, which represent around 25 to 30% of the rated power of the wind turbine [3–5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 675–684, 2022. https://doi.org/10.1007/978-3-031-02447-4_70

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To obtain a high quality of electrical energy from a wind system using DFIG, it is necessary to apply control techniques that allow the control of the power generated by the DFIG stator. However, this kind of machine demands complex protection and control systems. Yet, conventional control approaches such as Proportional-Integral (PI) control are susceptible to several problems of robustness since most of the electrical components present nonlinear behaviors, and the dynamic features of the grid coupled wind energy conversion system (WECS) lead to both parameter fluctuations and external perturbations, making it necessary to have a highly robust nonlinear control strategy to overcome these drawbacks. This paper aims to present a robust sliding mode control with integral action (ISMC) in which the gain of this controller is optimized using an artificial intelligence algorithm known as the ant colony optimization algorithm ACO. The proposed controller was simulated and verified using Matlab software to ensure its performance.

2 Wind Energy Conversion System Model Figure 1 presents the schematic diagram of a wind turbine system based on the DFIG. The wind turbine is coupled to the DFIG via a gear system. The stator of the DFIG is directly coupled to the grid, whereas the wound rotor is coupled to the grid via a frequency converter, made up of a rotor-side converter and a grid-side converter [6, 7].

Fig. 1. Schematic diagram of the DFIG based wind turbine system.

2.1 Wind Turbine Modeling The wind turbine uses the force of the wind to generate mechanical energy. The wind turns the blades, which make a shaft turn inside the nacelle. Then the gearbox increases the speed to that of the generator, and the generator transforms the mechanical energy of the wind into electrical energy [8]. The expression of power generated by the wind turbine is expressed in Eq. (1): Pm =

1 ρπ R2 V 3 CP (λ, β) 2

(1)

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where: ρ is the air density around the rotor, R is the turbine’s rotor radius, V is the wind speed, and C p is the power coefficient, which depends on the blade pitching angle β and tip speed ratio λ. this latter one is given by the following formula: λ=

t R V

(2)

where t presents the wind turbine’s angular speed. In this paper, the variations of C p (λ, β) are modeled by the following expression.    −21   CP (λ, β) = 0.5 116 λi − 0.4β − 5 exp λi + 0.0068λ (3) 1 1 0.035 λi = λ+0.08β − β 3 +1

2.2 Doubly Fed Induction Generator Modeling (DFIG) The dynamic equations for the DFIG voltages and active and reactive powers in the dq reference Park are expressed as follows [8]. Voltage equations: Vsd = Rs isd +

d φsd − ωs φsq dt

(4)

Vsq = Rs isq +

d φsq + ωs φsd dt

(5)

Vrd = Rr ird +

d φrd − (ωs − ωr )φrq dt

(6)

Vrq = Rr irq +

d φrq + (ωs − ωr )φrd dt

(7)

The active and reactive power equations: Ps =

3 (V ids + Vqs iqs ) 2 ds

(8)

Qs =

3 (V ids − Vds iqs ) 2 qs

(9)

where: V sd , V sq , V rd , and V rq are the voltages of the stator and rotor in the dq axes, respectively. isd , isq , ird , and irq are the currents of the stator and rotor in the dq axes, respectively. φsd , φsq , φrd , and φrq are the fluxes of the stator and rotor in the dq axes, respectively. Rs and Rr represent the resistances of the stator and the rotor, ωs and ωr represent the pulsation of the stator and rotor. The electromagnetic torque produced by the DFIG is expressed as follows: Tem =

 3 M P φsq ird − φsd irq 2 Ls

(10)

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where P is the generator pole numbers, M and L s are Mutual, and Stator inductances. In the vector control of the stator flux orientation, a reference frame related to the rotating field is required. The d axis of the Park reference frame would be oriented along with the stator flux. The frequent coupling of the machine to a large electrical grid of constant voltage and frequency justifies this choice [9]. If the stator resistance is ignored, φsq = 0 and φsd = φs , we obtain the following result  Vsd = 0 (11) Vsq = ωs φs = Vs The equations that represent the resulting voltages, as well as the stator active and reactive power, are expressed by Eqs. (12), (13), (14), and (15), respectively: d ird − (ωs − ωr )σ Lr irq dt

(12)

d M irq + (ωs − ωr )( φs + σ Lr ird ) dt Ls

(13)

Vrd = Rr ird + σ Lr Vrq = Rr irq + σ Lr

3 M Ps = − Vs irq 2 Ls   φs 3 M − ird Qs = Vs 2 Ls Ls where σ = 1 −

M2 Ls Lr

(14) (15)

is the DFIG dispersion coefficient.

3 Integral Sliding Mode Control Recently, sliding mode control is widely used as a robust controller of nonlinear systems. This is owing to its simple implementation, as well as its robustness against the nonlinearity of the system and its ability to handle uncertainties and external disturbances. The concept of this type of control is to bring the state trajectory back to the sliding surface and slide it to the equilibrium point [10, 11]. The sliding mode control generally consists of two parts, a continuous part, called the equivalent control U eq , and a discontinuous part called the switching control U n . U = Ueq + Un

(16)

Based on the integral sliding mode theory, the sliding mode surface can be indicated by the equation below:    ∞ d n−1 S(t) = δ + e(t) + Ki e(t)dt dt 0

(17)

where: e is the error on the controlled variable, δ presents a positive coefficient, n is the system order, and k i is a sliding gain.

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The integral sliding mode control adds an integral component to the classic sliding mode control technique to reduce the chattering phenomenon produced by the discontinuous control signal and enhance the performance of the sliding mode control. In this work we set δ = 1 and n = 1, then the designed sliding surface is expressed in (18).  ∞ e(t)dt (18) S(t) = e(t) + Ki 0

From Eqs. (14) and (15), it is clear that the stator active power is linked to the quadrature component of the rotor current, and the stator reactive power is linked to the direct component of the rotor current. The error function between the references and the measured rotor currents is presented in the equation below.  ∗ −I ed = Ird rd (19) ∗ −I eq = Irq rq Consequently, the derivative of the sliding surfaces is represented in Eq. (20). ⎧

⎨ S(I ˙ rd ) = I˙ ∗ − Vrd − Rr ird + (ωs − ωr )irq + Ki e rd σ Lr

σ Lr (20) ∗ − Vrq − Rr i − (ω − ω )( M φ + i ⎩ S(I ˙ rq ) = I˙rq s r σ Lr Ls s rd + Ki e) σ Lr σ Lr rq The sliding surface becomes null in the steady-state and the slipping mode, therefore the derivative of the surface function and the discontinuous control become null. Consequently, the equivalent commands can be determined by the equation below.  eq ∗ + R i − (ω − ω )i σ L − K e Vrd = σ Lr I˙rd r rd s r rq r i (21) eq ∗ + R i + (ω − ω )( M φ + σ L i ) − K e Vrd = σ Lr I˙rq r rq s r Ls s r rd i The Lyapunov function ensures the sliding mode control’s stability. This function is defined by: 1 2 S 2 To ensure the convergence of V, the derivative of Eq. (22) must be negative: V =

V˙ = S S˙ = S(˙e + Ki e) < 0

(22)

(23)

The principal aim of the switching control is to check the attractiveness conditions. Therefore, the discontinuous part is given by the Eq. (24):    ∞ Un = ε Sat e(t) + Ki e(t)dt (24) 0

where: Sat is the saturation function, and 1 is a constant that is chosen to ensure stability and rapidity and to overcome the perturbations that can affect the system. Hence, as long as the constant 1 > 0 is verified, V˙ < 0 can be assured. This proposed control strategy contains an indeterminate parameter, which is the sliding gain ki. In this work, the ant colony optimization (ACO) algorithm is used to optimize this parameter.

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4 Ant Colony Optimization (ACO) The ant colony optimization algorithm (ACO) is a set of probabilistic meta-heuristics developed by Marco Dorigo in 1992. This algorithm is inspired by the social behavior of real ants. These ants communicate with the other ants through a chemical messenger known as a pheromone. This pheromone that the ants deposit evaporates, causing the least reinforced paths to eventually disappear, and leading the ants to travel to nodes linked by short ones with high concentrations of pheromone, thus choosing the shortest path possible [12, 13]. To orient the ants to choose their solution to the problem, a probability rule is used. Equation (25) shows the probability rule between two nodes, I and j. ⎧ α β ⎨  [τij(t)] [ηij ]  , j ∈ J k α [η ]β i k [τ (t)] k il il (25) Pij = l∈Ji ⎩ 0 ,j ∈ / Jik where: α and β represent the parameters that control the importance of the path intensity, τ ij (t) and the visibility ηij , respectively. The quantity of pheromone τ kij deposited by each ant depending on the quality of the solution found is given by.  Q , (i, j) ∈ T k (t) k (26) τ ij (t) = Lk 0, (i, j) ∈ / T k (t) where: Q is a constant, T k (t) is the path of the ant k at iteration t, and L k is the length of the tour. Finally, to avoid local minima, an update of the paths is performed using the equation below: τij (t + 1) = (1 − ρ)τij (t) + τ ij (t)

(27)

m k where: τ ij (t) = k=1 τ ij (t), m presents the number of ants, and ρ presents the pheromone evaporation factor. The ant colony optimization algorithm (ACO) is proposed in this work to optimize the parameters of the gains ki and kp for the proportional-integral (PI) controller, and the sliding gain ki. The objective function f to minimize has been chosen as the integral time absolute error ITAE, which is defined by the following equation:  ∞ (28) t|e(t)|dt f = ITAE = 0

According to the above analysis, this algorithm was developed as shown in the flowchart below (Fig. 2).

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Fig. 2. The flowchart of the ant colony optimization algorithm (ACO).

5 Simulation Result and Discussion To validate the performance of the ACO_ISMC controller, MATLAB/Simulink software is used for simulation validation. The proposed controller ACO_ISMC is simulated and compared to the ACO_PI controller. The parameters of the DFIG, wind turbine, and ant colony optimization, are listed in Tables 2 and 3 in the Appendix. The wind speed used in the simulation is in the range of 9.5 m/s to 14.5 m/s as shown in Fig. 3.

Fig. 3. Wind speed profile.

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Figure 4 and 5 show the simulation results of active and reactive power, respectively. The active power is negative, implying that the DFIG behaves like a generator. The reactive power is zero, which means that the operation with a unit power factor is verified. Both figures show that the active and reactive powers follow their reference values, but the proposed controller converges faster than the ACO_PI controller which oscillates near the equilibrium position. Therefore, the proposed controller ACO_ISMC, has higher performance than the ACO_PI controller, with slower response time, minimal overshoot of power curves, and fewer oscillations and disturbances.

Fig. 4. Simulation result of the stator active power Ps.

Fig. 5. Simulation result of the stator reactive power Qs.

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Table 1 presents the numerical values of the objective function (fitness) for the three controllers, PI, ACO_PI, and ACO_ISMC. Based on the obtained values, it is apparent that the error obtained for the conventional method is higher than those obtained by the methods based on the ACO technique, and that the ITAE is minimal when using the proposed method ACO_ISMC compared to ACO_PI. Table 1. Optimal gain and objective function results summary Controllers

ITAE

Conventional PI

7.818e+004

ACO_PI

3.877e+004

ACO_ISMC

3.746e+004

6 Conclusions In the present paper, the turbine and DFIG models are developed, then optimization of the ISMC controller with an ACO algorithm is proposed to control the active and reactive powers of the DFIG. A simulation test was performed to validate the performance of the proposed controller, and a comparison with the ACO_PI controller showed that the ACO_ISMC controller has good performance with high precision, fast response time, and minimal ITAE compared to the ACO_PI controller.

Appendix

Table 2. Wind turbine and DFIG parameters Parameter

Value

Parameter

Value

Number of blades

3

Rotor resistance Rr

0.021 m

Radius of the wind R

28 m

Rotor inductance Lr

0.0137 m

Gain multiplier G

46

Stator inductance Ls

0.0137 m

Friction coefficient f

0.0024

Mutual inductance M

0.0135 m

Moment of inertia J

1 kg × m2

Pole pairs P

2

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Value

Pheromone degree α

0.8

Visibility parameter β

0.2

Iterations number

7

Number of ants k

30

References 1. Abdelli, R., Rekioua, D., Rekioua, T., Tounzi, A.: Improved direct torque control of an induction generator used in a wind conversion system connected to the grid. ISA Trans. 52, 525–538 (2013) 2. Li, P., Xiong, L., Wu, F., Ma, M., Wang, J.: Sliding mode controller based on feedback linearization for damping of sub-synchronous control interaction in DFIG-based wind power plants. Int. J. Electr. Power Energy Syst. 107, 239–250 (2019) 3. Ebrahimi, F.M., Khayatiyan, A., Farjah, E.: A novel optimizing power control strategy for centralized wind farm control system. Renew. Energy 86, 399–408 (2016) 4. Torkaman, H., Keyhani, A.: A review of design consideration for Doubly Fed Induction Generator based wind energy system. Electr. Power Syst. Res. 160, 128–141 (2018) 5. Ahmed, A.H., Mohd, H.A.: Comparison among series compensators for transient stability enhancement of doubly fed induction generator based variable speed wind turbines. IET Renew. Power Gener. 10(1), 116–126 (2015) 6. Boyu, Q., Hengyi, L., Xingyue, Z., Jing, L., Wansong, L.: Low-voltage ride through techniques in DFIG-based wind turbines: a review. Energies (2020) 7. Muller, S.M., Deicke, M., De Doncker, R.W.: Doubly fed induction generator systems for wind turbines. IEEE Ind. Appl. Mag. 8, 26–33 (2002) 8. Tanvir, A.A., Merabet, A., Beguenane, R.: Real-time control of active and reactive power for Doubly Fed Induction Generator (DFIG)-based wind energy conversion system. Energies 8, 10389–10408 (2015) 9. Elouatouat, H., Essadki, A., Nasser, T.: Control of a Doubly-Fed Induction Generator for wind energy conversion systems. In: 4th International Conference on Electrical and Information Technologies (2020) 10. Fdaili, M., Essadki, A., Nasser, T.: Comparative analysis between robust SMC & conventional PI controllers used in WECS based on DFIG. Int. J. Renew. Energy Res. 7, 2151–2161 (2017) 11. Linyun, X., Penghan, L., Hao, L., Jie, W.: Sliding mode control of DFIG wind turbines with a fast exponential reaching law. Energies 10, 1788 (2017) 12. Mokhtari, Y., Rekioua, D.: High performance of maximum power point tracking using ant colony algorithm in wind turbine. Renew. Energy 126, 1055–1063 (2018) 13. Gu, B., Li, X., Qiu, D., Zhang, L.: Study on PI parameters dynamic tuning based on ant colony algorithm for doubly-fed wind turbines. Int. J. Control Autom. 7, 327–340 (2014)

An Improved Supervised Fuzzy PI Collective Pitch Angle Control for Wind Turbine Load Mitigation Seif Eddine Chehaidia1(B) , Hamid Kherfane1 , Hakima Cherif2 , Abdallah Abderrezak1 , Hamid Chojaa3 , Laila Kadi4 , Boubekeur Boukhezzar5 and Mohammed Taoussi3

,

1 Research Laboratory of Industrial Risks, Non Destructive Control and Operating Safety,

University of Badji Mokhtar-Annaba, Annaba, Algeria [email protected] 2 Laboratoire de Génie Electrique, Biskra (LGEB), Department of Electrical Engineering, University of Mohamed Khider Biskra, Biskra, Algeria 3 Laboratory of Technologies and Industrial Services, Higher School of Technology, Sidi Mohamed Ben Abdellah University, 30000 Fez, Morocco [email protected] 4 SECNDCM Team, L2MC Laboratory, ENSAM, Moulay Ismail University, Meknes, Morocco 5 Laboratoire d’Automatique et de Robotique de Constantine, Mentouri Brothers University, Constantine, Algeria [email protected]

Abstract. Wind energy is a promising energy vector, attracting the consideration of industrialists and scientists in the light of the current energetic-environmental challenges. Hence the need to develop control laws to extract maximum profit from wind turbines. In this context the following paper addresses the problem of wind turbine power limitation in full load region using a novel supervised fuzzy PI pitch angle control. The proposed controller schedule proportional and integral gains based on real-time measurement of pitch angle and its rate. The proposed control has been validated on Controls Advanced Research Turbine (CART) using Fatigue, Aerodynamics, Structures and Turbulence (FAST) simulator by considering 9 DOFs. The obtained results confirm that the proposed control law provides better power and speed regulation with best load reduction and damping to the flexible modes of wind turbine than baseline PI pitch controller. Keywords: Wind turbine · Collective pitch control · Fuzzy · PI · FAST

1 Introduction As a result of the large-scale environmental damage caused mainly by the overexploitation of fossil fuels, the world has realized the need for a generalized energy transition towards renewable resources [1, 2]. Indeed, by late 2019, global wind development has touched a cumulative installed capacity of 650 [GW], with an annual growth rate of 19% © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 685–695, 2022. https://doi.org/10.1007/978-3-031-02447-4_71

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over the previous year. Offshore installation is taking 10% growth share with 6 [GW] added to global capacity in 2019 [3]. Horizontal axis wind turbines are the most common for commercial purposes, due to their simple design and highly aerodynamic efficiency. According to their speed, two types of horizontal axis wind turbines can be distinguished, which are, fixed and variable speed wind turbines. The latter are designed in order to operate under various wind speed conditions. One can distinguish three operating zones, namely: • Region I: below cut-in wind speed. • Region II (Partial Load): between cut-in wind speed and rated wind speed. • Region III (Full Load): between rated wind speed and cut out wind speed as shown in Fig. 1.

Fig. 1. Power curve of CART wind turbine [4]

When the wind speed is below its nominal value, the objective is to maximize power capture by acting on electromagnetic torque in order to ensure an optimal rotor speed tracking. While for high wind speeds, the pitch actuator is used to reduce the captured power and maintain it around its nominal value [4]. The control strategy consists of a feedback policy, commonly implemented controller corrects the measured error between rated power and the measured one [5–7], or, equivalently, the rotor speed error [8, 9]. Conventional PI controller is the most commonly used pitch controller. Unfortunately, PI is not robust against wind speed variation [10]. To compensate for the non-linearity effects, parameter variations or ill known dynamic behavior, various techniques are used, including PI gain scheduling [6, 10], which consists of correcting gains via an adaptive non-linear relationship between pitch angle and rotor speed in order to reduce the slip induced when the operating point is changed. One can see a modified fuzzy PI gain scheduling pitch angle control, in which authors substitute the nonlinear relationship with a fuzzy inference system. The main limitation of the proposed controller is that the last is a single input system, don’t taking into consideration the interdependency of proportional and integral actions. fuzzy

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PI gain scheduling [11]. Fuzzy logic control [6, 7, 12], obtained results show that the fuzzy command gives a better result compared to PI controller and considerably reduces structural loads. However, its main disadvantage in the proper choice of rules and the form of membership functions, but it remains one of the most widely used techniques, as it does not require knowledge of the mathematical model and works perfectly well with unknown systems. Learning-based control such as neural networks [13], ANFIS [14], the disadvantage of learning-based techniques is that they require the acquisition of quality data, containing sufficient information on the dynamics of the system, in order to achieve good results. As a solution to overcome the limitation of baseline PI, PI gain scheduling controller being described in [10] and the fuzzy gain scheduling proposed by [9], the present paper proposes a new supervised fuzzy PI collective pitch angle control for wind turbine. The rest of the paper is structured as follows, the system modeling is briefly described in Sect. 2. The proposed controller is detailed in the Sect. 3. Then main results are discussed in Sect. 4. Finally, drawn conclusion are presented in the last section.

2 Wind Turbine Modeling Wind turbines are mechatronic systems that convert the captured kinetic energy of the wind into electric energy. Typically, the aerodynamic torque of a wind turbine is given by (1), such as [15]: Pa 1 ρπ R2 Cp (λ, β)v2 = (1) ωr 2λ   where Pa [W] is the wind power, ρ kg · m−3 is the air density, R[m] is rotor radius   and v m · s−1 denotes wind speed. The nonlinear coefficient Cp (λ, β) depending on tip-speed ratio λ and blade pitch angle β[◦ ] shown in Fig. 2 given by a lookup table developed by National Renewable Energy Laboratory (NREL) [2, 16]. Ta =

Fig. 2. Power coefficient contours of CART wind turbine

The one mass model is expressed as follows: Jt ω˙ r = Ta − Dt ωr − Tg

(2)

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  = Jt = Jr + n2g Jg denotes the turbine total inertia where Jt kg · m2   −1 −1 Dt N · m · rad · s = Dt = Dr + n2g Dg is the turbine total external damping and Tg [N · m] = Te ng is generator torque in the rotor side. The generator is modeled by a first-order system, defined by the following differential equation [4]: 1 1 ref Te T˙ e = − Te + τTe τTe

(3) ref

where τTe = 0.01 [s] is the time constant of the generator and Te is the reference electromagnetic torque [4]. Pitch actuators for wind turbines can be either hydraulic or electric motors. It is necessary to alter the pitch angle of a blade in order to regulate the produced power and keep it on its rated value [6]. A first-order actuator model is adopted to represent the actual electric motor drive installed in CART. 1 1 β˙ = − β + βref τ τ

(4)

where τβ is a time constant that depends on pitch actuator.

3 Supervised Fuzzy PI Collective Pitch Angle Control One of the problems with the variable-fixed speed control is the one reported by A.D. Wright in [10]. In fact, when the operating point changes the performance of the controller decreases significantly, because each operating point requires particular gains. In order to overcome this problem, authors have proposed a PI gain sequencing technique, which allows the gain to be corrected from a nonlinear relationship based on the measurement of the pitch angle, thereby solving the problem of slipping when the operating point is changed. In order to improve this gain scheduling technique, we propose a new supervised fuzzy PI pitch angle control, that is conceptually in accordance with the PI gain scheduling controller proposed by the reference herein [10], although the adaptation is provided by a fuzzy inference system (FIS) that allows the PI gains correction based on the real-time measurement of pitch angle its rate. The proposed control law, which bloc is diagramed in Fig. 3 is given as follows: ⎛ ⎞ t   (5) βd = KP .KβP ε + ⎝KI .KβI ε(τ ) d τ ⎠ 0

where ε = ωr − ωref is the rotor speed error and KP , KβP , KI , KβI are respectively proportional gain, fuzzy proportional gain corrector, integral gain and its corrector. The main improvements of the proposed Fuzzy PI controller are the following: • Gain correction using human expertise, without the need for complex mathematical relationships.

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• Consideration of pitch rate, resulting in reduced loads on pitch actuators. • Each controller action is corrected separately, taking into account gains interdependency. • The proposed controller does not need the aerodynamic sensitivity depending on aerodynamic torque measurement. Even if theoretically the aerodynamic torque is easily calculated, its measurement for field wind turbines remains difficult. The principle of fuzzy logic control is to design a controller emulating the human reasoning in its behavior. This type of controller has already proved its effectiveness against strongly nonlinear, or even mathematically unknown, systems. This is achieved by assigning linguistic variables to fuzzy subsets, making it easier to set up fuzzy rules deduced from expertise.

Fig. 3. Bloc diagram of the proposed supervised fuzzy PI collective pitch angle controller

Table 1. Fuzzy rules Gains

Kβp

β

N

ZO

P

N

ZO

P

N

S

B

B

B

S

S

ZO

B

B

B

S

B

S

P

B

S

B

S

B

S

dβ/dt

KβI

Regarding the proposed supervised fuzzy PI collective pitch angle controller, we opted to reduce the number of Membership Functions (MFs) for reasons of stability and practical implementation. The fuzzy MFs for inputs, namely, pitch angle β and pitch rate dβ/dt being shown in Fig. 4a, b, are linguistically labeled as follows: N: Negative, P: Positive, ZO = Zero, S = Small and B = Big. During preliminary tests of the proposed controller, we observed that using the triangular membership function gives more sensitivity as inputs approached zero. The fuzzy range [−1,1] with a symmetric distribution to zero. For outputs KβP KβI , sigmoidal function labeled S = small and a Z-shaped function labeled B = big are used for each one, as one can see in Fig. 4c, d. The proposed FGSPI is a Mamdani type fuzzy inference system managed on a knowledge

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basis using the fuzzy rules of the IF-THEN type, given by (6). Table 1 summarizes the 18 rules scheduling the fuzzy output. IF β is N and d β/dt is N, THEN, Kβp is B and KβI is S

(6)

The output of the Mamdani type fuzzy inference system consists of several weighted fuzzy sets. Each component of the weight vector corresponds to a specific rule. In order to transition from the fuzzy domain to the numerical domain, the center of gravity method is used. Accordingly, the output is equal to the weighted sum of the numerical values of the corresponding labels, as follows: m 

CG =

μi ci

i=1 m 

(7) μi

i=1

where ci is the center of gravity of the ith MFs μi and M the number of fuzzy rules. After tuning of the proposed FIS, the obtained fuzzy surface is shown in Fig. 4e, f.

Fig. 4. MFs and fuzzy surfaces, a) β, b) dβ/dt, c)Kβp , d) KβI e) Fuzzy output Kβp , f) Fuzzy output KβI

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4 Result and Discussions In order to highlight the effectiveness of the proposed supervised fuzzy PI controller, the latter were validated by the FAST simulator [17] shown in Fig. 5, using the parameters of the CART shown in Fig. 6, located located at National Wind Technology Center (NWTC). Its characteristics are described in [10, 18]. Simulations were performed by enabling first flapwise blade mode (2 × 1 DOF); second flapwise blade mode (2 × 1 DOF), first edgewise blade mode (2 × 1 DOF), rotor-teeter (1 DOF), drivetrain rotational-flexibility (1 DOF), generator mode (1 DOF). All simulations were carried out using the wind speed profile shown in Fig. 7a. It has been performed using Turbsim [19].  It consists of 10 min turbulent wind series,  with a mean value of v = 18 m · s−1 at hub-height. The turbulence is introduced via Kaimal spectra with an intensity of 10%. Figure 7b shows the distribution of the wind speed profile. It can be seen that varies approximately from 15 to 21 [m·s−1 ] with a high frequency of values in the range [17–19], which can be explained by the moderate turbulence intensity. Figure 7c shows the measured rotor speed signal, we observe that both controllers provided good regulation of the rotor speed around its nominal value of 41.7 [rpm], with a notable superiority of the proposed supervised PI controller over the baseline PI. It can be seen that the PI controller exhibits more oscillations with a speed varying between 40.21 and 42.95 [rpm] resulting in a large structural load on the rotor shaft (Low Speed Shaft) with a variance of 0.45 [rpm]. This was reduced to 0.35 [rpm] by correcting the gains using the proposed supervised fuzzy PI controller.

Fig. 5. FAST simulator Simulink blocs

We should notice that PI controller is not suitable for variable wind speed. This is explained by the fact that this controller is synthesized from a linearized model around a specific operating point. Whereas the proposed controller provides a real-time correction of the gain according to the change of the operating point, leading to a controller similar in dynamics to a series of local controllers, which are responsive to wind speed changes. Figure 7d shows the measured electrical power. The performance of the proposed controller is reflected in its ability to provide a good regulation of the produced electrical power around its nominal value of 600 [kW], with a small variance of 5.14 [kW]. T will

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Fig. 6. CART wind turbine (From [20] with permission)

Table 2. Statistical comparison of control strategies Criterion

Performance PI

Proposed

minωr rpm   maxωr rpm   ωr rpm   stdωr rpm

40.21

40.71

42.95

42.55

41.71

41.70

0.45

0.35

minPe [kW]

578.59

579.96

maxPe [kW]

619.63

619.49

P e [kW]

599.99

599.93

stdPe [kW]   minβ ◦   maxβ ◦   β ◦   stdβ ◦

8.34

5.14

3.00

0.00

18.58

15.33

14.06

13.09

1.80

0.79

min (dβ/dt)

−3.24

−8.09

max (dβ/dt)

20

20.00

moy (dβ/dt)

0.02

0.00

std (dβ/dt)

1.15

0.47





lead to higher quality energy with fewer harmonics, allowing stability for an eventual connection of the machine to the grid. Figure 7e, g shows the obtained pitch angle and its Power Spectral Density (PSD) spectrum, it can be seen that the supervised fuzzy PI

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Fig. 7. Results. a) Wind speed,v = 18 m · s−1 ,b) Wind speed distribution, c) Rotor speed, d) Electrical power, e) Pitch angle, f) PSD (β), g) Pitch rate, h) PSD (dβ/dt)

control action has significantly reduced the oscillations, which contributes to the reduction of the forces on the blades, which not only ensures a good regulation, but also contributes to the longevity of the blades, which are continuously subjected to important aerodynamic loads. The same remark can be made about the pitch rate signal in time domain and its power spectral density illustrated by Fig. 7f, h. We notice that the pitch rate did not exceed the physical limits imposed by the manufacturer of 20 [° · s−1 ], with a reduction of the power of the control signal of a value in the vicinity of 4 [dB], with the ability of the proposed controller to attenuate the 1P component amplitude. Detailed statistical analysis of obtained results is given in Table 2.

5 Conclusion The main concern for wind turbines control when the wind is above its nominal value is to maintain the electrical power around its rated one. In this context, a new supervised fuzzy PI pitch angle controller is proposed. The use of fuzzy reasoning to online adjust gains. Obtained results showed that this technique is more efficient compared to baseline PI controller. The adaptation policy must be understood as a set of local linear controllers

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acting each one around a specific point, which gives a good robustness against non-linear systems. In order to validate the proposed control law, the FAST simulator was used. Using the parameters of the CART wind turbine with 9 DOFs show that the proposed method gives a better result with a notable load reduction and damping to the flexible modes of wind turbine working in full load region.

References 1. Chehaidia, S.E., et al.: Fuzzy gain scheduling of PI torque controller to capture the maximum power from variable speed wind turbines. In: 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). IEEE (2020) 2. Chehaidia, S.E., et al.: An improved machine learning techniques fusion algorithm for controls advanced research turbine (Cart) power coefficient estimation. UPB Sci. Bull. C 82, 279–292 (2020) 3. GWEC. G.W.E.C., Global Offshore Wind Report 2020 (2020) 4. Mérida, J., Aguilar, L.T., Dávila, J.: Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization. Renew. Energy 71, 715–728 (2014) 5. Nayeh, R.F., Moradi, H., Vossoughi, G.: Sliding mode robust control of the horizontal wind turbines with model uncertainties. In: 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE (2020) 6. Van, T.L., Nguyen, T.H., Lee, D.-C.: Advanced pitch angle control based on fuzzy logic for variable-speed wind turbine systems. IEEE Trans. Energy Convers. 30(2), 578–587 (2015) 7. Zhang, J., et al.: Pitch angle control for variable speed wind turbines. In: 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. IEEE (2008) 8. Hosseini, E., Aghadavoodi, E., Ramírez, L.M.F.: Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning. Renew. Energy 157, 897–910 (2020) 9. Saravanakumar, R., Jena, D.: Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. Int. J. Electr. Power Energy Syst. 69, 421–429 (2015) 10. Wright, A., Fingersh, L.: Advanced control design for wind turbines. In: Part I: Control Design, Implementation, and Initial Tests, National Renewable Energy Lab., USA (2008) 11. Xu, B., et al.: A pitch angle controller based on novel fuzzy-PI control for wind turbine load reduction. Energies 13(22), 6086 (2020) 12. Rashid, A., Ying, D.: Fuzzy inference based approach for pitch angle control of variable speed variable pitch wind turbine. In: 2020 Asia Energy and Electrical Engineering Symposium (AEEES). IEEE (2020) 13. Dahbi, A., Nait-Said, N., Nait-Said, M.-S.: A novel combined MPPT-pitch angle control for wide range variable speed wind turbine based on neural network. Int. J. Hydr. Energy 41(22), 9427–9442 (2016) 14. Sahoo, S., Subudhi, B., Panda, G.: Torque and pitch angle control of a wind turbine using multiple adaptive neuro-fuzzy control. Wind Eng. 44(2), 125–141 (2020) 15. Chojaa, H., et al.: Integral sliding mode control for Dbased WECS with MPPT based on artificial neural network under a real wind profile. Energy Rep. 7, 4809–4824 (2021) 16. Boukhezzar, B., Siguerdidjane, H.: Comparison between linear and nonlinear control strategies for variable speed wind turbines. Control. Eng. Pract. 18(12), 1357–1368 (2010) 17. Jonkman, J.: NWTC Design Codes FAST (v7. 01a), December 2012 (2012). http://wind.nrel. gov/designcodes/simulators/fast

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18. Stol, K.A.: Geometry and Structural Properties for the Controls Advanced Research Turbine (CART) from Model Tuning, 25 August 2003–30 November 2003. National Renewable Energy Lab., Golden (2004) 19. Jonkman, B.J., Buhl Jr, M.L.: TurbSim User’s Guide, National Renewable Energy Lab. (NREL), Golden (2006) 20. Wright, A.D., Balas, M.J.: Design of controls to attenuate loads in the controls advanced research turbine. J. Sol. Energy Eng. 126(4), 1083–1091 (2004)

Maximum Power Generation and Pitch Angle Control of a PMSG-Based WECS Connected to the Grid Adil El Kassoumi1(B)

, Mohamed Lamhamdi1 , Azeddine Mouhsen1 and Ahmed Mouhsen2

,

1 The Faculty of Sciences and Technology, Laboratory of Radiation-Matter and Instrumentation

(RMI), Hassan First University of Settat, Settat, Morocco [email protected] 2 The Faculty of Sciences and Technology, Laboratory of Engineering, Industrial Management and Innovation (IMII), Hassan First University of Settat, Settat, Morocco

Abstract. This article discusses the modeling and control of a wind energy system that utilizes a PMSG (permanent magnet synchronous generator) that is connected to the electrical grid. First of all, the modeling of the different elements constituting the wind power system is exposed. Then, the control schemes of the wind power system are developed. The MSC is controlled using the OTC-FOC technique to regulate the stator currents of the generator, in order to extract the maximum power. Moreover, the GSC control ensures the synchronization with the grid using the VOC technique based on the SRF-PLL, where the primary objective of this controller is to manage the reactive and active power fed into the grid while maintaining the DC-link voltage at its rated value. In addition, pitch angle control is also featured, so as to protect the PMSG from overload in high wind speed. To certify the validity of the theoretical study and the utility of the pre-sented control approach, the suggested wind turbine system’s overall scheme is implemented in MATLAB Simulink. The achieved findings validated the effectiveness of the control strategy based on PI controllers. Keywords: PMSG · Wind turbine · Pitch control · OTC-MPPT · VOC

1 Introduction With the improvement of the standard living of the populations and because of the increase of the world electricity consumption, the use of new energy sources to produce electricity in order to solve the energy crisis and the environmental pollution has become an international challenge [1]. In this context, different countries have thought of using renewable energy sources (RES) as a sustainable and clean solution. Among these countries, we can find Morocco, which has launched its national energy strategy aimed at moving away from dependence on oil and gas imports to become a major producer of renewable energy [2]. The strategy’s objective is to increase the installed capacity © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 696–705, 2022. https://doi.org/10.1007/978-3-031-02447-4_72

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of renewable energy to 52% by 2030, namely 20% solar, 20% wind, and 12% hydroelectric sources [2]. Morocco’s geographical location is favorable, giving it significant wind potential, estimated at around 6,000 MW, and to take advantage of this, it has put in place an ambitious wind power program which aims to install a capacity of 2600 MW by 2030 [3]. As a result of this, the research and investment in wind energy have increased dramatically. However, there are several technical problems related to their integration into the distribution networks, namely: - the maximum power extraction, the transient stability, the voltage and frequency response and the power quality [4]. In general, wind turbines are divided into two main categories: fixed speed and variable speed wind turbines. This latter is providing more electrical energy to the grid due to its greater range. Variable speed wind turbines (VSWT) are mostly categorized based on the type of generator used. The two most common types are the Permanent Magnet Synchronous Generator (PMSG) and the Dual Feed Induction Generator (DFIG) [5]. For the second type, the generator terminals are connected directly to the grid and the AC/DC/AC converters are scaled at 30% of the rated power, while in the first type, the generator is coupled to the grid terminal by two full scaled back-to-back converters. The chosen structure in this paper is based on the PMSG because it has many advantages such as good control accuracy, high efficiency and low maintenance, in addition, it is recommended as the only robust solution for gearless constructions [5]. Among the different power converter topologies possible for variable speed Wind Power Conversion Systems (WECS), the grid side converter GSC and the machine side converter MSC are common and essential elements not only for the injection of the generated power into the electrical grid as well as to comply with the restrictions of the grid codes [6]. Therefore, the control of the GSC and the MSC is important for the proper operation and efficiency of the WECS. MSC maximizes wind energy harvesting by executing MPPT in relation to the generator’s torque, speed, or power control. While the GSC is responsible for DC bus voltage management, grid reactive power regulation, and grid synchronization [7]. In this paper, we focused on modeling and controlling of the PMSG-based directdrive WECS, where the main objective is to control by mean of the MSC, the stator currents of the generator so as to ensure the maximization and limitation of the captured power as well as to ensure the synchronization with the power by the VOC technique through the control of the GSC, while making sure that the active and reactive power that is put into the grid is controlled, as well as the reference voltage of the DC bus being stabilized.

2 WECS Based PMSG Modeling A wind turbine system converts the energy from the wind into mechanical energy, which is then transformed into electrical energy. It consists of three-blades, 1.5 MW-PMSG, two bidirectional converters (GSC and MSC) interconnected by a DC bus, and three-phase RL filter. The structure of the studied system is shown in Fig. 1.

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Fig. 1. The structure of a grid-connected wind energy conversion system

2.1 Wind Turbine Model The basic function of the wind turbine is to intercept and transform the wind’s energy into mechanical energy. The expression of the aerodynamic power is [7]: E=

1 Cp ρSV 3 2

(1)

where Cp is the power coefficient, ρ is the air density; S is the the surface swept by the blades of the turbine of radius R.; V is the wind speed. The characteristics of the wind turbines can be differentiated according to the power coefficient Cp [8]:   −21 1 116 − 0, 4β − 5 e λi + 0, 0068λ (2) Cp (λ, β) = 2 λi With :

1 1 0, 035 = − λi λ + 0, 08β 1 + β3

where β is the blade orientation angle and λ is the tip speed ratio defined as follows [7]: λ=

Rm V

(3)

The wind turbine studied in this paper is connected directly to the PMSG by means of a mechanical shaft, and then we can obtain [7, 8]: J

d m = Tt − Tem − f m dt

(4)

where J represents the total inertia of the system, f is the coefficient of friction and Tem is the generator electromagnetic torque. 2.2 PMSG Modeling Considering some simplifying assumptions, the mathematical model of the PMSG is described in the dq reference frame as follows [8]:            vds Rs −ωr Lqs ids L 0 d ids 0 = + ds + (5) vqs ωr Lds Rs iqs 0 Lqs dt iqs ωr ψ r

Maximum Power Generation and Pitch Angle Control of a PMSG-Based WECS



   ids ψr + iqs 0   3Pp [ψr iqs + Lds − Lqs ids iqs ] = 2

ψds ψqs

Tem





=

Lds 0 0 Lqs

699



(6) (7)

2.3 Back-to-Back Converter Modeling The PMSG is connected to the power grid through two bidirectional converters (GSC and MSC). These converters are based on IGBT transistors, monitored by the pulse width modulation (PWM). They are both the same and can be used either as a rectifier or an inverter [8]. The dq frame equivalent model of the GSC is given by: vdi = sd Vdc vqi = sq Vdc

(8)

With : iPi = sd idg + sq iqg

2.4 Modeling DC-Links and Filters The DC Link model can be represented by the following equation [8]: dV dc 1 = (idc − iPi ) dt C

(9)

The CCR output currents are filtered by using RL filter. The model of this filter is defined as follows [8]: didg dt

+ Li ωg iqg

di vqi − Ri iqg − Li dtqg

− Li ωg idg

vdg = vdi − Ri idg − Li vdq =

(10)

3 MSC Control 3.1 MPPT Control Strategy According to the relation among the wind speed V and the tip speed ratio λ, and by replacing λ with λopt and placing Cp = Cpmax , we obtain [8]: K opt =

Cp max 1 ρπ R5 2 λopt 3

(11)

Therefore, the electromagnetic torque reference Tem ref is expressed as follows: Tem ref = K opt m 2

(12)

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3.2 Pitch Angle Control Strategy In pitch control, below rated wind speed, the blade angle of the wind turbine is changed to change the amount of power that can be made. Proportional-Integral (PI) control is the classic type of control for the control of the pitch angle. the power deviation from its reference has been taken into consideration. Below the nominal wind speed, the pitch angle is set to the optimal value 0° and the control system is then inactive. When the wind speed, on the other hand, surpasses the rated value, the error between the reference power and the power captured by the wind turbine is sent to a PI controller which gives a blade pitch reference βref to the internal control loop [9]. In order to place the blades in the desired position, the actuator used, consists of a mechanical system that must be considered as a first-order dynamic system given by the following equation [9]: β k0 = uβ 1 + τs

(13)

3.3 FOC-Based ZDC Control of MSC In this paper, the flux-oriented control is used to control the machine side converter, therefore the d-axis stator current of the generator is controlled to zero during operation to obtain a linear relationship between the stator current and the electromagnetic torque [10]. The global control scheme is shown in Fig. 2.

Fig. 2. Control based on orientation stator flux of the MSC

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Zero D-axis Current Control (ZDC) allows you to get current references that are used in the control laws [8, 10]. The three-phase stator currents isabc are converted to dq frame, so if the d-axis current ids is set to zero, the q-axis current iqs is equal to the stator current. For ids = 0, we obtain:  − → is = ids + jiqs = jiqs (14) is = ids 2 + iqs 2 = iqs With : ids = 0 The electromagnetic torque of the PMSG, described by Eq. 9 is then simplified as: Tem =

3Pp ψr iqs 2

(15)

We deduce the reference current iqs ref allowing to regulate the electromagnetic torque to its reference given by the MPPT: iqs ref =

2 1 Tem ref 3 Pp ψr

(16)

The simplified model of the PMSG shows that we can independently control the two direct and quadrature components of the current, each component has its own regulator. This control of the currents allows us to generate the reference voltages Vds ref and Vqs ref to be applied to the MSC [11]. The control of the ids and iqs currents is provided by two PI-type controllers as illustrated in Fig. 2. The expressions of the stator voltages vds and vqs are given as follows:  vds = −Rs ids − Lds didtds + ωr Lqs iqs (17) di vqs = −Rs iqs − Lqs dtqs − ωr Lds ids + ωr ψr

4 GSC Control The grid-side converter can be controlled according to different schemes. One of these schemes is known as Voltage-Oriented Control (VOC), which is shown in Fig. 3. VOC control is based on closed-loop current control in the dq frame. The Phase-Locked Loop (PLL) is used for the detection of the grid voltage magnitude the angle θg by orientation of the q-axis voltage to zero, this ensures the synchronization of the system with the electrical power grid [11]. To synthesize the regulators of the current inner loop, the GSC grid voltages are expressed as follows: di

vdg = vdi − Ri idg − Li dtdg + Li ωg iqg di vqg = vqi − Ri iqg − Li dtqg − Li ωg idg

(18)

For vqg = 0 and vdg = Vg , we have: di

vdi = Vs + Ri idg + Li dtdg − Li ωg iqg di vqi = Ri iqg + Li dtqg + Li ωg idg

(19)

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Fig. 3. Control scheme of the GSC

5 Simulation Results To prove the theoretical analysis and the utility of the control approach proposed, a complete WECS setup is developed in the MATLAB Simulink environment using a variable speed wind turbine, a PMSG, and power electronic converters connected to the grid. For these simulations, we consider that the wind system operates in partial load and full load, either by ensuring the extraction of the maximum power or by adjusting the pitch angle. The reference voltage of the DC bus, noted Vdc ref , is set at 1250 V. The reference reactive power Qf ref , is set to 0 VAR, which guarantees a unity power factor at the connection of the CCR with the power grid. The switching frequency of the MSC and GSC is set to 5 kHz. The wind profile used in this simulation is shown in Fig. 4(a). From the results presented in Fig. 4, it can be seen that the wind turbine operates in MPPT mode for wind speed lower than 11 m/s, As a result, the power coefficient is kept at its optimum (Cp max = 0.48) and the machine’s mechanical speed follows the fluctuation of the wind speed profile, demonstrating the efficacy of the MPPT control. On the other hand, the blade pitch angle is adjusted when the wind speed exceeds its rated value, this can be seen by the figures of λ and β, which allows to maintain the extracted power at its nominal value of 1.5 MW. The Fig. 5. Shows that the stator currents were regulated to their references where ids current was maintained to zero and iqs has been regulated to the reference obtained by the MPPT bloc. These results prove the effectiveness of the control strategy applied to the MSC.

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The figures above show the response of the GSC controlled by VOC strategy. It can be seen that the DC bus voltage is kept constant for a variable wind profile and likewise the currents idg and iqg follow their reference perfectly, hence the current iqg is set to zero in order to ensure zero reactive power and therefore a unitary power factor at the PCC. However, it can be seen that the power injected to the grid has the same shape as that of the wind turbine, which confirms the injection of all the extracted power. The Fig. 6(d). presents the current and the voltage of the phase a at the PCC, it is noted that the current and the voltage are in phase which confirms the flow of power towards the electrical power grid, in addition, the period of these signals is 0.02 s which corresponds to a frequency of 50 Hz.

Fig. 4. Results obtained for MPPT and pitch angle control strategies

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Fig. 5. Regulation of ids and iqs currents by PI controller

Fig. 6. Results obtained for the control of the GSC

6 Conclusion and Perspectives This paper presents the structure of the WECS as well as the modeling of the different components and their control. The main objective of the adopted controls was to ensure the maximization and limitation of the captured power as well as to ensure the synchronization with the power grid by the VOC technique through the control of the GSC, while regulating the active and reactive power fed into the grid and maintaining the DC bus voltage at its reference value. The implementation of various control laws proposed in this paper was carried out using the physical simulation tool Matlab/Simulink, the obtained results demonstrated the effectiveness of these control strategies based on PI controllers. Despite the effectiveness of the PI control under normal operating conditions, it presents poor results in the case of internal and external disturbances affecting the system which requires the implementation of other robust methods like the model predictive control technique.

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References 1. Schandl, H., Hatfield-Dodds, S., Wiedmann, T., et al.: Decoupling global environmental pressure and economic growth: scenarios for energy use, materials use and carbon emissions. J. Clean. Prod. 132, 45–56 (2016) 2. El Mourabit, Y., Derouich, A., Allouhi, A., et al.: Sustainable production of wind energy in the main Morocco’s sites using permanent magnet synchronous generators. Int. Trans. Electr. Energy Syst. 30(6), e12390 (2020) 3. Allouhi, A., Zamzoum, O., Islam, M.R., et al.: Evaluation of wind energy potential in Morocco’s coastal regions. Renew. Sustain. Energy Rev. 72, 311–324 (2017) 4. Jie, W.: Control technologies in distributed generation system based on renewable energy. In: 3rd International Conference on Power Electronics Systems and Applications (PESA), pp. 1–14. IEEE, Hong Kong (2009) 5. Polinder, H., Ferreira, J.A., Jensen, B.B., et al.: Trends in wind turbine generator systems. IEEE J. Emerg. Select. Top. Power Electr. 1(3), 174–185 (2013) 6. Blaabjerg, F., Liserre, M., Ma, K.: Power electronics converters for wind turbine systems. IEEE Trans. Ind. Appl. 48(2), 708–719 (2011) 7. Dahbi, A., Reama, A., Mehdi, A., et al.: Control and analysis of the wind turbine in different operation regions. In: International Conference on Communications and Electrical Engineering (ICCEE), pp. 1–5. IEEE, El Oued (2018) 8. Aboudrar, I., El Hani, S., Mediouni, H., et al.: Modeling and robust control of a grid connected direct driven PMSG wind turbine by ADRC. Adv. Electric. Electron. Eng. 16(4), 402–413 (2018) 9. Hwas, A., Katebi, R.: Wind turbine control using PI pitch angle controller. IFAC Proc. Vol. 45(3), 241–246 (2012) 10. Nasiri, M., Milimonfared, J., Fathi, S.H.: Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines. Energy Convers. Manage. 86, 892–900 (2014) 11. Gajewski, P., Pie´nkowski, K.: Advanced control of direct-driven PMSG generator in wind turbine system. Arch. Electr. Eng. 65(4), 643–656 (2016)

Design a Power Quality Analyzer Using an ARDUINO Card and Display Signals in the LABVIEW Environment Yassine Taleb(B) , Azeddine Bouzbiba, and Ahmed Abbou EREEC Laboratory, Department of Electrical Engineering, Mohammadia School of Engineering, Mohammed 5 University in Rabat, Rabat, Morocco {yassinetaleb,azeddinebouzbiba}@research.emi.ac.ma, [email protected]

Abstract. The monitoring of the quality of energy is a key element in the energy optimization of the installation analyzed. The measurement that is done on-site, using instruments provides an accurate picture of the data grid of the power system. The observation and analysis of electricity consumption, as well as any disturbances affecting the installation, will make it possible to establish a diagnosis and implement the appropriate solutions. This paper presents a design of a power quality analyzer based on an ARDUINO card and visualization of the signals in an interface developed in a LABVIEW environment, our system is validated by CMC 356 Omicron universal solution for testing. Our equipment will help us meet several needs in terms of energy quality power on the electrical grid and allowing to materialize the rate of disturbances transferred on the different points of the power grid and to use it as a portable console for measuring and recording voltage. Keywords: Power quality · Harmonics · LABVIEW · ARDUINO

1 Introduction Power quality is a term that means a wide variety of electromagnetic phenomena that characterize the voltage and current at a given location and time on the electrical grid. The increasing use of electronic equipment is likely to cause electromagnetic disturbances or to be sensitive to these phenomena, to this end the study of the quality of electrical signal provided becomes a primary issue. The various attempts to describe these phenomena have been accompanied by an increase in operating problems [1]. Power quality analyzers are offered by some manufacturers, these instruments are similar to oscilloscopes, and they have features adapted to power quality measurements. For a good power quality measurement, we need to be able to trigger events that are not continuous (such as voltage sag) or transient (such as those caused by lightning or power failures). Typical power quality meters/analyzers have the following features and characteristics: they shall capture and record waveform data in real-time for later display. It shall have the ability © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 706–717, 2022. https://doi.org/10.1007/978-3-031-02447-4_73

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to trigger power quality events such as swells, transients, or troughs. It shall calculate total harmonic distortion in real-time. It analyzes the spectrum. And that it has Input for high voltage probes and high current probes [2]. In this article, we have developed a prototype similar to the SICAM Q80 [3] power quality analyzer, based on an Arduino UNO card controlled in real-time by LabView software for disturbance analysis electric on a single phase of very high voltage 225 kV and high voltage 60 kV electric grids. Secondly, we will proceed with the validation tests of the equipment by injection analog signals and the analysis of experimental results. This paper is organized as follows. Section 1 is an introduction that gives the background of this work, Sect. 2 gives an overview of the power quality issues and technical standards used and a description of our system is presented. Section 3 describes the experimental results of simulations of our quality analyzer. The results were obtained by a prototype. Finally, conclusions and perspectives for future work are presented in Sect. 4.

2 Methods and Materials 2.1 Power Quality Problems Power quality problems are usually complex and difficult to detect. Technically, the ideal AC line feed from the public power grid should be a pure sine wave of fundamental frequency (50/60 Hz). In addition, the peak voltage value should be nominal. Generally, the AC power we receive every day deviates from the ideal specifications. Table 1 lists various power quality problems, their characterization methods, and possible causes [4]. Table 1. Power quality problems and their causes Broad categories

Specific categories

Methods of characterization

Typical causes

Transients

Oscillatory

Peak magnitude, frequency components

Line or capacitor or load switching

Impulsive

Peak magnitude, rise time, Lightning strike, and duration transformer energization, capacitor switching

Swell

Magnitude, duration

Ferro-resonant transformers, single line-to-ground faults

Interruption

Duration

Temporary(self-clearing) Faults

Sag

Magnitude, duration

Ferro-resonant transformers, single line-to-ground faults

Short duration voltage variation

(continued)

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Y. Taleb et al. Table 1. (continued)

Broad categories

Specific categories

Methods of characterization

Typical causes

Long duration voltage variation

Sustained interruptions

Duration

Faults

Overvoltage

Magnitude, duration

Switching offloads, capacitor energization

Undervoltage

Magnitude, duration

Switching on loads, capacitor de-energization

Symmetrical components

Single-phase loads, single-phasing condition

Voltage imbalance Waveform distortion

Notching

THD, Harmonic spectrum Power electronic converters

Harmonics

THD, Harmonic spectrum Adjustable speed drives and other nonlinear loads

DC offset

Volts, Amps

Geo-magnetic disturbance, half-wave rectification

Frequency of occurrence, modulating frequency

Arc furnace, arc lamps

Voltage flicker

2.2 Sense of a Deformed Quantity: The RMS Value The study of Root Mean Square (RMS) values of voltage and current can be calculated according to the RMS value of the different harmonic components.   H 2 Ih2 = I12 + I22 + · · · + IH (1) Irms =  Vrms =

h=1

H h=1

Vh2 =



2 V12 + V22 + · · · + VH

(2)

When a signal has harmonic components, its RMS value is different from the RMS value of the fundamental signal. It is therefore possible to roughly evaluate the distortion of the signal by comparing the RMS value and the RMS value of the fundamental component [5]. 2.3 THD: Total Harmonic Distortion The THD (Total Harmonic Distortion) is obtained by measuring the effective value of the harmonic components of a distorted waveform. In another way, it is the potential heating power of the harmonics concerning the fundamental. THD can be calculated for current or voltage [6]. The Total Harmonic Distortion formula of a VOLTAGE waveform is:   2 2  Vrms − V1,rms (3) THD =  2 V1,rms

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where Vrms is the RMS voltage value of the total waveform and V1,rms is the RMS voltage value of the first harmonic. The THD of a square wave is 48%, and The THD of a sine wave is 0% [7]. The main advantages of THD are: In most cases, it is used for a quick measurement of the disturbance. And it can be easily calculated. Some disadvantages of THD are: It does not provide amplitude information. And the detailed information of the spectrum is lost [8]. 2.4 Description of the Electrical Network Analyzer System Our system is a power quality analyzer composed of a card Arduino UNO, an analog card (TT voltage transformer), and a DC battery; it is illustrated in the following Fig. 1.

Fig. 1. Voltage acquisition card of power quality analyzer

Arduino UNO Card. To control our equipment we choose the Arduino board. This board is composed of two main parts the hardware part represents the electronic card based on a single component with microcontroller (mini-computer) ATMEGA328 (from the AVR family) and the software part represents the Arduino C programming environment. This environment software and hardware allow the user to formulate his projects through direct experimentation with the help of many resources available online [9]. Analog Card (TT: Voltage Transformer). The analog card is composed of a voltage transformer that transforms the input voltage of 57,73 V to a voltage of 1 V which is transferred to the Arduino card for the analysis of the voltage wave. DC Battery. To analyze a sinusoidal voltage of 1 V coming from the analog transformation board. The analog input of the ARDUINO only processes positive sinusoids between 0 V and 5 V. For this, we used a 1,7 V DC battery in offset to analyze the negative sinusoid wave of the voltage.

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LabVIEW. Virtual Instrument Engineering Workbench- LabVIEW- is software for instrumentation applications. Developed by the American company National Instrument, this software, which can be used in a large number of fields, is more particularly intended for data acquisition and signal processing. Indeed, it offers wide possibilities of communication between the computer and the physical world (by cards analog or digital acquisitions, GPIB cards, network, serial and parallel links, USB, etc.) as well as important mathematical libraries allowing to realize multiple processing on the measured signals. The programming language used by LabVIEW is graphic language G [10]. The LabVIEW environment offers, in three other separate windows, palettes independent of tools and objects used to edit the two program windows and test its operation. Our front panel is the interface that contains the various system views (Fig. 2 below).

Fig. 2. The front panel of the system

3 Simulation Results of Power Quality Analyzer The test bench used during our study project allowed us to validate our analyzer by injecting electrical voltages using the omicron box and to simulate the results with the LabView software, through an acquisition card with an Adriano UNO card. The test bench used in our study is illustrated in the following Fig. 3:

Fig. 3. The test bench

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3.1 MC356 Omicron Injection Box The CMC 356 is a measuring and testing device for all generations and types of protective relays. With its six powerful current sources (up to 64 A/860 VA per channel in threephase) and high dynamic range capability, the CMC 356 unit is capable of testing even heavily loaded electromechanical relays with very high power demands. The CMC 356 is a first-choice measuring device for applications requiring maximum amplitude, power, and versatility. With the Omicron CMC356 injection box, it is possible to generate analogy signals, including distorted signals containing harmonics. Technicians and commissioning engineers will appreciate its tremendous ability to perform wiring and plausibility checks of current transformers, using the primary injection of high currents from the test set [11]. 3.2 Experimental Tests and Results Experiment N º 1. We inject electrical voltages and we read the results measured in low voltage (LV) and very high voltage 225 kV (VHV). The nominal voltage injection is 57,73 V. Example of voltage injection by the omicron box Fig. 4 below:

Fig. 4. Injection of nominal voltage in the interface omicron

After the injection of the nominal voltage, we can see on the front panel or user interface LabView the waveform obtained, its amplitude, its frequency, and its low voltage and very high tension. The following Fig. 5 shows that the results obtained are almost close to injected values.

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Fig. 5. Measurement interface LabView of 225 kV side

After the injection of 25%, 50%, and 75% of the nominal voltage: we see the shape of a sinusoid also its RMS value and its frequency. Table of measurement tests (Table 2): Table 2. Equipment validation tests on the 225 kV side Value in % of nominal Voltage injected voltage (57,73 V) –LV– (V) – LV–

Voltage measured in low voltage –LV– (V)

Voltage measured in very high voltage –VHV– (kV)

Difference (V)

25%

14,43

14,49

54,647

0,06

50%

28,86

29,10

109,748

0,24

75%

43,29

43,78

164,81

0,49

100%

57,73

57,40

220,098

−0,33

Experiment N º 2. We repeat the same injection but here we interpret the results as low voltage (LV) and High Voltage (HV) (60 kV). The simulation of the injection results in the 60 kV (HV) is presented in Table 3. Table 3. Equipment validation tests on the 60 kV side Value in % of nominal voltage (57,73 V) –LV–

Voltage injected –LV– (V)

Voltage measured in low voltage –LV– (V)

Voltage measured in very high voltage – HV– (kV)

Difference (V)

25%

14,43

14,50

14,90

0,07

50%

28,86

28,92

29,92

0,06

75%

43,29

43,80

44,96

0,51

100%

57,73

57,51

60,02

−0,22

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Experiment N º 3. We inject electric voltages at a variable frequency and we read the results measured at Very High Voltage (VHV) and High Voltage (HV). After injecting the voltages at nominal frequency, we notice that the waveform is sinusoidal and that the value of the measured frequencies is equal to the nominal frequency of injected voltages. • Injection of a voltage at nominal frequency 50 Hz (225 kV) (Fig. 6):

Fig. 6. Waveform injected at nominal frequency 50 Hz (225 kV)

• The spectrum obtained after the injection of a voltage at frequency 50 Hz (225 kV) is in Fig. 7 below:

Fig. 7. Fundamental frequency spectrum on 225 kV side

Secondly, we inject an alternating voltage with a frequency of 25 Hz (225 kV) (Fig. 8):

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Fig. 8. Frequency spectrum at frequency 25 Hz on 225 kV side

• 225 kV side waveform at 25 Hz frequency (Fig. 9):

Fig. 9. The waveform on the 225 kV side at a frequency of 25 Hz

We can observe from the figures that the frequency of the injected voltage is also equal to the frequency measured on the voltages of 225 kV and 60 kV. The values of the voltage at variable frequency are shown in Table 4 below.

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Table 4. Voltage values injected at variable frequency Value in % of nominal frequency

Frequency injected Frequency in Hertz measured in HV –60 kV– (Hertz)

25%

12,5

50%

25

Frequency measured in VHV –225 kV–(Hertz)

Difference (Hertz)

12,501

12,501

0,001

25,0025

25,0029

0,0025

75%

37,5

37,508

37,508

0,008

100%

50

50,001

50,001

0,001

Experiment N º 4. We inject electrical voltage that contains harmonics and read measured results (Fig. 10).

Fig. 10. The waveform on the 225 kV side contains odd harmonics

We find that the odd harmonics, multiples of the fundamental frequency, are proportional to the injected harmonics and the value of the distortion rate harmonic THD is closer to the THD of the injected voltage (Fig. 11):

Fig. 11. FFT (Fast Fourier Transform) injected wave on the 225 kV side

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We repeat the same experiment and deduce the results of measurements in the 60 kV voltage view. Figure 12 shows the deformation of the sinusoid.

Fig. 12. The waveform on the 60 kV side contains odd rank harmonics

4 Conclusion We studied the different measured factors that determine the degree of pollution in the power system. In particular, our study focused on the design of a power quality analyzer used to measure and display in real-time the frequency, the RMC value of the voltage, the harmonics, and the harmonic distortion rate. The experimental simulation results of the analyzer are in perfect agreement and open new tempting perspectives, to decide its installation on the electrical grid and in particular on the 225 kV and 60 kV bus bars. In the next study, we will compare the measurement results recorded with the power quality analyzer SICAM A80 7KG8080 from SIEMENS.

References 1. IEEE Recommended Practice for Monitoring Electric Power Quality, pp. 9–10. The Institute of Electrical and Electronics Engineers, Inc., New York. ISBN:1-55937-549-3 2. Alexander Kusko, P.E., Thompson, M.T.: Power Quality in Electrical Systems, p. 206. The McGraw-Hill Companies. ISBN:0-07-151002-8 3. LNCS. https://assets.new.siemens.com/siemens/assets/api/uuid:a9f7d711-a436-47df-8775467ebbb70582/sicamq80systemmanual.pdf 4. Ghosh, A., Ledwich, G.: Power Quality Enhancement Using Custom Power Devices, pp. 4– 5. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-1153-3. ISBN:978-1-46135418-5 5. Philippe FERRACCI quality of electrical energy-Schneider electric-

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6. Santos, S., Wayne Beaty, H., Dugan, R.C., McGranaghan, M.F.: Electrical Power Systems Quality, 2nd edn, p. 181. The McGraw-Hill Companies 7. Alexander Kusko, P.E., Thompson, M.T.: Power Quality in Electrical Systems, p. 53. The McGraw-Hill Companies. ISBN 0-07-151002-8 8. Fuchs, E.F., Massoum, M.A.S.: Power Quality in Power Systems and Electrical Machines, pp. 19–20. Academic Press (2008). ISBN:9780128009888 9. LNCS. https://www.researchgate.net/figure/Arduino-Uno-FrontandBack_fig10_317386157/ download 10. LNCS. https://www.ni.com/en-lb/shop/labview/labview-details.html 11. LNCS. https://www.omicronenergy.com/en/products/cmc-356/

IoT, Comparative Study Between the Use of Arduino Uno, Esp32, and Raspberry pi in Greenhouses Zaidan Didi(B)

and Ikram El Azami

Computer Science Research Laboratory (LaRI), Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco {Zaidan.didi,ikram.elazami}@uit.ac.ma

Abstract. In recent years, there has been a strong integration of Internet of Things (IoT) technology in greenhouses, however, determining the choice between the main elements for processing the data from the sensors remains a big problem. Using an Arduino microcontroller or a Raspberry pi is a major challenge for farmers. In this article, we present a detailed comparative study between the different achievements aimed at this technology in the field of agriculture, and especially in greenhouses to maximize monitoring and to manage resources such as energy and water quantity to properly adapt uniform irrigation and maintain an acceptable humidity value. In this comparative study, we determined the protocols and technologies used for each approach. On the other hand, we have cited the advantages of each approach as well as the limitations that characterize each study to have an integral vision of the approaches that deal with the same subject. Finally, we adopted the different perspectives linked to this comparative study. Keywords: IoT · Raspberry pi · Arduino Uno · Esp32

1 Introduction Currently, statistics show that the field of agriculture has undergone intense changes with the strong use of IoT techniques [1, 2]. In this area, several studies have been carried out to make agriculture in greenhouses much more profitable and less expensive with the integration of Internet of Things technology, studies have used the Arduino board as the main processing element with the integration of humidity sensors [3, 4]. Other studies have used the Arduino Uno microcontroller and the Soil Moisture Sensor sensors as well as the GSM module to ensure communication between the different blocks [5– 7]. Other studies have used the ESP32s microcontroller with the DHT11 temperature and humidity sensor and soil moisture sensor as well as the SI1145 ultraviolet sensor [8]. Another interesting study that aims to ensure communication between the different blocks used L’esp32 with an approach based on radio frequency technology [9]. In the same context, other achievements have used the Raspberry pi as a processing and control unit [10–12]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 718–726, 2022. https://doi.org/10.1007/978-3-031-02447-4_74

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In this paper, we have developed a comprehensive comparative study to implement the protocols and technologies used for each study as well as the advantages and limitations that characterize each approach.

2 Materials and Methods 2.1 Materials In the various studies that are the subject of this comparative study, we have distinguished the following hardware as the main element of processing and control. Arduino Uno. Arduino Uno is an ATmega328 based microcontroller with a supply voltage in the range of 5 V and 2 K of RAM, for storing programs Arduino Uno has 32 KB of flash memory and 1 KB of EEPROM for storing parameters. This microcontroller executes 300,000 lines of source code per second using a 16 MHz clock, (see Fig. 1).

Fig. 1. Arduino Uno microcontroller.

The characteristics of the Arduino Uno board are grouped in the following table (see Table 1). Table 1. Characteristics of the Arduino Uno board. Function

Characteristic

Microcontroller

ATmega328

Voltage

5 V supply

Input voltage (recommended)

7–12 V

Input voltage (limits)

6–20 V

Digital I/O pins

14 (of which 6 provide PWM output)

Analog input pins

6

Flash memory

32 KB

Clock speed

16 MHz

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ESP32 Microcontroller. The ESP32 is a microcontroller on a chip developed by the company Espressif, it is a cheaper and very powerful development kit intended for the Internet of Things and embedded applications, the Esp32 microcontroller is characterized by the integration of two Wi-Fi and Bluetooth modules, (see Fig. 2).

Fig. 2. Esp32 Microcontroller.

The characteristics of the Arduino Uno board are grouped in the following table (see Table 2). Table 2. Characteristics of the Esp32 Microcontroller. Function

Characteristic

Number of cores

2

Supply voltage

2,7 V–3,6 V

GPIO pins

36

Flash memory

16 Mo

RAM

320 Ko

Wi-Fi

Wi-Fi: 802.11

Bluetooth

v5.0 et v 5.1

Dimension

48 × 26 × 11,5 mm

Raspberry pi. The Raspberry Pi is a single board nano-computer based on an ARM type processor, the Raspberry pi is characterized by its low weight and small size, moreover, it is cheaper and easy to use with a Linux type system, (see Fig. 3).

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Fig. 3. Raspberry pi 4 Model B 4 GB

The characteristics of a Raspberry Pi 4 Model B 4 GB are grouped in the following table (see Table 3). Table 3. Characteristics of a Raspberry Pi 4 Model B 4 GB. Function

Characteristic

Processor

Quad-Core 64 Bits

RAM

4 Go

Bluetooth

Bluetooth 5.0

Ethernet

Ethernet Gigabit

Port USB

USB 3.0

Micro HDMI

Yes

The Raspberry pi is part of a free Raspbian operating system based on Debian and designed specifically for this hardware, (see Fig. 4).

Fig. 4. Raspbian operating system on Raspberry pi.

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Comparative Approach. In this part, we have carried out a detailed study on the different IoT technologies used in greenhouses. An interesting study was developed with an Ethernet protocol from Arduino Uno [3], this study integrates the stationary particle sensor (PM) SDS01, and the temperature sensor DHT22 as well as an SD card for recording, it is a simple design with effective control of the parameters. We noticed limitations of this study such as, renewable energy is not used, data is not collected, no security, and we noticed the absence of a web/mobile application. Another study handles the same subject [5], this study uses a wireless protocol and point-to-point communication as well as a cloud server, with the integration of an Arduino Nano microcontroller and a LoRa RFM95 module as well as a temperature and humidity sensor, the realization uses cloud communication and a web application. This study takes into account the lighting conditions and the stages of plant growth, it uses easy communication between the different blocks. Unfortunately, renewable energy is not implemented correctly, Data transfer is limited as well as poor security of the data collected. Other very interesting studies have in particular fixed the research on the technology used in greenhouses [5–7], they have used the GSM module protocol and Arduino Ethernet as well as a wireless sensor network (WSN), in the hardware part, these studies use an Arduino Uno is the temperature and humidity sensors DHT11 and DHT22, as well as the soil moisture sensor and a SIM 800 GSM/GPRS and finally a communication system, smart meter, smart grid. Among the advantages of these achievements, we have marked a simple design at low cost, wireless sensor network technology makes the system more reliable as well as real-time data analysis reduces complexity and informs administrators by GSM notification, finally the algorithm is efficient and robust. In these last studies, we noted the following limitations: There are no details on the security system, moreover, the maintenance of the system is difficult as well as renewable energies are not exploited. In the same vision, very interesting studies have worked on the same subject [8], this study is well exploited the HTTP protocol via the Wi-Fi module of the ESP32 microcontroller, for the hardware part, this realization is based on the microcontroller MCU of the node ESP32 as well as the DHT11 sensor and the soil moisture sensor, we also noted the SI1145 Digital UV Index/IR/Visible Light Sensor, this achievement makes good use of the Blynk mobile app. The advantages of this study are very important, such that the design is simple and very good energy saving thanks to the use of standby mode to increase the life of the external battery. Another study aimed at data transmission may be of interest in this field [9], this study carries out data transmission by radiofrequency as well as the HTTP protocol, for the hardware part, this realization is based on the Esp32 microcontroller and a photovoltaic panel system, this study made good use of the 433 MHz RF transmitter (FS1000A) and associated receiver. As well as the cloud/ThingSpeak ™ platform. This study has advantages such as simple design and easy maintenance, unfortunately in this study, the antenna forces unsecured omnidirectional data transmission. In the same subject [10], this study is based on communication via the TCP/IP protocol, it uses the LoRa IoT protocol and the MYSQL server. In the hardware part, this realization uses the Raspberry pi3 as a processing element two SHT31 temperature

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and humidity sensors, an Android device, and a MySQL remote database as well as an SX1278 RF chip with a transmission frequency of 433 MHz. For the advantages, this greenhouse monitoring system is developed by LoRa technology, which allows low power consumption and long-distance transmission, this study provides the technical basis for building greenhouse models and analyzing big data. Unfortunately, this study makes system maintenance difficult, moreover, there are no details on the security system, and renewable energy is not used. Another interesting study [11], this study uses the Raspberry pi 3 and the HTTP protocol, and the Wi-Fi module built into the Raspberry pi as well as a wireless sensor network protocol and radio frequency communication. In the hardware part, we noted the following, a Raspberry pi 3 and a soil moisture sensor, solenoid valves, and online storage as well as a wireless sensor network (WSN), finally, we noted the presence of remote access (PC, Smartphone). This beautiful achievement ensures remote monitoring with secure communication this irrigation is based on zoning - High precision (zoning, FLC). Unfortunately, this realization should bring an improvement for the security of the data transfer between the server and the different blocks with the technique of cryptography, we also noticed that a database must be built from the data collected to perform analyzes. Finally, another interesting study uses the Raspberry pi [12], this study uses the HTTP protocol and the Wi-Fi module integrated into the Raspberry pi. In the hardware part, we have marked the following elements: a Raspberry pi 3 and a photovoltaic system, a DHT22 sensor, a soil moisture sensor (VH400), a water level sensor (HCSR04), a DC/AC/motors, an AC air compressor and a 24 V DC solar pump. Among the advantages of this study we noted, the design is simple, the complexity is reduced, and easy maintenance with a low price. This study has limitations such as the lack of a database to collect sensor information and data security issues.

2.2 Methods In this part, we have developed a study whose operating principle is based on the use of Raspberry pi 4 and an Arduino Uno microcontroller, the protocols and hardware technologies as well as the advantages are grouped in the Table 4. Table 4. Characteristics of our project. Protocols Our study • Wireless sensor • HTTP Protocol • Wi-Fi Module (Raspberry pi)

Technologies

Advantage

• Raspberry pi 4 with Raspbian • Arduino Uno [13] • DHT11 sensor • Sending data with 433 MHz radio RF • Cloud platform ThingSpeak platform [15]

• Remote monitoring • Secure communication • Use of wireless sensor networks (WSN) [14] • Using radio frequency modules is very easy

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In this paper, the operating principle is based on the distinction between the three zones. Zone A “Admin” is reserved for the farmer who can view the evolution of data in real-time via the ThingSpeak platform. As a result, farmers can ensure remarkable and efficient quantity monitoring at any time via a web interface. We designed this IoT solution to connect digital data hardware and services. Zone B is the main part of this project, it consists of a Raspberry pi 4 - 4 Go with a Raspbian operating system, this Raspberry manages the data processing and reads the temperature and humidity values measured by the sensor DHT11 (see Fig. 5). The decisions are sent to the Arduino Uno board in zone C, to control the watering solenoid valves via a radiofrequency transmitter. We used a Radiofrequency receiver that receives the data sent from zone B (see Fig. 6). We noticed here that the radio frequency receiver has four pins, Vcc, the middle two pins represent DATA and GND. The main characteristics of the DHT11 sensor are: Power supply: 3 to 5 V, Consumption (max): 2.5 mA, Precision: Humidity: ±5% Temperature: ±2 °C, Measuring range: Humidity: 20 to 100% - Temperature: 0 to + 50 °C, Dimensions: 16 × 12 × 7 mm.

Fig. 5. DHT11 sensor.

Fig. 6. The Arduino board and the RF receiver.

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3 Results and Discussion The results described in Fig. 7 present the results in real-time after the completion of our study. It is strongly noted that in this experiment an antenna was soldered to the ANT pin of the transmitter/receiver. The duration of this study is 31 days. With the use of the ThingSpeak platform, we have accumulated the different humidity values shown in Fig. 7.

Fig. 7. Humidity variation on the ThingSpeak platform.

This proposed study presents a cheaper IoT solution for real-time data monitoring based on Raspberry Pi, Arduino, and radiofrequency communication especially in isolated “no internet” locations. After this detailed and precise comparative study between the different approaches that integrate IoT technology in the field of agriculture and especially in greenhouses, we can lead to a transparent architecture, complete and secure for our future project, that targets the same skills to increase profitability and production, our future project also aims at high energy savings by exploiting different renewable energy resources. This comparison shows the obligation to take good advantage of water resources, with the use of submerged solar pumps and solenoid valves. Table 4 summarizes the characteristics of our project, “technologies and advantages.”

4 Conclusion This paper shows in detail the different studies that integrate IoT technology in agriculture, we have compared very well between the approaches that use the Arduino and the ESP32 microcontroller, and finally, the studies which use the Raspberry pi as the main element of processing and control, we have summarized in the first table the advantages and limitations of several studies, we have also determined the technologies adopted, the sensors and the protocols adopted for each approach. In Table 4 we have proposed our

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project with perspectives and recommendations for correcting the limitations cited in this comparative study to control and monitor the main elements in greenhouses because our goal is to define an intelligent approach based on the IoT.

References 1. Akhter, R., Sofi, S.A.: Precision agriculture using IoT data analytics and machine learning. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.05.013 2. Ronaghi, M.H., Forouharfar, A.: A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of unified theory of acceptance and use of technology model (UTAUT). Technol. Soc. 63, 101415 (2020). https://doi.org/10.1016/j.techsoc.2020.101415 3. Gkiolmas, A., Dimakos, C., Chalkidis, A., Stoumpa, A.: An environmental education project that measures particulate matter via an Arduino interface. Sustain. Futures 2, 100027 (2020). https://doi.org/10.1016/j.sftr.2020.100027 4. Shamshiri, R.R., et al.: Model-based evaluation of greenhouse microclimate using IoT-Sensor data fusion for energy efficient crop production. J. Clean. Prod. 263, 121303 (2020). https:// doi.org/10.1016/j.jclepro.2020.121303 5. Kumar, A., Singh, V., Kumar, S., Jaiswal, S.P., Bhadoria, V.S.: IoT enabled system to monitor and control greenhouse. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020. 11.040 6. Priya, C.G., Pandu, M.A., Chandra, B.: Automatic plant monitoring and controlling system over GSM using sensors. In: IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pp.173–176 (2017). https://doi.org/10.1109/TIAR.2017.8273710 7. Bagdadee, A.H., Hoque, M.Z., Zhang, L.: IoT based wireless sensor network for power quality control in smart grid. Proc. Comput. Sci. 167, 1148–1160 (2020). https://doi.org/10.1016/j. procs.2020.03.417 8. Doshi, J., Patel, T., Bharti, S.K.: Smart farming using IoT, a solution for optimally monitoring farming conditions. Proc. Comput. Sci. 160, 746–751 (2019). https://doi.org/10.1016/j.procs. 2019.11.016 9. Didi, Z., El Azami, I.: IoT design and realization of a supervision device for photovoltaic panels using an approach based on radiofrequency technology. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 365–375. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-73882-2_34 10. Yong, W., et al.: Remote-control system for greenhouse based on open-source hardware. IFAC-PapersOnLine 52(30), 178–183 (2019). https://doi.org/10.1016/j.ifacol.2019.12.518 11. Benyezza, H., Bouhedda, M., Rebouh, S.: Zoning irrigation smart system based on fuzzy control technology and IoT for water and energy saving. J. Clean. Prod. 302, 127001 (2021). https://doi.org/10.1016/j.jclepro.2021.127001 12. Selmani, A., et al.: Towards autonomous greenhouses solar-powered. Proc. Comput. Sci. 148, 495–501 (2019). https://doi.org/10.1016/j.procs.2019.01.062 13. Fan, O., et al.: Automatic delivery and recovery system of Wireless Sensor Networks (WSN) nodes based on UAV for agricultural applications. Comput. Electron. Agricult. 162, 31–43 (2019). https://doi.org/10.1016/j.compag.2019.03.025 14. Harikrishnan, R.: An integrated Xbee arduino and differential evolution approach for localization in wireless sensor networks. Proc. Comput. Sci. 48, 447–453 (2015). https://doi.org/ 10.1016/j.procs.2015.04.118 15. Kanakaraja, P., Syam Sundar, P., Vaishnavi, N., Gopal Krishna Reddy, S., Sai Manikanta, G.: IoT enabled advanced forest fire detecting and monitoring on Ubidots platform. Mater. Today Proc. 46(Part 9), 3907–3914 (2021). https://doi.org/10.1016/j.matpr.2021.02.343

Numerical Resolution of the LWR Method for First Order Traffic Flow Model Hamza El Ouenjli(B) , Anas Chafi, and Salaheddine Kammouri Alami Faculty of Science of Fez, Sidi Mohammed Ben Abdellah University, Imouzzer Road, B.P. 2202, Fez, Morocco {hamza.elouenjli,Salaheddine.kammourialami}@usmba.ac.ma

Abstract. Congestion in Moroccan roads especially in urban areas is increasing and gaining in scale more and more nowadays, it can be induced by roads perforated, absence of traffic signs, behaviors of drivers, overcapacity of roads, illegal parking etc. Also, disproportional traffic lights cycle in a junction also serves as cause of congestion. One of the most innovative solutions is to build an intelligent transportation system (ITS) to collect data in real time and adjust the cycle time of traffic lights in every road intersection, provide alternative paths and predict when and where congestion will occur. Having a numerical model that give reliable solutions in real time using accurate data is essential. In this perspective, we use the LWR macroscopic model to model traffic flow and we adapt numerical resolution methods of Lax-Friedrichs and Godunov Schemes to test their accuracy and adaptability for traffic situations. Keywords: Traffic flow · Macroscopic model · Numerical solution · Space-time discretization · Partial differential equation

1 Introduction Road traffic includes several factors, such as the geometry of the road infrastructure, the behavior of drivers, the variety of vehicle flows, etc. The situations we are confronted with require the intervention of models which allow us to better understand traffic dynamics, in particular the generation and propagation of congestion. Since the beginning of the twentieth century, two types of models have been developed in order to respond to simulation demand of the roads situations: Microscopic or car-following model and macroscopic model. In the microscopic model, each vehicle is modeled individually by a differential equation depending on its velocity and next vehicle’s position. However, it got complicated when the scale is too large and individual modeling became unrealistic. In the other hand, a Macroscopic traffic model represents the overall behavior of the traffic stream in similarity with fluid dynamics that define each state by the average values of the entire traffic which gives a large field of applications in more complexed situations. The first macroscopic attempt was proposed by Lighthill Whitham Richard (LWR) providing a partial differential equations of the evolution of traffic flow in time and space. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 727–736, 2022. https://doi.org/10.1007/978-3-031-02447-4_75

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Numerous types of solutions are proposed in order to have realistic solutions in a reasonable time laps. We can cite the characteristics method [1], Green function method [2], the separation of variables [3] and adomian decomposition method [4]. All those methods present a major inconvenient of application in complexed real time situations that requires fast and easily adaptable solution to all kind of changes. The main challenge is to have a reliable solution in reasonable amount of time and in different scales and the numerical resolution is presented as one of the most practical ways to solve this kind of problems. The goal of this paper is to adapt some space-time numerical schemes used in partial differential equations (PDE) to solve the LWR model and compare their solutions with the exact one in a simple case of the spread of a traffic shockwave. And also open up to the possibility of extending this method to more complexed situations in urban areas (traffic lights optimizing, public transport time prediction etc.) This paper is organized as follows: Sect. 2 give an introduction to the LWR model and present different ways of resolution. Section 3 dig deep in numerical resolution by explaining basics for Lax-Friedrichs and Godunov space-time discretization schemes and its adaptation to traffic modeling, and we expose the simple shockwave example in the end of the section before concluding and giving perspectives for more developed applications.

2 LWR Method for First Order Traffic Flow Model 2.1 Terminology In the case of macroscopic models, we introduce the following variables: • Number of vehicles noted N(x,t); • Flow rate classically noted Q(x,t) corresponding to the number of vehicles flowing at a point of abscissa x and time t per unit time: Q(x, t) =

N (x, t → t + dt) dt

(1)

Concentration noted K(x,t) corresponding to the number of vehicles per unit of length located on a section near the point of abscissa x, at time t: K(x, t) =

N (x, t → t + dt) dx

(2)

• Fow velocity: V(x,t), corresponding to the spatial average speed of the vehicles located in the section [x; x − x] at time t. Furthermore, it can be shown that the flow velocity is equal to the spatial average velocity of the individual speeds: 1 vi n n

Vs (x → x + dx, t) =

i=1

(3)

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2.2 First Order Model Hypothesis This model involves the three variables described above: velocity, flow and concentration. For the system to be fully determined, it consists of the following three equations [5, 6]: – Velocity definition equation, seen earlier as the ratio of flow rate to concentration; – The conservation equation, derived from the conservation of the number of vehicles over a section of infinitesimal length and over a period of time; – The fundamental diagram, which postulates that the flow velocity is obtained permanently for a state of equilibrium, which depends only on the instantaneous concentration. It is an equation of state generally separating a fluid part and a congested part. Let the following system of equations be used: Q(x, t) = K(x, t) × V (x, t)

(4)

∂Q(x, t) ∂K(x, t) + =0 ∂x ∂t

(5)

V (x, t) = Ve (K(x, t))

(6)

Qe (K(x, t)) = K(x, t) × Ve (K(x, t))

(7)

With:

The above equation corresponds to the fundamental diagram, which characterizes the network on which the vehicles travel. The main characteristics are based on logical observations: when the concentration is close to 0, the interactions between vehicles are so limited that these vehicles drive at their maximum desired speed, and when it increases, the interactions between vehicles are stronger and stronger, so their speed decreases. It can therefore be said that the concentration is bounded by a certain value noted Kmax (this bound corresponds to the limit case of a road on which all the vehicles are stopped one behind the other). Fundamental flow diagrams can be presented by the following generalized formula [7, 8]:  l p  K Qe (K) = K · Vmax · 1 − (8) Kmax The choice of the fundamental diagram will have no influence on the theoretical results which will be valid for any form of diagram. In the case of road traffic flow systems, the macroscopic LWR model of traffic flow chosen for this study, presented by equation (Eq. 5) is similar to a transport equation that expresses the distribution of vehicles on a section of the road with a speed dependent on its density. The LWR model is described by an homogeneous non-linear partial differential equation [9, 10].

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2.3 Numerical Resolution of the LWR Equation: Discretization Schemes The road traffic flow’s theory, based on fluid mechanics, shows in the same kind of phenomena such as shock waves, fans etc. These describe and explain the phenomena usually encountered in the process of vehicle flow, especially on highways. Then, the numerical methods can be also 1st or 2nd order always using the same discretization schemes based on the solution of the Riemann problem but with a high accuracy for the 2nd order schemes compared to the 1st order ones. Then we can divide the different space-time discretization schemes [11–13]: – For 1st order models: Lax-Friedrichs scheme, Godunov scheme, Finite difference numerical scheme. – For 2nd order models: Lax-Wendroff scheme, Godunov scheme. 2.4 Basic Principle of the Lax-Friedrichs Scheme The space-time discretization is performed on the Taylor series development of the differential equation. Starting from the transport equation the road traffic flow is assimilated to the flow of a fluid in a pipe of length L. The spatial discretization requires choosing intervals of space x such that L = M·x, with M the number of computational steps according to the space considered. Similarly, temporal discretization requires dividing time into intervals t such that T = N·t, where N is the number of calculation steps over time. This consists in establishing an approximate expression of the system of equations (Eqs. 1–3) using the Taylor series expansion of the differential operators of the equations. This development is based on the explicit schemes centered in x, and in t:     j j j+1 j k − ki−1 ∂k xi , tj ∂k xi , tj − ki k ≈ i+1 and ≈ i ∂x 2 · x ∂t t

(9)

Applying the relations (Eq. 6) in equation (Eq. 3), we obtain the following explicit centered scheme:

v t

t j m j+1 j j j j ki+1 − ki−1 + (ki+1 )2 − (ki−1 )2 (10) ki = ki − vm . 2x km 2x The linear part in (Eq. 10) constitutes the explicit scheme that is unstable, for the linear advection equation (with c = vm): ∂u ∂u +c = 0, tel que c = 0 ∂t ∂x To stabilize the scheme, we can replace the time discretization (Eq. 9) by:

j+1 j j   ki − 21 ki+1 + ki−1 ∂k xi , tj ≈ ∂t t

(11)

Numerical Resolution of the LWR Method for First Order Traffic Flow Model

We then obtain the following Lax-Friedrichs scheme (Eq. 12):     1 t 1 t j+1 j j k + k ki = − vm . + vm . 2 2x i+1 2 2x i−1

vm t j 2 j (ki+1 ) − (ki−1 )2 + ρm 2x

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(12)

Plotting the Trajectory Let us start from the figure below (Fig. 1), which represents the (x,t) plane discretized in (x,t). We define the velocity field distribution for each node. These values are calculated from the concentration field results given by the program. Finally, we can plot the trajectory of every node using the equation linking velocity value with time mesh and position.

Fig. 1. Velocity range illustration scheme

2.5 Basic Principle of the Godunov Scheme The basic principle of the Godunov scheme is as follows: – creation of a grid splitting the road into sections (Fig. 2) of length x of homogeneous concentration on each section [14] and time splitting by a step of t knowing the initial state of the system when t = 0).

Fig. 2. Godunov scheme: spatial-temporal discretization scheme decomposition of a segment into several homogeneous sections

It is assumed that the concentration is constant on each section (cell) k(x) = const and that the flow rate is constant during each time step q(t) = const. The principle of

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the discretization method is to calculate the concentration for a time step and to average it over each cell (Fig. 3).

Fig. 3. Godunov scheme: average concentration per segment

Fig. 4. Godunov scheme conditions

The initial conditions for solving by the Godunov scheme are to average the concentration over each cell and to know the inflow and outflow over each cell for one time step. As it is easier to calculate the flow rate at the outlet of each cell than the concentration, the actual and demanded inflow functions are introduced with these particular conditions as follows: – supply capacity - is the maximum number of cars that can enter a cell in a time step; – demand - corresponds to the number of vehicles that wish to leave a section in a time step (Fig. 4).   Qmax (K)if K ≤ Kc Qeq (K)if K ≤ Kc (K) = (13) (K) = Qmax if K > Kc Qeq if K > Kc Thus, the general form of the discretization can be developed by passing the output variables to the input variables of each cell. ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨

K1 =

Qin1 −Qout1 L

......

⎪ Qin −Qout ⎪ ⎪ Kn−1 = n−1 L n−1 ⎪ ⎪ ⎪ ⎪ ⎪ Q −Q ⎩ Kn = inn L outn

→

⎧ ⎪ ⎪ Qin1 = Qout0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ...... ⎪ ⎪ ⎪ Qinn = Qoutn−1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ Qinn+1 = Qoutn

→

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨

Qin1 = Qout1 + K1 L ......

⎪ ⎪ ⎪ Qinn−1 = Qoutn−1 + Kn−1 L ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ Qinn = Qoutn + Kn L

(14)

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Replacing for the segments [(n − (n − 2)) to (n)] the inputs by the outputs of the previous segment we should receive a dependence of the densities only on: – the input flow on the first segment. – the output flows corresponding to each segment. – the density of the previous segment. In fact, the output variable can be replaced by the function of the fundamental laws of the traffic flow model by expressing the flow from the state of the system (the density) and the velocity (Eq. 15).  Qout (t) = K(t) · V (K, t)

(15) K(t) V (K, t) = Vm 1 − K(t) → Q (t) = K(t) · V · 1 − out m Km Km For each segment, we have to replace the output by the relation that gives us the state of the system and then the output of the model.

3 Presentation of Results and Analysis 3.1 Analogy of the Exact Solutions with a Real Situation For example, at the back of a traffic jam, the cars are stopped and more and more cars arrive. The cars arriving in the traffic jam go, in most cases, from a fluid situation to the traffic jam where they are stopped. It is therefore a shock wave type situation. The graph shows the linear density as a function of the location on the road at a given time. Here we have for example Kg = 2 and Kd = 4. Shock waves that occur in traffic flow are very similar to the waves produced by dropping stones in water. A shock wave propagates along a line of vehicles in response to changing conditions at the front of the line. In our case, it travels against the traffic stream (Fig. 5).

Fig. 5. Exact solution shock wave at a certain t

It is now relevant to compare our exact solutions obtained in the theoretical part with the approximate solutions obtained with the two different schemes studied. We will be

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able to compare the accuracy of the results as well as the computation time for each scheme using a Sage algorithm and the example above. We consider an interval in space going from a = −5 to b = 5 divided into 50 points and a 300 points time mesh, we have dx = 1/10 and dt = 1/300. 3.2 Analysis of the Results of the Lax-Friedrichs Scheme Three diagrams are shown, the first at t = 0 and then two others at later dates, in order to see the evolution of the density. The red curve is the exact solution and the blue curve the one given by the diagram (Fig. 6).

Shock wave – Start

Shock wave – Middle

Shock wave - End

Fig. 6. Results of the Lax-Friedrichs scheme

Calculation time for the approximate solution 2.520 s. It can be seen that the calculation of the approximate solution is very fast. On the other hand, we observe that the both diagrams are identical after a transition phase relatively important which give this model less reliability. 3.3 Analysis of the Results of the Godunov Scheme Similar to Lax Friedrichs scheme, we model the same situations using Godunov Approximation (Fig. 7):

Shock wave – Start

Shock wave – Middle

Shock wave - End

Fig. 7. Results of the Godunov scheme

Calculation time for the approximate solution (case of a shock Kg = 2 and Kd = 4): 22.804 s. The calculation of the approximate solution is a little slower than the first LaxFriedrichs scheme. However, the approximation is better with a non important transition phase.

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4 Conclusion and Discussion In this paper, we applied numerical schemes to solve LWR model and to have an approximate solution in a reasonable time and with a high reliability. We found out that Godunov scheme provide a more accurate and closest to the exact solution even with a higher calculation rate than Lax-Friedrich scheme. The advantage of the numeric solutions is that they can be applied in more complex situations which is the case for most traffic problems especially in urban areas. Comparing to analytical solutions that their applicability limits remain in simple and often unrealistic cases. The fast calculation time and reliable results make this approach more attractive and able to be extended to more fields such as multimodal traffic modeling and travel time calculation. The application of numeric LWR model resolutions to simulate interaction between different transport modes. Also, applying numeric algorithms can be used in the public transport field to prevent bus irregularities and offer a higher level service especially with the emergence of Bus Rapid Transit projects as a substitution for high cost Tramways.

References 1. Strub, I.S., Bayen, A.M.: Weak formulation of boundary conditions for scalar conservation laws: an application to highway traffic modelling. Int. J. Robust Nonlin. Contr. IFAC-Affiliat. J. 16(16), 733–748 (2006) 2. Nikolov, E.: Transfer function representation approach of the LWR macroscopic traffic flow model based on the Green function. In: Proceedings of the 9th WSEAS International Conference on Evolutionary Computing (EC 2008), Sofia, Bulgaria, May 2008 (2008) 3. Winckelmans, G.: Classification des équations aux dérivées partielles, méthode de caractéristiques pour les cas hyperboliques, Octobre 2007 (2007) 4. Sentürk, ¸ E., Co¸skun, S.B., Atay, M.T.: Solution of jamming transition problem using adomian decomposition method. Eng. Comput. 35(5), 1950–1964 (2018) 5. Lighthill, M., Whitham, G.: On kinematic waves I. Flood movement in long rivers. In: Richards, P.I. (ed.) Proceedings of the Royal Society, London, Shock Waves on the Highway. Operations Research, May 1955, vol. 229A, pp. 281–316 (1956) 6. Richards, P.I.: Shock waves on the highways. Oper. Res. 4, 42–51 (1956) 7. Gerlough, D.L., Huber, M.J., National Research Council (U.S.): Traffic Flow Theory: A Monograph. Transportation Research Board, National Research Council, Washington (1975) 8. Guelfi, N., Mammar, A.: A formal framework to generate XPDL specifications from UML activity diagrams (2006) 9. Rascle, M.: An improved macroscopic model of traffic flow: derivation and links with the Lightill-Whitham model. Math. Comp. Model. 35(5–6), 581–590 (2002) 10. Lebacque, J.-P., Khoshyaran, M.M.: Modelling vehicular traffic flow on networks using macroscopic models. In: Proceedings of the FVCA II, pp. 551–558 (1999) 11. Zhang, Y., Chien, C., Iouannou, P.: Traffic density control for automated highway systems, Automatica 33(7), 1273–1285 (1997). PII: S0005-1098/97/00050-2 12. Chanut, S.: Modélisation dynamique macroscopique de l’écoulement d”un trafic routier hétérogène poids lourds et véhicules légers, Thèse de doctorat INSA de Lyon, ENTPE/INRETS (2005)

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13. Kachroo, P., Ozbay, K.: Solution to the user equilibrium dynamic traffic routing problem using feedback linearization. Transp. Res. B 32(5), 343–360 (1998). PII:S0191-2615(97)00031-3 14. Giorgi, F.: Prise en compte des transports en commun de surface dans la modélisation macroscopique de l’écoulement du trafic, Thèse de doctorat, INSA de Lyon, ENTPE/INRETS (2002)

Transverse Controller Design for a Self-driving Car Model Based on Stanley’s Approach Younesse El Hamidi(B) and Mostafa Bouzi Laboratory of Mechanics, Computer Science Electronics and Telecommunications, Faculty of Sciences and Technology, Hassan First University Settat, Settat, Morocco [email protected]

Abstract. Path tracking is a key element of self-driving cars. The capacity of a car to follow a predetermined path with zero inaccuracy in the steady-state is the notion of trajectory tracking. This paper proposes a nonlinear control rule that allows a vehicle to pursue a real-time path across constantly shifting off-road terrain independently. Conventional approaches may have suffered from inconsistencies instability, a limitation of tracking precision, or even a reliance on smooth traffic conditions, which all might lead to the car’s loss in unmanned off-road driving. The above study proposes a new approach to the automotive trajectory tracking that mimics human driver behavior by evaluating the placement of front tires - in relation to the anticipated trajectory - instead of the car’s frame, permitting for co-located management of the system. The suggested control strategy exploits the Stanley controller as a base controller using adequate parameter selection. By using kinematic equations of motion, a steering control rule is built for which global asymptotic stability is demonstrated. This controlling law is then expanded to account for the dynamic of the wheels and driver’s seat, which is driven by a servo motor. A switchable proportional-integral (PI) controller operates the brake and throttle to regulate the vehicle speed. Keywords: Trajectory tracking · Autonomous vehicle · Path following · Stanley control

1 Introduction Because of breakthroughs in processing and sensory technology, the development of autonomous vehicles has accelerated in recent years. The concept of an autonomous car, on the other hand, started in the late 1920s. Ai technology, big data, and computer processing technologies have all advanced in recent years. The popularity of self-driving cars is growing. The goal of self-driving technology is to improve safe driving, comfort, and efficiency while reducing road accidents [1–3]. Path tracking is an essential element of the mobility control module of an autonomous car, allowing the reference trajectory to be precisely followed. It was intended to correspond toward the reference trajectory. The above is the most current research hotspots in autonomous cars, according to [4, 5]. This study is purely concerned with path tracking control for driverless cars. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 737–744, 2022. https://doi.org/10.1007/978-3-031-02447-4_76

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The difficulty of trajectory tracking in self-driving cars is one of the most challenging difficulties owing to mobility limits, the sudden shifting of reference trajectories (severe maneuvers), and high vehicle speeds. Existing studies have looked at several techniques, such as the proportional-integral-derivative control method (PID) [6]. This approach is simple to develop and seems to have a basic structure. Without the requirement for large amounts of computing resources. This technology, although, cannot immediately switch to the surroundings or have appropriate accuracy for a wide range of shifting or for high-speed vehicles. Therefore as an outcome, adaptive techniques have got a lot of attention because of their capacity to adjust to a broad variety of scenarios [7]. A nonlinear adaptive control technique is proposed that follows predetermined paths based on laterally error, which is a function of transverse and vertical A different sort of lateral controller is the geometric controller. This kind of controller keeps a reference simply based on the vehicle’s kinematics, disregarding forces acting on the vehicle and presuming negligible wheel sliding. Pure tracking is one of the most essential geometric controllers [8, 9]. This would be the major approach to compute how much steering is necessary to keep the automobile on the highway [10]. Ollero et al. [11] created a supervisory controller (fuzzy controller) enabling online change of purest tracking attributes, consisting of autonomously modifying overall look-ahead distance based on trajectory features, velocity, and tracking errors. Shan et al. [12] clothoid fit tracking is a revolutionary tracking technology that replaces the rounds used in pure tracking with a clothoid C-curve, which reduces fitting error. Researchers also used a fuzzy algorithm to calculate the required anticipation distance based on the curvature of the path. A Stanley controller is a specific variety of geometrical path-following control that enabled Stanford to win the DARPA competition in 2006 [13]. Furthermore, a Prior study has shown that it performs well [14, 15]. Additional sorts of kinematics exist, such as the “carrot-following” and other geometric controllers [16, 17]. To recapitulate, the majority of the approaches discussed previously attempted to create a supervisory controller to keep the parameters of the fundamental controllers adaptable to maintain the fundamental controller’s properties adjustable, as described in the literature [11, 14] or to even modify the error computation method provided in the literature. The basic problem is that some of these methods compute overall inaccuracy using a single reference point, which is generally the one closest to the vehicle. This limits the controller’s capacity to maintain fast changes in the heading angle of the high-speed trajectory. The high-speed trajectory’s heading angle changes abruptly. The current research provides a novel technique that mimics human driving behavior and tries to decrease error along the route. The kinematic model and the dynamic model are the two-vehicle models utilized for controller design in this article. The path-following control laws are then developed. The kinematic control law is shown to have global asymptotic stability. Finally, the results are shown.

2 Vehicle Models This part contains two schematics that are used to represent the vehicle’s movement. For example, the kinematic model implies that the vehicle has no inertia. It is conceivable to build a controller that is globally reliable based on this concept. The dynamic model,

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which incorporates the impacts of inertia, tire slide, and guidance servo actuation, may be found at the second site: tires sliding and guiding servo actuation. 2.1 Kinematic Model A car kinematic motion with velocity v(t) may be defined by the distance error e(t), of steerable guide tires as well as their angle concerning the nearest part of the path to be followed., (ψ(t) δ(t)), As seen in Fig. 1, ψ(t) represents the vehicle’s yaw angle (heading) relative to the nearest route segment, and δ(t) represents the angle of the front wheels relative to the vehicle. Because of this reference frame, the actuator may respond to the error measurement instantly, allowing for collocated detection and control. In forward drive, the steering wheels act as the front wheels, as well as the derivatives of cross-track error is e˙ = v(t)sin(ψ(t) − δ(t)) in which the direction is technically limited to deltamax. The yaw angle’s derivative is equal to   v(t)sin(δ(t)) ˙ = r(t) ψ= − a+b

(1)

(2)

where a and b are the distances between both the center of gravity and front and rear wheels, respectively. 2.2 Dynamic Model The impacts of wheel sliding and the guiding servo are taken into account while modeling the vehicle’s non-linear dynamic motion. The front and back tires components are modeled to produce a force, respectively. Fyf (t), Fyr(t), transversal to the tire’s direction perpendicular. This is the vehicle’s bicycle model [18], or the anticipated driving regime. Fyf (t) ≈ −Cy αf (t)

Fig. 1. Kinematical model of an automobile

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Fyr (t) ≈ −Cy αr (t) where C y represents the tire rigidity of the tire pairs, and   Uy (t) + r(t)a + δ(t) αf (t) = arctan Ux (t)   Uy (t) − r(t)b αr (t) = arctan Ux (t) Given constant lateral and longitudinal body velocities Ux(t), Uy(t). This simulates the impact of the wheels deforming instantly enough to generate force. Differential equations of motion (DEMs):   (3) m U˙ x (t) − r(t)Uy (t) = Fxr + Fxf cos δ(t) + Fyf sin δ(t)   m U˙ y (t) + r(t)Ux (t) = Fyr − Fxf sin δ(t) + Fyf cos δ(t)

(4)

Iz r˙ (t) = −aFxf (t) sin δ(t) + aFyf (t) cos δ − bFyr (t)

(5)

3 Tracking Control Law This controller guarantees that the desired vehicle route, as specified by the path planner, is tracked in a closed loop. To calculate the lateral and heading error, the Stanley controller requires two inputs: the reference trajectory and the vehicle’s current position in relation to the overall frame. A controller is chosen using Eqs. (1) and (2) so that the resulting differential equation has a globally asymptotic stability equilibrium at zero cross-track error. ⎧



  ke(t) ke(t) ⎪ + arctan ψ(t) + arctan ψ(t) < δ(max) ⎪ ⎪ V (t) ⎨

V (t) ke(t) ψ(t) + arctan V (t) ≥ δ(max) δ(t) = δ(max) (6) ⎪

 ⎪ ⎪ k(t) ⎩ −δ(max) ψ(t) + arctan V (t) ≤ −δ(max) According to the literature [13], As shown by Eq. (6), this is divided into three regions: the saturated low region, the saturated high region, and the nominal region, where is the vehicle heading psi with respect to the path heading ψtraj at the closest point to the vehicle location, e(t) is the horizontal error, and K is the controller gain.

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4 Results Multiple simulation studies on various sorts of movements were carried out to validate the suggested technique (hooked road and curved road).

Desired Simulated

260 208

Desired Simulated

206

Y (m)

240

204 202

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760

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X (m) 180

160

Curved road 140 620

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660

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700

720

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X (m)

Fig. 2. A comparison of the desired curved path and the stimulated path

The vehicle’s route reaction is seen in Fig. 2. The answer is positive. According to the answer, the vehicle is always able to follow this type of reference trajectory with small inaccuracy. 300

Desired Simulated

238.5 280

Y (m)

260

Desired Simulated

238

Y (m)

237.5 240 382

382.5

383

X (m) 220

200

180

Hook road 280

300

320

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360

380

400

420

440

X (m)

Fig. 3. A comparison of the desired Hook trajectory and the simulated trajectory

Figure 3 depicts the vehicle’s trajectory reaction. The reaction is also good for the hook trajectory. Our results reveal that the suggested Stanley controller follows the reference trajectory effectively (Fig. 4).

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0.2 0.15

position error (m)

0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 0

50

100

150

200

250

300

350

400

450

500

time(sec)

Fig. 4. The positioning error

The graph depicts the positioning error; we can see that this error actually reduces to zero which validates our command.

5 Comparison Kinematic & Geometric controllers are the most basic sort of controllers utilized in autonomous vehicle trajectory tracking, as shown in Table 1. Table 1. Comparison between different types of controllers Controller types

Strength

Weakness

Geometric & Kinematic

Because of the fewest complicated state variables, this is the most basic sort of controller

The dynamics of the vehicle are not taken into account in the controller

Dynamic

The vehicle’s dynamic effect is considered Into the control law

In an experiment, achieving The vehicle’s dynamic states (e.g., forces and torques) may be difficult

Classical

A well-established strategy in the field Complex derivations and choices of control (such as, SMC and H∞) may be Particularly in nonlinear system control required

Adaptive

Adapt the controller to changing operating circumstances by making it more resilient If intelligence techniques are used the controller will be simplified and need less computations

Robustness should only be assessed in the context of a certain circumstance Some adaptive algorithms will be extensive increasing the cost of the overall approach’s calculation

Model based

To determine the control commands, evaluate the whole vehicle model

Immerse the controller in the online optimization issue The algorithms methodology will necessitate the use of computational resources approach’s calculation

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This sort of controller has been employed in several experiments. However, in some circumstances, its basic configuration may cause some robustness issues. Other controllers, such as kinematic and dynamic controllers, might have robustness issues. The kinematics controller is one of the most basic controllers utilized in this discipline. Because of its simple setup and very few parameters, it is simple to execute and establish the relationship between the vehicle’s location and the planned trajectory, allowing it to drive with little lateral error.

6 Conclusion A non-linear control law for a car was developed in order to autonomously track a realtime provided route over quickly changing off-road terrain by regulating the alignment of the front wheels relative to the intended trajectory, allowing for co-located system control. The control law’s global asymptotic stability has been proved by the use of kinematic equations. It can now adjust the characteristics of the tires and the power steering thanks to improved features. The speed controller employs a switchable PI controller, and the simulation results show the capacity to follow trajectories precisely in a range of off-road conditions, including hook and curved roads.

References 1. Jalti, F., Hajji, B., Mbarki, A.: The potential outcomes of artificial intelligence applied to the powered two-wheel vehicle: analytical review. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 1595–1605. Springer, Cham (2021). https://doi.org/10.1007/9783-030-73882-2_145 2. Nam, H., Choi, W., Ahn, C.: Model predictive control for evasive steering of an autonomous vehicle. Int. J. Automot. Technol. 20(5), 1033–1042 (2019) 3. Kayacan, E., Chowdhary, G.: Tracking error learning control for precise mobile robot path tracking in outdoor environment. J. Intell. Robot. Syst. 95, 975–986 (2019) 4. Bai, G., Liu, L., Meng, Y., Luo, W., Gu, Q., Ma, B.: Path tracking of mining vehicles based on nonlinear model predictive control. Appl. Sci. 9, 1372 (2019) 5. Bai, G., Meng, Y., Liu, L., Luo, W., Gu, Q., Li, K.: A new path tracking method based on multilayer model predictive control. Appl. Sci. 9, 2649 (2019) 6. Le-Anh, T., De Koster, M.B.: A Review of Design and Control of Automated Guided Vehicle Systems. Erasmus Research Institute of Management (ERIM), Rotterdam (2004) 7. Zhao, P., Chen, J., Song, Y., et al.: Design of a control system for an autonomous vehicle based on adaptive PID. Int. J. Adv. Robot. Syst. 9(2), 1 (2012) 8. Elbanhawi, M., Simic, M., Jazar, R.: The role of path continuity in lateral vehicle control. Proc. Comput. Sci. 60, 1289–1298 (2015) 9. Campbell, S.F.: Steering control of an autonomous ground vehicle with application to the DARPA urban challenge. Master’s Thesis. Massachusetts Institute of Technology (2007) 10. Wallace, R., Stentz, A., Thorpe, C.E., et al.: First results in robot road-following. In: Proceedings of 9th International Joint Conference on Artificial Intelligence (IJCAI 1985), Japan, vol. 2, pp. 1089–1095 (1985) 11. Ollero, A., Garca-Cerezo, A., Martinez, J.: Fuzzy supervisory path tracking of mobile reports. Control Eng. Pract. 2, 313–319 (1994)

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12. Shan, Y., Yang, W., Chen, C., et al.: CF-pursuit: a pursuit method with a clothoid fitting and a fuzzy controller for autonomous vehicles. Int. J. Adv. Robot. Syst. 12, 134 (2015) 13. Hoffmann, G.M., Tomlin, C.J., Montemerlo, M., et al.: Autonomous automobile trajectory tracking for off-road driving. In: American Control Conference, New York, pp. 2296–2301 (2007) 14. Amer, N.H., Hudha, K., Zamzuri, H., et al.: Hardware-in-theloop simulation of trajectory following control for a light armoured vehicle optimised with particle swarm optimisation. Int. J. Heavy Veh. Syst. 2007, 1–25 (2015) 15. Paden, B., Cap, M., Yong, S.Z., et al.: A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1, 33 (2016)

Road Safety Enhancement of Intelligent Transportation Systems: From Cellular LTE-V2X Toward 5G-V2X Adil Abou El Hassan(B)

, Imane Kerrakchou , Abdelmalek El Mehdi , and Mohammed Saber

SmartICT Laboratory, ENSAO, Mohammed First University Oujda, Oujda, Morocco {a.abouelhassan,i.kerrakchou,a.elmehdi,m.saber}@ump.ac.ma

Abstract. Nowadays, intelligent transportation systems (ITS) are getting significant attention to improve road traffic safety and efficiency. For this purpose, cellular vehicle-to-everything (C-V2X) technology supporting ITS services was initially defined by the Third Generation Partnership Project (3GPP) as part of Long Term Evolution (LTE) Rel-14 enhancements. C-V2X is considered a key technology for ITS, as it evolves from LTE-V2X to 5G New Radio V2X (NR-V2X) to fulfill specific requirements depending on C-V2X applications. This paper presents an overview of V2X technology by describing the main 3GPP’s use case categories and the physical layer design of LTE-V2X and NR-V2X. Based on the assessment of the LTE-V2X sidelink communications performance conducted by the 5G automotive association (5GAA) and using the AASHTO model, we evaluate the alert delivery reliability rate, which is assessed in terms of packet reception ratio (PRR). Finally, we highlight potential technologies that will be improved in V2X communications to enhance road safety through V2X technology. Keywords: LTE-V2X · NR-V2X · 5GAA · AASHTO model · PRR

1 Introduction The US Federal Communications Commission (FCC) assigned in 1999 a spectrum band of 75 MHz within the 5.9 GHz band for intelligent transportation systems (ITS). The research led to an initial set of radio standards for Vehicle-to-Everything (V2X), finalized in 2010. V2X allows a road vehicle to be interconnected with any entity associated with it. Therefore, V2X communications include vehicle-to-vehicle (V2V), vehicle-tonetwork (V2N), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communications. The original V2X radio standards are built on IEEE 802.11p technology and are known as Dedicated Short-Range Communications (DSRC) [1]. DSRC supports ad-hoc networking based on wireless local area network (WLAN) technologies for vehicles and is defined in the US by the American IEEE 1609 family of standards, known as Wireless Access in Vehicular Environments (WAVE) [2]. Another alternate access layer technology for ITS has been defined by the Third Generation Partnership Project © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 745–754, 2022. https://doi.org/10.1007/978-3-031-02447-4_77

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(3GPP), which is Cellular-based V2X (C-V2X) that is built on Long-Term Evolution (LTE) and known as LTE-V2X. 3GPP has introduced for the first time in Release 12 (Rel-12) direct Device-toDevice communications as support for proximity services (D2D ProSe) based on cellular technologies [3]. Release 14 (Rel-14) is the initial 3GPP standard that introduced LTE-V2X by providing LTE enhancements for V2X communications [4], whereas V2X services enhancements were developed as part of Release 15 (Rel-15) [5]. Release 14 (Rel-14) is the initial 3GPP standard that introduced LTE-V2X by providing LTE enhancements for V2X communications [4], whereas V2X services enhancements were developed as part of Release 15 (Rel-15) [5]. 3GPP has defined a novel cellular V2X standard in Release 16 (Rel-16) that uses the 5G New Radio (NR) air interface and is known as NR-V2X [6]. Additionally, the NR-V2X enhancements in future Rel-17 specifications include resource allocation improvements, NR Multiple-Input MultipleOutput (MIMO) for beam management, sidelink positioning, and carrier aggregation enhancements/multi-radio dual connectivity [2]. 3GPP has defined from Rel-14 to Rel-16 its use cases (UCs) of V2X to determine requirements, together with the respective evaluations for different scenarios. 3GPP supports three main categories of UCs, which are: (a) Non-safety V2X services (e.g., mobile hotspots, connected vehicles, dynamic updates of digital maps, etc.); (b) V2X services related to safety (e.g., vehicle platooning, autonomous vehicles, etc.); and (c) V2X services in various Radio Access Technologies (RAT) of 3GPP including LTE and 5G NR [7, 8]. To meet the 3GPP’s UCs requirements, a further collaboration between telecommunication technologies and the automotive industry is becoming a necessity. For this purpose, the 5G Automotive Association (5GAA) was created in 2016 to study the V2X architecture proposals developed by 3GPP and the defined UCs, and solve the regulatory and deployment issues of V2X communications [9]. The rest of the paper is organized as follows. Section 2 describes V2X use cases defined by 3GPP and 5GAA, as well as the physical layer design of V2X technology. Section 3 describes the performance metrics and the use of AASHTO model to evaluate the alert delivery reliability rate of LTE-V2X communications. Section 4 presents the methodology to evaluate the alert delivery reliability rate of LTE-V2X communications, which is assessed in terms of packet reception ratio, and also the comparative performance analysis evaluated in the highway, urban grid, and rural scenarios. It also highlights potential technologies that will be further improved in future V2X communications, specifically NR-V2X, to enhance road traffic safety and efficiency. Finally, Sect. 5 concludes the paper.

2 Overview of Cellular V2X Technology 2.1 Use Cases and Architecture of Cellular V2X Technology 3GPP has defined in Rel-16 the NR-V2X UCs into four groups: (a) Advanced Driving; (b) Vehicles Platooning; (c) Extended Sensors; and (d) Remote Driving [7]. For each UC, 3GPP also classifies various automation levels according to the Society of Automotive Engineers (SAE) levels of automation [10], starting from 0 (no automation) to 5 (complete automation) [8]. On the other hand, 5GAA has classified the UCs as

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“initial/day-1” or “advanced” UCs, either according to the support level in the different 3GPP specification releases of C-V2X or according to the study conducted by the 5GAA to determine whether they will be part of the C-V2X deployments of the first or subsequent phases. Given that “Day 1” UCs fall into the general category of basic V2X services that require less stringent requirements on the network related to key performance indicators (KPIs) and can therefore be supported by LTE networks. Whereas “Advanced” UCs (e.g., autonomous driving or remote driving) impose more stringent requirements, and therefore the use of 5G networks becomes primordial [11]. The LTE-V2X architecture was firstly introduced in Rel-14 with two additional functional entities, which are the V2X application server (AS) and the V2X control function (CF) [12]. The role of the former entity is to collect unicast uplink data from the user equipment (UE) and transfer it to the targeted UEs in unicast, broadcast, or multicast. Whereas the V2X CF is used to monitor and report to the UE the required parameters for V2X communications access. The V2X communications are based on the transmission of two message types: Cooperative Awareness Message (CAM) and Decentralized Environmental Notification Message (DENM) [13]. CAMs are small awareness messages generated periodically and broadcast information about the vehicle like its speed, direction, and geographical location. While DENMs are transmitted following specific events, like abrupt braking, traffic jam, or imminent collision. 2.2 Physical Layer Design and Resource Allocation of LTE-V2X The key features on which the LTE-V2X PHY layer is based are: (a) single-carrier frequency division multiple access (SC-FDMA); (b) blind message retransmission that is not based on feedback from the receiver (e.g., CAM); and (c) turbo codes. In the time domain, the resource allocation is based on the transmission time interval (TTI), which corresponds to a 1-ms subframe. Each subframe contains 14 orthogonal frequency division multiplexing (OFDM) symbols using a normal cyclic prefix. The data is transmitted by nine symbols, while four symbols (3rd, 6th, 9th, and 12th) convey the demodulation reference signals (DMRS) for channel sensing and mitigation of the Doppler effect caused by the high velocity of vehicles [14]. The subchannel notion in the frequency domain was defined as one multiple of 180 kHz resource block (RB) bandwidth, which is equivalent to 12 subcarriers with a subcarrier spacing (SCS) of 15 kHz. The data is structured into transport blocks (TBs), and a TB may employ one or more subchannels according to the modulation and coding scheme (MCS) which is used, the amount of RBs by subchannel, and the message size. The TB transmission is based on the turbo codes and QPSK, 16-QAM modulations, and also 64-QAM modulation introduced in Rel-15 [2]. Based on the LTE air interface, 3GPP has introduced in Rel-14 two V2X communication modes. The first mode is device-to-device (D2D) communication via the PC5 interface, a novel feature that allows direct communication through the so-called sidelink (SL). The second mode allows connecting vehicle UEs to an evolved Node B (eNB) over the LTE Uu interface [15]. Two channels are transmitted at different frequencies and within the same TTI in sidelink communications: the physical sidelink shared channel (PSSCH), which conveys data through TBs, and the physical sidelink control channel (PSCCH), which carries control information in the sidelink control information

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(SCI) message occupying 2 RBs and including frequency resource location, modulation, and coding scheme (MCS), message priority, etc. [14]. The SCI and the TB to which it refers must always be sent within a single subframe. A TB can only be received correctly if the related SCI is successfully decoded since the SCI contains vital information for the successful decoding of its associated TB. For LTE-V2X SL communications, two new modes of resource allocation were defined: modes 3 and 4. In mode 3, the UE performs sidelink communications on radio resources that are managed by the eNB, which selects and configures the communication resources, i.e., subchannels. However, the UEs that use mode 4 may be operating outside of network coverage since they autonomously select resources from one or more contention-based resource pools provided by the eNB [15]. Therefore, mode 3 performs better than mode 4 due to the centralization of transmission scheduling throughout the eNB. 2.3 Physical Layer Design and Resource Allocation of NR-V2X V2X sidelink communications built on the 5G NR radio interface were first introduced in Rel-16 and are known as NR-V2X SL. NR-V2X SL aims at supporting the use cases of enhanced V2X, such as connected and autonomous vehicles [16]. The NR PC5 interface handles both V2X sidelink communications for LTE and 5G NR. Furthermore, the V2X communications for downlink (DL) and uplink (UL) transmissions via NR Uu interface are supported in both NR deployments: Standalone (SA) and Non-Standalone (NSA) [17]. The physical layer of sidelink NR-V2X reuses certain LTE-V2X specifications defined in Rel-14 but also introduces supplementary procedures to support groupcast and unicast communications. Thus, Rel-15 has introduced the Low-Density Parity-Check (LDPC) coding to encode the TBs instead of turbo codes, whereas the TBs transmission is based on four modulation schemes: QPSK, 16-QAM, 64-QAM, and 256-QAM [6]. Additionally, the SCS of the OFDM waveform and the number of slots included in a 5G NR subframe may be flexibly set in NR-V2X communications. Therefore, NR-V2X supports 15 kHz multiples as SCS values of OFDM waveform since various OFDM numerologies are possible with an adjustable SCS at the following values: 15 kHz, 30 kHz, 60 kHz, and 120 kHz [5]. Besides the PSCCH and PSSCH channels used in LTE-V2X, NR-V2X uses two more channels in sidelink, which are: Physical Sidelink Broadcast Channel (PSBCH) and Physical Sidelink Feedback Channel (PSFCH). The transmitted PSBCH is included in the SL synchronization signal block (S-SSB) to support SL synchronization of proximate UEs that may be beyond network coverage [18]. Unlike the LTE-V2X SL, where only broadcast communications are supported, the physical layer of NR-V2X SL supports broadcast, groupcast and unicast communications. NR-V2X uses the hybrid automatic repeat request (HARQ) feedback carried by PSFCH in groupcast and unicast communications to enhance the reliability of the sidelink communications [14]. Furthermore, as opposed to LTE-V2X, where the SCI is sent in a single stage, two stages are used to send the SCI in NR-V2X. The first-stage SCI is conveyed by the PSCCH, while the secondstage SCI is conveyed by the associated PSSCH [18]. For the subchannel selection in NR-V2X sidelink communications, two new modes were defined in Rel-16, namely,

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mode 1 and mode 2 [18]. These two modes are the alternatives to the LTE-V2X modes 3 and 4 that were described in Subsect. 2.2.

3 Performance Metrics and Simulation Assumptions 3.1 Packet Reception Ratio Evaluation The average packet reception ratio (PRR) defined by 3GPP is the performance metric used for transmission reliability of V2X services. It is calculated around receivers vehicles located in a ring area of 20-m width around a transmitter vehicle, as the ratio of the number of vehicles that received the transmitted packets successfully to the overall number of vehicles in the ring area [19]. Thus, the average PRR is calculated as follows: n Xk (1) PRRAvg = k=1 n k=1 Yk where n is the total number of messages generated by the transmitter vehicle, Yk is the total number of vehicles located inside the ring area from the vehicle transmitting the message, Xk is the number of vehicles that received message k successfully in the same ring area [13]. The average PRR is calculated for the five messages forming the CAMs (i.e., one 300-byte message followed by four 190-byte messages), and an average PRR of at least 90% is considered sufficient as a reliability level [13]. 3.2 Alert Delivery Reliability Rate Evaluation and Simulation Scenarios Alert delivery reliability is the probability that a CAM transmitted by a road user would be received correctly by the targeted road users. Therefore, the alert delivery reliability rate for V2X communications is calculated based on the vehicle speed while accounting for the required safe stopping sight distance (SSD). The determination of the correct alert delivery reliability rate for a given speed is based on the calculation of the required SSD for this speed using the AASHTO model [20] that is defined as follows: SSD = 0.278 · TBR · V + 0.039 · V 2 / 

(2)

where TBR is the time of brake reaction in seconds, V is the vehicle speed in km/h, and  is the rate of deceleration in m s−2 . The values of TBR and  are recommended at 2.5 s and 3.4 m s−2 , respectively [20]. For a given vehicle speed within the range of speeds associated with the modeled accident scenario that is used in simulation, the PRR may be evaluated at the corresponding SSD by referring to the speed-distance relationship indicated by formula (2). The performances of V2V and V2P communications are evaluated in three scenarios, two defined by 3GPP and that are the highway and urban grid scenarios [13]. While the third scenario is the rural scenario which has been covered by 5GAA for road safety improvement. The highway and urban grid scenarios are shown in Fig. 1 and Fig. 2, respectively [13]. Whereas the rural scenario models a 2-lane highway with the same length and lane width as the highway scenario [19]. The remaining simulation settings are summarized in Table 1.

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Fig. 1. Road configuration of highway scenario [13].

Fig. 2. Road configuration of urban grid scenario [13].

Table 1. Simulation parameters [19]. Parameter

Value

Carrier frequency

6 GHz

Bandwidth

10 MHz

Resource allocation mode

Mode 4 (autonomous mode)

Frequency resource allocation

12 RBs for 190-byte message 16 RBs for 300-byte message

Pathloss model

Urban grid scenario: WINNER+ B1 Manhattan grid Highway and rural scenarios: LOS in WINNER+ B1

Shadowing standard deviation

Urban grid scenario: 3 dB for LOS and 4 dB for NLOS Highway and rural scenarios: 3 dB

Vehicle density

Average distance between vehicles on the same lane: 2.5 s × absolute vehicle speed

Total number of pedestrian UEs

Highway scenario: 10 Urban grid scenario: 500 Rural scenario: 50

Absolute pedestrian speed

3 km/h

CAM frequency

V2V traffic: 10 Hz; V2P traffic: 1 Hz

Maximum transmit power

23 dBm for vehicle and pedestrian UE

Antenna gain

Vehicle UE: 3 dBi; Pedestrian UE: 0 dBi

Antenna configuration

2 Rx and 1 Tx antennas

Noise figure

9 dB for vehicle and pedestrian UE

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4 Performance Analysis 4.1 LTE-V2V and LTE-V2P Performance Evaluation The performance evaluation results in terms of PRR regarding LTE-V2V (PC5) communications for highway, urban grid, and rural scenarios are presented in Fig. 3a, Fig. 3b, and Fig. 3c, respectively [19].

(a) Highway scenario

(b) Urban grid scenario

(c) Rural scenario

Fig. 3. PRR evaluation of LTE-V2V communications.

The PRR evaluation results related to LTE-V2P (PC5) communications for the highway, urban and rural scenarios are depicted in Fig. 4a, Fig. 4b, and Fig. 4c, respectively [19]. According to Fig. 3a, the PRR for a vehicle speed of 140 km/h is above 90% for transmitter-receiver (Tx-Rx) communication ranges up to 370 m in the highway scenario. Also, as can be seen in Fig. 3b, a vehicle speed of 60 km/h provides a PRR above 90% for Tx-Rx communication ranges up to 81 m in an urban grid scenario.

(a) Highway scenario

(b) Urban grid scenario

(c) Rural scenario

Fig. 4. PRR evaluation of LTE-V2P communications.

As for the rural scenario, the required PRR of 90% shown in Fig. 3c is achieved for Tx-Rx communication ranges up to 527 m corresponding to the vehicle speed of 100 km/h. According to PRR’s evaluation results for V2V and V2P communications in the three scenarios, the maximum distance between the transmitting and receiving vehicle providing at least 90% PRR is greater the higher the speed of the vehicles. Furthermore,

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LTE-V2V and LTE-V2P communications in the rural scenario perform better than those in the highway and urban scenarios due to the long Tx-Rx communication ranges they provide, and this is because of the low vehicle density and low interference level in the rural scenario. 4.2 Evaluation of Alert Delivery Reliability Rate The PRR evaluation results of LTE-V2X presented in Subsect. 4.1 are used to determine the alert delivery reliability rate for LTE-V2X as a function of speed vehicle. The speed ranges associated with each modeled accident scenario are 50–80 km/h for rural at junction accident scenario; 50–100 km/h for rural not at junction accident scenario; 15–40 km/h for urban grid accident scenario; and 70–140 km/h for highway accident scenario [19]. Based on the required SSD relating to the vehicle speed defined by formula (2), the alert delivery reliability rate of V2V and V2P communications for the highway, urban grid, and rural scenarios depicted in Fig. 5 is determined according to the speed range related to each modeled accident scenario. As shown in Fig. 5, both V2V and V2P communications provide over 90% alert delivery reliability for all vehicle speed values, only in highway, urban grid, and rural not at junction accident scenarios. Furthermore, V2V communications perform better than V2P communications in urban grid accident scenario and rural accident scenarios, both with and without junctions. Averaging the alert delivery reliability rates from Fig. 5 over the appropriate speed ranges provides the averaged alert delivery reliability rates shown in Fig. 6 for the four modeled accident scenarios.

Fig. 5. Alert delivery reliability rate of LTE-V2X communications.

Fig. 6. Average rates of alert delivery reliability of LTE-V2X communications.

As shown in Fig. 6, V2V and V2P communications perform best in the rural nonjunction accident scenario, and this is mainly due to the limited density of vehicles in the rural environment leading to the minimum of interference and the line-of-sight (LOS) radio propagation. On the other hand, V2P communications in the highway accident scenario perform better than the urban grid accident scenario since the latter is characterized by a high level of interference due to the high density of pedestrians and vehicles.

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However, the V2V and V2P communications in the rural at junction accident scenario present the weakest performance compared to the other accident scenarios. The performance degradation in this scenario is strongly related to the non-line-of-sight (NLOS) radio propagation due to the obstacles encountered, besides the vehicle NLOS (NLOSv) where the line-of-sight path is blocked by nearby vehicles. It should be noted that further performance improvements of LTE-V2X communications are possible by using 5G NR, which offers very high-performance scalability as it provides a flexible framework compatible with the previous C-V2X. Therefore, NR-V2X will be complementing LTE-V2X to meet the stringent requirements of some C-V2X applications. It also reuses some existing generic technologies, such as mobile edge computing (MEC) which is not specific to V2X applications, to further reduce endto-end (E2E) latency. The main benefit of MEC is that it offers the ability to build a cloud environment at the mobile network edge that is nearer to UEs, using storage and computing resources to serve V2X applications. Additionally, the NR-V2X network core, which is proposed in Rel-16, includes network management using network slicing and software-defined networking (SDN) technologies, along with 5G virtualized capabilities to further enhance the performance of the V2X communications. On the other hand, the multi-antenna technology known as NR-MIMO, which will be improved in future Rel-17 specifications, is recognized as a key technology of NR-V2X sidelink communications. This technology will enable improved spectral efficiency in high-mobility V2X scenarios.

5 Conclusion To conclude, this paper has provided an overview of V2X technology that presents its evolution within 3GPP from Rel-14 to Rel-17. We described the performance evaluation methodology that was established by 3GPP, including performance metrics and the assumptions on which simulation scenarios were based. We also performed a comparative analysis of LTE-V2X performance in terms of PRR in four modeled accident scenarios, and performance evaluation results have shown that LTE-V2V and LTE-V2P communications in the rural at junction accident scenario perform the worst compared to the other accident scenarios. To overcome this performance decrease in this accident scenario, NR-V2X is the only technology able to improve performance despite the NLOS radio propagation that strongly characterizes this accident scenario. Finally, the paper highlights key future technologies that will be used by NR-V2X leveraging the flexible framework of 5G NR, to ensure very high levels of safety and efficiency of the future road traffic along with minimal E2E latency of V2X communications.

References 1. Kenney, J.B.: Dedicated short-range communications (DSRC) standards in the united states. Proc. IEEE 99(7), 1162–1182 (2011) 2. Garcia-Roger, D., Gonzalez, E.E., Martin-Sacristan, D., Monserrat, J.F.: V2X support in 3GPP specifications: From 4G to 5G and beyond. IEEE Access 8, 190946–190963. IEEE (2020)

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3. Lin, X., Andrews, J.G., Ghosh, A., Ratasuk, R.: An overview of 3GPP device-to-device proximity services. IEEE Commun. Mag. 52(4), 40–48 (2014) 4. ETSI: TR 21.914 release description; release 14 (V14.0.0). Technical report, ETSI, June 2018 5. ETSI: TR 21.915 release description; release 15 (V15.0.0). Technical report, ETSI, October 2019 6. 3GPP: TR 21.916 (Release 16); technical specification group services and system aspects (V0.4.0). Technical report, 3GPP, March 2020 7. 3GPP: TR 22.886 study on enhancements and support for 5G V2X services; release 16 (V16.2.0). Technical report 3GPP, December 2018 8. 3GPP: TS 22.186 enhancement of 3GPP support for V2X scenarios; release 16 (V16.2.0). Technical Spec., 3GPP, June 2019 9. 5GAA: TR S-200137 working group standards and spectrum study of spectrum needs for safety related intelligent transportation systems - day 1 and advanced use cases. Technical report 5GAA, June 2020 10. SAE: Standard J3016 - taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. Technical spec., SAE International, September 2016 11. 5GAA: White paper C-V2X use cases - methodology, examples and service level requirements. Technical report 5GAA, June 2019 12. 3GPP: TS 23.285 architecture enhancements for V2X services; release 16 (v16.2.0). Technical report, 3GPP, December 2019 13. 3GPP: TR 36.885 study on LTE-based V2X services (v14.0.0, release 14). Technical report, 3GPP, June 2016 14. Garcia, M.H.C., et al.: A tutorial on 5G NR V2X communications. IEEE Commun. Surv. Tutor. 23(3), 1972–2026. IEEE (2021) 15. 3GPP: TS 36.300 evolved universal terrestrial radio access (E-UTRA) and evolved universal terrestrial radio access network (E-UTRAN); overall description; stage 2 (v15.7.0, release 15). Technical Spec., 3GPP, September 2019 16. 3GPP: TR 22.886 study on enhancement of 3GPP support for 5G V2X services (v16.2.0, release 16). Technical report, 3GPP, December 2018 17. 5G Americas: The 5G Evolution - 3GPP Releases 16-17. Technical report, 5G Americas, January 2020. https://www.5gamericas.org/wp-content/uploads/2020/01/5G-Evolution3GPP-R16-R17-FINAL.pdf 18. 3GPP: TR 37.985 overall description of radio access network (RAN) aspects for vehicle-toeverything (V2X) based on LTE and NR (v16.0.0, release 16). Technical report, 3GPP, June 2020 19. 5GAA: An assessment of LTE-V2X (PC5) and 802.11p direct communications technologies for improved road safety in the EU. Technical report, 5GAA, December 2017 20. Abdulhafedh, A.: Highway stopping sight distance, decision sight distance, and passing sight distance based on AASHTO models. Open Access Libr. J. 7(3), 1–24 (2020)

Mechatronic, Robotic and Control System

Evaluation of the Dynamic Behavior of a Rotor Based on a Vibration Diagnosis Mohammed Bouaicha1(B) , Imad El Adraoui2 , Hassan Gziri2 , Nadia Machkour1 , and Mourad Zegrari1 1 Laboratory of Structural Engineering, Intelligent, and Electrical Energy (LISSIEE), The National Higher School of Arts and Crafts (ENSAM), Hassan II University, Mers Sultan, BP 9167, Casablanca, Morocco [email protected] 2 Laboratory of Engineering, Industrial Management and Innovation (IMII), The Faculty of Sciences and Technology, Hassan 1st University, PO Box 577, Settat, Morocco

Abstract. This article attempts to evaluate the dynamic behavior of a rotor of a hydroelectric power station. The diagnostic approach adopted is predicated on vibration analysis using multi-sensor scanning technologies in the field of industry. 4.0. The proposed study helps monitor the dynamic state of health of the rotor in a manner relevant to maintenance 4.0. To achieve this, tests were carried out in different operating modes of the hydroelectric group (MAVNE, MAVEX, etc.). The tests culminated in a set of measurements, which are collected using several sensors (accelerometers, displacement probes) installed on the rotor bearings. The rotor is considered to be in good dynamic operating condition if two conditions are met, as the first requirement, its overall level of vibration is good or acceptable according to Standard ISO 10816-5. The second condition concerns the maximum eccentricity of the shaft line that does not exceed the theoretical clearance recommended by standard ISO 7919-5. If one of the thresholds is exceeded, a predictive maintenance intervention should be anticipated. Keywords: Industry 4.0 · Maintenance 4.0 · Diagnosis · Vibration analysis · Rotor · Standard ISO 10816-5 · Standard ISO 7919-5

1 Introduction The degraded dynamic behavior of a rotating machine is manifested in a set of degradations, namely: Wear, vibrations, cracking, etc. In [1], the authors established a diagnostic model of wear of a guide bearing for maintenance 4.0. A tribological approach is adopted to determine the model describing the evolution of wear. The latter helps predict the remaining service life before the bearing failure. Diagnosis based on vibration analysis is an effective technique used in the strategy of predictive maintenance 4.0. It generally targets rotating machines. Its main objective is to monitor the health of the machines during operation, using the collection of signals at several measuring points [2, 3] on the one hand, and to meet cost requirements and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 S. Motahhir and B. Bossoufi (Eds.): ICDTA 2022, LNNS 455, pp. 757–766, 2022. https://doi.org/10.1007/978-3-031-02447-4_78

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security [4] on the other hand. A study is conducted on a test bench, by stressing the shaft to an unbalance fault [5]. The authors installed an accelerometer on one of the two bearings. The data collected show vibratory acceleration signals where its behavior evolves according to an exponential law after a certain time. In the same context, the signals collected from the accelerometer are treated differently by [6] according to an operational aspect of automation of a process based on learning techniques, and an energy aspect of which the key point is the spectrum analysis. However, the work carried out in [7] declares the limits observed in the technique of detection of imbalance [5], which are due to the dependence of the information collected for the diagnosis. For this, the authors proposed a technique based on the antagonistic neural networks which display mergers of information acquired by several sensors. The objective is the generation of new information for use in Data-Clearing. To do this, two different modes are established based on the logic of information fusion: the pre-fusion GAN mode and the post-fusion GAN mode. The relevance of the cited work lies in its ability to integrate into complex systems. So, to identify faults in a rotating machine, vibration analysis is a priority approach. However, direct implantation remains complex. At this stage, the authors in [8] have accessed the feasibility by using two identical platforms. First, the vibrational information is collected with different foundations under different conditions. Second, the vibrational information is analyzed in the frequency domain for spectral calculation. Similarly, the authors in [9] have established an approach for fault diagnosis based on deep convolutional neural networks (DCNN). The particularity of this approach is the consideration of the characteristics of the raw information and the optimization of the combinations between several levels of fusion. Therefore, these two points mentioned help meet all the diagnostic requirements for maintenance 4.0. In [10], fuzzy neural networks are used for the diagnosis of a pump. The researchers conducted information training to predict future failures of the pump and optimization of the discrete model by a continuous pattern. A technique proposed in [11] is called the endto-end technique using long-term memory networks. In this technique, the information is processed in two stages, namely: extraction and assembly. It is obtained by using the Internet of Things (IoT) technique in the context of industry 4.0. This technique allows diagnostic accuracy, long-term extraction, and reduction in computational complexity. Another application was made in [12]. The researchers carried out measurements on a test bench, the purpose of which is to diagnose a bearing undergoing a forced radial load. Eventually, this work is extended to a prognosis of the health of the failing component. This work, as per the relevant approach discussed in the literature, aims to evaluate the dynamic behavior of the rotor of a hydroelectric group according to ISO 10816-5 [13] and ISO 7919-5 [14]. This rotating machine has cracked at certain welds of the cross members. The diagnosis is based on the vibration analysis of the signals acquired from several measuring points at the rotor bearings. To do this, two types of sensors (displacement probes and accelerometers) are used for the measurement of vibrations and the displacements of the bearing centers. This paper is organized as follows: materials and methodology, results and discussion, and conclusion and perspective.

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2 Materials and Methodology Rotors animate the hydraulic power stations. The latter is responsible for driving the alternators for the production of electrical energy using torque. The latter is created by the flow of water arriving from the guidelines towards the vanes. The components of the rotor are shown in the Fig. 1.

Fig. 1. Components of a hydroelectric group

The rotor is of the Francis type, the position of which is vertical. It rotates with a rotation frequency of around 333 rpm to produce a maximum electrical power of 80 MW. The turbine consists of 17 vanes distributed equidistantly over the shaft. The rotor is guided in rotation by 3 bearings, including the turbine bearing (PTU), the lower alternator bearing (PAI), and the upper alternator bearing (PAS). This work aims to evaluate dynamic behavior through vibration analysis. In the context of Industry 4.0, the collection of data reflecting the dynamic health of the rotor is carried out by: – Three accelerometers on each bearing in the Upstream Radial (Am), Right bank Radial (RD), and Axial (Ax) directions for measuring vibrations (points 1 to 9). – Two displacement probes for each landing in the Upstream Radial (Am) and Right Bank Radial (RD) directions at 90° for measuring the displacements of the centers of the bearings (points 10 to 15). Figure 2 shows the location of the different sensors.

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Fig. 2. Location of sensors

Table 1 describes the location of the accelerometers in each point and their associated direction. Table 1. Sensor number, designation, and direction Sensor number

Designation

Direction

01

PTU

Am

02

PTU

RD

03

PTU

Ax

04

PAI

Am

05

PAI

RD

06

PAI

Ax

07

PAS

Am

08

PAS

RD

09

PAS

Ax

10

PTU

Am

11

PTU

RD

12

PAI

Am

13

PAI

RD

14

PAS

Am

15

PAS

RD

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The tests were carried out on the rotor according to the following operating modes: • • • •

MAVNE: No-load operation not excited; MAVEX: No-load excited; MQUART: Operation at 20 MW; MDEMI: Half-load operation (40 MW) with supply and absorption of reagent (MDEMI +Q/MDEMI –Q); • M3QUARTS: Operation at 60 MW; • MPMAX: Full load operation (80 MW).

3 Results and Discussion This section first presents an analysis of vibrations by the scalar energy indicator representative of low and medium frequency vibrations “Global Velocity Level (GVL)”, then an examination of misalignment or eccentricity of the three bearings (PTU, PAI, and PAS) based on the displacement measurement of their centers. Table 2 represents all the vibration measurements (GVL) taken, then illustrated in Fig. 3. The point (03 Ax + PTU) installed in the axial direction of the turbine bearing has a maximum vibration speed level of around 1.95 (mm/s eff) for the MQUART test. Table 2. Global velocity level (GVL) GVL (2–1000 Hz) (mm/s eff)

MAVNE

MAVEX

MQUART

MDEMI

M3QUARTS

MPMAX

01 Am + PTU

1.00

0.94

0.96

1.37

0.29

0.56

02 RD + PTU

0.94

0.88

0.93

1.39

0.31

0.63

03 Ax + PTU

1.70

1.70

1.95

1.76

0.50

1.32

04 Am + PAI

0.38

0.38

0.43

1.23

0.25

0.38

05 RD + PAI

0.30

0.32

0.36

1.18

0.19

0.34

06 Ax + PAI

0.56

0.60

0.66

1.26

0.21

0.42

07 Am + PAS

1.21

1.10

1.08

1.54

1.02

1.14

08 RD + PAS

1.18

1.15

1.14

1.55

1.07

1.09

09 Ax + PAS

0.73

0.80

1.13

1.72

0.75

0.75

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GVL (2-1000Hz) (mm/s eff)

2

1.5

1

0.5

0 01 Am + PTU

02 RD + PTU

MPMAX

03 Ax + PTU

M3QUARTS

04 Am + PAI

05 RD + PAI

MDEMI

06 Ax + PAI

07 Am + PAS

MQUART

08 RD + PAS

MAVEX

09 Ax + PAS MAVNE

Fig. 3. Evolution of the overall level of the GVL velocity (2–1000 Hz)

Figure 4 represents the evolution of the peak-to-peak vibratory displacement indicator (Spp) of the bearings. The movements observed from: • Turbine bearing (PTU) is about 500 µm during half-load tests (MDEMI) (Spp max = 508 µm in MDEMI -Q operating mode). • Alternator lower bearing (PAI) is acceptable with a max Spp of 152 µm in the MAVNE mode of operation. • Alternator upper bearing (PAS) are good with a maximum of 129 µm in MAVNE mode.

Displacement measurement (µm) 600 500 400 300 200 100 0

MAVN E

MAVE X

MQUA RT

Spp PTU

190.39

221.74

Spp PAI

152.32

116.8

Spp PAS

129.35

102.86

Spp PTU

MDEM I

M3QU ARTS

250.91

485.77

258.77

98.57

100.03

86.17

84.62

98.83

101.7

97.52

100.4

96.86

104.09

101.45

102.49

Spp PAI

MPMA X 297.66

MDEM I +Q 491.37

MDEM I -Q 507.92

Spp PAS

Fig. 4. Evolution of the peak-to-peak vibratory displacement indicator (Spp) of the bearings

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The displacement orbits of the centers of the three rotor bearings are shown in Figs. 5, 6 and 7.

Fig. 5. Axis displacement orbits (PTU)

Fig. 6. Axis displacement orbit (PAI)

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Fig. 7. Axis displacement orbit (PAS)

The study proposed in this work provides relevant results on the overall level of rotor vibration velocity. They present acceptable speed thresholds according to Standard ISO 10816-5 [13]. The machine rotor is located in the area (A/B) which is around 2.5 (mm/s eff) for the upper alternator bearing (PAS) (Table 3). Table 3. ISO 10816-5 [13] GVL (2–1000 Hz) (mm/s eff)

Good (A/B)

Acceptable (B/C)

Not satisfying (C/D)

Unacceptable (D)

Upper alteration bearing (PAS)

2.5

4

6.4

>6.4

The measured displacements of the alternator bearings are good. The maximum Spp is 152 µm on the lower alternator bearing (PAI) and 129 µm on the upper alternator bearing (PAS). The displacement values respectively classify these bearings in zones B, A of Standard ISO 7919-5 [14] (Table 4). The turbine bearing has acceptable displacement indicators (Spp max = 298 µm) for the various operating modes, except during the MDEMI mode and during tests with variations of reagent (Spp = 508 µm in MDEMI –Q mode) which is due to a partial load torch. It is recommended to avoid this operating regime. The use of the experimental approach based on vibration analysis provides significant results to assess the dynamic behavior of the rotor. The validation of this assessment is linked to the references of Standards ISO 10816-5 [13] and ISO 7919-5 [14] to avoid any

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Table 4. ISO 7919-5 [14] 333 rpm

A

B

C

D

Spp (µm)