Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science: ICMMCS 2023 (Advances in Intelligent Systems and Computing, 1450) 9819936101, 9789819936106

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Advances in Intelligent Systems and Computing 1450

Sheng-Lung Peng Noor Zaman Jhanjhi Souvik Pal Fathi Amsaad   Editors

Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science ICMMCS 2023

Advances in Intelligent Systems and Computing Volume 1450

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen , Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Indexed by DBLP, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST). All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Sheng-Lung Peng · Noor Zaman Jhanjhi · Souvik Pal · Fathi Amsaad Editors

Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science ICMMCS 2023

Editors Sheng-Lung Peng Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan, Taiwan Souvik Pal Department of Computer Science and Engineering Sister Nivedita University Kolkata, West Bengal, India

Noor Zaman Jhanjhi School of Computer Science, SCS Taylor’s University Subang Jaya, Malaysia Fathi Amsaad College of Engineering and Computer Science, Joshi Research Center 489 Wright State University Dayton, OH, USA

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-99-3610-6 ISBN 978-981-99-3611-3 (eBook) https://doi.org/10.1007/978-981-99-3611-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organizing Committee and Key Members

Conference Committee Members Conference General Chair Sheng-Lung Peng, National Taipei University of Business, Taiwan Program Conveners Noor Zaman Jhanjhi, Taylor’s University, Malaysia Fathi Amsaad, College of Engineering and Computer Science, Wright State University, USA Hanaa Hachimi, Secretary General of Sultan Moulay Slimane University USMS of Beni Mellal. Morocco Satyendra Narayan, Sheridan Institute of Technology, Ontario, Canada Conference Convener M. Pushpa Rani, Mother Teresa Women’s University, India Conference Organizing Chairs D. Balaganesh, Berlin School of Business and Innovation, Germany Souvik Pal, Sister Nivedita University, India Program Chairs D. Akila, Saveetha College of Liberal Arts and Sciences, SIMATS Deemed to be University, India K. Kavitha, Mother Teresa Women’s University, India Ahmed J. Obaid, Faculty of Computer Science and Mathematics, University of Kufa, Iraq R. Esther Felicia, Shri Krishnaswamy College for Women, India International Advisory Board Members Ahmed A. Elnger, Beni-Suef University, Egypt Alok Satsangi, Director, NSHM Knowledge Campus, Durgapur, India v

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Organizing Committee and Key Members

Abdelilah Jraifi, Cadi Ayad University of Marrakech, Morocco Bikramjit Sarkar, JIS College of Engineering, India Chakib El Mokhi, Cadi Ayyad University of Marrakech, Morocco Dac-Nhuong Le, Haiphong University, Vietnam. Debashis De, Maulana Abul Kalam Azad University of Technology, India Gia Nhu Nguyen, Duy Tan University, Vietnam J. M. Chang, National Taipei University of Business, Taiwan John Petearson Anzola, Los Libertadores University, Colombia Faiez Gargouri, University of Sfex, Tunisia Kusum Yadav, University of Hail, Kingdom of Saudi Arabia M. J. Diván, National University of La Pampa, Argentina Mohamed Amine Boutiche, University of Sciences and Technology Houari Boumediene, Algeria Mohammed Kaicer, Ibn Tofail University of Kenitra, Morocco Noor Zaman, Taylor’s University, Malaysia Mostafa Ezziyyani, Abdelmalek Essaâdi University, Morrocco Srinath Doss, Botho University, Botswana S. K. Hoskere, Shanghai United International School, China Sanjeevikumar Padmanaban, Aalborg University, Denmark Vasaki Ponnusamy, Universiti Tunku Abdul Rahman, Malaysia Vicente García Díaz, University of Oviedo, Spain Organizing Committee K. Kavitha, Mother Teresa Women’s University, India D. Usha, Mother Teresa Women’s University, India S. Vimala, Mother Teresa Women’s University, India M. P. Indra Gandhi, Mother Teresa Women’s University, India V. Selvi, Mother Teresa Women’s University, India Technical Program Committee Members Anand Paul, Kyungpook National University, South Korea Abdullah, Chandigarh University, India Amanpreet Kaur Sandhu, Chandigarh University, India Ashish Mishra, Gyan Ganga Institute of Technology and Science, India Abhishek Dhar, Swami Vivekananda University, India Bijoy Kumar Mandal, NSHM Knowledge Campus, Durgapur, India B. Jagadhesan, Dhanraj Baid Jain College, Chennai, India C. K. Lin, Fuzhou University, China Dac-Nhuong Le, Haiphong University, Haiphong, Vietnam Debabrata Roy, NSHM Knowledge Campus, Durgapur, India D. Napoleon, Bharathiar University, India Dibyendu Mukherjee, NSHM Knowledge Campus, Durgapur, India E. Ramaraj, Alagappa University, India K. Kavitha, Chellammal College for Women, India K. J. Pai, Ming Chi University, Taiwan K. Hema Shankari, Women’s Christian College, India

Organizing Committee and Key Members

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Kamal Wadhwa, Government of PG College Pipariya, India K. Kavitha, Mother Teresa Women’s University, India Kusum Yadav, University of Hail, Kingdom of Saudi Arabia Jarnail Singh, Chandigarh University, India J. Kamalakumari, Agurchand Manmull Jain College, Chennai, India L. J. Hung, National Taipei University of Business, Taiwan N. Pradeep, Bapuji Institute of Engineering and Technology, India Mohammed Kaicer, Ibn Tofail University, Morocco Manisha Malhotra, Chandigarh University, India Pradeep Laxkar, Mandsaur University, India P. R. S. Choudhry, Govt Model Science College, India P. Kandan, Annamalai University, India R. Amutha, Tiruvalluvar University College Arts and Science, India Ranbir Singh Batth, Lovely Professional University, India R. Kalaiarasi, Tamil Nadu Open University, India R. Velmurugan, Presidency College, India Sayed Ameeddnuddin Irfan, Universiti Teknologi Petronas, Malaysia Srinath Doss, Botho University, Botswana S. Vimal, Jeppiaar Engineering College, Chennai, India Suresh Rasappan, University of Technology and Applied Sciences—Ibri, Sultanate of Oman S. Mathivilasini, Ethiraj College for Women, India S. M. Tang, National Defense University, Taiwan Saurabh Adhikari, Swami Vivekananda University, India Sonjoy Pan, Swami Vivekananda University, India T. Nathiya, New Prince Shri Bhavani Arts and Science College, Chennai, India T. Nagarathinam, MASS College of Arts and Science, Thanjavur, India Vicente García Díaz, University of Oviedo, Spain V. R. Elangovan, Agurchand Manmull Jain College, Chennai, India V. Vijayalakshmi, SRM Institute of Science and Technology, Kattankulathur, India Vasaki Ponnusamy, Universiti Tunku Abdul Rahman, Malaysia.

Session Chair(s)

Arthi Ganesan, PSGR Krishnammal College for Women, India Ton Quang Cuong, Vietnam National University, Vietnam Bikramjit Sarkar, JIS College of Engineering, India Mathiyalagan Kalidass, Bharathiar University, India Midhunchakkaravarthy Janarthanan, Lincoln University College, Malaysia V. R. Elangovan, Agurchand Manmull Jain College, India Nguyen Ha Huy Cuong, Department of Computer Science, The University of Danang, Vietnam Kusum Yadav, Department of Computer Science, University of Hail Saudi Arabia Jarnail Singh, Department of Computer Application, Chandigarh University, Punjab, India Hanaa Hachimi, Department of Applied Mathematics and Computer Science, Sultan Moulay Slimane University, USMS of Beni Mellal. Morocco A. Meenakshi, Vels Institute of science technology and Advance Studies, India M. L. Suresh, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India

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Invited Speaker(s)

Prof. Ts. Dr. Saravanan Muthaiyah, Professor, Department of Information Technology, Multimedia University, Malaysia Prof. Farshad Badie, PROFESSOR, Berlin School of Business and Innovation, Berlin, Germany Dr. K. Kala, Vice-Chancellor, Mother Teresa Women’s University, Kodaikanal, India Dr. Noor Zaman Jhanjhi, Associate Professor, School of Computer Science, Taylor’s University, Malaysia Prof. Dr. Sayan Kumar Ray, Professor, School of Computer Science, Taylor’s University Malaysia

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Preface and Acknowledgment

The main goal of this proceedings book is to bring together top academic scientists, researchers, and research scholars so they can share their experiences and research results on all aspects of intelligent ecosystems, data sciences, and mathematics. ICMMCS 2023 is a conference that aims to bring together academic scientists, professors, research scholars, and students who work in different areas of engineering and technology. On ICMMCS 2023, you will have the chance to meet some of the best researchers in the world, learn about some new ideas and developments in research around the world, and get a feel for new Science–Technology trends. The conference will give authors, research scholars, and people who attend the chance to work together with universities and institutions across the country and around the world to promote research and develop technologies on a global scale. The goal of this conference is to make it easier for basic research to be used in institutional and industrial research and for applied research to be used in real life. ICMMCS 2023 has been jointly organized by Society for Intelligent Systems and Mother Teresa Women’s University, Madurai, Tamil Nadu [NAAC “A” Accredited Government University in Tamil Nadu] in association with National Taipei University of Business, Taiwan; Statistical and Informatics Consultation Center (SICC), University of Kufa, Iraq; and Sultan Moulay Slimane University, Beni Mellal—Khénifra region of Morocco, in Hybrid mode (Physical mode and Google Meet Platform) on 24 and 25 February, 2023. The conference brought together researchers from all regions around the world working on a variety of fields and provided a stimulating forum for them to exchange ideas and report on their researches. The proceeding of ICMMCS 2023 consists of 51 best selected papers, which were submitted to the conferences, and peer-reviewed by conference committee members and international reviewers. The presenters have shown their slides either virtually or in person. Experts in the field of education have gathered from all over the world, including India, Malaysia, Vietnam, Iraq, Spain, Pakistan, Taiwan, Canada, and Morocco, to discuss how to better prepare the next generation of leaders through education. Knowledge domains from many countries’ research cultures were brought together at this meeting. Academic conferences rely heavily on its authors and presenters for

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their credibility. In light of the current global pandemic, we appreciate the authors’ decision to present their works at this conference. We are very grateful to Almighty for always being there for us, through good times and bad, and for giving us ways to help ourselves. From the Call for Papers to the finalization of the chapters, everyone on the team worked together well, which is a good sign of a strong team. The editors and organizers of the conference are very grateful to all the members of Springer, especially Mr. Aninda Bose, for his helpful suggestions and for giving them the chance to finish the conference proceedings. We also appreciate the help of Prof. William Achauer and Prof. Anil Chandy. We are also thankful to Mrs. Ramya Somasundaram, who works for Springer as a project coordinator, for her help. We’re grateful that reviewers from all over the world and from different parts of the world gave their support and stuck to their goal of getting good chapters submitted during the pandemic. Last but not least, we want to wish all of the participants’ luck with their presentations and social networking. This conference can’t go well without your strong support. We hope that the people who went to the conference enjoyed both the technical program and the speakers and delegates who were there virtually. We hope you have a productive and fun time at ICMMCS 2023. Taoyuan, Taiwan Subang Jaya, Malaysia Kolkata, India Dayton, USA

Sheng-Lung Peng Noor Zaman Jhanjhi Souvik Pal Fathi Amsaad

Contents

Neuro-Fuzzy Logic Application in Speech Recognition . . . . . . . . . . . . . . . . D. Nagarajan, Khusbhu Chourashia, and A. Udhayakumar

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A Machine Learning Model for Predicting COVID-19 . . . . . . . . . . . . . . . . Lawrence Ibeh and Sulekha Mohamud

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Thyroid Disease Prediction Using a Novel Classification Enhancing MLP and Random Forest Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Akila, Bikramjit Sakar, Saurabh Adhikari, R. Bhuvana, V. R. Elangovan, and D. Balaganesh YouTube Sentimental Analysis Using a Combined Approach of KNN and K-means Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . Saurabh Adhikari, Ruchi Kaushik, Ahmed J. Obaid, S. Jeyalaksshmi, D. Balaganesh, and Falah H. Hanoon Big Data Analytics: Hybrid Classification in Brain Images Using BSO and SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Souvik Pal, Saikat Maity, Saurabh Adhikari, Mohammed Ayad Alkhafaji, and Hanaa Hachimi Breast Cancer Detection Using Hybrid Segmentation Using FOA and FCM Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Souvik Pal, Saikat Maity, Saurabh Adhikari, Mohammed Ayad Alkhafaji, and Vicente García Díaz Hybrid Optimization Using CC and PSO in Cryptography Encryption for Medical Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saurabh Adhikari, Mohammed Brayyich, D. Akila, Bikramjit Sakar, S. Devika, and S. Revathi

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Boundary Element Method for Water Wave Interaction with Semicircular Porous Wave Barriers Placed over Stepped Seabed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Santanu Kumar Dash, Kailash Chand Swami, Kshma Trivedi, and Santanu Koley

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Fostering STEM Education Competency for Elementary Education Students at Universities of Pedagogy in Vietnam . . . . . . . . . . . . 107 Tiep Quang Pham, Tuan Minh Dang, Huong Thi Nguyen, and Lien Thi Ngo Blockchain Based E-Medical Data Storage for Privacy Protection . . . . . . 125 Suja A. Alex, Noor Zaman Jhanjhi, and Sayan Kumar Ray A Study on Different Fuzzy Image Enhancement Techniques . . . . . . . . . . 135 Lalit Kumar Narayan and Virendra Prasad Vishwakarma A Review on Different Image Enhancement Techniques . . . . . . . . . . . . . . . 143 Lalit Kumar Narayan and Virendra Prasad Vishwakarma Cryptocurrency and Application of Blockchain Technology: An Innovative Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Chetan Trivedi and Sunil Kumar Efficient Cluster-Based Routing Protocol in VANET . . . . . . . . . . . . . . . . . . 165 Hafsah Ikram, Inbasat Fiza, Humaira Ashraf, Sayan Kumar Ray, and Farzeen Ashfaq Type II Exponentiated Class of Distributions: The Inverse Generalized Gamma Model Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Salah H. Abid and Jumana A. Altawil A Comparative Study of Masi Stock Exchange Index Prediction Using Nonlinear Setar, MS-AR and Artificial Neurones Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Saoudi Youness, Falloul Moulay Mehdi, Hachimi Hanaa, and Razouk Ayoub Application of Differential Transform Method for Solving Some Classes of Singular Integral Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Subhabrata Mondal, Arijit Das, Sonjoy Pan, Biman Sarkar, and Santu Ghorai Training Elementary Teachers in Vietnam by Blended Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Tiep Quang Pham, Tuan Minh Dang, Hung Van Nguyen, and Huong Thi Nguyen

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Doubly Truncated Type II Exponentiated Generalized Gamma Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Salah H. Abid and Jumana A. Altawil Detailed Review of Challenges in Cloud Computing . . . . . . . . . . . . . . . . . . . 251 Sneha Raina IoT-Enabled Smart Warehousing with AMR Robots and Blockchain: A Comprehensive Approach to Efficiency and Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Sumathi Balakrishnan, Amal Danish Azman, Jinan Nisar, Osezua Ehizogie Ejodame, Phung Shun Cheng, Tang Wai Kin, Yeo Jia Yi, and Shamp Rani Das Measuring the Feasibility of Using Fuel Cells in Marine Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Anastasia Kiritsi, Anastasios Fountis, and Adil Abbas Alwan Blockchain-Based Healthcare Research with Security Features that Can Be Applied to Protect Patient Medical Records . . . . . . . . . . . . . . 281 Mahfuzul Huda, Abdullah, Saurabh Adhikari, Adnan Allwi Ftaiet, and Niti Dey Wave Scattering by Thin Multiple Bottom Standing Vertical Porous Walls in Water of Uniform Finite Depth . . . . . . . . . . . . . . . . . . . . . . 291 Biman Sarkar, Priya Sharma, Santu Ghorai, Arijit Das, Subhabrata Mondal, and Sonjoy Pan Mobile Learning Integration into Teaching Kinematics Topic in English in Vietnam High School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Pham Thi Hai Yen and Ton Quang Cuong On Density of Grid Points in l ∞ -Balls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Nilanjana G. Basu, Partha Bhowmick, and Subhashis Majumder Performance Validation and Hardware Implementation of a BLE Mesh Network by Using ESP-32 Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Ziyad Khalaf Farej and Azhar Waleed Talab Laplace Transformation of Eigen Maps of Locally Preserving Projection (LE-LPP) Technique and Time Complexity . . . . . . . . . . . . . . . . 345 Soobia Saeed, Manzoor Hussain, Mehmood Naqvi, and Hawraa Ali Sabah A Systematic Literature Review of How to Treat Cognitive Psychology with Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Soobia Saeed, Manzoor Hussain, Mehmood Naqvi, and Kadim A. Jabbar

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Study of SEIRV Epidemic Model in Infected Individuals in Imprecise Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Ashish Acharya, Subrata Paul, Manajat Ali Biswas, Animesh Mahata, Supriya Mukherjee, and Banamali Roy Study of a Fuzzy Prey Predator Harvested Model: Generalised Hukuhara Derivative Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 Balaram Manna, Ashish Acharya, Subrata Paul, Subhabrata Mondal, Animesh Mahata, and Banamali Roy Overview of Applications of Artificial Intelligence Methods in Propulsion Efficiency Optimization of LNG Fueled Ships . . . . . . . . . . . 391 Anastasia Kiritsi, Anastasios Fountis, and Mohammed Ayad Alkhafaji Interval Neutrosophic Multicriteria Decision Making by TODIM Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Najihah Chaini, D. Nagarajan, and J. Kavikumar The Scenario of COVID-19 Pandemic in Brazil Using SEIR Epidemic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Subrata Paul, Ashish Acharya, Manajat Ali Biswas, Animesh Mahata, Supriya Mukherjee, Prakash Chandra Mali, and Banamali Roy Ldetect, IOT Based Pothole Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Sumathi Balakrishnan, Low Jun Guan, Lee Yun Peng, Tan Vern Juin, Manzoor Hussain, and Sultan Sagaladinov Speech Synthesis with Image Recognition Using Application of CNN and RNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Bijoy Mandal, Dibyendu Mukherjee, Anup Kumar Ghosh, and Rahul Shyam GeoGebra-Assisted Teaching of Rotation in Geometric Problem Solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Hoang Vu Nguyen, Thi Minh Chau Chu, Ton Quang Cuong, Vu Thi Thu Ha, Pham Van Hoang, Ta Duy Phuong, and Tran Le Thuy Pandai Smart Highway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Sumathi Balakrishnan, Jing Kai Ooi, Shin Kir Ti, Jer Lyn Choo, Ngui Adrian, Qiao Hui Tai, Pu Kai Jin, and Manzoor Hussain Hand Gesture Recognition: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Shefali Parihar, Neha Shrotriya, and Parthivi Thakore Application of Big Data in Banking—A Predictive Analysis on Bank Loans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Saurabh Banerjee, Sudipta Hazra, and Bishwajeet Kumar

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An Image Enhancement Algorithm for Autonomous Underwater Vehicles: A Novel Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Mahfuzul Huda, Kumar Rohit, Bikramjit Sarkar, and Souvik Pal Proposing a Model to Enhance the IoMT-Based EHR Storage System Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Shampa Rani Das, Noor Zaman Jhanjhi, David Asirvatham, Farzeen Ashfaq, and Zahraa N. Abdulhussain Synthetic Crime Scene Generation Using Deep Generative Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Farzeen Ashfaq, Noor Zaman Jhanjhi, Naveed Ali Khan, and Shampa Rani Das Co-opetition Reloaded: Rethinking the Role of Globalization, Supply Chains, and Mechanism Design Theory . . . . . . . . . . . . . . . . . . . . . . . 525 Anastasios Fountis Throughput Performance Analysis of DL-MU-MIMO for the IEEE 802.11ac Wireless LAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Ziyad Khalaf Farej and Omer Mohammed Ali Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555

About the Editors

Sheng-Lung Peng is a Professor and the director (head) of the Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan. He received the Ph.D. degree in Computer Science from the National Tsing Hua University, Taiwan. He is an honorary Professor of Beijing Information Science and Technology University, China, and a visiting Professor of Ningxia Institute of Science and Technology, China. He is also an adjunct Professor of Mandsaur University, India. Dr. Peng has edited several special issues at journals, such as Soft Computing, Journal of Internet Technology, Journal of Real-Time Image Processing, International Journal of Knowledge and System Science, MDPI Algorithms, and so on. His research interests are in designing and analyzing algorithms for Bioinformatics, Combinatorics, Data Mining, and Network areas in which he has published over 100 research papers. Prof. Dr. Noor Zaman Jhanjhi is currently a Professor in Computer Science, Program Director for the Postgraduate Research Programme in computer science at the School of Computer Science at Taylor’s University, Malaysia. He has been nominated as the world’s top 2% research scientist globally for 2022. He has been nominated as outstanding faculty by the MDEC Malaysia for 2022. He has highly indexed publications in WoS/ISI/SCI/Scopus, and his collective research Impact factor is more than 700 plus points. His H index is 44, while more than 500 publications are on his credit. He has several international Patents on his account, including Australian, German, and Japanese. He edited/authored over 40 research books published by world-class publishers, including Springer, Taylors and Frances, Willeys, Intech Open, IGI Global USA, etc. He has excellent experience supervising and co-supervising postgraduate students, and more than 30 Postgraduate scholars graduated under his supervision. Prof. Jhanjhi serves as Associate Editor and Editorial Assistant Board for several reputable journals, such as PeerJ Computer Science, CMC Computers, Materials and Continua, Computer Systems Science and Engineering CSSE and Frontier in Communication and Networks. He received

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About the Editors

Outstanding Associate Editor for IEEE ACCESS. Active reviewer for a series of top-tier journals has been awarded globally as a top 1% reviewer by Publons (Web of Science). He is an external Ph.D./Master thesis examiner/evaluator for several universities globally. He has completed more than 40 internationally funded research grants successfully. He has served as a Keynote/Invited speaker for more than 60 international conferences globally, and chaired international conference sessions internationally. He has vast experience in academic qualifications, including ABET, NCAAA, and NCEAC, for 10 years. His research areas include Cybersecurity, IoT security, Wireless security, Data Science, Software Engineering, and UAVs. Dr. Souvik Pal is an Associate Professor in the Department of Computer Science and Engineering at Sister Nivedita University (Techno India Group), Kolkata, India. Prior to that, he was associated with Global Institute of Management and Technology; Brainware University, Kolkata; JIS College of Engineering, Nadia; Elitte College of Engineering, Kolkata; and Nalanda Institute of Technology, Bhubaneswar, India. Dr. Pal received his M.Tech., and Ph.D. degrees in the field of Computer Science and Engineering from KIIT University, Bhubaneswar, India. He has more than a decade of academic experience. He is author or co-editor of more than 15 books from reputed publishers, including Elsevier, Springer, CRC Press, and Wiley, and he holds three patents. He is serving as a Series Editor for “Advances in Learning Analytics for Intelligent Cloud-IoT Systems”, published by Scrivener-Wiley Publishing (Scopusindexed); “Internet of Things: Data-Centric Intelligent Computing, Informatics, and Communication”, published CRC Press, Taylor & Francis Group, USA; “Conference Proceedings Series on Intelligent Systems, Data Engineering, and Optimization”, published CRC Press, Taylor & Francis Group, USA; Dr. Pal has published a number of research papers in Scopus/SCI/SCIE Journals and conferences. He is the organizing chair of RICE 2019, Vietnam; RICE 2020 Vietnam; ICICIT 2019, Tunisia. His professional activities include roles as Associate Editor, Guest Editor, and Editorial Board member for more than 100+ international journals and conferences of high repute and impact. His research area includes cloud computing, big data, internet of things, wireless sensor network, and data analytics. He is a member of many professional organizations, including MIEEE; MCSI; MCSTA/ACM, USA; MIAENG, Hong Kong; MIRED, USA; MACEEE, New Delhi; MIACSIT, Singapore; and MAASCIT, USA. Dr. Fathi Amsaad is an Assistant Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. In 2017, he received a Ph.D. in Engineering, with an emphasis on Computer Science and Engineering, from the Electrical Engineering and Computer Science Department at the University of Toledo (UT), Toledo, Ohio, USA. He taught several courses in Computer Science, Computer Engineering, Electronics and Electrical Engineering, and Cybersecurity domain, including Hardware Security, CMOS-VLSI Design/Testing, Computer Architecture, Design of Computer Systems with FPGA, and Wireless Communications and

About the Editors

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Networking. His research in the area of Hardware-oriented Security aims to enable Trust, Assurance, Authentication, and Reliability in Microelectronics and Embedded System Applications. This includes hardening chip-level security for IoT Applications, Reconfigurable Computing Platforms and Very Large-Scale Integration (VLSI) Systems.

Neuro-Fuzzy Logic Application in Speech Recognition D. Nagarajan, Khusbhu Chourashia, and A. Udhayakumar

Abstract Speech recognition is the ability of a system to recognise words and phrases in speech and convert them to readable or text format. In general, speech recognition will be accomplished by activities such as call routing, speech-to-text processing, voice dialling, voice audibility, and language modelling. Although neural networks are good classifiers, their effectiveness is based on the calibre and quantity of training data they are given. The use of fuzzy approaches enhances performance when training data is lacking or not entirely representative of the possible range of values. In other words, adding fuzzy approaches enables the classification of erroneous data. This paper presents a neural network that handles fuzzy numbers as the neuro-fuzzy system. This characteristic gives the system the ability to classify erroneous input in the proper way. The performance of the neuro-fuzzy system for speaker-independent voice recognition is significantly better than a regular neural network, according to experimental findings. Due to variances in voice frequency and pronunciation, speaker-independent speech recognition is a particularly challenging categorisation challenge in this study. Keywords Speech recognition · Neural network · Neuro-fuzzy system · Artificial neural network · Soft computing

D. Nagarajan (B) Department of Mathematics, Rajalakshmi Institute of Technology, Chennai, India e-mail: [email protected] K. Chourashia Department of Mathematics, Vels Institute of Science, Technology and Advanced Studies, Chennai, India A. Udhayakumar Vels Institute of Science, Technology and Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_1

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1 Introduction Among all the real-time applications, recognising a human voice is both a critical and difficult challenge. It has been discovered that combining neural networks and fuzzy logic is a very efficient way to consistently identify unknown sounds. It is the most important sub-discipline of soft computing. It has been used in the investigation process due to its significance. Artificial neural networks can be used to include human learning through mathematical operations and design. Due to a lack of information on a particular technique, we may meet uncertainty in decisionmaking processes in real-world applications. Fuzzy logic can be used to tackle this problem because it deals with uncertainty. Therefore, while there is insufficient information exists in a human voice, the process of recognising the voice will be done by assigning membership values to the components of the process. A neural network informs the human brain about performance based on actions such as learning, reasoning, and adjusting, whereas fuzzy logic deals with uncertainty by incorporating a human approach to comprehending linguistic variables. The Neuro-Fuzzy System (NFS) is developed by combining these two key fields and has been used in a variety of applications. As a result, it’s a learning design hybrid with fuzzy reasoning. In NFS, fuzzy logic (FL) handles IF–THEN rules, while the neural network (NN) decides parameter values. FL will perform well for diverse types of noise during speech recognition because it is a multi-valued logic [11]. A mathematical model is a condensed representation of an artificial neural network’s supporting brain-like system, which is a network of distributed parallel computing (ANN). NN’s greatest strength is how adaptable it is. NN will adjust the weights automatically to optimise the system’s behaviour as pattern recognisers, decision-makers, system controllers, predictors, and other roles. Even if the system’s control alters over time, the NN’s adaptability will result in strong performance or allow the system to operate well. Another advantage of NN over the inventor’s analytical growth is the requirement of the ability to learn from instances. Researchers are interested in NN because of its capacity to frame robots with biological organism awareness. NN has been employed in principle biological computations for its judgement or intuition. In 1965, Zadeh invented fuzzy sets as a way to represent and use imprecise data. In knowledge-based systems, interpretational morphology offered by FL will be able to approximate the capacities of human thinking. Thinking and learning are the functions of a human mind called cognitive process, which hold uncertainty in nature, and this uncertainty can be captured by fuzzy logic properly by the mathematical fortitude Thinking and learning are cognitive processes in the human mind that contain uncertainty in nature, and this uncertainty can be captured by fuzzy logic and mathematical models. The Fuzzy Logic approach is a sophisticated mathematical branch that provides control solutions. Human common sense knowledge is inherently imperfect and hazy. The first-order logic and possibilistic theory methods give a useful theoretical framework. Therefore, a system with a mathematical model that is challenging to derive is easy with FSs. With imprecise information, the decision-making process is quietly possible using FL. Cognitive uncertainties can be dealt with by neuro-fuzzy networks

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(NFN). In controlling the system, membership function of FSs is to be tuned for decision-making, and it will be done by NN. To model the qualitative aspects of human knowledge, Fuzzy Inference Systems use linguistic IF–THEN rules. Expert knowledge will be encoded in FL using fuzzy rules (FRs), and constructing and tweaking membership functions that will quantitatively describe the linguistic labels will take more time, but NN requires less time than FL due to its automatic learning approaches in enhancing the system’s performance Backpropagation method and FRs will find the weights of the neuron in NN and FS, respectively, when collecting knowledge. In NFN, triangular norms can be used to aggregate the incoming data. The NFN’s inputs, outputs, and eight processing elements are all real values in the range [0, 1], and the processing element is known as a fuzzy neuron. In a fuzzy IF–THEN rule, the degree of the input data should be matched with the condition of the rule. The two sections IF and THEN are referred to as antecedent and consequent parts, respectively. These two elements, respectively, describe the state of the system that should activate the rule and the action that the operator takes to govern the system to put it another way, IF a set of conditions is met, THEN a set of outcomes can be deduced. Organisation of this paper is a literature survey in Sects. 2 and 3 explains the basic concepts and the details of neural network, in Sect. 4, experimental results and discussion, and Sect. 5 included the conclusion.

2 Literature Review Somarathi and Vamshi [1] employed a neural network to solve the problem of assembling a fuzzy logic controller in a novel method. Guz and Guney [2] looked into the advantages and disadvantages of creating fuzzy rule bases for NFSs. Kumari and Sunita [3] showed that a neuro-fuzzy integrated methodology is ideal for detecting cardiac problems. To recognise the pattern, [4] developed a Neuro-fuzzy algorithm. Vaidhehi [5] The Sugeno type ANFIS model was used to present a way for constructing a web-based neural fuzzy advising system. Petchinathan et al. [6] used a Local Linear Model Tree and an ANFIS to build and regulate a pH neutralisation procedure. ANFIS was utilised by Ramesh et al. [7] to recover temperature and evaporation portraits up to 10 km over the hot station Gadanki. ANFIS was studied by Dragomir et al. [8] as a scenario for predicting and controlling the energy produced by Renewable Sources. Junior et al. [9] investigated the application of NFS for series design and pricing estimation. Chauduri et al. [10] focused on mental health and the use of soft computing and neuro-fuzzy techniques to provide a better way of identifying an illness using various tools and approaches. Maskara et al. [11] demonstrated that, in the presence of noise in attention and uncertainty in disease diagnosis, intelligent techniques such as ANN and ANFIS have a stable behaviour. An ANFIS for anticipating surface roughness in end milling was presented by Markopoulos et al. [12]. Shaabani et al. [13] employed a hybrid strategy in ANFIS to identify a disease, combining Back Propagation and Least Square Error, and exhibiting fuzzy systems’ linguistic strength and neural networks’ quantitative capabilities. Mathur

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et al. [14] employed ANFIS to predict in-socket continuous limb temperature and compared expected and actual data. For the development of intelligent trustworthy and reclamation robots, [15] offered a combined technique employing ANF and Bayesian procedure to achieve rapid and proper choice, as well as to calculate and adapt its own performance. Sahin and Erol [16] created a model that used NN and ANFIS to anticipate soccer game attendance percentages. Mamak et al. [17] compared ANFIS and FAO 56 formula using mean square error and mean absolute error, it was discovered that ANFIS accurately forecasted daily evapotranspiration. Hadroug et al. [18] employed ANFIS to regulate the speed and fatigue temperature of a gas cylinder in order to achieve optimal performance. Pradeep et al. [19] used an ANFIS-based UPQC to reduce current and voltage exaggeration at the distribution system’s consumer end. Atsalakis [20] used ANFIS and NN to offer and confer two data-driven models for estimating the ailment of professional welders. An et al. [21] focused a study on using ANFIS to calculate and determine lost information in data, as well as using Fuzzy DE to deal with differential equations while missing information in equations. Wending [22] explained the hyper neuro-fuzzy systems. Vani and Anusuya [23] detailed the review of fuzzy speech recognition.

3 Basic Concepts 3.1 Artificial Neural Networks (ANNs) ANNs combine mathematical behaviour and algorithms with the way humans learn, and they can learn to do tasks depending on training data. The vast majority of the neural network must be taught. To perform better as pattern recognisers, decision-makers, system controllers, predictors, and other functions, they automatically modify their weights. Due to its adaptability, the ANN can operate effectively even when the system or environment changes over time. During learning time, it can establish its own representation of the message received which is the self-organisation of ANN. It carries a parallel computation using specially designed hardware devices and produces a real-time operation. Partial elimination of a network directed to decline of the corresponding performance. Even when there is network damage in ANN can maintain network capabilities it leads to a fault tolerance.

3.2 Fuzzy Logic (FL) Fuzzy Logic contributes to conjecture design, which empower approximate reasoning applied in knowledge based systems also provides a mathematical vitality in capturing uncertainties related to thinking and reasoning called human intellectual process. Since the conventional methods fail to provide theoretical framework

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to deal with the depiction of common sense knowledge which is naturally imprecise and non-categorical, an evolution of fuzzy logic is persuaded. In this case, knowledge is understood as a hazy limitation on a set of variables, and decision-making is achievable in uncertain situations. Fuzzification is possible for any system. A system with fuzzy logic is called fuzzy system. This system is suitable for reasoning with uncertainty as well as the system which has a difficult mathematical design.

3.3 Relation between Neural Network and Fuzzy Logic NNs can be employed only when the training data are attainable. One cannot interpret the solution obtained from learning process. Most of the NNs designed as to a black boxes so final result cannot be described in terms of rules. Here the learning process is initiated without any prior knowledge, and thus it must learn from scrape. It takes long time and also there is no assurance for success. Whereas in fuzzy logic, it is difficult to establish a model from a fuzzy system and stand in need for fine-tuning and reproduction before working with appropriate membership values. Only if there is intelligence about to the answer in the form of lexical if–then rules can it be used. Every intelligent method has specified computational properties in learning ability, confession of decisions, etc. for particular real-world problems. NNs are doing well in pattern recognition but they fail to explain the way of reaching the decision. Whereas Fuzzy logic systems are doing well in explaining their way of reaching the decision but cannot pick up the rules automatically for decision process. Because many complex domains contain a variety of peculiar component difficulties and may necessitate multiple sorts of processing, hybrid systems are often quite useful for a variety of application domains. These constraints are the motivation of bringing NN and FL together and create a hybrid system named Neuro-Fuzzy Systems (NFSs). Neural networks and fuzzy logic can be used to produce a system that can deal with intellectual uncertainty in a human-like manner.

3.4 Neuro-Fuzzy System (NFS) The Neuro-Fuzzy System is a realistic integration of the benefits of both neural and fuzzy logic, allowing for the creation of more intelligent decision-making systems. In this system, neural network contributes immense parallelism, stability and data learning into the system or simply learning ability to optimise the parameters whereas fuzzy logic contributes design of uncertainty, transference of uncertainty, and subjective knowledge or simply for the representation of the knowledge in an intelligible manner. NFS provides the specific merits of the corresponding application.

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Fig. 1 Speech recognition process

3.5 Speech Recognition Speech recognition is categorised into identification and verification. It is a mechanism of recognising the speaker on the basis of speech waves automatically. This procedure makes it attainable to worn speaker’s tone to authenticate their identity, information service security control for intimate information field and distance connection to computers are all under supervision. The technique of finding the speaker who contributes a given assertion is called Speaker Identification. The technique of accepting/rejecting the identity request of a speaker is called speaker verification. Here, usually, a voice is used as the pivotal to approve the identity of a speaker. The identity of the speaker is associated with the physiological and observable characteristics, exist in the vocal plot characteristics (spectral envelope) and in the characteristics of the voice source and dynamic features spanning level segments (supra-segmental features). Figure 1 shows that speech recognition for the process, input speech is extract using feature extraction then check the similarity selection after the result is processing.

4 Experimental Results The neuro-fuzzy network implementation was done using simulation. Using the equations developed in the previous part, the simulation was created in the C programming language and assessed using numerous conventional data sets. Vowel, one of a number of data sets used as neural network benchmarks, will be the application problem used as the testbed for this study. It is used to recognise the eleven vowel sounds from various speakers without regard to the speaker. The Vowel data set utilised in this study was originally compiled by Deterding for a “non-connectionist”

Neuro-Fuzzy Logic Application in Speech Recognition Table 1 Recognition rates for neuro-fuzzy and existing neural networks

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Type of network

Hidden neurons

Recognition rate

Neural network

22

57.3

Neural network

44

64.8

Neural network

88

53.1

Neuro fuzzy system

22

89.3

speaker normalisation investigation. Deterding recorded instances of the 11 steadystate vowels of English spoken by 15 speakers. The training data were produced using four male and four female speakers, while the testing data were produced using the remaining four male and three female speakers. These results were promising, with performance on par with or better than a standard neural network. The voice signals were carefully transformed to 12 bits with a sampling rate of 10 kHz and low pass filtered at 4.7 kHz. Twelve-order linear predictive analysis was carried out on 10 samples of 653 data. Windowed vowel fragments from the constant part are hammered. The reflection coefficients were used to compute 10 log area parameters, resulting in a 10-dimensional input space. Each speaker used 11 vowels to create 10 speech frames. The outcome is that the 10 speakers in the testing set create 678 frames whereas the 8 speakers in the training set produce 853 frames. Vowel is an useful testbed for the neuro-fuzzy system since it has a wide range of values and utilises a minimal training set compared to other neural network applications. Even when limited to the 11 vowel sounds that make up Vowel, speaker-independent speech detection is still quite challenging. Using these data, which serve as a benchmark for comparisons, Robinson conducted a research comparing the performance of feed-forward networks with various structural configurations in Table 1.

5 Conclusion Numerous applications of fuzzy theory have proved successful. This study demonstrates how it can be applied to boost neural network efficiency. Fuzziness has a lot of benefits, and one of them is that it can deal with imperfect data. Although neural networks are well renowned for being great classifiers, the quantity and calibre of the training set can have an adverse effect on their performance. The problem class of speaker-independent speech recognition is one illustration of how neuro-fuzzy methods are beneficial. As mentioned in the previous section, simulation experiments made use of the Vowel data collection. This well-known data collection has been used in numerous studies with dismal outcomes. As a result, one researcher stated that “bad outcomes seem to be inherent to the data”. This is accurate to such an extent. This issue with subpar performance lends greater support to effective approaches. Speech recognition is a good fit for the neuro-fuzzy model. The block box attitude of the NN and the speech recognition to the challenge of selecting adequate. This combination can be used to avoid membership values for fuzzy systems. It can also

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establish a model’s learning efficiency and prior knowledge to specify the problem, therefore neuro-fuzzy models are only suitable for application areas where interpretation is required. In the future, develop the idea to the speech recognition using neutrosophic neural network system.

References 1. Somarathi, S., & Vamshi, S. (2013). Design of NEURO fuzzy systems. International Journal of Information and Computation Technology, 3(8), 819–824. 2. Guz, Y. K., & Guney, I. (2010). Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system. Turkish Journal of Electrical Engineering & Computer Sciences, 18(4), 625–645. 3. Kumari, N., Sunita, S. (2013). Comparision of ANNs, fuzzy logic and neuro-fuzzy integrated approach for diagnosis of coronary heart disease: A survey. International Journal of Computer Science and Mobile Computing, 2(6), 216–224. 4. Balbinot, A., & Favieiro, G. (2013). A neuro-fuzzy system for characterization of arm movements. Sensors, 13, 2613–2630. 5. Vaidhehi, V. (2014). A framework to design a web based neuro fuzzy system for course advisor. International Journal of Innovative Research in Advanced Engineering, 1(1), 186–190. 6. Petchiathan, G., Valarmathi, K., Devaraj, D., & Radhakrishnan, T. K. (2014). Local linear model tree and neuro-fuzzy system for modelling and control of an experimental pH neutralization process. Brazilian Journal of Chemical Engineering, 31(2), 483–495. 7. Ramesh, K., Kesarkar, A. P., Bhate, J., Ratnam, M. V., Jayaraman, A. (2015). Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations. Atmosphere Measurement Techniques, 8, 369–384. 8. Dragomir, O. E., Dragomir, F., Stefan, V., Minca, E. (2015) Adaptive neuro-fuzzy inference systems as a strategy for predicting and controlling the energy produced from renewable sources. Energies, 8, 13047–13061. 9. Junior, C. A. A., Silva, L. F. D., Silva, M. L. D., Leite, H. G., Valdetaro, E. B., Donato, D. B., & Castro, R. V. O. (2016). Modelling and forecast of charcoal prices using a neuro-fuzzy system. Cerne, 22(2), 151–158. 10. Chauduri, N. B., Chandrika, D., Kumari, D. K. (2016) A review on mental health using soft computing and neuro-fuzzy techniques. International Journal of Engineering Trends and Technology, 390–394. 11. Maskara, S., Kushwaha, A., Bhardwaj, S. (2016). Adaptive neuro-fuzzy system for cancer. International Journal of Innovative Research in Computer and Communication Engineering, 4(6), 11944–11948. 12. Markopoulos, A. P., Georgiopoulos, S., Kinigalakis, M., & Manolakos, D. E. (2016). Adaptive neuro-fuzzy inference system for end milling. Journal of Engineering Science and Technology, 11(6), 1234–1248. 13. Shaabani, M. E., Banirostam, T., & Hedayati, A. (2016). Implementation of neuro fuzzy system for diagnosis of multiple sclerosis. International Journal of Computer Science and Network, 5(1), 157–164. 14. Mathur, N., Glesk, I., & Buis, A. (2016). Comparision of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. Medical Engineering and Physics, 38(2016), 1083–1089. 15. Hernandez, U. M., Solis, A. R., Panoutsos, G., Sanij, A. D. (2017). A combined adaptive neurofuzzy and Bayesian for recognition and prediction of gait events using wearable sensors. IEEE International Conference on Fuzzy Systems, 34–34.

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16. Sahin, M., & Erol, I. R. (2017). A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Mathematical and Computer Applications, 22(43), 1–12. 17. Mamak, M., Unes, F., Kaya, Z. Y., Demirci, M. (2017). Evaporation prediction using adaptive neuro-fuzzy inference system and Penman FAO. In “Environmental Engineering” 10th International conference vilnius gediminas technical university (pp. 1–5). 18. Hadroug, N., Hafaifa, A., Guemana, M., Kouzou, A., Salam, A., & Chaibet, A. (2017). Heavy duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: Speed and exhaust temperature control. Mathematics-in-Industry Case Studies, 8(8), 1–20. 19. Pradeep, M., Padmaja, V., & Himabindu, E. (2018). Adaptive neuro-fuzzy based UPQC in a distributed power system for enhancement of power quality. Helix, 8(2), 3170–3175. 20. Atsalakis, G. S. (2018). Applications of a neuro-fuzzy system for welders’ indisposition forecasting. Journal of Scientific and Engineering Research, 5(4), 171–182. 21. An, V. G., Anh, T. T., Bao, P. T. (2018). Using genetic algorithm combining adaptive neuro-fuzzy inference system and fuzzy differential to optimizing gene. MOJ Proteomics Bioinformatics, 7(1), 65–72 22. Wending, L. (2022). Implementing the hybrid neuro-fuzzy system to model specific learning disability in special University education programs. Journal of Mathematics, 2022:6540542 23. Vani, H., Anusuya, M. (2020). Fuzzy speech recognition: a review. International Journal of Computer Applications, 177(47), 39–54

A Machine Learning Model for Predicting COVID-19 Lawrence Ibeh and Sulekha Mohamud

Abstract The pandemic has had a significant impact on both public health and the global economy, leading to widespread lockdowns and disruptions to daily life. Despite the rollout of vaccines, the virus continues to spread and the situation remains fluid, with new variants emerging and the threat of further waves of infections. Efforts are underway to keep the infection from spreading and to find treatments and cure. The aim of this paper is to demonstrate the usefulness of machine learning techniques and algorithms in recognizing and predicting COVID-19 instances. The study improved the understanding of the mechanisms that lead to the spread of COVID-19 as well as the efficacy of various treatment methods. Our findings suggest that machine learning can be useful in recognizing, investing in, and forecasting COVID-19 situations. Machine learning techniques and algorithms can help address these gaps and improve our ability to respond to the pandemic. The use of supervised learning algorithms especially Random Forest demonstrated favorable outcomes, achieving a testing accuracy of 92.9%. The study concluded that predictive models are necessary in the fight against COVID-19 and can lead to better public health outcomes. In the future, recurrent supervised learning is expected to yield even better accuracy. Keywords COVID-19 · Machine learning · Model · Random forest · Prediction

1 Introduction The COVID-19 pandemic continues to present a significant threat to global health. The virus first emerged in the Hubei Province of China in the year of 2019 and ever since then, it spreads to more than 180 countries, with confirmed cases exceeding 39.5 L. Ibeh (B) · S. Mohamud Faculty of Computer Science and Informatics, Berlin School of Business and Innovation, Berlin, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_2

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million and deaths exceeding 1.1 million [1]. The pandemic has put immense pressure on medical systems worldwide, leading to shortages in hospital beds, medical equipment, and trained healthcare workers. Effective screening and diagnosis are crucial for mitigating the burden on healthcare systems and for making timely clinical decisions. Tests for reverse transcriptase polymerase chain reaction (RT-PCR), the most validated diagnostic test for COVID-19, have been in short supply in many developing countries [1–3]. To meet the challenges posed by the COVID-19 pandemic, researchers have developed prediction models that aim to help medical personnel in prioritizing patients and assessing the risk of infection [1]. These models take into consideration factors such as confirmed cases and death rates. This technique has the potential to improve the COVID-19 patients’ planning, treatment, and reported results. In this study, we introduce a machine learning algorithm that identifies a probable SARS-CoV- 2 positive outcome [1]. Our model was built using data from all persons tested for SARS-CoV-2 throughout the epidemic year (2020). As a consequence, our approach may be used to efficiently filter and prioritize SARS-CoV-2 testing in the general population [4].

2 Methods The following packages and libraries are required for the project: Datetime, Numpy, Pandas, SciPy, Scikit Learn, and Jupyter Notebook.

2.1 Collecting Initial Data Before getting the data, we need to define measurable and quantifiable goals. Defining measurable and quantifiable goals prior to obtaining data helps ensure that the data collected are relevant and useful for achieving the desired outcomes [5]. Given that, our goal here is to predict if the COVID-19 is going to increase or not, using random forest model. The data utilized in this research were obtained from Kaggle under the name “2019 Corona Virus Dataset”. It was developed using information from many sources, including the World Health Organization and John Hopkins University (26). Additionally, considering the availability of complete data, our main concentration was on 12 countries, which include Belgium, China, France, Germany India, Iran, Italy, Pakistan, Spain, Turkey, US, and the United Kingdom.

2.2 Data Pre-processing The COVID-19 data are organized into columns, including date, string, and numerical data types. Additionally, there are categorical variables. To prepare the data for the

A Machine Learning Model for Predicting COVID-19

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Fig. 1 Overview of confirmed cases

machine learning model, label encoding was performed on the categorical variables [1]. This involves assigning a numerical value to each unique categorical value in the column [5]. The data contain multiple missing values, which can result in errors when used as input. To resolve this issue, the missing values are filled with “NA”.

2.3 Data Exploratory When it comes to the impact of COVID-19 on countries, data exploratory can provide an overview of how the virus has spread and the measures taken by different governments to control its spread [6]. Figure 1 shows an overview of number of cases in the countries represented below [7]. Figure 2 shows the confirmed cases of Belgium, China, France, Germany India, Iran, Italy, Pakistan, Spain, Turkey, US, and the United Kingdom [7]. Figures 3 and 4 show the number of daily cases and number of daily new fatalities, respectively.

2.4 Selecting the Model Techniques The model used in this study to predict the increase of COVID-19 was Random Forest [8]. Because of its capacity to handle high dimensionality, non-linearity, and complexity in data, Random Forest is a popular machine learning technique for predicting outcomes [7]. It is an ensemble approach for making predictions that include numerous decision trees. The mathematical equation for the random forest:

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Fig. 2 Cumulative trend for confirmed cases

Fig. 3 Number of daily cases

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Fig. 4 Number of daily new fatalities

MSE =

N 1  ( f i − yi )2 N i=0

where N is the number of data points, f i is the model’s output, and yi is the actual value for each data point.

2.5 Building the Model For our case, we predicted if the COVID-19 will increase in the coming months. This is clearly a scoring problem which means predicting or estimating the probability of an occurrence [3, 7]. The modeling process started with importing sklearn and label encoder library as shown below from sklearn. preprocessing import LabelEncoder LE = LabelEncoder() Then we select the target variables, the one we are going to predict(x) target = ‘ConfirmedCases’ Then we import the Random forest classifier from sklearn. ensemble import RandomForestClassifier Defining our model rfc = RandomForestClassifier(n_estimators = 10, max_samples = 0.8, random_ state = 1).

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2.6 Test Design Our data were divided into training and testing sets. Before separating the data, we ensured that the training and testing sets have the same class balance as the dataset [5]. The models are trained using 80% of the data and tested with 20%. Training the model. rfc.fit(train_df[features], train_df[target]) The next step was to make prediction based on the feature from the test data predictions2 = rfc.predict(test_df[features]) predictions = predictions2[0:500] Creating a dataframe to store the target columns Final_work = pd.DataFrame({‘ForecastId’: test_df[‘ForecastId’], ‘ConfirmedCases’: predictions, ‘Fatalities’: predictions2}).

2.7 Model Validation Once the model has been built, we have to determine if : Accurate enough for our needs? Do the results of the model make sense in the context of problem domain? These are important questions to consider if we need to find if the model designed does or does not fulfill expected needs, then we must go back and redefine our goals [5]. From our study, our model had quantifiable and defined goal, which made it easier to build an accurate model to give the expected results. Table 1 shows the outcome of the random forest model [7, 9]. Table 1 The outcome of random forest

Forecast Id

Confirmed cases

Fatalities

1

273.0

6.0

2

281.0

6.0

3

299.0

7.0

4

349.0

7.0

5

367.0

11.0

6

423.0

14.0

7

444.0

14.0

8

484.0

15.0

9

521.0

15.0

10

433.0

23.0

A Machine Learning Model for Predicting COVID-19

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2.8 Model Relevance Deploying predictive models that forecast the spread of COVID-19 is important because it can help reduce the impact of the pandemic and save lives.

3 Results From Fig. 2, Belgium has a lot of confirmed cases from mid-March to mid-April with more than 500,000 thousands. In addition, China managed to stabilize the pandemic while other countries shows an increase in the trend. Figure 3 shows the trend of confirming numbers of daily cases. China started with an increase and reached a peak at February 15th with more than 10000 cases then the cases started to reduce, United States showed an increase in the daily cases from March 17 with more than 5000 daily cases and increased all the way to 35000 cases. Other countries started low and the trend started to increase in the middle March to the end. Figure 4 shows the number of daily new fatalities. Countries like Italy, Spain, US, France, United Kingdom, Belgium showed an increase in trend at the end of March and beginning of April.

4 Discussion From the outcome of the predictive model shown in Table 1, the Random Forest model result indicated that the rise in confirmed cases and fatalities rate will increase in the coming months. This prediction was based on various factors such as demographic data, past trends, and other relevant variables. Figure 2 shows the confirmed cases of Belgium, China, France, Germany India, Iran, Italy, Pakistan, Spain, Turkey, US, and the United Kingdom. The findings show that close proximity with a person diagnosed with COVID-19 was a significant factor. This supports the high level of transmission of the virus and emphasizes the significance of maintaining social distancing measures [1, 10–12]. Belgium has a lot of confirmed cases from mid-March to mid-April with more than 500,000 thousands while China stabilized the cases and other countries showed an increase in the trend. The reason why Belgium had a large number of confirmed cases from mid-March to mid-April with over 500,000 cases could be due to various factors such as high levels of community transmission, inadequate measures for controlling the spread of the virus, and a higher rate of testing that revealed more positive cases. As for China, it managed to stabilize the situation by implementing strict measures such as lockdowns, widespread testing, and contact tracing. This, along with the country’s vast resources and infrastructure, helped in containing the spread of the virus. In

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contrast, other countries may have faced challenges in effectively implementing similar measures, leading to a high number of cases [13]. As seen in Fig. 3, confirming the daily number of COVID-19 cases is vital in several aspects. It helps in monitoring the spread of the virus and assessing its impact on the community. This information is crucial in making informed decisions and implementing appropriate measures to control its spread [1, 14, 15]. For example, if there is a sudden increase in the number of cases, it may indicate a surge in community transmission, and prompt authorities to take measures such as imposing lockdowns or increasing testing. China started with an increase and reached a peak at February 15th with more than 10000 cases then the cases started to reduce, United States showed an increased daily cases from March 17 with more than 5000 daily cases and increased all the way to 35000 cases. Other countries started quite low, but the trend started to increase in the middle March to the end. The high record of daily cases of COVID-19 in the US from March 2020 was due to a combination of factors including lack of preparedness, a large and highly mobile population, inconsistent implementation of measures, and challenges in vaccine rollout [4]. Figure 4 shows the number of daily new fatalities. Countries like Italy, Spain, US, France, United Kingdom, Belgium showed an increase in trend at the end of March and beginning of April. This rise in fatalities can be attributed to several factors. Firstly, the healthcare systems in these countries were overwhelmed by the rapid increase in cases, leading to a shortage of medical supplies, hospital beds, and trained medical staff. This resulted in a delay in treatment for critically ill patients and increased mortality rates [1–4]. Secondly, many of these countries had an aging population, which is a known risk factor for severe illness and death from COVID-19. The high number of cases in these countries may have resulted in a higher proportion of elderly individuals becoming infected and succumbing to the disease [1]. Lastly, the availability and distribution of personal protective equipment (PPE) and testing kits were limited at the time, leading to a delay in the identification and treatment of infected individuals. This could have also contributed to the increase in fatalities during this time period. According to Table 1, the outcome of the predictive model, using supervised machine learning with a Random Forest algorithm, indicated that the rise in confirmed cases and fatalities rate will increase in the coming months. This prediction was based on various factors such as demographic data, past trends, and other relevant variables [1, 16].

5 Conclusion In conclusion, the prediction of the increase in COVID-19 has important implications for public health and decision-making. The results of this study highlight the potential for continued spread of the virus and the need for proactive measures to be taken to mitigate its impact. The use of predictive models allows for early warning of

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outbreaks and hot spot areas, which can help allocate resources more effectively and inform the development and implementation of effective prevention measures. Acknowledgements Sincere gratitude to my Professor, Dr. Lawrence Ibeh for guiding me through this entire project.

References 1. Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Npj Digital Medicine, 4(1), 3. https://doi.org/10. 1038/s41746-020-00372-6 2. Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S., & Jo, O. (2020). COVID-19 patient health prediction using boosted random forest algorithm. Frontiers in Public Health, 8, 357. https://doi.org/10.3389/fpubh.2020.00357 3. Oži¯unas, D. O. (2021). Identifying severity of COVID-19 in patients using machine learning methods. University of Twente. 4. Babukarthik, R. G., Adiga, V. A. K., Sambasivam, G., Chandramohan, D., & Amudhavel, J. (2020). Prediction of COVID-19 using genetic deep learning convolutional neural network (GDCNN). IEEE Access: Practical Innovations, Open Solutions, 8, 177647–177666. https:// doi.org/10.1109/ACCESS.2020.3025164 5. Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied Artificial Intelligence: AAI, 17(5–6), 375–381. https://doi.org/10.1080/713827180 6. Yan, L., Zhang, H-T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Zhang, M., Huang, X., Xiao, Y., Cao, H., Chen, Y., Ren, T., Wang, F., Xiao, Y., Huang, S., Tan, X., Yuan, Y. (2020). An interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence, 2(5), 283–288. https://doi.org/10.1038/s42256-020-0180-7 7. The Class of AI. (n.d.). Covid_19_Analysis_Week4.ipynb at master · the classofai/COVID_19. 8. Schott, M. (2019). Random Forest Algorithm for machine learning - capital one tech medium. Capital One Tech. https://medium.com/capital-one-tech/random-forest-algorithmfor-machine-learning-c4b2c8cc9feb 9. Li, Y., Zhang, C., & Zhang, S. (2003). Cooperative strategy for web data mining and cleaning. Applied Artificial Intelligence: AAI, 17(5–6), 443–460. https://doi.org/10.1080/713827173 10. Pasupuleti, R. R. (2021). Rapid determination of remdesivir (SARSCoV-2 drug) in human plasma for therapeutic drug monitoring in COVID-19-Patients. Process Biochemistry, 102(3), 150–156. 11. Scarpone, C., Brinkmann, S. T., Große, T., Sonnenwald, D., Fuchs, M., & Walker, B. B. (2020). A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: A cross-sectional case study of COVID-19 incidence in Germany. International Journal of Health Geographics, 19(1), 32. https://doi.org/10.1186/s12942-020-00225-1 12. National center for biotechnology information. (n.d.). Nih.gov. Retrieved February 22, 2023, from https://www.ncbi.nlm.nih.gov/ 13. Frontiers. (n.d.). Frontiersin.org. Retrieved February 22, 2023, from https://www.frontiersin. org/ 14. Moulaei, K., Shanbehzadeh, M., Mohammadi-Taghiabad, Z., & Kazemi-Arpanahi, H. (2022). Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Medical Informatics and Decision Making, 22(1), 2. https://doi.org/10.1186/s12911-021-01742-0

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15. Podder, P., Bharati, S., Mondal, M. R. H., & Kose, U. (2021). Application of machine learning for the diagnosis of COVID-19. In U. Kose, D. Gupta, V. H. C. de Albuquerque, & A. Khanna (Eds.), Data Science for COVID-19 (pp. 175–194). Elsevier. 16. Prakash, K. B. (2020). Analysis, prediction and evaluation of COVID-19 datasets using machine learning algorithms. International Journal of Emerging Trends in Engineering Research, 8(5), 2199–2204. https://doi.org/10.30534/ijeter/2020/117852020

Thyroid Disease Prediction Using a Novel Classification Enhancing MLP and Random Forest Algorithms D. Akila, Bikramjit Sakar, Saurabh Adhikari, R. Bhuvana, V. R. Elangovan, and D. Balaganesh

Abstract It has just become apparent how important it is to anticipate thyroid sickness. Thyroid problems impact people all over the world. This disease has become a significant problem in India as well. The disease thyroiditis is one of them that is growing as people’s lives change, with several study findings estimating that 42 million Indians experience “thyroid problems.“ Thyroid illness affects individuals rather often. As a result, thyroid disease prediction is currently necessary. This study used a brand-new hybrid categorization to forecast thyroid illness. We anticipate that this study will provide a helpful overview of recent findings in this area and show how to apply Random Forest methodologies as a tool for thyroid ailment prediction innovations. The multi-layer perception (MLP) techniques as well as the random forest method are used in the hybrid classification. The findings clearly show that our hybrid approach is superior, and as a result, it is advised for this task in thyroid ailment prediction. Keywords Thyroid disease · MLP · Random forest · Hybrid method D. Akila (B) Department of Computer Applications, Saveetha College of Liberal Arts and Sciences, SIMATS, Chennai, India e-mail: [email protected] B. Sakar Department of Computer Science and Engineering, JIS College of Engineering, Kalyani, India S. Adhikari School of Engineering, Swami Vivekananda University, Kolkata, India e-mail: [email protected] R. Bhuvana Department of Computer Science, Agurchand Manmull Jain College, Chennai, India e-mail: [email protected] V. R. Elangovan Department of Computer Applications, Agurchand Manmull Jain College, Chennai, India D. Balaganesh Berlin School of Business and Innovation, Berlin, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_3

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1 Introduction Thyroid disorders have become more common in recent years. The thyroid gland plays a pivotal role in regulating metabolism. Hyperthyroidism and hypothyroidism are two of the most common conditions brought on by abnormalities in the thyroid gland. Hypothyroidism and hyperthyroidism, two common thyroid disorders, are diagnosed in a sizable number of people every year. Both hyperthyroidism and hypothyroidism can arise from insufficient levels of thyroid hormones, which the thyroid gland produces as levothyroxine (T4) and triiodothyronine (T3), respectively. Several techniques are discussed in the literature as potential means of identifying thyroid disease. Proactively diagnosing thyroid illness is essential for timely patient management and reducing mortality and healthcare expenditures. The incidence of thyroid disease has increased recently. One of the thyroid gland’s primary roles is to control metabolism. Hyperthyroidism and hypothyroidism are the most prevalent diseases caused by thyroid gland dysfunction. Thyroid conditions such as hypothyroidism and hyperthyroidism affect a sizable population. Both hyperthyroidism and hypothyroidism can result from insufficient levels of the thyroid hormones levothyroxine (T4) and triiodothyronine (T3). The medical literature suggests a plethora of methods for identifying thyroid illness. Predicting the onset of thyroid problems ahead of time is crucial for saving lives and reducing healthcare costs [1, 2]. Heartbeat, body temperature, and, most importantly, metabolism—the body’s usage and absorption of nutrients—are all regulated by thyroid hormones. Major problems may develop when the thyroid gland functions excessively (hyperthyroidism with high hormone levels) or inadequately (hypothyroidism with low hormonal changes). Additionally, the thyroid gland may become inflamed (thyroiditis) or expand as a result of one or more swellings that develop there (nodules, multinodular goiter). These nodules may include cancerous tumors in some cases. Because of this, treating thyroid problems is a crucial concern [3]. According to Few, there are 38,000 persons worldwide who suffer from congenital hypothyroidism on average. Nearly 42 million people in poor nations like India suffer from thyroid illness. Indians appear to experience it more frequently, as seen by the one out of 2640 ratio reported for Mumbai. More than 25,000 hospitals worldwide now gather patient data in a variety of ways. In the conventional approach, statistical testing and traditional analysis are used to conduct clinical and medical investigations [4]. Analyzing thyroid illness is one of the most difficult and brutal projects since it requires a ton of knowledge and information. If this illness is discovered at an early stage, the patient will receive proper care from the doctor. Specialist examination or multiple blood tests are the usual ways to diagnose thyroid disease. Thyroid hormone replacement is a safe and effective medication that helps manage one’s adverse effects and clears up confusion with early diagnosis and treatment. The detection of diseases with high accuracy is currently one of the essential challenges in medical sciences that require innovation. Many cutting-edge tactics and computational frameworks have been created in this decade to promote their operations [5]. Artificial intelligence has already been extensively employed in recent years for a variety of purposes, including

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the better categorization of thyroid conditions. In addition to clinical exams, machine learning (ML) methods have been successfully employed to obtain accurate thyroid data interpretation and early thyroid disease detection. The effectiveness of these strategies has been the subject of several studies [6–8]. Machine learning has different classification models that we can use to train our prototype with proper thyroid patient training data and can predict and honestly give the results with a greater degree of accuracy [9]. A proper training dataset results in an accurate predicting model, reducing the overall price of the thyroid patient’s treatment and also saving time. The best algorithms for making decisions and resolving issues in the real world are classification algorithms.

2 Related Works The medical field creates a lot of complicated data that is hard to handle. In the past few years, machine learning techniques have been used more and more to study and classify different illnesses. In this part, we looked into numerous methods for anticipating thyroid problems. The many machine learning methods employed in the field of illness prediction are shown in this section. The thyroid is a crucial organ that produces several hormones that the body uses for a variety of crucial functions, according to Asif et al. [10]. So, thyroid illness threatens the health of every part of the body, including the endocrine, circulatory, neurological, respiratory, digestive, muscular, and reproductive systems. Heart failure, losing consciousness, and mental illnesses are frequent events that can all result in death. Because of this, good clinical diagnosis and early identification of thyroid illnesses help maintain the physiological equilibrium of the human body and potentially save countless lives. They explored several machine learning techniques for the early diagnosis and prediction of thyroid illness in their study, and they recommended the multilayer perceptron (MLPC), which had the greatest accuracy of 99.70%. It may thus be used realistically, which will help medical professionals detect thyroid problems early. Thus, their suggested approach can aid in the fight against thyroid illness and promote human welfare. The thyroid dataset was studied by Yadav et al. [11] using a variety of machine learning classifiers, including decision trees, random forest trees, additional trees, and bagged ensemble models. Using bagging ensemble approaches, the seed value of 35 and the n-fold value of 10 have been shown to have the maximum accuracy. Therefore, when compared to the other three classification methods, the bagging ensemble methodology is the best. Abbad Ur Rehman et al. [12] Early disease diagnosis and identification are crucial for human survival. Specific and reliable identification and detection have been easier to achieve because of machine learning algorithms. Due to the symptoms of thyroid illness being confused with those of other conditions, diagnosis is difficult. The three newly added characteristics in the thyroid dataset have a beneficial influence on classifier performance, and the findings reveal that they outperform previous studies

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in terms of accuracy. Nave Bayes obtained 100% accuracy in all three portions of the experiment after analyses of KNN, SVM, decision tree, logistic regression, and Nave Bayes, whereas logistic regression achieved 100 and 98.92% accuracy in L1and L2-based feature extraction, respectively. KNN also produced great results, with a 97.84% accuracy rate and a 2.16% error rate. The benefits and resilience of the new dataset are evident after analysis and would enable clinicians to obtain more exact and accurate findings in less time. The study that deals with categorizing thyroid disorders into hyperthyroidism and hypothyroidism was described by Salman & Sonuc [13]. Algorithms were used to classify their illnesses. Using multiple methods, machine learning produced positive results and was developed using multiple models. A total of 16 inputs and 1 output were used in the first model, and the correctness of the random forests method produced a result of 98.93%, the greatest accuracy of the other algorithms. Based on prior research, the following features were left out of the second embodiment: The characteristics that were removed were thyroid hormone replacement, hypothyroidism, and hyperthyroidism. Here, they have found that certain algorithms’ accuracy has been retained while others have improved. The Naive Bayes method was shown to optimize the model by a ratio of 90.67. The MLP algorithm’s greatest precision was 96.4 percent accuracy. According to Jajroudi et al. [14], one of the most important considerations in scheduling therapy for cancer patients is survival (6). To predict survival, data mining techniques like decision trees, ANNs, regression, and others are available. The ANN model was applied to survival analysis recently. According to reviews of other prior studies, ANN has demonstrated promising results in the prediction of lung, breast, and esophageal cancer survival in their study. Regression and ANN were used to forecast thyroid cancer survival. In their investigation, MLP effectively served as an appropriate technique for predicting survival in thyroid cancer patients. It is advised to employ additional ANN techniques, such as genetic processes with more precise data, to get better outcomes. Due to a lack of supporting data, certain useful aspects were left out of their analysis. A more accurate model might be used to depict them. For the assessment of thyroid nodules, Ouyang et al. [15] examined three linear and five nonlinear machine learning systems. Overall, the performance of the linear and nonlinear methods was comparable. According to their findings, RF and kSVM, two nonlinear machine learning algorithms, performed marginally better than competing techniques. Their machine learning technique may make it simpler to diagnose malignant nodules since it is simple to use, repeatable, and inexpensive. Multiple machine learning methods were created and verified by them for the prediction of cancerous thyroid nodules. Many FNAs detect nodules with an acceptable low risk of cancer in order to Toing. For the diagnosis of diseases, Krishnamoorthi et al‘s. [16] ML method is considered advantageous. Early diagnosis and treatment benefits patients. In their research, they have investigated a handful of accurate machine learning (ML) classification algorithms for the identification of diabetic patients. The classification issue involves an expression of precision, and ML was applied to the PIDD data set. On the testing

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dataset, the algorithm was trained, verified, and validated. The results of their implementation technique demonstrate that the LR algorithm beats rival ML algorithms. The results of association rule mining indicate that glucose and BMI are significantly associated with diabetes. LR’s ROC values have been required to be 86%. In the future, unstructured data will be taken into consideration, which is the study’s primary weakness. For the prediction of cancer, Parkinson’s disease, cardiovascular disease, and COVID-19, other healthcare areas may employ or be recommended +e models. Uddin et al. [17] looked at how well different machine learning methods predicted diseases. Because clinical data and study focus varied so much, it was not possible to compare studies on predicting disease until a standard baseline for the dataset and scope was set. They only compared studies that used more than one machine learning method to predict sickness with the same data. Even though there are differences in how often and how well they work, the results show that there could be more than one algorithmic family for predicting sickness. Sreejith et al. [18] wrote about a system that lets users access the functions of a healthcare management system whenever they want. Based on the user’s readings, being able to predict cardiac illness lets patients get the help they need as soon as possible. By giving the doctor the ability to examine the medical histories of diverse patients, the quality of the medication provided by the doctor is also enhanced. Here, the paper evaluates several methods and suggests using the random forest approach to predict cardiac disease. They may include different sensor fusion techniques to outperform wearable technology. It will result in the inclusion of different health metrics [19]. According to Rahman et al. [20], the main goal of their research is to develop a system that can accurately diagnose patients with chorionic liver infections using six unique supervised classifier models. They investigated how each classifier performed when given patient data and found that the LR classifier provided the highest order exactness (75% based on the F1 measure) to predict liver disease, while the NB classifier provided the lowest precision (53%). The decision support system and diagnosis of chronic diseases will now use the outperform classification technique. The program can forecast liver infections in advance and provide health status advice. In low-wage countries without enough medical infrastructure or specialists, their implementation can be unexpectedly profitable. There are some implications from their findings for the next worthy KS in their field. More algorithms may be chosen to create an ever-more accurate model of liver disease prediction, and performance can be steadily enhanced. They have only examined a few well-known supervised machine learning systems. Additionally, their work is poised to play a vital part in medical research as well as provide therapeutic targets to prevent liver infection. Several image processing methods, pattern matching strategies, and inferred machine learning algorithms have been presented by Suseendran and his research group [21– 25] in order to improve precision, comparability, and performance. Singh et al. [26] have given assessment methods for heart disease prediction utilizing soft computing algorithms. In order to improve efficiency and effectiveness in healthcare, Rakshit

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et al. [27] have suggested a variety of healthcare approaches based on the Internet of Things. Summary: • For more accurate findings, classifiers with various KNN distance functions and data augmentation approaches can be applied. • By employing various and sizable datasets for various illnesses, we may witness the identification of various thyroid dataset dataset-influenced and test more.

3 Proposed Method Preprocessing, feature selection, and classification are the three steps used in this suggested technique to process the task. Preprocessing is a crucial stage since the database is repetitive and noisy. By looking at the data, we may do feature extraction, data combination to fill in missing values, and excess data removal because lacking quality as excess data would result in inaccurate results. The feature selection process employs linear discriminant analysis. Additionally, classification methods such as KNN, SVM, MLP, and Random Forest are discussed. MLP and Random Forest make up the Hybrid Algorithm. The overall process of the proposed system is shown in Fig. 1. (i) Data We were able to get a lot of information about thyroid hormone levels, and we are now using this information to classify diseases in our study. Deep learning techniques are used to quickly and effectively treat thyroid problems and other illnesses because they play an important role in the healthcare industry and help us diagnose and classify diseases. The information was collected on 1250 people, respectively, males and females, for whom the age group ranged from 1 to 1 year. The data were obtained from outside hospitals but also labs that focus on analyzing and able to diagnose diseases, and the sample taken from the data was the information of Indian citizens as well as

Fig. 1 Diagram in block

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the type of data linked to thyroid problems. Since 90 years, these samples contain both healthy people and those without thyroid disease who both have hyperthyroidism and hypothyroidism. The information was obtained during a one to four-month period with the main goal of employing machine learning techniques to categorize thyroid diseases. As the data collected included 17 variables or attributes, all of which were relevant to the study (For example, ID, age, gender, the message “thyroid hormones,” “on anti-thyroid medicine,” “sick,” “during pregnancy,” “thyroid surgery,” “query hypothyroid,” “query hyperthyroid,” “TSH M,” “TSH,” “T3 M,” “T3, T3, T4”, and the category”) were taken into consideration. (ii) Preprocessing Outliers are taken out and the data is standardized in this part. A model was developed using the processed data. Before trying to apply classifier to the data index, the data must be properly preprocessed and organized. Before connecting, such data should be carefully handled [16]. During this stage, inconsistent data have been handled and eliminated to produce more precise results. The pre-processing method carefully checks the data to disclose the data through analysis and the identification of lost data. Data preparation and cleaning are all part of the pre-processing process. Missing values can be found in this data collection. (iii) Feature Selection Techniques The Feature Selection Technique (FST) consistently improves classification accuracy while reducing computational expense. Additionally, FST removes unimportant characteristics and makes machine learning less time-consuming. The following are the feature selection methods that are employed: Linear Discriminant Analysis (LDA): LDA is a supervised approach used to extract the key features from a dataset. It is used to decrease computing costs and prevent overfitting of the data. To do this, a feature space is projected onto a more condensed, lower-dimensional space with the best class separability. In LDA, the axes that maximize the partition among the various classes are given more consideration [28]. (iv) Classification (a) KNN KNN is one of the first and most straightforward statistical learning techniques or classification algorithms. “K” refers to the number of nearest neighbors, which can be supplied explicitly in the item constructor or estimated using the upper bound made available by the stated value. Hence, classifications for similar scenarios are comparable, and a new sample is classified by comparing it to each of the existing examples. When an unidentified chemical is acquired, the nearest neighbor technique searches the patterns region for k-training instances that are geographically adjacent to the unknown sample. Two distinct approaches are introduced to translate the distance between nodes into a weight, and predictions for several nodes can be

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derived from a training sample situated far away. The automated system has numerous advantages, including its user-friendliness and analytic tractability. Due to the fact that it only utilizes a single instance, the classifier is both highly efficient and must perform well in disease prediction, notably in HD prediction [29]. KNN is one of the supervised machine learning methods. It is commonly employed in classification issues. KNN is frequently used to classify items according to the distance or nearest measure, i.e., the separation between the item and all other objects in the training set. The item is classified utilizing K-neighbors. The procedure is executed before the positive integer K is defined. The Euclidean distance is widely employed to determine the dimensions of various objects [16]. The following gives the computation for the Euclidean distance equation: [ | k |∑ Euclidean = | (Xi − Yi)2

(1)

i=1

X, Y—Two points in Euclidean space. Xi , Yi —Euclidean vectors, starting from the origin of the space (initial point). K = K-space. (b) SVM Support vector machine (SVM) is a supervised machine learning technique that can be used for classification, regression, and even outlier identification. The features of the dataset are displayed in n-dimensional space. A hyperplane, which is a straight line, is utilized to differentiate between the two classes. SVM, a popular supervised machine learning algorithm, can be applied to classification and regression issues. The objective of SVM [10] is to locate a hyperplane in an X-dimensional space (X is the number of features) that classifies the data points separately and has the highest margin. SVM aids in selecting the line that best classifies the data. In addition to selecting a line between the two classes, the SVM algorithm avoids getting too close to neighboring samples. The “support vector” in “support vector machine” refers to two state vectors drawn from the origin to boundary points [12]. Figure 2 displays the operating system of the SVM. Fig. 2 SVM [12]

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The equation for a Hyperplane w = aiSi, y = wx + b where w = weights, b = bias,

(2)

(c) MLP (Multi-layer Perceptron) Classifier The human nervous system serves as an inspiration for the multilayer perceptron idea [24]. The benefits of MLP include being: Highly fault-tolerant, meaning that even if neurons and the connections between them fail, they continue to function; and (ii) Nonlinear in nature, making it appropriate for a variety of real-world issues [28]. The complicated function known as MLP receives numerical inputs and returns the same numbers. A fully linked MLP network is shown in Fig. 3. It has three layers: the domain’s raw input is taken in by the input layer, feature extraction is done by the hidden layer, and prediction is done by the output layer. A deep learning network has multiple hidden layers. On the other side, adding additional hidden layers might cause vanishing gradient issues, which call for the adoption of unique techniques to fix. The parameters of the model of the MLP, which include the number of layers hidden and neurons, must be carefully selected [30]. Cross-validation methods are routinely employed to determine optimum values for these hyperparameters. The MLP networks’ output and hidden neurons use activation mechanisms (f). Normally, the activation function used by all hidden neurons is the same. As opposed to the concealed layers, the output layer often has a distinct activation function. The option is made based on the intention or kind of prediction of the model. An operational amplifier is used to give the neural network non-linearity [30]. When the bias is present, a node in a multilayer perceptron may be described as a neuron that computes the weight value of the inputs and sends it via the input signal [31]. This is how the entire procedure is described: Fig. 3 Three-layer MLP neural network architecture in its standard form [30]

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Vj =

p ∑

Wij Xi + θ j

(3)

i=1

Yj = Fi(V j)

(4)

where fj(vj) is the input layer of the jth neuron, yj is the output, and vj is the concatenation of inputs X1, X2,… XP, j is the bias, and Wji is the network here between input Xi as well as the neuron j. A popular choice for the activation function is the sigmoid function, as follows: F(a) =

1 1 + e−a

(5)

There are many distinct kinds of neural networks, but multilayer neural channels are the most often used. Due to the presence of several hidden layers in their structure, multilayer neural networks are popular because they may occasionally assist in the resolution of difficult issues that a single convolutional-level neural network cannot [31]. (d) The Random Forest In the same way that wood is made up of many trees, randomized forests (RF) are a group of DTs that work together [17]. When DTs are made in a lot of detail, the trained data are often over fitted, which means that a small change in the input data can cause a big difference in the classification results. They are very sensitive to the data they were taught on, which makes it easy for them to make mistakes on the test data. The different DTs of an RF were taught how to use its different training data components. For a sample to be put into a category, its entry must give back to every DT of the two trees. Then, each DT gives a classification result that takes a different part of the input vector into account. The trees then choose the classification that gets a certain number of “votes” (for a discrete classification outcome) or the average of all the trees in the forest (for a numeric classification outcome). The RF technique, which takes into account the results of several different DTs, can reduce the differences caused by the evaluation of a single DT within the same dataset. This program evaluates a lot of different decision trees, creating a forest. Another name for it is a collection of decision tree methods [28]. Algorithm 1 Forest random 1. A random selection of m features is made from a maximum of n features; 2. The optimal split point is used to determine node d, which is a member of the set of m nodes; 3. Additionally, the best-split technique is used to divide d into daughter nodes; 4. Repeat Steps 1–3 until the target becomes the leaf node of a tree with a root node; 5. The formation of a tree is represented by Steps 1–4. To make a forest, repeat them as many times as necessary [28].

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The RF approach combines random feature selection with bagging. The following three random forest tuning settings are crucial: (1) the number of trees (n tree), (2) the minimum node size, and (3) the number of characteristics used to divide each node (m try). The benefits of the random forest algorithm are described below [32]. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

The ensemble learning algorithm known as the random forest is precise. Large data sets may be processed using random forest effectively. It can cope with a large number of input variables. Random forest calculates the key classification variables. Missing data can be accommodated. Techniques for balance error for class-unbalanced data sets are available in random forests. With this technique, generated forests may be preserved for later use. Overfitting is overcome by random forest. RF is much less sensitive to anomalies in training data. Parameters in RF may be simply adjusted, negating the requirement for tree trimming. Accuracy and variable importance are generated automatically in RF [32].

(e) Hybrid (MLP and RF) Classification A randomized forest tree is one of many trees in a forest that helps with prediction decisions. It offers the finest division of all medical data qualities or other characteristics [11]. DT and ensemble learning is the foundation of the data categorization method known as RF. It creates a large number of trees and a forest of choice trees when it’s in beginner mode. Throughout the testing period, every tree in the forest forecasts the classifier for every occurrence. The final decision for every test set is taken by majority voting once a classification is generated from each tree. According to this theory, test data should be provided to the classifier who obtains the most votes. For each piece of information contained in the data gathered, this process is repeated [29]. An artificial neural network called a multilayer perceptron produces several outputs from a collection of inputs (MLP). A directed graph is formed between the hidden layer and output layer of an MLP by numerous layers of input nodes. Backpropagation is used by MLP to prepare the network. A deep learning method is MLP. A directed graph, in which the signal only moves in one way between nodes, is what distinguishes a multilayer perceptron from other neural networks. Except for the input nodes, every node has a nonlinear activation function. Backpropagation is a supervised learning technique used by an MLP. A deep learning technique called MLP uses many units called neurons [13].

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Algorithm 2 Hybrid classification algorithm 1. A fresh bootstrap sample should be chosen from the training set. 2. On this bootstrap sample, grow on an unpruned tree. 3. Do this for I = 1 to c 4. To construct T1i, randomly select the training examples with replacements. 5. Make a root node with T1i in it called Gi. 6. Telephone Generate Tree (Gi) 7. Stop for 8. Generate Tree(G): 9. If G includes just one class’s instances, then 10. Return 11. Else 12. Determine the optimal split by selecting (m attempt) at random at each internal node. 13. If every tree is completely developed. You shouldn’t prune. 14. Using the results of all the trees, produce the overall forecast.

The weights and biases of an MLP network are represented by the location of a particle. Finding a position/weight combination that leads the network to provide computed output that resembles the output of the labeled training data is the goal [30]. The RF technique employs a tree-based solution known as a forest to educate an MLP network. The tree’s potential solutions are all referred to as particles. An ensemble learning technique called Random Forest builds a “forest” of many decision trees. To categorize a new item based on its characteristics, each tree is assigned a class, and each tree “votes” for that class. The forest selects the categorization that receives the most votes. It uses bagging and the random subspace approach to build trees. (v) Result This portion of the study project—which was carried out using MATLAB 2016a and an i5 CPU and 4 GB of RAM—discusses the suggested thyroid classifier performance results of the proposed classification compared to existing methodologies and also collects the thyroid dataset from the UCI repository. Different classification methods are employed to identify the classes. Using a classification technique, it classifies data from the thyroid. Hypogonadism, thyroid issues, goiter, thyroid nodules, and thyroid cancer are among the specific types of thyroid illnesses that have been recognized. Using a classification system, our suggested technique locates the specific thyroid illness. Accuracy specifically refers to the percentage of a test dataset that the model correctly predicts or the precision of the model. Accuracy is determined as follows by identifying true positives (TP) & true negatives (TN) as examples that are correctly categorized, and false positives (FP) & false negatives (fn (FN) as cases that are incorrectly classified: Accuracy =

TP + TN T P + FP + T N + FN

(6)

Thyroid Disease Prediction Using a Novel Classification Enhancing … Table 1 Prediction of thyroid disease accuracy

Techniques

Accuracy (%)

SVM

95

KNN

93

RF

94

MLP

92

Hybrid (MLP + RF)

98

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On the other hand, precision and recall aim to measure, respectively, rates for True Positive (TP) and True Negative (TN). Precision is the ability of classifiers to prevent misclassifying positive instances as really negative ones. Precision =

TP T P + FP

(7)

Instead, recall evaluates how sensitive the model is. It is defined as the proportion of a class’s correct predictions to the total cases in which they occur. Recall =

TP T P + FN

(8)

The reliability of the prediction of thyroid disease is displayed in Table 1 and Fig. 4. The aforementioned result demonstrates that the suggested hybrid classification approach has a 98% accuracy rate. The performance and accuracy of our suggested methods are high when compared to other approaches like SVM and KNN.

Fig. 4 Prediction of thyroid disease accuracy

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4 Conclusion One of the disorders that affect the global population and are becoming more prevalent is thyroid disease. Our work focuses on the categorization and prediction of thyroid disorders since medical reports indicate major imbalances in thyroid diseases. In this article, a unique hybrid classification is applied for thyroid prediction and diagnosis. The following result demonstrates that our suggested hybrid classification approach has an accuracy value of 98%. Hybrid classification comprises the MultiLayer Perception (MLP) method and the Random Forest Algorithm. Our suggested methods provide great performance, accuracy, and support for the identification of thyroid illness when compared to existing methods like SVM and KNN. We may suggest other deep learning and machine learning techniques in categorization in the future to improve illness prediction. Future work: Many types of thyroid exist these days which remain unclassified. So the above work may be extended to the detection of the type of thyroid and the stage of the same. Which may help medical practitioners to suggest respective treatment procedures.

References 1. Chaganti, R., Rustam, F., De La Torre Díez, I., Mazón, J. L. V., Rodríguez, C. L., Ashraf, I. (2022) Thyroid disease prediction using selective features and machine learning techniques. Cancers 14 (16), 1–23. https://doi.org/10.3390/cancers14163914 2. Turanoglu-Bekar, E., Ulutagay, G., & Kantarcı-Savas, S. (2016). Classification of thyroid disease by using data mining models: A comparison of decision tree algorithms. Oxford Journal of Intelligent Decision and Data Science, 2016(2), 13–28. https://doi.org/10.5899/2016/ojids00002 3. Aversano, L., Bernardi, M. L., Cimitile, M., Iammarino, M., Macchia, P. E., Nettore, I. C., & Verdone, C. (2021). Thyroid disease treatment prediction with machine learning approaches. Procedia Computer Science, 192, 1031–1040. https://doi.org/10.1016/j.procs.2021.08.106 4. Raisinghani, S., Shamdasani, R., Motwani, M., Bahreja, A., & Raghavan Nair Lalitha, P. (2019). Thyroid prediction using machine learning techniques. In: Communications in computer and information science (vol. 1045). Springer Singapore. https://doi.org/10.1007/978-981-139939-8_13 5. Shankar, K., Lakshmanaprabu, S. K., Gupta, D., Maseleno, A., & de Albuquerque, V. H. C. (2020). Optimal feature-based multi-kernel SVM approach for thyroid disease classification. Journal of Supercomputing, 76(2), 1128–1143. https://doi.org/10.1007/s11227-018-2469-4 6. Akhtar, T., Gilani, S. O., Mushtaq, Z., Arif, S., Jamil, M., Ayaz, Y., Butt, S. I., & Waris, A. (2021). Effective voting ensemble of homogenous ensembling with multiple attribute-selection approaches for improved identification of thyroid disorder. Electronics (Switzerland), 10(23). https://doi.org/10.3390/electronics10233026 7. Dharmarajan, K., Balasree, K., Arunachalam, A. S., & Abirmai, K. (2020). Thyroid disease classification using decision tree and SVM. Executive Editor, 11(03), 3234. 8. Olatunji, S. O., Alotaibi, S., Almutairi, E., Alrabae, Z., Almajid, Y., Altabee, R., Altassan, M., Basheer Ahmed, M. I., Farooqui, M., & Alhiyafi, J. (2021). Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian

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YouTube Sentimental Analysis Using a Combined Approach of KNN and K-means Clustering Algorithm Saurabh Adhikari, Ruchi Kaushik, Ahmed J. Obaid, S. Jeyalaksshmi, D. Balaganesh, and Falah H. Hanoon

Abstract Sentiment analysis is the method for learning what users think and feel about a service or a product. YouTube, one of the most widely used video-sharing websites, receives millions of views daily. Many businesses utilize YouTube, a well-known social media platform, to sell their goods through videos and advertisements. Popular YouTube channels are seeing a sharp increase in the daily volume of comments. We cannot easily notice and comprehend this enormous volume of comments, which are largely unstructured, so we need some applications or methods that use large amounts of data to perform sentiment analysis. So the sentiment analysis is, therefore, necessary to categorize the comments on a bigger platform to find meaningful ways to categorize. In this paper, we employed sentimental analysis and methods that may be applied to comments on YouTube videos. Additionally, it S. Adhikari School of Engineering, Swami Vivekananda University, Kolkata, India e-mail: [email protected] R. Kaushik Department of Computer Science, CHRIST University, Bangalore, India A. J. Obaid Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq e-mail: [email protected] S. Jeyalaksshmi Department of Information Technology, Vels Institute of Science Technology and Advanced Studies, Chennai, India D. Balaganesh (B) Berlin School of Business and Innovation, Berlin, Germany e-mail: [email protected] F. H. Hanoon Department of Physics, College of Science, University of Thi-Qar, Nassiriya, Iraq Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq F. H. Hanoon e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_4

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describes and groups various techniques that are helpful in sentiment analysis and data mining studies. For sentimental analysis, we merged the K-Nearest Neighbor (KNN) and K-means clustering approaches. For more discussion, the proposed technique is compared with the SVM classifier and Naive Bayes for better accuracy. Keywords Sentiment analysis · YouTube · KNN · K-means · SVM · Naïve Bayes

1 Introduction The Internet’s growing popularity has changed how we think about the things we do every day. With the rise of social media, faster and easier Internet access, and smart devices, this effect has become stronger. With the ease and time savings that have been made possible by the shift from the physical world to the cyber world, there is no doubt that the quality of life has been increasing quickly [1]. Today, it is impossible to think of doing research or keeping up with the newest news without utilizing the Internet. In reality, as a result of the Internet’s widespread use, more complex ideas, including big data and the Internet of Things (IoT), have emerged. However, despite the Internet’s widespread use, not all websites and platforms receive the same amount of traffic because of their popularity and advantages [1]. The virtual space for social networking that online social media provides allows users to express their opinions and provide reviews on media material. Social media data mining aims to unearth meaningful knowledge from information gathered from user interactions with web content. Monitoring suspicious activity on the internet has become essential. Data may be communicated in text, audio, and video formats in online forums. However, a text corpus is the most popular and helpful format for debate on the internet. The best approach to using it and discussing information in the textual format is through text corpora. Data from online media can be utilized for good or bad, even by criminals. Data from online media may be utilized in both positive and negative ways, and criminal authorities might use it to incite irrational opposition to lawful activity. To spot any suspicious activity, the discussion boards need to be continuously monitored. Many law enforcement organizations around the world are seeking ways to monitor these forums and detect any potential unlawful activity. However, there are several difficulties in analyzing these suspicious activities, including locating suspicious published materials and publications produced by users and examining user behavior in social media [2]. Social media platforms like Facebook, Twitter, and YouTube have evolved dramatically, changing how people live their lives. These platforms allow users to post videos, exchange messages, and communicate their opinions with others. The most popular medium for sharing videos is YouTube. With 30 million daily users, it is the second-most-frequented website in the whole globe. Over one billion videos are seen daily on YouTube, and 500 h of footage are uploaded every minute. YouTube divides each video into relevant categories to make it easier to find this material. Like, dislike, and commenting are just a few of the new options that YouTube has included to allow

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users to review videos [3]. The public is accustomed to using the video-hosting site YouTube. According to Our World Data, with over a billion active users, YouTube has had one of the highest numbers of social media users during the previous 5 years. The popularity of YouTube is presently being utilized by several businesses to grow their clientele and increase their marketing footprint through product videos [4]. Sentiment analysis may assist users in comprehending the user’s perspective and is effective for rapidly grasping the big picture when employing a lot of text data. The term “sentimental analysis” is also used to refer to the process of identifying the positive, negative, or neutral thoughts, perspectives, attitudes, perceptions, emotions, and sentiments expressed in a text. Current YouTube usage numbers give an idea of the site’s size: at the time of writing, over 1 billion active users are watching video material each month, totaling approximately six billion hours of video. Additionally, YouTube is responsible for 10% of all internet traffic and 20% of visits to websites [5]. Sentiment analysis is the process of identifying, extracting, and categorizing the views, feelings, and attitudes stated in the text as they relate to various issues. Sentiment analysis is also known as information extraction, evaluation mining, appraisal extraction, and attitude analysis. Through the study of criticism (or review) language, opinion mining, and sentiment analysis studies seek to understand the thoughts of people throughout the Web [6]. Researchers are now interested in research on knowledge extraction from a corpus of texts. The text of opinions is among the most popular sources for information excavation. Social media is the source of many opinions. Nearly all human actions revolve around opinions, which also heavily impact people or organizations [7]. The outcomes of opinion analysis are excellent or negative, build or decline, and so on. In this research, opinions from Twitter and Facebook are divided into positive and negative categories. Text mining uses several algorithms to categorize views into positive or negative sentiments. In this work, the K-Means cluster and KNN are used in conjunction. The goal of K-Means clustering is to group a collection of items so that they are more similar to one another than to other groups.

2 Related Works In this part, we looked into several emotional analytics and data mining techniques using a variety of published articles. The technique of looking for or extracting valuable information from textual material is known as text mining [8]. It looks for intriguing patterns in huge datasets. It employs a variety of pre-processing techniques, including stemming and stopword deletion. Their study included comprehensive information on the stop-word deletion and stemming algorithms, two text mining pre-processing approaches. They anticipate that the community of text-mining researchers will benefit from this study and get a solid understanding of the various pre-processing strategies. Ezpeleta et al. [9] described a brand-new social spam filtering technique. We offer ways to support the idea that by capturing the mood of the words, it is feasible to

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enhance the outcomes of the present social spam filtering. First, several tests are run with and without the mood feature using a social spam dataset. We then compare the outcomes and show how mood analysis might enhance social spam filtering performance. Results indicate that utilizing the Online Comments Dataset and the validation dataset, respectively, the best accuracy attained with the dataset increased from 82.50 to 82.58% and also from 93.97 to 94.38%. Allahyari et al. [10] tried to give a concise overview of the field of text mining. We provide a summary of the most essential methods and algorithms that are widely applied in the text domain. Additionally, various significant text-mining techniques in the biomedical field were reviewed in this work. Despite the limitations of this page, it is hard to thoroughly detail all of the many approaches and algorithms, but it should provide a general picture of how text mining is progressing at the moment. Given the enormous amount of scholarly literature that is created each year, text mining is crucial for scientific study. Due to the regular addition of numerous new papers, these vast online archives of scientific literature are substantially expanding. Although this expansion has made it easier for scholars to acquire more scientific knowledge, it has also made it very challenging for them to find papers that are more relevant to their interests. Researchers’ interest in analyzing and mining this enormous volume of text is therefore high. To find YouTube, Wahyono et al. [11] developed a mobile application that was based entirely on students’ emotions and online learning while viewers saw online learning materials. When figuring out the results of a website, algorithms for artificial intelligence use text files instead of comments. This study uses a text-based set of guidelines for a text-based emotion class type with k-NN to determine each student’s sentiments based only on user comments on YouTube and online learning resources. By using this program, teachers may learn how their students feel after seeing videos of the study materials they provide for YouTube and online courses. Among others, the legal field is one of several whose primary foundation is information that is preserved as text [12]. Each case that a legal analyst is working on is a research challenge. The legal or judicial argument is based on thorough research to create arguments. The intricacy and quantity of papers that must be looked for and examined make the aforementioned process highly challenging. Today’s search possibilities are largely keyword based. To make this procedure simpler, researchers have introduced the TM approach and associated techniques. The study suggests using an unsupervised text mining approach called clustering to organize papers to improve document search. Hashimi et al. [13] mentioned that the majority of text mining methods rely on several strategies, including clustering, classification, relationship mining, and pattern matching. These methods have been applied to finding, locating, and extracting pertinent facts and information from unstructured and disorganized textual resources. To provide a framework and design, mining approaches have been provided along with various algorithms and classifications. Classification, clustering, linear regression, cluster analysis learning, anomaly detection methods, summarization, and other supervised training approaches are just a few of the diverse methods that have been found. Each of those strategies is essential for creating and putting

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into use data warehouses that are useful for various purposes. Most often, academics, researchers, development centers, etc. employ data warehouses. These days, social networking sites like YouTube, Facebook, and others are quite popular [14]. The best feature of YouTube is that users can subscribe to and comment on videos. However, flooding the comments on those videos attracts spammers. As a result, this study uses K-Nearest Neighbor and Support Vector Machine (SVM) to construct a YouTube identification framework (k-NN). This study is divided into five (5) steps, including data gathering, pre-processing, feature selection, classification, and detection. The use of Weka and RapidMiner is made for the experiments. SVM and KNN accuracy results employing both machine learning methods demonstrate good accuracy results. Naive Bayes often comes up on top, followed by Decision Tree and Logistics. Weka’s results, in contrast, demonstrate an accuracy of at least 90%. A further defense against spam attacks is to attempt to avoid clicking links in comments. By incorporating qualitative analysis into the already-existing quantitative analysis method, Lee et al. [15] demonstrated a way to assess the effect of identifying future signals. This methodology offers an improved method for confirming the validity and reliability of analytical results. The feasibility of the updated technique is beneficial to determining the progress of the issue, expanding from prospective to emergent concerns, as we discovered in the study case on the ethical dilemmas of AI. It is commonly regarded in many fields of research and administration that the updated methodology, which combines qualitative content analysis, is an ambidextrous approach that allows analysts to strike a balance between rigor and flexibility. In practice, the strategy is anticipated to benefit government and commercial stakeholders by giving them a thorough understanding of the current state of affairs, including both hidden and well-known signals as well as their significance. Singh et al. [16] have given assessment methods for heart disease prediction utilizing soft computing algorithms. Many Internet of Things (IoT) strategies and techniques for improving healthcare performance have been described by Rakshit et al. [17]. In order to improve accuracy, comparison results, and performance, Suseendran and his research team [18–22] have reviewed several image processing approaches, pattern matching techniques, and inferred machine learning algorithms that may be analyzed for sentiment analysis. Summary: • The improvement in the absolute number of comments on YouTube and also the daily active visitors on this website is remarkable. • This indicates that mood analysis can distinguish between spam and valid social media comments. • The mood feature gives each type of video a unique feature for comments. This modification aids classifiers in removing spam comments and enhances performance.

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3 Proposed Method Sentiment analysis may be defined as the study, comprehension, and classification of views on a given subject [4]. Opinion mining, or sentiment analysis, is the technique of automatically comprehending, extracting, and analyzing textual data to get sentiment data from opinion phrases. The dataset is taken from YouTube and it is preprocessed and feature extracted for our proposed sentimental analysis (KNN + K-means) to get the final result. Figure 1 shows the block diagram of our proposed method, it shows the steps followed in our sentimental analysis. (i) Dataset Using the YouTube Data API, the used datasets were taken directly from YouTube [20]. The attractiveness of the channel as well as the availability of recent comments serves as the foundation for the retrieved datasets. Other than these two features, there was nothing else taken into account. Consequently, the datasets were chosen at random (not based on celebrities or whatsoever). The overall number of YouTube channels used is 100, and there are 10,000 total samples. (ii) Pre-processing Pre-processing involves cleaning up the raw dataset using operations like tokenization, stop-word removal, and stemming. For the subsequent feature extraction and selection phase, the clean dataset would be utilized. (iii) Feature extraction Features extraction is a procedure for converting data that was previously in text form into a machine-understandable format. Making the information into vectors is one

Fig. 1 Block diagram for the suggested approach

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of them, which makes it simpler for robots to learn. There are other ways to create vectors, however, in this study, we only employ two categories of vectorizers: • The hash function known as HashingVectorizer is a useful tool for efficiently mapping words to features. The hashing function is used by Hashing Vectorizer to determine how many frequencies are present in each text. With this technique, a text document is transformed into a tokens event matrix [4]. • The term frequency-inverse documents frequency vectorizer is TFIDFVectorizer. It uses statistics to determine each word’s weight in the sample document [5]. IDF is the weight of how broadly dispersed the word is over the whole dataset, and TF is the frequency with which the phrase appears in the dataset. The amount of the IDF increases with the amount of information that does not include the relevant phrase. • The CountVectorizer technique turns a group of text documents into a token count matrix. Digitization is the process not only offers a quick approach to alter a collection of text files and create a vocabulary of recognized different words but it can also be used to encrypt new documents [4]. Weights that are often employed in information retrieval and text mining are term frequency and inverse document frequency (TF-IDF) [18]. The formula to determine TF is TF(d , t) = f (d , t)

(1)

According to Eq. 1, f(d,t) is the frequency of the word t in record d. TF is term frequency and IDF is inverse documents frequency. Equation 2 below proves the formula for calculating TF-IDF. TFIDF = TF(d , t).IDF(t)

(2)

If there are checkable facts that may be utilized as a query inside the TF-IDF technique, TF-IDF weighing may be completed. (iv) Sentimental analysis (a) SVM SVM is effective in distinguishing between good and bad issues, like spam. A supervised learning model called SVM examines the information utilized in classification and regression. SVM is frequently used for classification issues. For binary classification problems, SVM is utilized together with kernel functions [14]. In the field of machine learning, a support vector classifier is one such supervised training method that makes sufficient progress on a range of tasks, specifically while analyzing the feelings. The more complicated the data, the more correct the forecast will be, making SVM algorithms superb classifiers [5]. A “good” linear separator across different classes is what Support Vector Machines seek to identify. Only two classes—a positive class and now a negative class—can be

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distinguished by a single SVM. The SVM method looks for a hyperplane that is the farthest away from both positive and negative instances (also known as the margin). Support vectors are papers that define the hyperplane’s precise location and have a distance from it. If the documentation vector of the two classifications cannot be separated linearly, a hyperplane is chosen so that the fewest document vectors may be found on the incorrect side [10]. (b) Naive Bayes The word “naive” refers to the assumption that the characteristics in a dataset are independent of one another. This classifier is a probabilistic learning approach based on the Bayesian theorem. This classifier may be used for sentiment analysis, document classification, text categorization, spam filtering, etc. Few explained how generative classifiers, also known as Bayesian classifiers, aim to construct a probabilistic classifier by modeling the underlying word properties in various classes. The next step is to categorize the text using the posterior probabilities that the documents belong to the various groups based on the occurrence of particular words in the texts [2]. A class c Naive Bayes is as follows for a document d: P(c|d ) =

P(d|c)P(c) P(d)

(3)

P(c|d) stands for the likelihood function of the classification, P(c) for the posterior distribution of a class, P(d|c) for the likelihood, or the likelihood of the predictor provided the lesson and P(d) for the posterior distribution of the predictor [2]. (c) KNN A supervised learning technique is KNN. Data in the KNN method is shown as a vector space. KNN emphasizes the k training data points that are most comparable to a test data point. The method will integrate the neighbors’ labels to decide the label of the testing data point after identifying the K-Nearest Neighbors [14]. A method for categorizing objects based on educational data that is nearest to the item is the K-Nearest Neighbor (KNN) criteria set [11]. Friendship distances, whether close or far, are often determined using the general method shown in the equation below, based on the Euclidean distance. [ | n |∑ d = | (ai − bi)2 i=1

In the aforementioned equation, where the component is “distance,” a = check statistics/testing, b = pattern statistics, i = variable statistics, and. n = Dimension of statistics.

(4)

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KNN is a straightforward and effective classifier. Known as a lazy learner since the training phase just involves classifying all of the training instances. To store the training values, KNN uses a lot of memory. In essence, the K-nearest neighbor technique stated that for a particular value of K, it would locate the K-nearest neighbor of an unknown data point and then assign a class to the unknown data point based on which class had the greatest number of points across all class of K neighbors. (d) K-means clustering One of the partitioning techniques that is frequently used in data mining is k-means clustering. In the case of text data, the k-means clustering divides n texts into k groups. The clusters are constructed around a representative object. Algorithm 1 Clustering with K-means 1. Determine the cluster K number. 2. Setting up the cluster center (centroid). It can be carried out in several ways. However, using a random approach is the most popular method. Random numbers are used to assign cluster centers. 3. Distribute all information and items to the nearby cluster. The distance between two items is used to calculate how near they are to one another. The Euclidean Distance concept is used to determine how far all data are from each centroid point. 4. Update the centroid using the current membership of the cluster. The centroid represents the mean (mean) of all the data and objects in a given cluster. The cluster’s median may alternatively be utilized if preferred. 5. If the cluster doesn’t change, assign each item using the new cluster center; otherwise, continue Step 3 once there is no transition for each cluster.

(e) Combined KNN and K-means algorithm The closest neighbor classifier is a closeness classifier that performs the classification using distance-based measurements. The fundamental contention is that, based on similarity metrics like cosine established, documents that are members of the same class are much more likely to be “similar” or close to one another (2.2). From the different classifiers of the documents related to the training set, the categorization of the test dataset is deduced. The method is known as k-nearest neighbor categorization and the most prevalent class from such k neighbors is given as the classifier [10] if we take into account the k-nearest neighbor in the train data set.

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Algorithm 2 Combining KNN and K-means is a proposed algorithm 1. Input: D is for the document set, S is for similarity, and k is the cluster count. 2. Output: k-cluster collection. 3. Initialization. 4. Choose k data points at random to serve as initial centroids. 5. Determine the cluster K number. 6. Setting up the cluster center. 7. Distribute all information and items to the nearby cluster. 8. Update the centroid using the current membership of the cluster. 9. Do, but do not converge. 10. Based on the most comparable papers, assign them to the centroids. 11. Determine the cluster centers for each cluster. 12. End. 13. Change each object’s assignment using the new cluster center. 14. The clustering process is complete if the cluster doesn’t change. 15. Else. 16. carry out Step 7 once more until each cluster shows no change. 17. give back k clusters.

(v) Results YouTube is used in this study as a helpful resource for collecting text remarks in the comment column. Figure 2 demonstrates the beneficial source from the Channel on YouTube that includes study material. The overall amount of YouTube channels used is 100, and there are 10,000 total samples.

Fig. 2 An example of a YouTube remark [11]

YouTube Sentimental Analysis Using a Combined Approach of KNN … Table 1 SVM, Naive Bayes, and proposed method accuracy

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Technique

Accuracy (%)

SVM

95.58

Naïve Bayes

92.58

KNN

94.32

K-means

93.56

Proposed (KNN + K-means)

98.13

One of the criteria used to gauge how accurately an algorithm is applied is performance evaluation. The Confusion Matrix is employed in this review. By contrasting the outcomes of the categorization of the training data, the worth of accuracy, recall, clarity, and F1 score for the classified testing data will be examined. The accuracy score measures the algorithm’s effectiveness using an Eq. (5). Accuracy =

TP + TN TP + TN + FN + TN

(5)

The equation defines recall as the proportion of the chosen special attention to the entire number of relevant things accessible (6). Recall =

TP TP + FN

(6)

Precision is the proportion of the relevant item that was chosen to all other relevant items. When information requests and replies are matched using an equation, this is what is meant by precision (7). Precision =

TP TP + FP

(7)

where TP is true positive, TN is true negative, FP is false positive, and FN is false negative. The efficiency of the SVM, Naive Bayes, and the proposed approach is shown in Table 1 and Fig. 3. The emotional analysis of YouTube comments using the combined technique of KNN and K-means algorithms exhibits superior accuracy of 98.13%. When compared to other current algorithms like SVM, Naive Bayes, etc., the proposed methods exhibit promising results.

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Fig. 3 SVM, Naive Bayes, and proposed method accuracy

4 Conclusion In the past 10 years, it has become clearer that social media platforms are becoming more popular. These platforms are now more widely known and used, which has encouraged spammers, fraudsters, and other bad actors to attack them. YouTube has an unusually high number of users and traffic for one of the most popular social media sites. In this research paper, we introduce a sentiment analysis algorithm for YouTube comments. For emotional analysis in this research, we merged the KNearest Neighbor (KNN) and K-means clustering approaches. The suggested combination technique of the KNN and K-means algorithms yields a precision of 98.13% in the emotional analysis of YouTube comments. When compared to other current algorithms like SVM, Naive Bayes, etc., the suggested approaches provide promising results.

References 1. Abdullah, A. O., Ali, M. A., Karabatak, M., & Sengur, A. (2018). A comparative analysis of common YouTube comment spam filtering techniques. In 2018 6th international symposium on digital forensic and security (ISDFS) (pp. 1–5). IEEE. 2. Sharmin, S., & Zaman, Z. (2017). Spam detection in social media employing machine learning tool for text mining. In 2017 13th International conference on signal-image technology & internet-based systems (SITIS) (pp. 137–142). IEEE. 3. Alhujaili, R. F., & Yafooz, W. M. (2021). Sentiment analysis for youtube videos with user comments. In 2021 International conference on artificial intelligence and smart systems (ICAIS) (pp. 814–820). IEEE.

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Big Data Analytics: Hybrid Classification in Brain Images Using BSO and SVM Souvik Pal, Saikat Maity, Saurabh Adhikari, Mohammed Ayad Alkhafaji, and Hanaa Hachimi

Abstract Due to the fast growth of information technology, medical information technology has moved in a smarter direction. The healthcare data may be used to create a health predictive model that can enhance different methods of illness prevention. Medical health big data offers a fundamental “data promise” for medical service cognition and smart healthcare. Numerous novel methods have been proposed and put into practice for the improved categorization of normal and unusual medical images, which provides a framework for predicting several disorders. For the qualities required of medical information, the categorization of large amounts of data in health care is extremely important. In this study, big-data MRI brain pictures are classified using a brand-new hybrid algorithm. Particle Swarm Optimization (PSO) is used for segmentation, and Support Vector Machine (SVM) and Brain Storm Optimization (BSO) are used for classification. We perform classification on brain tumor images from the Medical Big Data dataset to get better results for diagnosis. Additionally, our technique outperforms the old method in terms of classification performance, especially with large or high-dimensional datasets. S. Pal (B) · S. Maity Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India e-mail: [email protected] S. Maity e-mail: [email protected] S. Adhikari School of Engineering, Swami Vivekananda University, Kolkata, India e-mail: [email protected] M. A. Alkhafaji Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] H. Hachimi Applied Mathematics and Computer Science, Secretary-General of Sultan Moulay, Slimane University USMS of Beni Mellal, Beni Mellal, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_5

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Keywords Big data · PSO · SVM · BSO

1 Introduction Due to information technology, digital medical technology has gotten better, medical data is growing at an exponential rate, and medical science has become a good example of how science works. This has led to the phenomenon of “big data”. Data has evolved into a new strategic asset and a key driver of innovation in the age of big data, and it is transforming how biomedical research is conducted as well as how people live and think. Parts of the healthcare profession can be used to improve the cataloging and management of healthcare big data and build a data foundation for future development and use. This can be done through the assimilation, analysis, and technical specification characterization of big knowledge in the healthcare service field. It also gives a strong theoretical and technological foundation for the creation and use of big data in the fields of medicine and health. The project study outcomes can enhance the conceptual and practical system inside the field of healthcare health big data research by supplying essential technologies and information models for big data in the medical and healthcare industries [1]. Medical data in the healthcare industry has grown quickly in recent years. A zettabyte of patient records was produced in the USA in 2018. The adoption of new techniques based on big data technology, machine learning (ML), and artificial intelligence (AI) has consequently become important as a result of this accumulation of medical data, particularly photographs [2]. Presently, several researchers have created machine-learning methods for the early diagnosis of chronic illnesses. Wearable technology provides healthcare facilities with simple, dependable, affordable, and light health monitoring solutions. The ongoing monitoring of bodily changes with smart sensors has become a way of life as a result of several medical awareness campaigns. The majority of health education projects call for illness prevention and early disease detection. The use of technology to create medical data using Spark and machine deep learning to forecast health problems is highly practical and valuable in the field of healthcare. People will benefit from receiving warnings about health problems and information about health threats earlier. In smartphone applications, it can also assist doctors in patient tracking. Using recommendation system-based machine learning techniques, also makes it easier to cure human diseases based on sophisticated testing [3]. A brain tumor is an abnormal development of cells within the area of the brain’s skull that can either be malignant or not. MR brain scans are being used to classify brain tumors, which is a new trend in medical imaging. Due to its rarity and fatal nature, tumor research is an intriguing field. Neurologists can assist the individual to live a longer lifespan in comparison by finding brain tumor tissues early [4]. One of the most deadly types of illnesses that are dramatically on the rise in brain tumors. According to statistics gathered from worldwide scientific organizations like the American Cancer Society (ASCO), the rate of cancer-related deaths is rising quickly

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globally. The growth of brain tumors, which can take many different shapes and sizes and manifest in a variety of sites, is one of the leading reasons for rising death rates in both children and adults. It has been discovered that during the past several decades, the overall number of persons suffering from and passing away from brain tumors has grown by 300 people annually [5]. A tumor is a strange growth in the tissues. The cells in a brain tumor proliferate and multiply uncontrollably, appearing uncontrolled by the mechanisms that control normal cells. Primary or metastatic brain tumors can be malignant or benign. Metastatic brain tumors that have migrated to the brain from another part of the body are what are referred to as cancers. Magnetic resonance imaging (MRI) imagery is frequently utilized while treating tumors in the brain, ankle, or foot [6]. One of the computer vision jobs is classification, where machine learning is used to extract information from a collection of input data, look for certain patterns, and then come to conclusions based on the facts they have discovered. The employment of machine learning algorithms in a variety of sectors, including medical, bioinformatics, economics, agriculture, robotics, etc., has led to their widespread usage and increased academic research. A supervised training task called classification produces a categorical output or the class to which a given instance belongs. The purpose of supervised learning is to create a decision matrix that accurately categorizes unknown examples using a model developed on the training dataset where the categories of cases are known. In the training set of data, the decision model looks for patterns that will allow it to classify newly discovered cases. Since medical datasets often contain a large number of attributes and examples, classifying them is a difficult challenge. The need for early and accurate diagnosis for patient recovery is driving the search for quicker and more accurate categorization algorithms in CAD systems [7]. Machine learning researchers have extensively researched the classification problem. Numerous categorization techniques have been devised and are often utilized in real-world settings. For instance, support vector machines (SVM), decision trees (DT), artificial neural networks (ANN), k-nearest neighbor (KNN), naive Bayesian classification (NBC), etc. However, many of these techniques have a locally optimal solution since they are structurally deterministic [8]. The Brain Storm Optimization (BSO) algorithms are a novel form of swarm intelligence that is based on the brainstorming process, a collective human activity. BSO involves the convergent operation and the divergent operation, which are its two main operations. Through iterative solution dispersion and convergence in the search space, an “acceptable” optimum might be attained. Naturally, both convergence and divergence are capabilities of the chosen optimization method [9]. In this article, brain tumor pictures obtained from big data are processed using a new, unique hybrid algorithm. Particle Swarm Optimization (PSO) is used for segmentation, but Support Vector Machine (SVM) and Brain Storm Optimization (BSO) are used for classification.

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2 Related Works The use of information technology and electronic health systems in the medical field has helped improve patient care, which brings up issues with segmentation and categorization. In this part, we investigated segmentation and classification methods for big data medical pictures using machine learning and optimization techniques. van Opbroek et al. [10] published an automatic method for brain extraction and brain tissue segmentation. By using the Gaussian scale-space features and the Gaussian derivative features, they were able to make segmentations that were usually pretty smooth and gave good results without any additional spatial regularization. Because it was hard to tell the difference between the basal ganglia and the white matter around it, segmentations were not always smooth in some slices, especially those that had the basal ganglia. The suggested multi-feature SVM classification generates appropriate segmentations quickly. Pourpanah et al. [11] came up with a hybrid FMM-BSO model to solve the problem of selecting features when putting data into groups. First, FMM is applied as a method of supervised learning to progressively build hyperboles. The optimal feature subset is then extracted using BSO as the underlying method to optimize classification accuracy and reduce model complexity. To assess the efficacy of the FMM-BSO model, ten benchmark classification tasks and an actual case study, namely, motor failure detection, were employed. The effectiveness of FMM-BSO has been compared to that of the classic FMM and other approaches described in the literature in terms of classification accuracy and the number of characteristics chosen. Overall, FMM-BSO is capable of producing promising outcomes that are comparable to, if not superior to, those from other cutting-edge techniques. FMM-BSO, however, necessitates higher execution times than FMM-PSO and FMM-GA. In a study by Zhang et al. [12], the use of artificial intelligence based on machine learning for large data analysis was examined. The use of SVM in classification algorithms for large data was researched, and it is non-linear and was used. Through the discussion of the multi-classification method, the KNN algorithm was utilized to enhance the one-to-one SVM approach. The reliability of the revised technique for massive data analysis was then confirmed by numerical tests and example analysis. The upgraded one-to-one SVM outperformed the neural network in terms of classification accuracy for faults in power transformers, reaching 92.9%. This paper offers a theoretical foundation for the use of support vector machines and other artificial intelligence tools in large data processing. The effectiveness of swarm intelligence algorithms is typically assessed using benchmark functions [13]. The theoretical study of the algorithm’s running times is lacking. Each member of the swarm is an answer in the search area as well as a sample of data from the search area. Better algorithms and search techniques could be suggested based on the assessments of these data. A new and intriguing swarm intelligence technique is brainstorm optimization (BSO). This work has studied the BSO algorithm’s evolution and uses fit from the standpoint of data analysis. The BSO algorithm may be thought of as combining data mining with swarm intelligence

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methods. Each participant in the brainstorming optimization algorithm serves as both a potential optimization solution and a data point for illuminating the problem’s topology. Swarm intelligence and data mining approaches can be coupled to obtain advantages that each approach could not achieve on its own. The classification model is based on feature selection developed by Xue and Zhao [14], and BSO is utilized to solve the optimum model in two steps. BSO searches for the best weights as well as the best feature subset. Studies using nine distinct datasets reveal that CBSOFS generally has a lower structure size and greater classification accuracy. It implies that feature selection may be used to successfully optimize the classification model’s structure. Additionally, it implies that EAs can hunt for the ideal structure and weights, which can greatly enhance classification performance. A notion of enhanced DPP based on big data analytics that combines all three optimizations was put forth by Ji et al. [15]. Each CNC resource is defined by its data properties inside the context. Following the preparation of the data, big data analytics methods, such as ANN, are used to mine the relationships and patterns among the milling resources to optimize the appropriate resources. AHP manages the interaction between the workpiece, which is seen as the initial limitation, and the optimized objectives of the machining needs. An EA or SI algorithm is used to acquire the optimal or nearly optimal machining resources for choosing the machine tool, cutting tool, and milling circumstances according to their capabilities based on the supplied fitness by merging the mining pattern with the AHP applicable goal. Each chosen resource is viewed as a constraint, limiting the possible solutions. A simple case study that employs a hybrid GA and DBN algorithm validates this strategy. It was suggested using fuzzy brainstorm optimization to classify and segment medical images [16]. The most significant notion for study and analysis is thought to be brain MRI image analysis. The procedure of segmenting and classifying brain tumors is difficult to complete, and the suggested FBSO approach has shown improved outcomes across all metrics. The BRATs’ 2018 set of data was applied to the brain MRI scans. The suggested FBSO had an F1 score of 95.42 percent, an accuracy of 93.85%, a sensitivity of 94.39%, a specificity of 88.96%, and a robustness of 95.42%. It also lowered the segmented time of the optimizer. Using three different types of algorithms, four different sets of classes, and two different sets of characteristics, Surantha et al. [17] evaluated the categorization of sleep stages using heart rate variability in ECG signals. As algorithms, ELM, SVM, and the combination of ELM and PSO were employed. The accuracy was evaluated for a set of classes consisting of 6, 4, 3, and 2 sleep phases. The PSO algorithm chose six of the 18 characteristics. According to the study, ELM and PSO integration, followed by ELM and SVM, had the best accuracy. Conclusion: PSO inclusion increased the ELM and SVM algorithms’ accuracy. The introduction of PSO also reduced the likelihood of model overfitting. The processing times for ELM with PSO for classes 2, 3, 4, and 6 did not change noticeably. Varuna Shree et al. [18] split MR images of the brain into normal brain tissue that was not affected and malignant tumor tissue (infected). Preprocessing is used to eliminate noise from the image and smooth it, which enhances the signal-tonoise ratio. The pictures were then divided up using a discrete wavelet transform,

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and textural characteristics were taken out of the gray-level co-occurrence matrix (GLCM), which was done after the morphological operation. Brain MRI scans are used to classify cancers using a probabilistic neural network (PNN) classifier. To acquire precise vessels, Wen et al. [19] suggested a unique cerebrovascular segmentation approach. First, a new FMM is used to match the intensity histograms of the photos, which leads to a better fit (two Gaussian probability density functions and a Rayleigh distribution function). The best FMM parameters are then obtained using the modified PSO method. Our met, therefore, has a higher impact on segmenting tiny blood vessels. It can cut down on the number of convergence iterations required by other methods like SA, SEM, or EM, improving performance. Our approach has two drawbacks. To reach a stable state, the PSO algorithm cycle is first repeated consecutively. They think that utilizing the parallel architecture of contemporary graphics technology will enhance its performance. Second, several fractured points in the tiny vessels result from our method’s failure to take into account the neighborhood link between the voxels. Rakshit et al. [20] talked about how IoT can be used in different ways to improve performance and results in the healthcare sector. Singh et al. [21] have shown how to use soft computing algorithms to assess and predict heart disease. Suseendran and his research team [22–26] have talked about different ways to process images, match patterns, and use machine learning to improve accuracy, comparison results, and performance. Summary: • To evaluate the performance of some methods, larger datasets must be used. • The field missing rate in the clinical health data is also rather high, and it greatly affects the classification outcomes.

3 Proposed Method (i) Big-data analysis is A big data age has gradually emerged in human civilization as a result of the advancement of information technology, and the volume of data produced throughout the course of production and daily life has been growing quickly. Big data contains enormous amounts as well as great complexity, a variety of data kinds, and quick transmission rates. Furthermore, big data often has more value than small data, which can offer some direction for corporate decision-making. As a result, big data processing and analysis have drawn increasing attention. Four primary components make up big data analysis: clustering, association analysis, classification, and prediction. Big data is categorized using past data, and it is placed in the appropriate category depending on the characteristics of the new sample, which are essential for information security, medical diagnosis, reducing financial risk, and other factors. Big data environments make it much harder to classify data, necessitating the urgent need for effective big data analysis techniques [12]. We preprocessed the medical dataset for the brain tumor, and PCA is used for feature extraction from the processed image. The feature

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Fig. 1 Block diagram

extracted image is used for segmentation using PSO, and hybrid (BSO + SVM) classification is used to get the final result. Figure 1 shows the block diagram of the proposed method. It shows the steps involved in our proposed hybrid classification method. (ii) Dataset This explains the components, the source where the brain imaging data was gathered, and the techniques for feature extraction and segmentation in brain MRI. The suggested approach applies to datasets with 256 × 256 and 512 × 512-pixel size brain MRI imagers to better improve it, it is transformed to greyscale. (iii) Preprocessing The preprocessing stage raises the caliber of the MR images of brain tumors and prepares them for upcoming processing by clinical professionals or imaging modalities. Additionally, it aids in enhancing MR image characteristics. The factors include increasing the signal-to-noise ratio, improving the aesthetic appeal of MR images, eliminating background noise and unimportant details, smoothing interior areas, and keeping important edges [18]. (iv) PCA Feature Extraction The technique of obtaining key information from segmented images, such as roughness, shape, contrast, and color properties, is known as feature extraction. It is necessary to reduce the number of characteristics since too many add to computation times and memory storage, which can occasionally complicate the categorization of tumors. Since (PCA) effectively reduces the dimension of the data and thus also lowers the computing cost of evaluating fresh data, it was applied. PCA is an excellent approach for reducing the dimensionality of a data set with many interconnected variables while retaining the majority of the variability.

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It works by rearranging the variables in the data set in air weights or variances. This method has three outcomes: it orthogonalized input vectors’ components so that they are uncorrelated with one another, it sorts the resulting orthonormal components in order of increasing variation, and it eliminates the components that contribute the least to the variance in the data set [5]. (v) PSO Segmentation Segmentation is a crucial step that directly affects the classification’s outcome. Even if the greatest classifier available is utilized segmentation will still result in a subpar classification result. Even with a relatively simple classifier, a solid segmentation will undoubtedly result in a higher classification rate. However, due to factors and methods outside of the MRI picture, precise segmentation is challenging [5]. Particle Swarm Optimization, a technique inspired by nature, is used to segment the tumor part from the MR picture (PSO). By initializing cluster centroids, PSO offers an optimum solution. It functions similarly to how swarms react and interact with one another as they move around in quest of a solution [13]. The PSO method [29] is simple to use, concurrent, and extremely efficient. The PSO approach is useful for addressing nonlinear, non-differentiable, and multi-modal function combinatorial optimization because of its parallel structure, which provides great performance. The PSO algorithm’s information distribution also makes it adaptable [19]. The two equations below serve as the foundation for applying particle swarm optimization: V p(i, j) = V p(i, j ) + C1 ∗ rand() ∗ (Pbest(i) − Par ticle(i, j ) + C2 ∗ rand() ∗ (Gbest( j ) − par ticle(i, j )

(1)

The coordinates of the intensity values are denoted by a particle (i, j)a, where Vp stands for particle velocity. Gbest() is the overall best fitness value determined by any particles in the solution set, while Pbest() is the fittest solution of a single particle. C2 and C1 are constants, while the method rand() creates random numbers between 0 and 1. The new location may be determined along with the new velocity: next Par ticle(i, j ) = V p(i, j ) + curr ent par ticle(i, j )

(2)

PSO is a population-driven optimization approach that draws inspiration from the cooperative behavior of behaviors in colonies, including bird swarms, which move as a collective, disperse, then regroup to accomplish tasks [17]. Each location a bird takes in PSO is identified as a particle where the value will be maximized. The maximized value for a particle is referred to as g-best, while the fittest value for every particle is referred to as p-best. Every particle has a velocity, that is used to gauge how quickly it will travel to the next point. Following is the PSO algorithm: • Initializing particle population and velocity at random; • Beginning a fresh iteration; • Analyzing each particle’s fitness function;

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• Determining the p-best for each particle and changing it when a newer one performs better; • Calculating the g-best value; • Changing each particle’s speed according to Eq. • When the termination requirements are satisfied, the iteration must end. If not, step 3 of the procedure must be redone. (vi) Classification (a) Support Vector Machine Support Vector Machines outperform other widely-used techniques in terms of organization and are therefore quite effective in classifying pixel-based data. Support vectors are what are utilized as training cases. Utilized to make the best use of the samples, no training scenario else than training examples is useful. An ideal hyperplane is fitted in this manner. High classification accuracy can only be attained by training tiny Big Data collections [6]. An ordinary machine learning algorithm is SVM. This extended linear classifier has several uses, including face identification, picture categorization, and image remote sensing analysis [12]. The knowledge management set was pre-summated to be {(xi, yi), i = 1, 2,.., l). If a categorization hyperplane exists, then ωx + b = 0,

(3)

ωxi + b ≥ 1, yi = 1

(4)

ωxi + b ≤ −1, yi = −1, i = 1, 2, · · ·, l,

(5)

Let

where w and x represented the inner product, this proves that the sample can be separated linearly. (b) Brain Storm Optimization (BSO) One well-known population-based algorithm that draws inspiration from nature and falls under the umbrella of swarm intelligence is the brainstorm optimizer (BSO). The brainstorming method used by people to generate ideas served as the model for this program [7]. Yuhui Shi introduced the BSO algorithm in 2011. This method was used to solve several challenging optimization issues, including path planning, satellite configuration, clustering, grid system energy optimization, positioning of drones for the best coverage, etc.

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The answers in a brainstorming optimization algorithm are divided into several clusters. If the newly produced answer in the same index isn’t superior, the population’s best solution will be retained. Depending solely on single or two persons in clusters, new individuals can be created. When a new person is on the verge of the best answer thus far, their capacity for exploitation is increased. The capacity to explore is improved when a new person is produced randomly or by people in two groups [9]. The original BSO method is easy to build and has a straightforward principle. The primary method is described in Algorithm 1. This algorithm uses three different tactics: clustering the solutions, creating new individuals, and selecting. Algorithm 1 The brainstorming optimization algorithm is used in this process [9]. 1. Initialization: assess the n probable solutions (individuals) that are generated at random; 2. While not having found a “good enough” solution or completing the specified number of iterations; 3. Clustering: a clustering algorithm that divides n people into m clusters; 4. To create new people, randomly choose one or two cluster(s); 5. Selection: Using the same individual index, the freshly created person is compared against the current individual, and the better one is preserved and registered as the new individual; 6. Evaluate the n people.

(c) Hybrid Algorithm (BSO + SVM) To find a better approach to a problem, a group of individuals is typically called to brainstorm the issue at hand. It can resolve a variety of challenging and creative issues that one individual would not be able to resolve. Shi comes up with a fresh concept for an optimization technique as a result of the brainstorming process, and he developed the BSO method in 2011. Combining swarm intelligence optimization with data analysis and selecting reasonably excellent solutions based on the study of the data are characteristics of BSO [6]. Every solution in the population is led to the best option in the problem space using the conventional swarm intelligence optimization process, which is prone to falling into a local optimum solution. To determine the overall best solution, BSO analyzes the solution set to obtain the dispersion of answers or the number of “peaks” in the optimal solution [14]. As a result, the hybrid algorithm in this research employs BSO and SVM to address the classification issues.

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Algorithm 2 Hybrid algorithm 1. Starts 2. Adding the brain image dataset 3. Big data processing before use 4. supplying the sample script 5. Determining the weights ceiling 6. Developing the feature list 7. From 1 to length, please 8. If weights exceed the threshold 9. Using (lambda Value: Value > dataset threshold) 10. Include a new entry as a column in (LF). Utilize cross-validation to carry out the partitioning 11. Start using Bridge on the data to educate and test the system 12. Evaluation 13. Prediction 14. Visualization 15. Initialization: Generate n probable solutions (individuals) at random and assess each one 16. Come up with fresh options 17. r = rand (0,1) 18. Choose a clustering cx with frequency p6 if r < P6 19. r1 = rand(0,1) 20. Potentially alter the cx cluster center by introducing random values if r1 < p6bi 21. Else 22. By adding a random value, you might potentially alter a random answer from the cx 23. if, end; 24. else 25. Pick two arbitrary clusters. 26. r2 = rand(0,1) 27. The new solution is the combining of two cluster centers if r2 > p6c 28. Else 29. A new solution is a pair of randomly selected answers from the selected clusters 30. if, then 31. If 32. Maintain the superior solution between the old and new. till a fresh answer is produced. until the maximum number of iterations has been completed 33. END

This improves the classifier’s ability to distinguish between normal and abnormal brain images. (viii) Result On MATLAB R2017b, all of the experimental models were carried out. The technology is used for 256 × 256 and 512 × 512 pixel brain MRI images on a dataset. Figure 2 shows the Grayscale used as an additional improvement. Accuracy: Accuracy =

TP + TN TP + FP + TN + FN

(6)

where TP is true positive, TN is true negative, FP is false positive, and FN is false negative.

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Fig. 2 Examples of brain MR training images

Table 1 The recommended method’s accuracy

Sample images

Accuracy (%)

Image a

98.20

Image b

97.80

Image c

97.60

Image d

98.40

Fig. 3 The proposed method’s accuracy

The suggested method’s accuracy is displayed in Table 1 and Fig. 3. The result above demonstrates how accurate the hybrid BSO + SVM classification is. The hybrid classification algorithm’s average accuracy is 98%. When compared to other current approaches, our suggested algorithm displays the greatest accuracy and performance.

4 Conclusion Big data is made up of huge amounts, a lot of complexity, different types of data, and fast transmission rates. As a result, processing and analyzing large data sets has drawn increasing interest. In big data MRI brain images, segmentation and classification are

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performed. The most important concept for study and analysis is thought to be brain MRI image analysis. In this study, big-data MRI brain pictures are classified using a brand-new hybrid algorithm. For segmentation and classification, we use Particle Swarm Optimization (PSO). Brain Storm optimization (BSO), and Support Vector Machine (SVM) methods are used in hybrid classification. The suggested hybrid method has shown improved outcomes across all criteria for the difficult task of segmenting and classifying brain tumors. The aforementioned outcome demonstrates that the hybrid BSO + SVM classification is 98% accurate. When compared to other approaches that are already in use, our suggested algorithm exhibits the best accuracy and performance.

References 1. Xing, W., & Bei, Y. (2020). Medical Health Big Data Classification Based on KNN Classification Algorithm. IEEE Access, 8, 28808–28819. https://doi.org/10.1109/ACCESS.2019.295 5754 2. TchitoTchapga, C., Mih, T. A., TchagnaKouanou, A., FozinFonzin, T., KuetcheFogang, P., Mezatio, B. A., &Tchiotsop, D 2021 Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms Journal of Healthcare Engineering 2021 https://doi. org/10.1155/2021/9998819 3. Ismail, A., Abdlerazek, S., & El-Henawy, I. M. (2020). Big data analytics in heart disease prediction. Journal of Theoretical and Applied Information Technology, 98(11), 1970–1980. 4. Dixit, A., & Nanda, A. (2019). Brain MR Image Classification via PSO-based Segmentation. 2019 12th International Conference on Contemporary Computing, IC3 2019, 1–5. https://doi. org/10.1109/IC3.2019.8844883 5. Faisal, Z., & El Abbadi, N. K. (2019). Detection and recognition of brain tumors based on DWT, PCA, and ANN. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 56–63. https://doi.org/10.11591/ijeecs.v18.i1.pp56-63 6. Alam, M., &Amjad, M. (2018). Segmentation and Classification of Brain MR Images Using Big Data Analytics. Proceedings - 2018 4th International Conference on Advances in Computing, Communication, and Automation, ICACCA 2018, 1–5. https://doi.org/10.1109/ICACCAF. 2018.8776742 7. Tuba, E., Strumberger, I., Bezdan, T., Bacanin, N., & Tuba, M. (2019). Classification and Feature Selection Method for Medical Datasets by Brain Storm Optimization Algorithm and Support Vector Machine. Procedia Computer Science, 162(Iii), 307–315. https://doi.org/10. 1016/j.procs.2019.11.289 8. Xue, Y., Zhao, Y., & Slowik, A. (2021). Classification Based on Brain Storm Optimization with Feature Selection. IEEE Access, 9, 16582–16590. https://doi.org/10.1109/ACCESS.2020.304 5970 9. Cheng, S., Sun, Y., Chen, J., Qin, Q., Chu, X., Lei, X., & Shi, Y. (2017). A comprehensive survey of brain storm optimization algorithms. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 1637–1644. https://doi.org/10.1109/CEC.2017.7969498 10. Van opbroek, A., Van der Lijn, F., & De Bruijne, M. (2022). Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification. The MIDAS Journal. https://doi.org/10.54294/ ojfo7q 11. Pourpanah, F., Lim, C. P., Wang, X., Tan, C. J., Seera, M., & Shi, Y. (2019). A hybrid model of fuzzy min–max and brainstorm optimization for feature selection and data classification. Neurocomputing, 333, 440–451. https://doi.org/10.1016/j.neucom.2019.01.011

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12. Zhang, Z. (2020). Big data analysis with artificial intelligence technology based on a machine learning algorithm. Journal of Intelligent and Fuzzy Systems, 39(5), 6733–6740. https://doi. org/10.3233/JIFS-191265 13. Cheng, S., Qin, Q., Chen, J., & Shi, Y. (2016). Brainstorm optimization algorithm: A review. Artificial Intelligence Review, 46(4), 445–458. https://doi.org/10.1007/s10462-016-9471-0 14. Xue, Y., & Zhao, Y. (2022). Structure and weights search for classification with feature selection based on the brainstorm optimization algorithm. Applied Intelligence, 52(5), 5857–5866. https:/ /doi.org/10.1007/s10489-021-02676-w 15. Ji, W., Yin, S., & Wang, L. (2019). A big data analytics-based machining optimization approach. Journal of Intelligent Manufacturing, 30(3), 1483–1495. https://doi.org/10.1007/s10845-0181440-9 16. Narmatha, C., Eljack, S. M., Tuka, A. A. R. M., Manimurugan, S., & Mustafa, M 2020 A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images Journal of Ambient Intelligence and Humanized Computing 0123456789 https://doi. org/10.1007/s12652-020-02470-5 17. Surantha, N., Lesmana, T. F., & Isa, S. M. (2021). Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-020-00406-6 18. Varuna Shree, N., & Kumar, T. N. R. (2018). Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Informatics, 5(1), 23–30. https://doi.org/10.1007/s40708-017-0075-5 19. Wen, L., Wang, X., Wu, Z., Zhou, M., & Jin, J. S. (2015). A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization. Neurocomputing, 148, 569–577. https://doi.org/10.1016/j.neucom.2014.07.006 20. Rakshit, P., Nath, I., & Pal, S. (2020). Application of IoT in healthcare. Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, pp. 263–277. https://doi.org/10.1007/978-3-03033596-0_10 21. Singh, D., Sahana, S., Pal, S., Nath, I., Bhattacharyya, S. (2020). Assessment of the Heart Disease Using Soft Computing Methodology. In: Solanki, V., Hoang, M., Lu, Z., Pattnaik, P. (eds) Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1125. Springer, Singapore. https://doi.org/10.1007/978-981-15-2780-7_1 22. Suseendran, G., Balaganesh, D., Akila, D., & Pal, S. (2021, May). Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams. In 2021 7th International Conference on Optimization and Applications (ICOA) (pp. 1–8). IEEE. doi: https://doi.org/10.1109/ICOA51614.2021.9442621. 23. Jeyalaksshmi, S., Akila, D., Padmapriya, D., Suseendran, G., & Pal, S. (2021). Human Facial Expression Based Video Retrieval with Query Video Using EBCOT and MLP. In Proceedings of First International Conference on Mathematical Modeling and Computational Science: ICMMCS 2020 (pp. 157–166). Springer Singapore. https://doi.org/10.1007/978-981-33-43894_16 24. Pal, S., Suseendran, G., Akila, D., Jayakarthik, R., & Jabeen, T. N. (2021, January). Advanced FFT architecture based on Cordic method for Brain signal Encryption system. In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 92–96). IEEE.doi: https://doi.org/10.1109/ICCAKM50778.2021.9357770. 25. Suseendran, G., Doss, S., Pal, S., Dey, N., & Quang Cuong, T. (2021). An Approach on Data Visualization and Data Mining with Regression Analysis. In Proceedings of First International Conference on Mathematical Modeling and Computational Science: ICMMCS 2020 (pp. 649– 660). Springer Singapore. https://doi.org/10.1007/978-981-33-4389-4_59 26. Suseendran, G., Chandrasekaran, E., Pal, S., Elangovan, V. R., & Nagarathinam, T. (2021). Comparison of Multidimensional Hyperspectral Image with SIFT Image Mosaic Methods for Mosaic Better Accuracy. In Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS 2021 (pp. 201–212). Springer Singapore. https://doi.org/10.1007/978-981-16-31535_23

Breast Cancer Detection Using Hybrid Segmentation Using FOA and FCM Clustering Souvik Pal, Saikat Maity, Saurabh Adhikari, Mohammed Ayad Alkhafaji, and Vicente García Díaz

Abstract Medical image processing has recently been widely applied in a variety of fields. Finding the anomaly problems in that image is highly beneficial for the early diagnosis of these ailments. There are several techniques available for segmenting MRI images to find breast cancer. Breast cancer is the second greatest cause of death in women. Early detection of breast cancer reduces the number of women who die from cancer. If caught in time, breast cancer is among the forms of cancer that can be cured. In this study, breast cancer is identified in medical photos using a unique hybrid segmentation technique. Fruitfy optimization technique (FOA) and FCM clustering are both used in hybrid segmentation. To get a more accurate value of the clustering centers in FCM Clustering, a Fruitfy optimization algorithm (FOA) approach was applied. The MRI images’ features are extracted using the Extended Gabor wavelet transform (IGWT). When compared to other approaches, the result demonstrates that the hybrids segment performs with great performance and good accuracy of 96.50%. Keywords Segmentation · MRI · FOA · FCM · IGWT

S. Pal (B) · S. Maity Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India e-mail: [email protected] S. Maity e-mail: [email protected] S. Adhikari School of Engineering, Swami Vivekananda University, Kolkata, India e-mail: [email protected] M. A. Alkhafaji Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] V. G. Díaz Department of Computer Science, University of Oviedo, Oviedo, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_6

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1 Introduction Unprecedented growth is a characteristic of a group of illnesses that includes cancer. It enables cells to expand and divide out of control, infecting surrounding bodily regions. These developed sections can be seen as a bulge on an X-ray. When it invades nearby tissues and distant areas, this growth turns cancerous. Microcalcification clusters in the breast might occasionally be a symptom of malignancy. Breast masses are often benign rather than malignant [1]. The most prevalent malignancy and the main reason for cancer-related deaths in women is breast cancer. Pathological diagnosis provides the most dependable gold standard of all approaches for early identification and diagnosis, which is essential for managing the disease and increasing the survival rate. Traditional diagnosis methods mostly rely on the medical professionals’ professional experience, and the results of the diagnostic procedure may, in some cases, be inaccurate. In recent years, artificial intelligence techniques and computer diagnostic tools have made it feasible to employ quantitative measures and machine learning techniques for medical diagnosis. Artificial intelligence-based strategies for diagnosing breast cancer have also been developed [2]. The second leading cause of mortality for women is breast cancer. The number of women who die from cancer is reduced by early identification of breast cancer. Mammograms, breast ultrasonography, functional magnetic resonance (MRI), breast thermography, and positron emission tomography (PET) are the most widely used techniques for finding breast cancer. Breast thermography is a quick, painless, non-invasive, and inexpensive imaging technique for the early diagnosis of breast cancer. Regardless of a woman’s age, breast size, or type, this imaging technique is successful. The technique can be used to keep tabs on the state of the breasts following surgery [3]. Breast cancer is currently the most prevalent illness affecting American women as well as the foremost cause of cancer-related death. Breast with improved contrast In addition to the normal anatomical structural information, MRI imaging of the mammary before, during, and after injection of a contrast material offers a non-invasive evaluation of the microcirculatory properties of tissues [4]. Breast MRI is a fantastic tool for finding solutions when it comes to identifying, diagnosing, and staging breast cancer. In identifying breast cancer, it has demonstrated high sensitivity and moderate specificity. The enhanced breast, including the breast implants and the mammary glands around the implant, may be seen using MRI. Breast MRI helps monitor patients following breast cancer therapy, staging breast cancer, and choosing the best course of action. Breast cancer diagnosis using contrast-enhanced MRIs obtained by contrast injection has been demonstrated to be quite sensitive, but they are both time-consuming and a waste of healthcare resources [5]. Image processing and signal processing techniques have become increasingly important in recent years. These methods have been used to assess a variety of pictures that are beneficial for remote sensing, healthcare, aerospace, control systems, and other fields. For the detection of breast cancer in women, the breast imaging method known as mammography has attracted more attention from researchers in the field of medical imaging [6].

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One of the fascinating areas of medical research is image processing as well as its segmentation. Both MRI and computed tomography scans (CT) are used in MRI image technology to provide images of the inside of the body, but MRI provides a more accurate viewing of anatomical features of tissues. MRI is superior to a CT scan since it does not have any negative effects on human health. To create the human body, many cell types are joined together. Cancer image screening has recently become the most essential medical imaging technology. Computerized tomography is a common method for identifying and evaluating cancer pictures. The nodules are separating benign from cancerous tissue. The ideal strategy for the current investigation is to pre-process the original sample picture to minimize noise detection and Gaussian blur using an adaptive bilateral filter and compare the results [7, 8]. In this study, we suggested a brand-new hybrid segmentation method for breast cancer diagnosis. Fruit fly optimization techniques and FCM are both used in hybrid segmentation.

1.1 Gap Analysis Some image segmentation techniques like SVM, FCM, and ANN may benefit in some particular methods. But we need better efficiency and accuracy methods to early detect breast cancer. The hybrid method suggested in our work helps in the early detection of breast cancer for diagnosis.

2 Related Works This section gives some basic information on breast cancer and the methods used to diagnose it, such as various optimization and clustering algorithms. The section below also contains further medical segmentation techniques. Al-Ayyoub et al. [9] wrote about how to use GPUs to make the SPFCM part of mammography images work better. The results showed that using the GPU’s computing power to split up data can be done quickly and accurately. This means that the technology could be used in the real world to make the process of diagnosing cancer faster and more accurate. An effective and computationally efficient method for addressing medical data categorization issues is FOA-SVM [10]. The proposed FOA-based method, which looks at the novel swarm-based technique for the optimum parameter tuning for the medical classification data, is what makes this study unique. It aims at maximizing the generalization capability of the classification model. The actual investigations have shown that in terms of numerous assessment factors, notably the computation time cost, the recommended FOA-SVM beat four other competing choices. This implies that the proposed FOA-SVM method can be a helpful alternative clinical choice for medical decision support. A novel FS-based categorization model for CKD was introduced by JerlinRubini and Perumal [11]. The usage of FFOA for effective FS

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and MKSVM for classification purposes is the work’s primary novelty. The MKSVM algorithm will be used to categorize the data once the FFOA has been run to produce a set of chosen features. Four scale datasets—renal chronic, Ohio, Hungarian, and Swiss—were used to determine the outcome of the anticipated work. The findings demonstrate that the suggested methodology delivers the greatest performance of the classifier of 98.5% again for the chronic renal function dataset compared to the standard HKSVM, FMMGNN, and SVM approaches. Additionally, it retains the least FNR and FPR as compared to existing approaches and achieves the highest sensitivities, specific, PPV, and NPV value. Kapila and Bhagat [12] in this study report propose brain tumor segmentation and classification, which is carried out in the MATLAB working environment. To determine the recital of the expected technique, “Sensitivity, Selectivity, Accuracy, PPV, NPV, FPR, and FNR” are used. The suggested tumor segmentation and classification achieves the best levels of specificity and accuracy when compared to the current method. To assess outcomes, the suggested methodology (HFFABC + ANN) is contrasted with the presently applied methods (Fruitfly + ANN) and (ABC + ANN). The suggested method generated brain MRI images with 98.1% sensitivity, 98.9% accuracy, and 99.59% dependability, respectively. The experimental findings clearly show that the suggested strategy works better than the existing methods. According to Cahoon et al. [13], using intensity alone as the main distinguishing characteristic would result in increased misclassification rates for both supervised and unsupervised segmentation algorithms in digital mammograms. But techniques like the K-NN algorithms are capable of significantly lower the frequency of incorrectly labeled pixels in certain sections of the picture when given additional information like window standard deviations and means. An enhanced FCM method called HisFCM was proposed by Zhang et al. [14] to better use the information included in the provided image. HisFCM does better than FCM, FCM S, and EnFCM at segmenting medical images because it uses the best parts of all three.HisFCM can also deal with medical data in real time and works much better than other improved algorithms. The suggested method, on the other hand, might not be able to find regions of interest (ROI) in pictures, especially when it comes to complicated medical images, because it is a segmentation method that only uses the image’s color features and statistical information. A novel approach for segmenting colored images was put out by Harrabi et al. [15] and was based on a customized fuzzy c-means technique and several color spaces. Using the accuracy classification degree, the most important components of the employed color spaces are chosen in the first stage. Then, these various bits of information are clustered into homogenous areas using a modified version of a Fuzzy C-means (FCM) method. The acquired findings demonstrate the method’s generality and robustness in that the fuzzy c-means approach included the most important component photos. The findings showed that segmentation performance has significantly improved. The segmentation of colored images can benefit from the proposed approach. Singh et al. [16] said that breast cancer is one of the main reasons why women die. Using fuzzy C-Means grouping and K-means clustering, the authors of this article

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show a new way to find exact clusters in mammograms that show cancer mass and calcification. By putting them together, they were able to figure out where the breast cancer was in mammograms that had not been processed. The results demonstrate that this technique can aid doctors in making a quicker diagnosis of breast cancer and identifying the entire area that the disease has affected. This will help the doctor figure out what stage of cancer the patient has so that important and effective treatments can be given. Their study is based on a visual detection approach using mammography processing pictures. Using the right data-collecting software or hardware connection with digital mammography devices, a real-time system may be developed. Ingo Kanungo et al. [17] One of the main reasons why women die is from breast cancer. The prevention of cancer has therefore been demonstrated to depend on early diagnosis by routine screening and prompt treatment. According to the paper, radiologists’ interpretation of the patient’s therapy from the patient’s raw mammography pictures, which are only 63% accurate, is misleading. Using fuzzy clusterings, such as K-means, fuzzy C-means, and FPCM, they have presented a novel technique in this study for identifying breast cancer masses or calcified in mammograms. They then recommended GA-ACO-FCM clustering for unequivocal mass identification. By combining these, they were able to precisely (92.52%) locate the breast cancer spot in the original mammography images. The findings suggest that this method can help the radiologist diagnose breast cancer at an early stage and categorize the whole cancer-affected region. This will assist the doctor in determining the patient’s cancer stage so that essential and effective treatment procedures may be taken. The suggested approach is inexpensive since it may be used with any type of computer. Using the right data-collection hardware and software interaction with digital mammography devices, a real-time system may be developed. Singh et al. [18] have presented assessment techniques using soft computing algorithms to predict heart disease. Suseendran and his research team [19–23] have discussed different image processing techniques, pattern matching techniques, and implied machine learning algorithms to get better accuracy, better comparison results, and better performance. Rakshit et al. [24] have discussed different IoT-based methodologies and techniques to get better performance and results in healthcare sector. Summary: • Investigate the aforementioned possibilities using current mammography equipment. • Color image segmentation may benefit in some particular methods. • The clustering approach used earlier improved categorization and decreased instances of incorrect classification to further improve classification accuracy.

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3 Proposed Method The overarching goal of early cancer diagnosis is the preservation of human life. From a medical standpoint, this is essential for keeping track of patients. Given that cancer is the top cause of cancer-related mortality in women globally, early identification of malignant growth is crucial for a doctor’s ability to make a proper diagnosis and choose the best course of action. If caught in time, cancer is among the forms of cancer that can be cured. Breast cancer is frequently diagnosed by self-examination, either by either a patient or by a clinician. This manually performed exam looks for lumps or other abnormalities in the size, shape, or location of the breasts [9]. The following four key steps make up the suggested MRI breast cancer diagnosis: (1) Pictures: In the initial stage of the inquiry, we get clinical data from MRI scans to diagnose breast cancer. (2) Preprocessing stage: In the investigation’s second stage, a preprocessing technique is offered. Any image processing technique’s initial step is often preprocessing. Enhancing picture quality and identifying those components of the picture that are necessary for further processing are the main objectives of the preprocessing approach. (3) Phase of feature extraction: The breast cancer picture features are extracted using the Improved Gabor Wavelet Transform (IGWT). (2) Breast MRI images are segmented using a segmentation algorithm employing the hybrid segmentation approach in the second phase. This method is a hybrid segmentation of breast MRI images using the FCM clustering algorithm with FruitFly optimization. Fig. 1 shows the key steps suggested for MRI breast cancer diagnosis using hybrid segmentation. (i) Images The most potential substitute to mammography for finding some tumors that mammography might miss is magnetic resonance imaging. Additionally, by determining the level of the disease, radiologists and other medical professionals can use MRI to help them make decisions about how to treat breast cancer patients. Six hundred and ninety-nine occurrences and nine characteristics from needle aspirates of patient breasts make up the Wisconsin dataset. To distinguish between

Fig. 1 Shows the proposed method’s block diagram

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benign and cancerous samples is the aim. Between both the malignant and benign samples, there were substantial differences in each of the nine characteristics. (ii) Preprocessing The raw input health records are supplied as input during preprocessing. These raw data are very susceptible to noise, missing numbers, and contradiction. The accuracy of categorization is influenced by the superiority of raw data. Preprocessing should be applied to unrefined data to improve the quality of medical data. Preprocessing is more useful in this article when a dataset with non-numerical information is received as a mathematical structure. The mathematical dataset for the allotted supplementary is obtained by grabbing the non-numerical data. Once preprocessing is complete, it is relatively simple to forecast if a disease will exist or not. As a result, the final results include facts that are difficult to accept. The primary objective of the preprocessing component is to increase the input image’s prominence so that it may be used for post-processing by minimizing or deleting isolated elements. The preprocessing component’s main objective is to remove or reduce isolated and undesired background segments from the input picture to improve its suitability for post-processing [12]. (iii) Feature extraction The recommended method uses the created Gabor wavelet transform for feature selection (IGWT). Here, an optimization method is used to alter the conventional Gabor wavelet transform. Here, an optimization method is used to alter the conventional Gabor wavelet transform. The oppositional fruit fly method is used to develop the Gabor filter’s efficacy. An improved Gabor wavelet is used in the preprocessed pictures as opposed to the GWT [8]. Below, we present the mathematical justification for IGWT. The fundamental wavelet for IGWT is    ∞ 2π 2 σ 2 (y − f )2  − j2π yt (1) g f,o = g f,o (t)e dt = exp − f2 −∞ where f is mentioned as a dominant factor and σ is denoted resolution factor and {g, f, y, t}, is generated by scaling of the wavelet. The picture quality develops the Gabor wavelet transform’s efficiency before the feature selection technique is used [8]. It is possible to find multiscale and multi-orientation textural segments and sub in the abnormal region by tuning Gabor kernels with different scales and orientations. The Gabor filter obtains its characteristics straight from the gray frames. Micropatterns in the segmented area are abnormal and come in different sizes and orientations [20]. These patterns can be used to identify breast abnormalities. Utilizing Gabor filters, such micro-patterns may be effectively examined [1]. (iv) Segmentation method (a) Fuzzy C-means algorithm One of the most popular approaches for pattern identification is the fuzzy C-means method, sometimes referred to as fuzzy ISODATA. One item of data may belong

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to several groups when clustering is done using fuzzy C-means (FCM) [17]. To accomplish a decent classification, it is dependent on minimizing the objective function. Solutions of reduction are the minimum mean square error stationary points of “b,” which is the perceptual errors clustering criteria. One item of information may belong to several groups when clustering using the fuzzy c-means (FCM) technique [4]. This method is frequently used in pattern recognition. This is based on the minimal objective function: Jm =

N  C 

m Uab ||Xa − Cb||2 , 1 ≤ m < ∞

(2)

a=1 b=1

where m is any actual figure greater than 1, and k*k is any norm attempting to convey the similarity between observational data and the center. In array b, ab represents the degree of affiliation of xi, xa represents the ascending triangle of d-dimensional measurement values, cb represents the cluster’s d-dimension center, and xa appears to be this triangle. When doing fuzzy partitioning, the objective function is iteratively optimized, and the membership uab and cluster centers cb are updated by 1

Uimj =  c

||Xa−Cb|| 2/(m−1) k=1 { ||Xa−Cb|| }

(3)

k

Cj =

ia1 U ab.Xa k a=1 Xa

(4)

When max ab is reached, the iteration will end. where k is the number of iteration steps, and e is an iteration criterion between 0 and 1. This process ends up at a saddle point or local minimum of bm. Bezdek created a fuzzy c-means (FCM), a fundamental kind of fuzzy clustering. It demonstrates a method for dividing data sets that span many dimensions into a specified number of clusters. To what degree each piece of data is part of a cluster is determined using a fuzzy c-means (FCM) cluster-based approach [4]. (b) Fruitfly optimization algorithm (FOA) Based on the fruit fly’s natural tendency to seek out food, the fruitfly optimization technique seeks out global optimization. This will utilize its apheresis capabilities to detect the scent of food. Fruit fly utilizes their abilities to detect scent to get close to the meal before utilizing eyesight to get it. Depending just on the group’s swarming behavior, extra flies also may fly in the vicinity of the meal. Various other flies may also fly toward food. Using this food-finding feature, the ideal input weight model parameters to optimize for ELM are discovered [1]. The “fruit fly algorithms” is a software that imitates the foraging habits of fruit flies. The fruit fly algorithm is a cutting-edge technique for searching for global optimization. The inquiry into the foraging behaviors of the fly swarms served as the catalyst. Fruit flies have acute vision and osphresis, making them expert superfood

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hunters. It initially looks for food by sensing a significant amount of smells floating across the area and sniffing about. It may fly to that exact place after getting so close to the meal [8] or find a fruit once there thanks to its keen vision. The optimum refers to the source of food, and the foraging process is replicated by iteratively searching for the optimum in the FOA. Based on the fruit fly’s concept of food identification, the FOA is an alternative theory. It is better to recognize and evaluate smell and visual cues when compared to various animals. Fruit flies’ olfactory apparatus is more sensitive to a wider range of odors that are pleasant and may even be able to identify a food source from a long distance away [11]. Once the material in the immediate area has been eaten, it may use any delicate eyesight to locate food and fly there. Based on the fruit fly’s concept of food identification, the FOA is an alternative theory. It is better to recognize and evaluate smell and visual cues when compared to various animals. Fruit flies’ sensory organs have a wider range of pleasant smells to pick up on in their surroundings, and they may even be able to pick up on a source of food from a vast distance away [11]. Once the material in the immediate area has been eaten, it may use any sensitive eyesight to locate food and fly there. Fruit flies are thus introduced, and their method of finding food is described as follows: (a) first, they use their olfactory kidney to analyze the source of food before trying to fly to a particular location; (b) alternatively, they use their sensitive eyes to get nearer to the food place; and (c) finally, they switch the location of their flock of fruit flies before trying to fly in that direction. Algorithm 1: Algorithm for Fruitfy Optimization (FOA) [1, 6] 1. Deploy a fruit fly at a random location to kick off the algorithm. Initialize the X and Y axes. 2. Randomly set the direction and distance for each fruit fly to travel in when looking for food 3. Xi = Random Value + X axis 4. Yi = Random Value + Y-axis 5. Because it is currently unable to detect the orientation of the food, The amplitude of the smell density (S), a quantity that is the opposite of the range, is estimated when the distance to the sources is first determined (Dist). 6. To compute the smell density (Smelli) for each fruit fy location, substitute the smell intensity judgment function for the smell density value (S) (or fitness function). Smelli as the function (Si) 7. In the fruity fly swarm, pick the fruit fly with the greatest scent density (get the highest value). maximum [best Smell Best Pointer] (Smell) 8. Decide on the place and fragrance density that works best (x, y). Currently, the fruit fly population is using eyesight to travel to that food source. 9. best Smell = smell best 10. Axis X = best Index X 11. Axis Y = best Y-Index 12. Repeat steps two through five. Check to see if the current fragrance density is higher than the one from the previous installment. If so, go to Step 8.

(c) Hybrid segmentation (FCM + FOA) The goal of this study is to choose characteristics efficiently using a hybrid technique. The proposed hybrid segmentation approach combines the fruit fly optimization and

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fuzzy C-Means model. The fundamental drawback of the fruit fly technique is that it is less computationally efficient and that, later in the evolutionary process, it easily becomes trapped at a local optimal value. Segmentation is more computationally efficient in the FCM. Consequently, the hybrid segmentation approach will be more effective than the separate methods. 1. Initialization: Create a matrix using all the picture pixels; 2. Initialization 3. Line by line scan the image to create a vector X that contains all of the image’s gray levels. 4. Initialize the class vectors’ centers at random 5. From the algorithm’s iteration t = 1 to its conclusion: 6. Calculate the membership matrix U(t) of element Uab using Eq. 5; 7. A matrix of size (cn) is called Uab; 8. Determine the vectors V(t) = [v1, v2,…, vc] 9. If 10. (V(t)-V(t-1)) < e; 11. Then 12. raise the iteration counter, 13. else 14. If necessary, go back to step 6 and end the algorithm. a selected positive threshold is. 15. Randomly initialize the fly pickup pixel positions; 16. Distribute a starting Fruit Fly position at random to start the algorithm. Axis initialization: X and Y 17. Set each fruit fly’s distance and direction at random. 18. Xj = Axis X + Random Value 19. Yj = Axis Y + Random Value 20. The initial computation is the distance to a source because the location of the meal cannot yet be determined (Dist). The characteristic that is assessed is the scent concentrations’ intensity (S), which is the antithesis of distance. 21. Predict where the optimal smell concentration will be. 22. After maintaining the smell concentration with the maximum benefit and merging it, the fruit fly swarm then travels to the area with the greatest fragrance concentration metric (FU, FV). It uses visualization to control its flying. 23. Good Smell = Good Smell 24. FU-axis = FU Excellent index ð Þ 25. FV-axis = FV Excellent index ð Þ 26. Create a consecutive optimization process to carry out steps 17 to 22 and then check to see if the scent strength is higher now than it was in the consecutive smell intensities before. If so, carry out job 2

(v) Result Six hundred and ninety-nine occurrences and nine characteristics from needle aspirates of patient breasts make up the Wisconsin dataset. To distinguish between benign and cancerous samples is the aim. It was discovered that the nine characteristics in each sample varied considerably between benign and cancerous samples. For this suggested study, a 3.20 GHz Intel Core i7 microprocessor, Windows 7 OS, 4 GB RAM, and MATLAB (version 2015a) are employed. The performance of the proposed model was evaluated using the accuracy of classification (ACC), the area around receiver-operating characteristic curve (AUC)

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parameters, sensitivity, and specificity. An ACC, sensitivities, and specificity all fall under the following definitions: Accuracy: Correctness determines the weighted percentage of input photos that can be segregated successfully [12]. It is the proportion of accurately anticipated observations to all observations. It may also be described as the likelihood that a testing procedure will be appropriately performed. Accuracy =

TP +TN × 100% (T P + F P + F N + T N )

(5)

where TP is true positive, TN is true negative, FP is false positive, FN is false negative. Sensitivity: Sensitivity is determined by the portion of input pictures that have been accurately segmented (The degree to which segmentation is carried out accurately for positive results). Sensitivity =

TP × 100% (T P + F N )

(6)

Specificity: The measure of accuracy is the proportion of input pictures that have been accurately segmented (the indicator of how accurately segmentation is carried out to prevent undesired results). Specificity =

TN × 100% (F P + T N )

(7)

The segmentation using the Fruit Fly Algorithms, Fuzzy C-means, and Hybrid Segment based on FOA and FCM are shown in Fig. 2. Table 1 and Figure 3 demonstrate the proposed method’s accuracy. The highest accuracy is 96.70% and we achieved an average efficiency of 96.50% in our hybrid classification based on the Fruit fly algorithm and fuzzy c-means. When compared to other current methods, our suggested hybrid technique has the highest accuracy and greatest performance. Fig. 2 a Segmentation of FOA, b segmentation of FCM [4], and c segmentation of our hybrid proposed

(a)

(b)

(c)

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Sample Images

Accuracy (%)

Image 1

96.40%

Image 2

96.60%

Image 3

96.70%

Image 4

96.30%

Fig. 3 The proposed method’s accuracy

4 Conclusion Breast cancer is one of the top causes of death among women. This article uses a novel hybrid segmentation technique to identify cancer in medical images. In the hybrid segmentation, FruitFly Optimization Algorithm (FOA) and FCM cluster are employed. The FruitFly optimization algorithm (FOA) method was employed to identify the FCM Clustering centers with the highest degree of accuracy. MRI images’ characteristics are extracted using the Improved Gabor wavelet transform (IGWT). The findings show that this method can help doctors diagnose breast cancer more quickly and define the entire region that has been impacted by the disease. This will assist the doctor in determining the patient’s cancer stage so that essential and effective treatment procedures may be taken. The findings demonstrate that hybrid segmentation operates and has a high accuracy of 96.50% when compared to other approaches. Our suggested techniques yield encouraging outcomes. In the next projects, we can do hybrid segmentation utilizing various clustering and optimization techniques.

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References 1. Melekoodappattu, J. G., Subbian, P. S., & Queen, M. F. (2021). Detection and classification of breast cancer from digital mammograms using hybrid extreme learning machine classifier. International Journal of Imaging Systems and Technology, 31(2), 909–920. 2. Huang, H., Feng, X. A., Zhou, S., Jiang, J., Chen, H., Li, Y., & Li, C. (2019). A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features. BMC Bioinformatics, 20(8), 1–14. 3. Prakash, R. M., Bhuvaneshwari, K., Divya, M., Sri, K. J., & Begum, A. S. (2017). Segmentation of thermal infrared breast images using K-means, FCM, and EM algorithms for breast cancer detection. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1–4). IEEE. 4. Kannan, S. R., Ramathilagam, S., Devi, R., & Sathya, A. (2011). Robust kernel FCM in segmentation of breast medical images. Expert Systems with Applications, 38(4), 4382–4389. 5. Hassanien, A. E., Moftah, H. M., Azar, A. T., & Shoman, M. (2014). MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Applied Soft Computing, 14, 62–71. 6. Melekoodappattu, J. G., & Subbian, P. S. (2020). Automated breast cancer detection using hybrid extreme learning machine classifier. Journal of Ambient Intelligence and Humanized Computing, 1–10. 7. Kavitha, P., & Prabakaran, S. (2019). A novel hybrid segmentation method with particle swarm optimization and fuzzy c-mean based on partitioning the image for detecting lung cancer. 8. Krishnakumar, S., & Manivannan, K. (2021). Effective segmentation and classification of brain tumor using rough K mean algorithm and multi-kernel SVM in MR images. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6751–6760. 9. Al-Ayyoub, M., AlZu’bi, S. M., Jararweh, Y., & Alsmirat, M. A. (2016). A GPU-based breast cancer detection system using single pass fuzzy c-means clustering algorithm. In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 650–654). IEEE. 10. Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., ... & Liu, D. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61–75 11. JerlinRubini, L., & Perumal, E. (2020). Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm. International Journal of Imaging Systems and Technology, 30(3), 660–673. 12. Kapila, D., & Bhagat, N. (2022). Efficient feature selection technique for brain tumor classification utilizing hybrid fruit fly-based ABC and ANN algorithm. Materials Today: Proceedings, 51, 12–20. 13. Cahoon, T. C., Sutton, M. A., & Bezdek, J. C. (2000). Breast cancer detection using image processing techniques. In Ninth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No. 00CH37063) (Vol. 2, pp. 973–976). IEEE. 14. Zhang, X., Zhang, C., Tang, W., & Wei, Z. (2012). Medical image segmentation using improved FCM. Science China Information Sciences, 55(5), 1052–1061. 15. Harrabi, R., & Braiek, E. B. (2014). Color image segmentation using a modified Fuzzy CMeans technique and different color spaces: Application in the breast cancer cells images. In 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 231–236). IEEE. 16. Singh, N., Mohapatra, A. G., & Kanungo, G. (2011). Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering. International Journal of Computer Applications, 22(2), 15–21. 17. Kanungo, G. K., Singh, N., Dash, J., & Mishra, A. (2015). Mammogram image segmentation using hybridization of fuzzy clustering and optimization algorithms. In Intelligent Computing, Communication and Devices (pp. 403–413). Springer, New Delhi.

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18. Singh, D., Sahana, S., Pal, S., Nath, I., Bhattacharyya, S. (2020). Assessment of the heart disease using soft computing methodology. In: Solanki, V., Hoang, M., Lu, Z., Pattnaik, P. (Eds.), Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1125. Springer, Singapore. https://doi.org/10.1007/978-981-15-2780-7_1. 19. Suseendran, G., Chandrasekaran, E., Pal, S., Elangovan, V. R., & Nagarathinam, T. (2021). Comparison of multidimensional hyperspectral image with SIFT image mosaic methods for mosaic better accuracy. In Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS 2021 (pp. 201–212). Springer Singapore. https://doi.org/10.1007/978-981-16-31535_23. 20. Suseendran, G., Balaganesh, D., Akila, D., & Pal, S. (2021). Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams. In 2021 7th International Conference on Optimization and Applications (ICOA) (pp. 1– 8). IEEE. https://doi.org/10.1109/ICOA51614.2021.9442621. 21. Jeyalaksshmi, S., Akila, D., Padmapriya, D., Suseendran, G., & Pal, S. (2021). Human facial expression based video retrieval with query video using EBCOT and MLP. In Proceedings of First International Conference on Mathematical Modeling and Computational Science: ICMMCS 2020 (pp. 157–166). Springer Singapore.https://doi.org/10.1007/978-981-33-43894_16 22. Suseendran, G., Doss, S., Pal, S., Dey, N., & Quang Cuong, T. (2021). An approach on data visualization and data mining with regression analysis. In Proceedings of First International Conference on Mathematical Modeling and Computational Science: ICMMCS 2020 (pp. 649– 660). Springer Singapore. https://doi.org/10.1007/978-981-33-4389-4_59 23. Pal, S., Suseendran, G., Akila, D., Jayakarthik, R., & Jabeen, T. N. (2021). Advanced FFT architecture based on Cordic method for Brain signal Encryption system. In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 92–96). IEEE.doi: https://doi.org/10.1109/ICCAKM50778.2021.9357770 24. Rakshit, P., Nath, I., & Pal, S. (2020). Application of IoT in healthcare. Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, pp. 263–277. https://doi.org/10.1007/978-3-03033596-0_10

Hybrid Optimization Using CC and PSO in Cryptography Encryption for Medical Images Saurabh Adhikari, Mohammed Brayyich, D. Akila, Bikramjit Sakar, S. Devika, and S. Revathi

Abstract Private patient data is extensively present in medical photographs. Patients and medical institutions would suffer catastrophic losses as a result of medical image theft and destruction. Because medical photos include sensitive patient data, security is the primary problem during the transmission of those images. When digitized photographs and the patient data they contain are transferred over public networks, image compression security is an important technique for protecting sensitive data. A variety of data encryption based on various techniques was examined by researchers due to the quick advancements in the fields of clinical picture encryption. In this research, we used an optimization strategy to create a novel cryptographic model. Particle swarm optimization (PSO) and the cuckoo search (CS) optimization approach are used to accomplish medical image cryptography. This article used a novel cryptographic framework with optimization techniques to explore the safety of medical pictures. The optimum key for enhancing the security of the encryption and decryption process will be chosen using optimization techniques developed for particle swarms and cuckoo search (CS) in cryptography with elliptic curves. The

S. Adhikari School of Engineering, Swami Vivekananda University, Kolkata, India e-mail: [email protected] M. Brayyich Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] D. Akila (B) Department of Computer Applications, Saveetha College of Liberal Arts and Sciences, SIMATS, Chennai, India e-mail: [email protected] B. Sakar Department of Computer Science and Engineering, JIS College of Engineering, Kalyani, India e-mail: [email protected] S. Devika · S. Revathi Department of Computer Applications, Agurchand Manmull Jain College, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_7

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effectiveness of the proposed method is measured using the Peak Signal-to-Noise Ratio (PSNR) and the entropy. Keywords Medical images · Encryption-decryption optimation · PSO · CS · ECC

1 Introduction Some security requirements must be completed to send the medical photos securely. These requirements include honesty, validity, and secrecy [1]. By encrypting the medical picture to achieve categorization and using computerized markers to assure validity and uprightness, cryptographic methods may be used to meet the stated security requirements. The pieces on display in this teaching activity show how encryption methods give medical symbols protection. The primary goal is to secure medical pictures both during transmission and while such cutting-edge information is recorded. The next test is to make sure that the code can tolerate harsh treatment, such as compression. Protection must be given top priority because there are still many security issues in the world of cloud computing. Since the child’s name, address, and other health details are accessible online, there is a chance that theft, unauthorized access, and security breaches might happen to the data. Effective protection of these records is required. By using cryptographic techniques to encrypt the first message, it is possible to grant outside access to all those records [1]. Owing to the need of the secure transmission of medical images, global healthcare organizations have been able to develop specific security protocols for medical data. One such standard is Digital Imaging and Communications in Medicine (DICOM). The standard provides guidelines and procedures for achieving the three telehealth security services of confidentiality, authenticity, and integrity. While the integrity and authenticity service is required to validate ownership and identify photo modifications, the secrecy service is required to prevent unwanted access to the transmitted images. Today, cryptography and digital watermarking technologies are used to build approaches and algorithms capable of providing the required security services for telemedicine applications [2]. In the field of medicine, prompt and reliable diagnosis is crucial. These days it is common to practice transferring photos, and thus it is crucial to find an effective way to do so over the network. Various security requirements must be followed to ensure the secure transmission of medical pictures. These requirements include confidentiality, sincerity, and dependability. Data assurance has become a crucial issue as a result of several communication security issues. Security for photos is a significant challenge, especially when sophisticated images include a significant amount of information. The necessity to meet the security requirements of digital pictures has encouraged the development of effective encryption techniques [3]. When it comes to private picture information, such as that from the military, commerce, or industry, data must be encrypted before it can be transferred over the Internet [4].

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Vibrant image data became one of the key ways that humans convey information in the multimedia age. The importance of information security is growing as the Internet expands so quickly. Since images, videos, and other types of information are the primary information carriers on the Internet, security concerns have risen to the top of the research agenda. However, conventional encryption techniques like the advanced encryption standard (AES) or data encryption standard (DES) aren’t appropriate for picture encryption due to the strong correlation of neighboring image neighboring pixels and high redundancy. The goal of the research is now to create a new picture encryption algorithm [5]. Electronic healthcare, or e-healthcare, is now practical and widely used because of the internet’s rapid development. E-healthcare is a term used to describe a web-based system where a patient may get in touch with a knowledgeable doctor for a diagnosis. Some medical photos are sent and kept online. These pictures could reveal a lot of patient privacy, and they’re incredibly private and delicate. Data encryption is the most effective approach to significantly protect this privacy concern [6]. Because practically all real-world applications have internal optimization difficulties, the optimization technique has been a core study topic that has attracted a variety of research groups from many areas. Finding the optimal solution while adhering to a set of restrictions is known as optimization. Applications of optimization have multiplied recently, appearing in fields including engineering, machine learning, cybersecurity, image processing, wireless sensor networks, and the Internet of Things (IoT). Numerous of these issues are multimodal, multi-objective, noisy, high dimensional, non-convex, and dynamic in nature. Several traditional and environmental (NIA) methods have been shown to deal with these hard optimization problems. Particle Swarm (PSO), one of these popular techniques that pique our interest, operates with a population known as a swarm. A set of guidelines that make use of both local and international information govern the particle’s movement [7]. We suggested a hybrid optimal cryptography approach in this work. The best key will be selected using a hybrid optimization method in elliptic curve cryptography that combines particle swarm optimization with cuckoo search optimization to increase the security level of the encryption and decryption processes.

2 Related Works We looked into numerous cryptography techniques in medical imaging in this area. For our suggested strategy, the different optimization techniques are investigated. The following list includes a few optimization-based cryptography techniques. Mohamed Elhoseny et al. came up with a hybrid encryption method for the Internet of Things (IoT) [8]. The study also came up with a plan for improving IoT by using a hybrid encryption method. This suggested paradigm, which combines ECC, PSO, and goes, uses multinomial encryption and decoding to produce the

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intended message. Because there is less financial ambiguity, this approach uses less memory. The researchers used crucial metrics like PSNR and SSI, which have shown controlled picture quality against all tests while proving the present job with a variety of data. As the method never provided significant impalpability, it is obvious that it is insufficiently secure; therefore, additional investigation is required to raise the security level. The suggested algorithm is quicker at both encryption and decryption. The ROI-optimized lossless medical picture encryption and decryption system based on game theory are suggested by JIAN ZHOU et al. [9]. The negotiation process is used to maximize the ROI criteria. This allows the ROI to be calculated precisely and adaptively, taking into account the various medical picture types and encryption standards. The encryption technique converts picture formats at the pixel level, achieves lossless decryption, and successfully safeguards the security of medical image data. Additionally, since the encoded ROI location data does not have to be handled individually, the chance of information leakage is reduced even further. The wavelet-based watermarking approach for medical picture authentication was created by Balasamy K. et al. [10]. The watermark is created using a chaotic tented map and the second image’s hash function, and then it is encrypted with a secret key. New, reversible watermarking algorithms that precisely identify altered areas in watermarked photos are our suggested approach. Using the best location and velocity data that are integrated with the pixels of the created watermark, PSO is utilized to generate random coefficients. Additionally, the proposed plan makes the watermark invisible. There is no requirement for additional information throughout the extraction process. The lossless host picture is required in telemedicine for diagnostic purposes on the receiving end. A comparison of chaos or ECC-based encryption techniques was offered by Mustapha Bensalah et al. in their paper [11]. Both methods offer strong security capabilities. Before any practical use, a thorough security study must be conducted because the transition process is not yet developed. The chaos-based technique does offer a straightforward implementation and a fast execution time. The discrete logarithm issue, on the other hand, is hard to solve but remains highly expensive in terms of execution time due to the data encoding phase, which requires ample time. This is where the ECC-based technique differs. Therefore, one of the objectives to accelerate the ECC-encryption and decryption procedures is the optimization of this operation. Mustapha Benssalah et al. [12] come up with a good way to evaluate the security of Dawahdeh et al.’s most recent cryptosystem, which combines ECC, linear cryptography, and chaos. This approach is discovered to still be vulnerable to a variety of attacks, including known and selected plain-text attacks. In addition, a more effective and secure medical image encryption technique for TMIS has been proposed to address and overcome the identified vulnerabilities. By incorporating a new ECIES that offers entity authentication and key sharing into the new system version, the matrix key negotiating scheme has been improved. Utilizing Arnold’s Cat map and the hyperchaotic Lorenz generator, unique processes that enable the addition of confusion and diffusion to encrypted clinical images are also guaranteed. It has been established that the improved approach works with both grayscale and medical

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images. The security and performance analysis of the IECCHC scheme shows that it can survive a variety of attacks and exhibits exceptional security properties. Alhayani, Bilal and others [13] proposed that real-time images, including crucial data, are captured by the visual sensor networks, which then safely and successfully transmit them to the required receiver via the wireless link. Applications like image data transfer demand a substantial amount of energy, and the study focuses on determining whether-hile sensor networks in WSNs have restricted processing power and battery life. Therefore, it is challenging to build an image transmission method for cooperative communications that is energy-efficient. In collaborative digital picture transmission over WSNs, the quality of the picture depends on how the network is set up and how the camera works. Three key performance indicators for the suggested cooperative image transmission strategy have been evaluated using both unique approaches like PSNR (Peak Signal to Noise Ratio) and vitality productivity and conventional methods. This work provided a thorough description of an optimal ECC-based secure and cooperative picture transmission paradigm. The simulation outcomes demonstrate the effectiveness of their suggested model. Sasi et al. [14] investigate solutions to some of the safety issues in a wireless sensor network. While some of the strategies made use of standard cryptographic procedures, others made use of cryptography optimization techniques. This paper presents several optimization-based theories while also highlighting their benefits and drawbacks. This study presents several concepts related to the different cryptographic optimizations and concludes that a large amount of energy and range is needed to store to reduce the key size, and that a complete conversion system must be created in future development. The energy use and delay brought on by the runtime when employed in the setting of a flexible security infrastructure in a wireless network of sensors are the other areas of analyze the use of GA for picture security, Sandeep Bhowmik et al. [15] combined block-based image processing and encryption methods. The examples demonstrate that when the suggested technique was applied to pictures, the correlation between n pixels was reduced. The four cases we looked at here with various block sizes demonstrate that, while the conventional Blowfish Transformation is algorithm better in terms of pixel connection when compared to the Genetic Algorithm, encryption performance significantly improves when GA is used after the traditional processing of images (here using the Blowfish Algorithm). Both GA and the Blowfish Algorithm are outperformed by the suggested Blow GA approach. Once more, the results of the experiments demonstrate that there is a negative connection between the block (we divided the image) and the pixel correlation. This fact bolsters the earlier study reference to forward this study, the performance of the method will be assessed using chromosomes of various sizes (key). It is anticipated that the algorithm is more effective at disrupting the association between the picture elements with a larger key size, resulting in a lower correlation coefficient value. It is possible to assess the use of meta-heuristics like Evolutionary Algorithms, and Tabu hybridized.

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Germine ACO-based metaheuristic algorithm was suggested by Mary G. et al. [16] to improve VC shag before transmitting through the network, eliminating the need for calculation at the recipient end. When utilized with optimized enormous dimensions, simple fitness produces good results more quickly. A different beneficial result might be obtained by adjusting the input parameters and experimenting with alternative values for the parameters q, pheromone, and q. ACO may be used to calculate pheromone deposits and increase the quality of color in color channels using a variety of objective functions. Without requiring a mathematical operation to betray the secret, the suggested method ensures extremely safe, secure, quick, and outstanding quality transfer of the hidden code in the form of an image. Future experiments with a hybrid strategy integrating multiple Nature-inspired Optimization Algorithms might improve VC shares. S. Pal et. al. [19] have discussed a hybrid intelligent scheduling model that may be utilized for healthcare-based task analysis. D. Doss et. al. [20] have discussed memetic optimization, which may be useful for secure healthcare data transmission. P. Rakshit et. al. [21] have presented on audio steganography which is pattern and intensity-based visual cryptography for more security. Summary: • We can create a reliable cryptosystem that ensures the authenticity, integrity, and secrecy of the DICOM information by combining chaos-based encryption and watermarking techniques. • For a deeper understanding, it is intriguing to look at how the linked works are implemented on hardware in intriguing practical applications, securing medical picture data required high levels of security, adaptability, and quickness. • Particle swarm optimization (PSO) and the cuckoo search (CS) optimization approach are used to accomplish medical image cryptography and ECC is used for better security in our study.

3 Proposed Method Random number plays a major role in the integrity of cryptographic primitives for protecting important data, and it is represented by encryption keys. It is crucial to maintain the picture’s integrity and confidentiality is a crucial security concern with the processing and transmission of digital medical images [3]. The protection, safety, and security of medical data kept in the information management system will primarily be ensured through the verification of medical pictures. Privacy, authenticity, integrity, and confidentiality are typically used to describe the transfer of image data via an unsecured network between two locations. As a result, the security of sensitive information included in medical photographs must be given additional consideration [11]. The credibility of the health industry may be compromised if health information management data is misused regarding patient security regarding their medical

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Fig. 1 Shows the proposed model’s block diagram

photographs. The current work’s ultimate objective is to suggest a hybrid optimization encryption strategy. Several studies are now being conducted to boost the security of medical imaging. from the optimization that arbitrarily generates the population keys. Using the generated key, decrypt the cipher picture. Compared to regular photos, medical images differ in various ways, including redundancy, a high data volume, and excellent pixel correlation. Strong encryption techniques are therefore needed. The best encryption method for protecting medical photos is a hybrid approach. In this article, we employ cryptography to secure medical pictures. The suggested model’s block diagram is displayed in Fig. 1. Elliptic curve encryption (ECC) is used to encrypt the medical pictures first, then a hybrid optimization technique combining particle swarm (PSO) but the cuckoo searching (CS) optimization model is used to choose the best public keys for this crypto method. (i) Medical images For the administration and transmission of electronic patient records (EPR) across a network, DICOM (digital imaging and communications in medicine) standards have been established. A header file or a medical picture that transmits important patient information and data are both included in the DICOM standard [12]. The interoperability of DICOM imaging equipment and arbitrary programs is condensed by this standard. (ii) ECC Since 1987, ECC has revolutionized public keys in part because of its shorter operand length than previous asymmetric methods [6]. ECC offers several advantages, including quick calculations, lower power, and memory use [18]. ECC is used for digital signatures, authentication, and key exchange, among other things. The following is the equation for an elliptical curve: y 2 = x 3 + ax + b

(1)

If the parameters a and b are both fixed, x and y are members of the finite field, and (binary or prime field). Point multiplication, in which a point P is divided by an integer

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k to produce a novel position Q that conforms to the curve, is the ECC operation that takes the longest to process. The foundation of ECC is scalar multiplication. ECC is an asymmetrical or public key method based on the algebraic structure of elliptic curves. Koblitz & Miller independently advocated for its application in cryptography in 1985. With noticeably lower key sizes than traditional asymmetric cryptosystems like RSA, the ECC offers comparable security levels [12]. To solve the security issue in many fields, particularly GFs, premier ordering domains GF(q), or characteristic-2 fields GF is suggested as the fundamental solution in the literature (2 m). Scalar point multiplication, denoted by Q = k, is the one-way function specified by ECC. P, QE (GF (q)), and a scalar k are all present (the key). Recurring point additions and doubling are used in this process. The following is a list of the ECC El-Gamal encryption: C1 = r.P

(2)

C2 = M + r.Q

(3)

M stands for the encrypted message as a point, but r is a randomized integer [11]. A secret key d links the two locations P and Q (Q = d.P). M = C2dC1 provides the decryption procedure. Algorithm 1: ECC [13] 1. N: extended generation of random binary strings 2. 2. According to the non-adjacent form, Update N 3. B: NAF technique extended portion generation 4. User A chooses an elliptic curve E as well as a base point G within it. 5. W=0; 6. c=0 7. Calculate (using the NAF method): 8. F=B*D (F-public key of the sender, D- a point on an elliptic curve, B- Private Key of the sender) 9. I=PP*D (I- public key of receiver, PP –private key of the receiver)) 10. If Lc = 1 and Nc = 1, then W=W+F and W=W-F, respectively. 11. W=2h W 12. c=c+1 13. Repeat steps 9 through 13 until the algorithm stops at step 9 after the last iteration. 14. W=2v W 15. W=W+O 16. revert W 17. User B determines C2 = M+r and C1 = rp. Q. 18. User B sends user A C1 and C2. 19. After M is decrypted, the plaintext is obtained.

Two components of the encryption method are the plaintext version of the data picture and the private keys [3]. The array’s byte components are stored in a row

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sequence from left to right, with each line corresponding to one of the image’s output lines. The image’s lines are then completely encrypted. (iii) Particle Swarm Optimization PSO is a randomized search strategy created by Eberhart and Kennedy [10] and is modeled after the social behavior of schools of fish or bird flocking. A swarm is a collection of mobile drugs that act in unison to accomplish a common objective. The swarm’s possible solutions are all classified as particles. The initialization of particles is created at random, and an iterative process is used to find the best solution. The velocity Mi of each particle traverses across the m-dimensional search space. PSO is a reliable stochastic global optimization technique that is based on animal social behavior. With an initial planning xi = (xi1, xi2,…, xin), also referred to as particles for I = 1, 2,…, N, where N is the initialized particle number, the PSO is initialized with an original population of possible solutions in n-dimensional space. The particles move along predetermined routes in n-dimensional space at a velocity of vi = (vi1,vi2,…,vin). Each particle saves the location in the n-dimensional space where the optimizing function had its best value (Pbest), as well as the best location overall (Gbest) in the surrounding area [7]. The following equations describe how these two best values affect their trajectory. The vector pbi = yields the Pbest distance matrix (pbi1,pbi2,…,pbin). The vectoring = (pgi1,pgi2,…,pgin) yields the Gbest global position, and the particle’s location and velocity are updated as     vi ← wvi + d1 s1 pbi − xi + d2 s2 pbi − xi

(4)

xi ← xi + vi

(5)

Every iteration ends with these modifications. Here, w stands for weight inertia, which is also known as the previous velocity’s contribution to the new velocity. Here, the numbers r1 and r2 are generated at random between [0, 1], and the acceleration coefficients c1 and c2 are also generated at random between [0, 2]. It’s interesting to think about the particle’s neighborhood. Numerous topologies may develop in the vicinity of the particles. The neighborhood in the original PSO is made up of all the particles, hence the global ideal situation (Gbest) of the optimizing function is the greatest in the neighborhood. The exchange of information occurs across the swarm and progressively iteration by repetition; the variety of the particle is destroyed as the swarm congregates in one area of the n-dimensional space that may or may not be the best location. The neighborhood is seen by the best as a large number of nodes linked by topological or dynamic magnitude networks. The problem under investigation is one such network [7]. (iv) CS optimization The CS algorithms are driven by various cuckoo species engaging in brood parasitism by depositing their eggs into the nest of the host °edgings. These parasitic cuckoo females may mimic the hues and patterns of the host species’ eggs. It is typical for

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a nest to just contain one egg at a time for ease of handling. The host nest’s exposed egg indicates a preliminary setup. The method is chatting to another arrangement through a cuckoo egg it lays [17]. • • • • •

Start with the answer. Hello, where Hello = “H1, H2,…” Assess the value of fitness. Fi = PSNR + CC. Utilize the Levy Flight Formula Hnew to update the new solution. Determine Hnew’s fitness. When Hnew > f (Hi). Obtain the ideal key and maximum fitness at last. Hoptimal Fi = max (PSNR + CC). The three rules that we applied to the CS algorithm were

(i) Each cuckoo only lay one egg contains, and she sets it in a nest that she picks at random. (ii) The best nests produce the best eggs (solutions), which are passed on to succeeding generations. (iii) There are a set number of host nests accessible, and hosting does have a probability Pa” of finding an alien egg [7]. The host bird in this scenario either discards the egg or leaves the nest and creates a fresh one in a different area. (iv) hybrid optimization (CS + PSO) To scramble and decode data from the medical picture, the optimal key selection procedure takes into account the “fitness function” as the maximum key with PSNR. The fitness function represents a design solution that is close to the set aims. The system of hybrids optimization creates the arrangement to evaluate each arrangement’s purpose. The next stage is how it is shown. Fitness = M AX (P S N R)

(6)

The primitives are taken into account when the secret solution is introduced to create a new population size for the optimum key selection procedure. Enter Sol = S1, S2, . . . Sn

(7)

The notion of creating the consolidation of specific ways of these strategies is covered in the aforementioned parts. The hybrid of CS with PSO is carried out to take better care of the optimum key for ECC using the most severe key, i.e., PSNR.

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Algorithm 2: generation of keys using hybrid optimization (CS + PSO). 1. View the original picture 2. determine its PSNR and MSE value 3. Start the cuckoo and swarm search. 4. Initialize every parameter, including the number of iterations and cuckoo eggs. 5. Set the positions of all eggs to their initial pixel intensities. 6. Continue looping until the maximum number of iterations. 7. Let’s use the input data o, the elliptic variables x, n, and y. 8. The numeric values for the chosen private key Prkare. 9. Create a public key using Pubk = PrK*R. 10. R stands for the random number within 1 and n-1 in this case. 11. Use optimization to obtain the best Prk and Pubk. 12. Update the particle and cuckoo solutions. 13. Until the process is perfect, it is repeated. 14. Obtain Opt Pubk & Opt PrK. 15. An encryption scheme Assumes that the sender is giving the recipient a picture. A applies an elliptic group point’s point-based encoding on a plain picture o.

The goal of this hybrid technique is accomplished by selecting the best outcome from the two methods to learn the hybridization form with the greatest focus. Up to that moment, the process is repeated until the best key for the medical picture is found. (vi) Results An assessment of DICOM picture encryption is carried out on a station with a Core i7 CPU and 16 GB of RAM. The encryption techniques under consideration are programmed in MATLAB r2017b (64-bit). A series of DICOM pictures are used to test the effectiveness and consistency of the two encryption techniques. The security simulation outcomes of our suggested encryption were contrasted with those of other current security methods using various metrics. For security analysis, the website’s medical images, such as “Brone” and “Foot Others,” were collected as shown in Fig. 2. The figure below displays some sample images. Peak Signal-to-Noise Ratio: P S N R = 10log(

2552 ) MSE

(8)

where PSNR is the Peak signal to Noise Ration and MSE is the mean squared error. Entropy: Entr opy =

2N −1  i=0

Pilog(

1 ) Ki

where N is the number of gray levels, Ki is the probability mass function.

(9)

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Fig. 2 Simulation outcomes

SSI: SS I =

(2mean( A ∗ B) + C1)(2con( A ∗ B) + C2)         (mean A2 + mean B 2 + C1)(con A2 + con B 2 + C2)

(10)

where A and B are the ith pixels in the processed and original image. C1 and C2 are the regular parameters. The PSNR level of the medical pictures is displayed in the above Table 2 and Entropy value is mentioned in Table 1. With the help of our Hybrid optimization approach, we can utilize the PSNR value but also SSI value 1, which we obtained from the particular photos, to decode that image. After selecting the optimum key, which isolates the information into chunks with the highest level of the fitness function, the entire input is encrypted.

Table 1 Various techniques’ entropies

Image

Entropy

Original image

1.66

Chaos-based encrypted image [11]

7.9977

Proposed image

7.9973

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Table 2 Value of PSNR

IMAGES

Encrypted image

Image1

PSNR 65.732

Image2

64.923

Image3

65.740

Image4

65.702

4 Conclusion In this study, a brand-new Elliptic Curve Cryptography (ECC)-based medical picture encryption technique is put forth with an ideal public key obtained by hybrid optimization employing the particle swarm optimization (PSO) and cuckoo search (CS) objective functions. This article used a novel cryptography model with optimization techniques to explore the safety of medical pictures. Particle swarm and cuckoo search (CS) in cryptographic algorithms will be used to select the best key to increase the security level of the encryption and decryption processes. We suggested that

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the hybrid optimization technique exhibits impressive outcomes and security when compared to other current methods using PSNR and entropy findings. Other optimization techniques may be included in cryptography research in the future to increase security, flexibility, and quickness.

References 1. Shankar, K., Elhoseny, M., Chelvi, E. D., Lakshmanaprabu, S. K., & Wu, W. (2018). An efficient optimal key-based chaos function for medical image security. IEEE Access, 6, 77145–77154. 2. Al-Haj, A., Abandah, G., & Hussein, N. (2015). Crypto-based algorithms for secured medical image transmission. IET Information Security, 9(6), 365–373. 3. Avudaiappan, T., Balasubramanian, R., Pandiyan, S. S., Saravanan, M., Lakshmanaprabu, S. K., & Shankar, K. (2018). Medical image security using dual encryption with the oppositionalbased optimization algorithm. Journal of Medical Systems, 42(11), 1–11. 4. Yin, S., Liu, J., & Teng, L. (2020). Improved elliptic curve cryptography with homomorphic encryption for medical image encryption. International Journal Network Secure, 22(3), 419– 424. 5. Yin, S., & Li, H. (2021). GSAPSO-MQC: Medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system. Evolutionary Intelligence, 14(4), 1817–1829. 6. Hafsa, A., Sghaier, A., Malek, J., & Machhout, M. (2021). Image encryption method based on improved ECC and modified AES algorithm. Multimedia Tools and Applications, 80(13), 19769–19801. 7. Bharti, V., Biswas, B., & Shukla, K. K. (2021). A novel multi-objective gdwcn-pso algorithm and its application to medical data security. ACM Transactions on Internet Technology (TOIT), 21(2), 1–28. 8. Elhoseny, M., Shankar, K., Lakshmanaprabu, S. K., Maseleno, A., & Arunkumar, N. (2020). Hybrid optimization with cryptography encryption for medical image security in the Internet of Things. Neural Computing and Applications, 32(15), 10979–10993. 9. Zhou, J., Li, J., & Di, X. (2020). A novel lossless medical image encryption scheme based on game theory with optimized ROI parameters and hidden ROI position. IEEE Access, 8, 122210–122228. 10. Balasamy, K., & Ramakrishnan, S. (2019). An intelligent reversible watermarking system for authenticating medical images using wavelet and PSO. Cluster Computing, 22(2), 4431–4442. 11. Benssalah, M., Rhaskali, Y., &Azzaz, M. S. (2018). Medical image encryption based on elliptic curve cryptography and chaos theory. In 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT) (pp. 222–226). IEEE. 12. Benssalah, M., Rhaskali, Y., & Drouiche, K. (2021). An efficient image encryption scheme for TMIS based on elliptic curve integrated encryption and linear cryptography. Multimedia Tools and Applications, 80(2), 2081–2107. 13. Alhayani, B. S., Hamid, N., Almukhtar, F. H., Alkawak, O. A., Mahajan, H. B., KwekhaRashid, A. S., ... & Alkhayyat, A. (2022). Optimized video internet of things using elliptic curve cryptography-based encryption and decryption. Computers and Electrical Engineering, 101, 108022. 14. Sasi, S. B., & Sivanandam, N. (2015). A survey on cryptography using optimization algorithms in WSNs. Indian Journal of Science and Technology, 8(3), 216. 15. Bhowmik, S., &Acharyya, S. (2011). Image cryptography: The genetic algorithm approach. In 2011 IEEE International Conference on Computer Science and Automation Engineering (Vol. 2, pp. 223–227). IEEE.

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16. Mary, G. G., & Rani, M. (2019). Application of ant colony optimization for enhancement of visual cryptography images. In Nature Inspired Optimization Techniques for Image Processing Applications (pp. 147–163). Springer, Cham. 17. Shankar, K., & Eswaran, P. (2016). RGB-based secure share creation in visual cryptography using optimal elliptic curve cryptography technique. Journal of Circuits, Systems, and Computers, 25(11), 1650138. 18. Shankar, K., & Eswaran, P. (2016). An efficient image encryption technique based on optimized key generation in ECC using a genetic algorithm. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 705–714). Springer, New Delhi. 19. Pal, S., Jhanjhi, N. Z., Abdulbaqi, A. S., Akila, D., Alsubaei, F. S., & Almazroi, A. A. (2023). An intelligent task scheduling model for hybrid internet of things and cloud environment for big data applications. Sustainability, 15(6), 5104. https://doi.org/10.3390/su15065104 20. Doss, S., Paranthaman, J., Gopalakrishnan, S., Duraisamy, A., Pal, S., Duraisamy, B., & Le, D. N. (2021). Memetic optimization with cryptographic encryption for secure medical data transmission in IoT-based distributed systems. Computers, Materials & Continua, 66(2), 1577– 1594. https://doi.org/10.32604/cmc.2020.012379. 21. Rakshit, P., Ganguly, S., Pal, S., Aly, A. A., & Le, D. (2021). Securing technique using pattern-Based LSB audio steganography and intensity-based visual cryptography. Computers, Materials & Continua, 67(1), 1207–1224. https://doi.org/10.32604/cmc.2021.014293.

Boundary Element Method for Water Wave Interaction with Semicircular Porous Wave Barriers Placed over Stepped Seabed Santanu Kumar Dash, Kailash Chand Swami, Kshma Trivedi, and Santanu Koley

Abstract This study examines the dispersion of water waves by inverted semicircular surface-piercing wave barriers installed on a stepped seabed. The “Boundary element method” is applied to handle the present “Boundary value problem”. In addition to this energy identity is derived to estimate the dispersion of wave energy by the pair of perforated wave barriers. In addition, the influence of porosity, geometrical configurations of pair of porous barriers, and stepped seabed on the energy dissipation are investigated. The study reveals that for smaller Keulegan-Carpenter (KC) number, the “energy dissipation” due to the perforated barriers is higher. However, the reflection coefficient shows the opposite pattern. Keywords Scattering · Boundary element method · Energy identity · Porous barriers

Nomenclature h1 h2 [j ω BCs E D1 E D2 ED g

Water depth on left hand far field boundary (m) Water depth on right hand far field boundary (m) jth Boundary of the domain Angular frequency Boundary conditions Energy dissipation by right semi-circular structure Energy dissipation by right semi-circular structure Energy dissipation Gravitational force

S. K. Dash (B) · K. C. Swami · K. Trivedi · S. Koley Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Hyderabad, Telangana 500078, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_8

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A RC or |R0 | TC or |T0 | BEM BVP

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Wave amplitude Reflection coefficient Transmission coefficient Boundary element method Boundary value problem

1 Introduction In recent years, using surface-piercing lightweight wave barriers for temporary protection of various marine structures has acquired a considerable interest in marine and coastal engineering field. These low cost and affordable wave structures mainly act as wave barriers and are attached with floating buoys, and therefore positioned from free surface up to certain depth to the water. Due to surface-piercing in nature, these lightweight wave barriers create an obstruction to the free surface ocean waves and helps to mitigate the incoming ocean wave energy to a large extent [1]. The use of surface-piercing barriers can effectively break the incident wave and thereby minimizes the destructive influence of wave forces on the existing marine infrastructures. In earlier days, bottom-standing and surface- piercing thick rigid and impermeable structures were used for coastal protection purpose. However, due to high wave loads on these rigid and impermeable structures, often these structures collapsed and this will have severe impact on coastal region economics [2–5]. To get rid of this financial loss, coastal engineers recommended to use porous structures which are often termed as breakwaters and are made of concrete caisson covered with armor blocks. These are permanent structure built in a particular location and cannot be moved to other locations depending on requirement. To get rid of this problem, flexible, light weight floating structures are required which can be easily moved to any locations and can also be used in deep water locations where bottom-standing structures cannot be constructed [6]. Various types of thin porous structures (different shapes and configurations) are used as temporary wave barriers. Out of these, one of the widely used shape is semicircular type wave barrier [7]. This shape was initially used in the early 1990’s at the Miyazaki port, Japan and later was used at the Tianjin port and harbor of Weihai city of China. One of the advantages of using these semi-circular shape wave barriers is that these frameworks are extremely secure against sliding and the moment of overturning induced by the collision of ocean waves is relatively insignificant. Porous boxes, which combine perforated vertical and horizontal screens, are suggested as a powerful wave-dampening shape to protect seaward amenities. These are lightweight, easy of assembly and disassembly, cost-effective, rapid deployment, and environmentally friendly structures. The porous surface-piercing boxes can also be used for marine aquaculture and are mostly used as fish farming cages [8]. With proper designing, the functionality of the perforated boxes can be boosted significantly as temporary wave barrier. In addition, the functionality of the perforated boxes can be enhanced

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significantly by implementing new design methods such as the addition of a couple of perforated boxes in the structure. Along with the reflecting waves and dissipation of wave energy, these couple of boxes frequently trap some of the wave energy, that aids in lowering the wave height on the porous box’s leeward side. Further, a couple of permeable boxes can behave as a wave-trapping device in addition to reflecting and dissipative properties [9]. In the current research, the scattering of water waves by inverted semicircular “surface-piercing” wave barriers placed over stepped sea-bottom is analyzed utilizing the BEM. The following is the outline of the main sections of the paper: Sections 2 and 3 provide the mathematical form of the problem and related methodology to solve the problem. The results of the present study are given in Sect. 4. Finally, findings and concluding remarks are given in Sect. 5.

1.1 Objective The main objective of the paper is to investigate the interaction of water waves with semicircular perforated wave barriers placed over a stepped seabed. In this regard, the energy identity relation is derived. The effect of various structural and porosity related parameters on the wave energy scattering and dissipation is analyzed in a detailed manner.

2 Mathematical Formulation Figure 1 depicts the graphical diagram of the given physical problem in which water waves propagating from −x direction towards +x direction impinge with floating inverted dual semicircular porous wave barriers placed over step type seabed. Based on the linear water waves theory in the Cartesian coordinate system with two dimensions, the associated BVP is represented where the origin is at the mean free water level, the y-axis points vertically upward, and the positive x-axis points to the right. The water having “density” ρ covers the region −∞ < x < ∞ , −h1 < y < 0 depth for left far field boundary and −h2 < y < 0 depth for right far field boundary. The mean free surface merges with the horizontal plane y = 0. The dual semicircular porous barriers having radius r 1 and r 2 , respectively and center at (b + r1, 0) and (−c − r2, 0), respectively float and are kept fixed in position using suitable floater/buoys. In presence of these dual porous barriers, the total water region is categorized into three sub-parts Rj for j = 1, 2, 3. It is taken that the water is “Incompressible”, “Inviscid”, and “Irrotational”. Additionally, the motion of water is “harmonic” in reference to the “angular frequency”. In view of these assumptions, the velocity potential function “Φ (x, y, t)” exists and is expressed of the form Φj (x, y, t) = Re(φ j (x, y)e−iωt ) with subscripts j represents domains Rj for j = 1, 2, 3. This scalar potential function satisfies the Laplace equation as

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Fig. 1 Schematic diagram of “inverted semicircular porous wave barriers placed over a stepped seabed”

(

) ∂2 ∂2 + 2 φ j = 0. ∂x2 ∂y

(1)

The BCs on mean water level is provided by ∂φ j − K φ j = 0. on y = 0 for j = 1, 2, 3. ∂n

(2)

where ∂/∂n denotes the normal derivative, and K = ω2 /g. The BCs on the fixed stepped bottom is given by ∂φ j = 0, on [ j for j = 2, 3, 4. ∂n

(3)

The dispersion of wave through the permeable structure follows a semi-empirical quadratic discharge equation which states that “the pressure drop through a permeable barrier is directly proportional to the square of the relative velocity” [10–12]. This quadratic boundary condition is given by ⎧ ∂φ j ∂φ 1 ⎪ ⎪ =− , ⎨ ∂n ∂n | | , | ∂φ1 | ∂φ1 ∂φ1 ⎪ ⎪ | | + β ⎩ φj − φ 1 = αj| j ∂n | ∂n ∂n

on [7 and [9 , for j = 2, 3,

(4)

where the coefficients α j , β j in the above equation represent the drag coefficient and the inertial coefficient, respectively. Finally, the far-field B.Cs. on the both sides ends are given by

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⎧ inc ⎪ ⎪ ∂(φ1 − φ ) − ik (φ − φ inc ) = 0, as x → ∞, ⎨ 0 1 ∂n ⎪ ⎪ ∂φ2 − i p φ = 0, as x → ∞. ⎩ 0 2 ∂n

(5)

where φ inc (x, y) denotes “incident wave potential” and it is denoted by φ inc (x, y) = eiκ 0x f 0 (κ 0 , y) with κ 0 being the “wave number” accompanied with the incident wave propagating in R1 satisfies the dispersion relation ω2 = gκ 0 tanh(κ 0 h1 ). On the other hand, p0 represents the positive real root of the dispersion relation ω2 = gp0 tanh(p0 h2 ). The form of f 0 (κ 0 , y) is given by ( f 0 (k0 , y) =

) −ig A cosh(k0 (y + h 1 )) , ω cosh(k0 h 1 )

(6)

with A being the incident wave amplitude. This eigenfunction f 0 (κ 0 , y) satisfies ∫0 −h 1

( 2 2) g A 2k0 h 1 + sinh(2k0 h 1 ) . f 0 (k0 , y) f 0 (k0 , y)dy = − ω2 4k0 cosh2 (k0 h 1 )

(7)

At this point, it is further noted that Eq. (5) can also be written as ⎧

φ1 (x, y) = (eik0 x + R0 e−ik0 x ) f 0 (k0 , y), on [1 , φ2 (x, y) = T0 ei p0 x f 0 ( p0 , y),

on [5 .

(8)

The unknown values R0 and T 0 in Eq. (8), are linked to the reflection and transmission of incident waves, respectively.

3 BEM-Based Solution Procedure The BEM doesn’t need the roots of the complex “dispersion relation” in the permeable region. Whereas, semi-analytical tools like Eigen-function expansion method needs the complex roots of the “dispersion relation”. Often finding these complex roots are complicated. Therefore, the BEM has significant advantages over other solution tools. Applying “Green’s second identity” to the complex velocity potential φ(x, y) and the fundamental solution G(x, y; x 0 , y0 ) over the domain of the physical problem surrounded by [, we get the subsequent expression for (x 0 , y0 ) ∈ [ as 1 − φ (x0 , y0 ) = 2

∫ ( φ (x, y) [

) ∂ G(x, y; x0 , y0 ) ∂φ(x, y) − G(x, y; x0 , y0 ) d[. ∂n ∂n (9)

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The fundamental solution (x, y; x 0 , y0 ) is expressed as follows G(x, y; x0 , y0 ) =

)1 ( 1 ln r, with r = (x − x0 )2 + (y − y0 )2 2 2

(10)

Implementing the B.Cs (2–5) into Eq. (9) throughout each region Rj for j = 1, 2, 3, the resulting set of integral equations are as follows ) ∫ ( ∫ ∂G ∂G 1 − ik0 G φ1 d[ + d[ − φ1 + φ1 2 ∂n ∂n [1 [2 ∪[3 ∪[4 ( ) ) ∫ ( ∫ ∂G ∂G + − i p0 G φ1 d[ + − K G φ1 d[ ∂n ∂n [5

∫

+ [7 ∪[9

(

[6 ∪[8 ∪[10

) ) ∫ ( inc ∂φ 1 ∂φ1 ∂G φ1 Gd[, − G d[ = − ik0 φ inc 1 ∂n ∂n ∂n

(11)

[1

( ) ) ) ∫ ( ∫ ( ∂G ∂φ1 ∂G ∂G 1 φ1 Θ12 +G d[ + − K G φ2 d[ = 0, − φ2 + 2 ∂n ∂n ∂n ∂n [7

[11

(12) ( ) ) ) ∫ ( ∫ ( ∂φ1 ∂G ∂G ∂G 1 Θ13 +G d[ + − K G φ3 d[ = 0, φ1 − φ3 + 2 ∂n ∂n ∂n ∂n [9

[12

(13) where the expression for Θ12 and Θ13 are given by | | | | | | ∂φ1 | | | + β2 , Θ13 = α3 | ∂φ1 | + β3 . Θ12 = α2 || | | ∂n ∂n |

(14)

By employing the BEM, the aforementioned set of “Fredholm integral equations”. (11–13) is now transformed into a system of “linear algebraic equations”. In the BEM, the boundaries of Rj for j = 1, 2, 3 are divided into a finite no. of small linesegments, known as “boundary elements”, and are directed in anticlockwise manner. The values of the quantities φ and ∂φ/∂n are supposed to be constants over each boundary element. Under this consideration, the system of integral Eqs. (11–13) are discretized in an appropriate manner.

3.1 Energy Identity “Energy Identities” ensure the veracity of numerically measured water wave interaction results. From the aforementioned BEM the components of the energy identity

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will be computed to ensure the validation of the work. When the water waves get interacted with the porous structures, energy dissipation will take place and therefore, derivation of appropriate energy identities are very much helpful [3, 7, 13–15] to account the percentage of incident “wave-energy dissipation” due to the existence of surface-piercing wave barriers. In this current Section, the “energy-identity” for incident waves interacting with porous barriers are derived. Implementing “Green’s second identity” to the velocity potential functions φ j for j = 1, 2, 3 and its complex conjugate φ ∗ j for j = 1, 2, 3 over the domain as defined before, we obtain the following expression ) ∫ ( ∂φ j∗ (x, y) ∂φ j (x, y) ∗ φ j (x, y) − φ j (x, y) d[ j = 0. ∂n ∂n

(15)

[j

In the above expression, [ j depicts all the boundaries of the regions Rj for j = 1, 2, 3. Now in region 1, the only contributions are from the boundaries [ j for j = 1, 5, 7, 9. These contributions are stated as follows ) g 2 A2 2k0 h 1 + sinh(2k0 h 1 ) . ω2 4k0 cosh2 (k0 h 1 ) ( 2 2) ˜ B= ˜ − g A 2 p0 h 2 + sinh(2 p0 h 2 ) , [5 : 2i p0 |T0 |2 B, ω2 4 p0 cosh2 ( p0 h 2 ) ) ∫ ( ∂φ ∗ (x, y) ∂φ1 (x, y) φ1 (x, y) 1 − φ1∗ (x, y) d[. [7 ∪ [9 : ∂n ∂n

[1 : 2ik0 (−1 + |R0 | )~ A, ~ A= −

(

2

(16)

(17) (18)

[7 ∪[9

Again, in region 2, the boundary [ 11 has no contribution. Based on the boundary condition (4), only [ 7 has the following contribution [7 :

) ( ) ) ∫ ( ( ∂φ1 ∂φ1∗ ∂φ ∗ ∂φ1 + φ1∗ + Θ∗12 1 d[. − φ1 + Θ12 ∂n ∂n ∂n ∂n

(19)

[7

In a similar manner, using the non-linear boundary condition (4) on region 3 we get the only contribution on boundary [ 9 and is given by ) ( ) ∫ ( ( ∗) ∂φ1 ∂φ1∗ ∗ ∗ ∂φ1 ∂φ1 − φ1 + Θ13 + φ1 + Θ13 d[. [9 : ∂n ∂n ∂n ∂n

(20)

[9

Using Eqs. (16–20) into Eq. (15), we get the final energy identity as | R0 | 2 + χ0 | T0 | 2 + E D1 + E D2 = 1 ,

(21)

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∫ || ∂φ1 ||2 ( / )( / ) 12 ) where χ0 = p0 k0 ~ A and the term E D1 = − җ(Θ B ~ | ∂n | d[ ,E D2 = A k0 ~ [7 ∫ || ∂φ1 ||2 13 ) − җ(Θ | ∂n | d[ represent the amount of energy dissipated due to the semi~ k0 A [9

circular porous barriers, respectively.

4 Results and Discussions In this portion, the physical parameters related to the water waves scattering by the inverted semi-circular perforated wave barriers are investigated using the iterative BEM. Various results such as the R0 , T 0 and ED by the porous structures are presented to analyze the effectiveness of the aforementioned physical problem in order to create “tranquil zone” in the leeward side of the porous barriers. The wave and structural input variables are configured to the following values: T 0 = 8 s, h1 = 10 m, h2 = 8 m, r1/h1 = r2/h1 = 1/4, KC = 10. The far-field false boundaries [ 1 and [ 5 are placed at three times distant from the structure so that far-field BCs are satisfied on [ 1 and [ 5 . Moreover, the drag coefficient (α , for j = 1, 2 ) and blockage coefficient (β j , for j = 1, 2 ) are evaluated by following formulae ( αj =

8i 3πω

)(

) KC ∗ b , A

βj =

h1 , 10

where b is termed as the submergence length from the mean free surface. The fraction of the height of the “reflected wave” to the height of “incident wave” is known as the reflection coefficient (R0 ) and it is expressed as | | |( | )∑ ∫y j nb1 | | −i g A | | |R0 | = | φ (− l, ymj ) cosh(k0 (h 1 + y))dy − e−i k0 l |. ˜ ω cosh(k0 h 1 ) | A | j=1 | | y j +1 Transmission coefficient (T 0 ) is the ratio of “transmitted wave” to “incident wave” height, and is expressed as | | |( | )∑ ∫y j +1 nbr | | −i g A | | |T0 | = | φ(r, ymj ) cosh( p0 (h 2 + y))dy |. | ~ | | B ω cosh( p0 h 2 ) j=1 | yj Here, the total number of boundary elements used to discretize [ 5 is shown by the summation’s upper bound, “nbr” (Table 1). In Fig. 2a, It has been noticed that E D1 goes down with an increase of KC number in the profile of short water-waves, and an opposite pattern is seen in case of long water-wave profile. Moreover, Fig. 2c shows that as the values of KC increases, the

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Table 1 Comparison between |R0 |2 , |T0 |2 , |ED1 |2 , and |ED2 |2 with total energy as shown in Eq. (21) KC

|R0 |2

χ 0 |T 0 |2

|E D1 |2

|E D2 |2

Total Energy

3

0.0057

0.7747

0.1052

0.1149

1.0006

10

0.0168

0.6191

0.1570

0.2077

1.0007

20

0.0339

0.5354

0.1674

0.2645

1.0011

|R0 | increases, based on the phenomenon that when the values of KC goes up, the porosity of the wave barrier decreases, and consequently the thin barriers behave as a non-porous structure [15, 16]. In Fig. 3a, it is found that the variation of E D1 is higher for moderate values of r 1 /h1 in short water-wave profile. Further, in long water-wave profile, E D1 takes higher values for higher r 1 /h1 . Moreover, the variation of E D1 increases with an increase in incident time period up to some extent and attains maximum before going to decrease further. On the other side, in Fig. 3b, it is demonstrated that E D2 does not alter much due to the variation in r 1 /h1 and it decreases gradually with rise in the time period which is depicted in Fig. 3c. In Fig. 4a, it is noticed that E D1 takes higher value for smaller r 2 /h1 . An opposite trend is revealed in Fig. 4b, c. Further, it is noted that E D1 attains its maximum for average incident time-period values and it decreases with an increment in time-period after reaching to maximum. A similar observation is found in Fig. 4b. Figure 4c can be interpreted from the observation of Fig. 4b as with the increase in E D2 , the wave reflection will decrease consequently.

Fig. 2 Variation of a E D1 , b E D2 , and c |R0 |2 versus T for various KC

Fig. 3 Variation of a E D1 , b E D2 , and c |R0 |2 versus T for various r 1 /h1

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Fig. 4 Variation of a E D1 , b E D2 , and c |R0 |2 versus T for various r 2 /h1

5 Conclusions In this study, water wave dispersion by inverted semicircular porous wave barriers over stepped seafloor is investigated. To handle the present BVP, the BEM is used. Further, energy identity is provided to determine the “wave energy dissipation” to be measured by the permeable wave barriers. The effect of porosity, geometrical configuration of pair of porous boxes, and stepped seabed on the energy dissipation is studied. The study shows that for smaller KC number, the ED due the pair porous boxes is higher. However, the reflection coefficient shows opposite pattern. Additionally, it is noted that the variation of the energy dissipations due to the porous boxes increases as the ratio of water depths decreases. An opposite trend is noticed in the variation of reflection coefficient in short wave regime and shows a similar pattern in long wave regime. Moreover, the variation of RC initially drops as the time period increases. Hereafter, the variation of RC increases with an increment in time period. The overall pattern of the EDs due to the porous barriers and reflection coefficient with the variation of radius of semicircular porous boxes are similar in nature as stated before. This study reveals that the KC number associated with the properties of the porous wave barriers plays a significant role in wave ED by the porous barriers and the maximum energy dissipation occurs in the intermediate wavelength regions. The present results are useful for the coastal engineers to design appropriate parameters and structural configurations to dissipate higher portion of “incident wave energy” and to reduce the scattering coefficients to create a tranquil zone as per the requirements.

References 1. Vijay, K. G., Venkateswarlu, V., & Nishad, C. S. (2021). Wave scattering by inverted trapezoidal porous boxes using dual boundary element method. Ocean Engineering, 219, 108149. 2. Koley, S., Behera, H., & Sahoo, T. (2015). Oblique wave trapping by porous structures near a wall. Journal of Engineering Mechanics, 141(3), 04014122. 3. Koley, S., Sarkar, A., & Sahoo, T. (2015). Interaction of gravity waves with bottom- standing submerged structures having perforated outer-layer placed on a sloping bed. Applied Ocean Research, 52, 245–260.

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4. Behera, H., Koley, S., & Sahoo, T. (2015). Wave transmission by partial porous structures in two-layer fluid. Engineering Analysis with Boundary Elements, 58, 58–78. 5. Koley, S. (2019). Wave transmission through multilayered porous breakwater under regular and irregular incident waves. Engineering Analysis with Boundary Elements, 108, 393–401. 6. Koley, S., & Sahoo, T. (2017). Oblique wave scattering by horizontal floating flexible porous membrane. Meccanica, 52(1), 125–138. 7. Koley, S., & Sahoo, T. (2017). Wave interaction with a submerged semicircular porous breakwater placed on a porous seabed. Engineering Analysis with Boundary Elements, 80, 18–37. 8. Shen, Y., Firoozkoohi, R., Greco, M., & Faltinsen, O. M. (2022). Comparative investigation: Closed versus semi-closed vertical cylinder-shaped fish cage in waves. Ocean Engineering, 245, 110397. 9. Vijay, K. G., & Sahoo, T. (2019). Scattering of surface gravity waves by a pair of floating porous boxes. Journal of Offshore Mechanics and Arctic Engineering, 141(5). 10. Molin, B. (2011). Hydrodynamic modeling of perforated structures. Applied Ocean Research, 33(1), 1–11. 11. Liu, Y., Li, Y. C., & Teng, B. (2016). Interaction between oblique waves and perforated caisson breakwaters with perforated partition walls. European Journal of Mechanics- B/Fluids, 56, 143–155. 12. Bennett, G. S., McIver, P., & Smallman, J. V. (1992). A mathematical model of a slotted wavescreen breakwater. Coastal Engineering, 18(3–4), 231–249. 13. Kaligatla, R. B., Koley, S., & Sahoo, T. (2015). Trapping of surface gravity waves by a vertical flexible porous plate near a wall. Zeitschrift für angewandte Mathematik und Physik, 66(5), 2677–2702. 14. Koley, S., & Sahoo, T. (2021). Integral equation technique for water wave interaction by an array of vertical flexible porous wave barriers. ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 101(5), e201900274. 15. Panduranga, K., Koley, S., & Sahoo, T. (2021). Surface gravity wave scattering by multiple slatted screens placed near a caisson porous breakwater in the presence of seabed undulations. Applied Ocean Research, 111, 102675. 16. Dean, R. G., & Dalrymple, R. A. (1991). Water wave mechanics for engineers and scientists (Vol. 2). world scientific publishing company.

Fostering STEM Education Competency for Elementary Education Students at Universities of Pedagogy in Vietnam Tiep Quang Pham, Tuan Minh Dang, Huong Thi Nguyen, and Lien Thi Ngo

Abstract This study focuses on determining the STEM educational competency of final year students majoring in Primary Education at pedagogical universities in Vietnam, thereby developing this important competency building program for students before graduating to become primary school teachers. Questionnaire surveys and in-depth interviews were conducted among 4th year Primary Education students at different pedagogical schools in Vietnam. The results reveal that final year students have an average STEM education competency. Despite the implementation of STEM education-related modules in the Primary Education curriculum, some key component competencies were found to be below average. The study also demonstrates that the lack of STEM education competency to meet the implementation of the primary education program is not only related to the content of the training program but also closely linked to the method and form as well as organize this competency training for students. Finally, the study proposes the program content and methods of fostering technology-based STEM educational competency for last-year students in Primary Education so that they gain the competence to carry out STEM educational activities for elementary pupils before graduation to become a professional primary school teacher. Keywords Competence · STEM education · Primary education · Elementary students · Teacher training

T. Q. Pham (B) · T. M. Dang · H. T. Nguyen VNU University of Education, Hanoi, Vietnam e-mail: [email protected] T. M. Dang e-mail: [email protected] H. T. Nguyen e-mail: [email protected] L. T. Ngo Hanoi Pedagogical University 2, Vinhphuc, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_9

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1 Introduction Humanity is entering the period of scientific revolution 4.0, the revolution of artificial intelligence, it has been changing extremely and rapidly in all aspects of social life. The advanced education systems in the world are undergoing great changes with the ultimate aim of training a young generation who has enough intelligence and sensitivity to the times to adapt and develop. Therefore, one of the modern educational models to realize the above educational goals is spreading and influencing all over the world, which is STEM education. STEM education is one of the major concerns of many countries today. Several studies have shown the great role of STEM education in the development of countries. According to Gonzales et al. [4], in the first decade of the twenty-first century, the STEM education model has created a great change in the field of education [4]. Banks and Barleks [1] affirm that STEM education has a positive influence on the development of industry in the world [1]. Wang et al. [12] argue that STEM education is closely linked with the development of industries [12]. According to Linh et al. [7], the development of STEM education aims to meet the demand for high-quality human resources to ensure important factors for personal life, the country’s political and economic position in the world [7]. Thus, it can be affirmed that STEM education has great significance for integration and development with many countries in the current period. In 2018, Vietnamese education made strong strides for the fundamental and comprehensive renovation of education. The introduction of the new general education program emphasized the integration of STEM educational contents which support students at all levels of orientation and the development of competencies and qualities for students. In the new general education program since 2018, it is affirmed: “STEM education is an educational model based on an interdisciplinary approach, helping students apply their knowledge of science, technology, engineering and mathematics to solve problems some practical issues in specific contexts” (Vietnam Ministry of Education and Training, 2018) [9]. Thus, in the new general education program, STEM education is both meant to promote education in the fields of science, technology, engineering, and mathematics, as well as demonstrating an interdisciplinary approach to development abilities and qualities of learners. In addition, STEM education also contributes to the realization of the following goals: Developing students’ specific competencies of subjects in the STEM field. It is the ability to apply knowledge and skills related to Science, Technology, Mathematical Engineering subjects, to link knowledge to solve practical problems. STEM education provides students with the foundation for higher learning and future careers. The requirement for the renovation of general education and the problem for primary school teacher training in Vietnam is the need to train a team of teachers with sufficient competency and professional qualifications to meet the challenges knowledge in educational innovation, especially with the implementation of STEM education in schools.

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However, in Vietnam today, the formal STEM teaching in schools faces many challenges [6], most schools do not have a team of teachers with good STEM education competency to be ready to perform STEM education tasks. Bien et al. [2] believes that most teachers have not implemented STEM education regularly and effectively because they do not know how to develop and implement STEM topics [2]. The problem is to create a team of teachers with good STEM education competency, especially with final year students at pedagogical universities so that they are ready to perform STEM education tasks in schools. Therefore, the fostering STEM education competency for students in primary education is an urgent issue today. Therefore, this study is part of a broad research project on STEM education strategies in Vietnam, which aims to answer the following questions: To answer the following three questions: 1. To what extent is the STEM education competency of 4th year students majoring in Primary Education achieved? 2. What is the content of STEM education competency building for students majoring in Primary Education? 3. Which teaching method should be used in fostering STEM educational competency for students majoring in Primary Education?

2 Competence in STEM Education Competence is referred to the category of capabilities, this is a competency approach often found in foreign research papers, “competency is the ability to effectively meet complex requirements in a contextualized environment concrete” [3]. This approach emphasizes that in terms of technique, competence is considered as a way of performing in accordance with the purpose and conditions of the action, not paying attention to the results achieved of the activity, leading to many difficulties in training and assessing competency. Competency is attributed to individual attributes, and this is the approach to competency commonly found in research papers in Vietnam: Competence is a combination of psychological attributes of an individual, formed and developed in a particular field of activity; is the potential human power in solving practical problems. This approach emphasizes the results of activities, knowing based on theory and practice, applying experience to each individual’s activities to achieve the set goals [9]. STEM education competency is one of the specific competencies in the teaching competency of teachers. According to author Hung [5], teaching competency in vocational education includes four components: teaching design competency, teaching organization competency, teaching assessment and assessment competency, and teaching competency, teaching management and identifying 15 components of those 4 competencies [5]. This is the reference base for us to determine the structure of teachers’ STEM educational competencies. In the article: “STEM integrated teaching in Korea: structure of teacher competency”, [11] has built a structure of STEM

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education competency including three competency components: cognitive characteristics, skills teaching ability, attitude characteristics; At the same time, 21 factors of three competency components were identified [11]. The factors fully demonstrate the integrated teaching competency for STEM education, but many factors are not suitable with the STEM teaching competency of Vietnamese teachers. Based on the research results, we determined the STEM educational competency to include the following components: cognitive competency of STEM education, competency to design STEM teaching plans, and competency to implement STEM teaching plans, the competency to evaluate—adjust the STEM teaching plan. In which, each component of STEM education competency is shown as follows: – Cognitive competence on STEM education: It is the ability to recognize, understand and analyze knowledge about STEM education. This includes understanding of knowledge about STEM education (concept, classification, benefits that STEM education brings…), understanding of engineering design process, understanding of research process science, understanding of scientific background on various STEM topics, readiness and enthusiasm for STEM teaching. – Competency to design STEM teaching plans: It is the ability to develop and design specific plans for STEM teaching. This competency component includes finding ideas in practice to build into STEM topics, developing STEM teaching goals, selecting and designing STEM-based teaching activities, building learning materials practice for STEM activities, and build and use equipment that supports STEM activities. – Competency to implement STEM teaching plans: It is the ability to implement built-in STEM teaching plans in the classroom. This competency component includes assigning tasks to students in a lively and engaging way, supporting students in STEM activities, organizing effective reporting and discussion activities, managing classrooms in the classroom of STEM topics. – Competency to evaluate—adjust the STEM teaching plan: is the ability to evaluate the learning outcomes of students after completing the STEM lesson, thereby orienting on adjusting the teaching plan accordingly. This component of competence includes selection and use of objective assessment tools, assessment of students’ abilities before, during and after the learning process, and determination of the appropriateness of learning activities with their ability, ability of students applying the lesson research process to adjust the STEM teaching plan. The above STEM educational competencies are described by the research team at four levels, including Level 1: Competency is initially formed Level 2: Competence achieved at a good level Level 3: Capability is at a good level Level 4: Capability is very good (See also the appendix).

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3 Learning Characteristics of Students Majoring in Primary Education First, elementary pedagogical students are learners who have learning experiences, have systematic study habits and most have basic study skills such as reading documents, finding and exploiting information in the library or online, knowing how to learn through sharing with friends or on forums, seminars, conferences… However, in a few students, those skills are not good, partly because the training method is not practiced enough but focus on skills, partly because these children have not actively learned and practiced. Some children have not yet adapted to the way of studying at university but still study like high school students, so they depend a lot on textbooks, books, and teachers. Second, although students are learners, students are mature learners, so their life experience is complete and more or less the precedents in life experience also affect learning. For example, many children always believe that textbooks and textbooks are always right, even though that belief has no convincing basis. They don’t think that books and textbooks are written and reviewed by people, so they are not always correct. These children rarely research and think about and refer to scientific publications other than textbooks and books assigned by their teachers. Especially, very few pedagogical students read and analyze scientific journals. Those are the early manifestations of conservatism and stagnation in learning. Third, the ability of primary school teachers to respond to modern learning strategies is generally low. They lack skills in cooperative learning, project-based learning, problem-based learning, case-based learning, constructivist learning, etc., and have not created conditions for students to learn like that. The other part is because they themselves are passive and just like to learn according to old habits. Currently, the basic way students learn is still listening, taking notes, reading books, remembering, understanding, and recalling when taking the exam, so only a few achieve the level of application and critical thinking, the rest learn without learning, do not know how to do, learn without really knowing right or wrong, just the right curriculum is suitable. Fourth, the learning style of primary pedagogical students in general is not rich and lively. Most students learn the same and are the same in listening, taking notes, reading books, remembering, understanding, and recalling when taking the test. Many students have not yet created for themselves the most effective learning style for them, have not taken advantage of their forte and strengths in learning, but still follow the general trend, just like everyone else. If you are like everyone else, you are assured that you do not know that you have your own strengths or weaknesses, so you need to choose the most suitable learning method for you to achieve the highest efficiency. Fifth, the learning attitude of primary pedagogical students is generally good and positive. Most of the students appreciate their studies, schools and teachers, and believe in the future of their careers. They are serious in studying, taking tests and exams, obeying study discipline and school rules, enthusiastically participating in activities of the Communist Youth Union and the school’s social movements,

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including cultural and artistic movements, support for exams, humanitarian activities, etc. These are the outstanding advantages of pedagogical students. But in particular in terms of learning, especially in the in-service, connected systems, the study discipline is not highly self-disciplined, mainly due to the compulsory regulations that students comply with.

4 Research Context In recent years, Vietnam’s education system is having strong turning points for the fundamental and comprehensive renovation of education. One of them is the implementation of the new general education curriculum program. The new general education program is designed in the direction of a competency approach, which has been implemented since 2019. The goal of the program is to help develop core qualities and competencies in learners, which include qualities: patriotism, kindness, honesty, hard work and responsibility and core competencies such as autonomy and self-study, communication and collaboration, problem-solving and creativity with specific competencies associated with specific subjects at each school level (Ministry of Education and Training [MOET], 2018) [9]. To achieve the goals of the new general education program, STEM education is an educational orientation that is concerned and implemented to concretize those educational goals. In the new general education curriculum, STEM education is both meant to promote education in the fields of science, technology, engineering and mathematics, and to demonstrate an interdisciplinary approach, competency development, and quality of learners (Ministry of Education and Training, 2018) [9]. At the same time, in the general education program, the content of subjects in the STEM knowledge block has emphasized and enhanced activities in the direction of STEM education. In the overall general education program, STEM education has been focused through the following manifestations: (1) The new general education program is full of STEM subjects: Mathematics; Natural Sciences; Technology; Informatics, Physics, Chemistry, Biology; (2) The position and role of Informatics Education and Technology Education in the new general education program has been significantly enhanced [8]. This not only clearly shows the thought of STEM education but also the timely adjustment of general education before the industrial revolution 4.0. In the general education program, it is also clearly stated: “Along with Mathematics, Natural Sciences and Informatics, Technology subject contributes to promoting STEM education, one of the educational trends that is being valued in many countries around the world and is given due attention in this time in Vietnam’s reform of general education” [9]. Thus, with the renovation of the general education program in 2018, STEM education is an inevitable consequence to achieve the set educational goals. Along with the promulgation of the new general education program of the Ministry of Education and Training of Vietnam, the aim is to concretize the implementation of STEM education in schools, on August 14, 2020. The Ministry of Education and Training issued Official Letter 3089/BGDÐT-BDTrH 2020 implementing

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STEM education in secondary education to support high schools in effectively implementing Science, Technology, Engineering and Mathematics (STEM) education. The Ministry of Education and Training guides some contents of implementing STEM education and organizing and managing STEM education activities in high schools (Ministry of Education and Training, 2020). The official letter clearly shows the purpose, contents, organization, and implementation of STEM education in schools. With the issuance of Official Letter 3089, it has been confirmed that the role of STEM education in schools is very important and needs to be implemented [10]. From the above context, it can be affirmed that STEM education for students in schools is necessary and the issue of fostering STEM educational competency for teachers in general and students of universities is urgent. In particular, students in the final year of primary education need to have specific orientations and implementations for fostering STEM educational competency for learners so that they have the competency to build and organize educational activities of STEM before becoming a full-time teacher in schools.

5 Research Methods 5.1 Respondents We conducted a survey of some lecturers and students of primary education at some key universities in Vietnam about the STEM education competency of 4th year students at universities in Vietnam (Table 1): University of Education—Thai Nguyen University, Hanoi National University of Education, Hanoi National University of Education 2, University of Education—University of Danang, Vinh University, Ho Chi Minh City University of Education Minh in Vietnam. In order to select universities with students and teachers of primary education, we have done this by participating in the ETEP program (Enhancing Teacher Education Program) which is a program to develop pedagogical schools to improve education competency of teachers and administrators of general education institutions funded by the World Bank, implemented from 2017 to 2022. These are the key pedagogical universities of Vietnam that are enrolled in ETEP program. Through participating in the ETEP program, we have the opportunity to exchange and conduct surveys with experienced and well-qualified lecturers of teachers’ schools, 4th year students of universities. Information on the number of lecturers and students participating in the survey is as follows:

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Table 1 Information on the number of lecturers participating in the survey and interview University

Number Lecturer

Students

Hanoi University of Education

09

55

Hanoi University of Education 2

08

60

Thai Nguyen University of Education—Thai Nguyen University

06

54

University of Vinh

08

53

University of Education—University of Danang

07

61

Ho Chi Minh City University of Education

09

57

5.2 Survey Content The survey was conducted to evaluate the following main contents: • Assessment of STEM education competency of 4th year students at universities; • Evaluate content of fostering STEM educational competency for students; • Evaluation of the method of fostering STEM educational competency for students.

5.3 Survey Method 5.3.1

Survey by Questionnaire (Data Source A)

We conducted a survey of 4th year primary education students in the pedagogical and educational universities of Vietnam mentioned above, during the period from February 10 to 25, 2022. Students participated in the survey. Survey participants answer questions in the form of multiple choice on the survey form. The questionnaires are designed based on the proposed STEM education. Based on these studies, we propose a model of students’ STEM education competency structure to develop expertise in STEM education and some orientations to build the structure of a career development program for students. Questionnaire survey was conducted on 340 students from primary school teacher training universities in Vietnam to determine the necessary activities of STEM education competency building for students. Closed questions were used Likert scale to assess students’ STEM educational competency. The set of questions is built from the framework of STEM education competencies required for 4th year students. The questions focus on the main content: STEM educational competence of 4th year students. The questionnaire uses a 4-point Likert scale to assess students’ performance of STEM competencies. This scale includes 4 options for students’ level of STEM education competency with a scale of 1 to 4. The questionnaire has been tested and adjusted to be more reasonable before being used in this study.

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5.3.2

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In-Depth Interviews, Consult Experts (Data Source B)

The in-depth interview method is used to exploit the experience of lecturers and students’ thoughts of pedagogical schools in Vietnam about the importance of STEM education competency building contents and methods that teachers used to foster STEM educational competencies for students, as these will be difficult to investigate by questionnaire due to the limitation of this research method. Interviews were conducted after the questionnaire survey was completed and initial results were provided. The interview also focused on the practice of organizing STEM education in schools about the extent and frequency of these activities. The questions we used in in-depth interviews focused on the following: 1. How important is it to foster STEM education competency for students? 2. What contents on fostering STEM education competency need to be done? 3. What methods have been used to foster STEM educational competencies for students? Which method is suitable for fostering STEM educational competency for students? 4. When conducting competency building in STEM education, which stage is the most difficult? There were 47 lecturers who participated in the interviews, who participated in fostering STEM education competencies for students and 36 students in primary education from regional teacher training pedagogical schools in Vietnam. We conducted 12 interviews, including 6 individual interviews and 6 group interviews with groups of trainers. Interviews were conducted by 2 or 3 authors directly. The two authors took notes during the interviews, conducting voice recordings of the interviewees with informed consent. The interviewers encouraged the lecturers to respond enthusiastically, according to their own thoughts, to the interview questions posed and to share their thoughts, as well as to consider the responses of the respondents. The teacher’s answers are carefully recorded and then transcribed verbatim to provide author with detailed and authentic data.

5.3.3

Data Analysis

Mathematical statistical methods and cross-checks were used to analyze the data and confirm the reliability of the data. The data from the questionnaire was used as primary data source, then processed by SPSS software version 20.0. Cronbach’s alpha test is used to evaluate the reliability of the scale and the type of variable if the obtained value is not within the allowable limit. Cronbach’s alpha coefficient is 0.74 (within the allowable limit from 0.6 to 0.9). This reliability test allows the authors to confirm the reliability of the scale. The survey results on the current status of students’ STEM educational competence were analyzed according to descriptive statistical parameters about the level of competence, mean score, and standard deviation to detect the level of STEM educational competence of students final year students.

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In addition, we compared the STEM educational competency of three groups of students: the Northern student group, the Central student group, and the Southern student group to clearly see the STEM educational competency of each region. An independent sample test was used to test three groups of students to find out the difference in their STEM educational competencies. Data from the in-depth interview source (source B) is processed and coded by writing words representing STEM educational competencies and divided into topics for the results. These results were then used to verify the data values obtained from the survey source using a questionnaire (Source A). Thus, the authors have determined the level of STEM education competency of students in universities in Vietnam.

6 Research Results 6.1 Current Status of STEM Education Competency of 4th Year Students The results of this study aim to answer the first question: To what extent is the STEM education competency of 4th year students majoring in Primary Education achieved? The current state of STEM education competency was surveyed through questionnaires. From the questionnaires that have been made, we collect students’ answers, then analyze and encode student answers into opinions, the views are arranged into 4 powerful components of competency as shown in Table 2. We build a professional standard STEM education competency structure in teaching according to the professional standards promulgated by the Ministry of Education and Training. The competency behaviors of this structure are necessary to provide seniors with the competency to teach them STEM well. Based on the students’ opinions through the questionnaire, we determined the students’ level of STEM education competency. Then, we used the method of consulting a Science expert and a Math expert to survey the proposed structure of STEM education competency. Based on the expert method, we adjust and change the components according to the competency composition. As a result, we have determined the structure of STEM education competency, including 4 components and 18 components as shown in Table 1. The survey results on STEM education competency of students in Vietnam are presented in Table 2. The survey results in Table 2 show that the common point of the component competencies of STEM education competency is the high level of awareness of STEM education (Mean = 3.22), the competency to implement the plan. The students’ ability to design STEM teaching plans (Mean = 2.65) is at an average level, the competency to design STEM teaching plans (Mean = 1.91), and the ability to evaluate and adjust the STEM teaching plan (Mean = 1.92) are at a low level. Thus, a high level of awareness about STEM education shows that students are equipped and have a relatively complete understanding of STEM education.

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Table 2 STEM education competency of students Number

Competency Components

Average score

Standard deviation

Cognitive competence in STEM education 1

Knowledge of STEM education (concepts, classifications, benefits that STEM education brings …)

3.19

0.907

2

Understanding of the engineering design process

3.18

0.979

3

Understanding the scientific research process

3.04

0.901

4

Scientific background on various STEM topics

3.29

0.824

5

Willingness and enthusiasm for STEM teaching

3.42

0.702

Ability to design STEM teaching plans 6

Find ideas in practice to build into a STEM theme

1.94

0.895

7

Developing STEM teaching goals

1.91

0.887

8

Selecting and designing STEM-based teaching activities

1.79

0.827

9

Develop learning materials for STEM activities

1.86

0.872

10

Build and use equipment to support STEM activities

2.04

0.926

Ability to implement STEM teaching plans 11

Assign tasks to students in a lively and attractive way

2.73

1.049

12

Supporting students in STEM activities

2.42

1.082

13

Organize effective reporting and discussion activities

2.65

1.038

14

Classroom management in STEM education

2.79

0.995

Evaluation competency—adjusting the STEM teaching plan 15

Selection and use of objective assessment tools

1.90

0.913

16

Assess students’ ability before, during, and after the learning process

1.97

0.935

17

Determine the appropriateness of the learning activity to the 2.00 student’s ability

0.953

18

Apply the lesson study process to adjust the STEM teaching plan

0.844

1.79

The competency to design STEM teaching plans is relatively low, including the following activities: • • • • •

Finding ideas in practice to build into STEM topics; Define STEM teaching goals; Select and design STEM-based teaching activities; Develop learning materials for STEM activities; Build and use equipment to support STEM activities.

The survey results in Table 2 show that students’ ability to find ideas in practice to build into a STEM topic is at the lowest level (Mean = 1.94). The highest competency is to build and use equipment to support STEM activities (Mean = 2.04). Combined with in-depth interviews, we received feedback from both faculty and students that

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finding ideas in practice to build STEM topics is very difficult and takes a lot of time. They think that because the content of the curriculum of subjects related to STEM education in Vietnam has not been closely related to find ideas that are suitable for the knowledge in the subjects: Science and technology, engineering, and math. In the survey results in Table 2, students’ ability to evaluate and adjust STEM teaching plans is also relatively low. Combined with the results of the in-depth interview method, we find that most of the students have not really paid much attention and have not been able to perform evaluations—adjust the appropriate teaching plan. Instructors believe that their students are still confused about the choice and use of objective assessment tools and the application of the lesson study process to adjust the STEM teaching plan because this is a form of assessment. prices, adjusting new lessons in Vietnam. We found that the survey results by questionnaire are consistent with the survey results through in-depth interviews. We draw the following conclusions about the assessment of STEM educational competency of primary education pedagogical students of universities as follows: Students have basic understanding of STEM education, but do not yet have the competency. Designed STEM education topics for students, grasped some basic methods to organize STEM educational activities for students. The competency to evaluate and adjust the STEM education plan is low. At the same time, through the results of the questionnaire survey, we also compared the STEM educational competencies of students in different regions. The survey results show that between groups of students from universities in different regions, there are differences in STEM educational competency, component competencies in STEM educational competency of students in universities. There are significant differences between universities. We found that 4th year students at universities in the North have higher cognitive abilities in STEM education than students in the Central and South. In which, the percentage of students from Hanoi National University of Education with good cognitive ability about STEM education is the highest. Combined with in-depth interviews, we find that the student training program of the Northern universities also has many activities in some modules that introduce the basics of STEM education to students. And in training activities on STEM education for students, lecturers at universities in this region are very focused on providing and forming knowledge about STEM education for students in a methodical way. However, in terms of competency to implement STEM teaching plans, students in the South have a higher level than students in the North and Central regions. Combined with in-depth interviews, we find that pedagogical universities in the South pay great attention to the formation and development of practical competencies for students, creating many opportunities for students to study through practice. In particular, the survey results show that the percentage of students at Ho Chi Minh City University of Education with the ability to implement STEM teaching plans is the highest among schools. For universities in the North and Central, they often attach great importance to forming knowledge for students in a methodical way, but the practice of students often assigns tasks to students without strict control. This is also one of the disadvantages in the way of

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training students of the universities in the North and the Central. As for the competencies of the components for designing STEM teaching plans and evaluating and adjusting the teaching plans, the competency levels of all three groups of students are at the same level. All three groups of students said that it is difficult to design a STEM teaching plan and evaluate and adjust the plan with them.

6.2 Assessment of the Necessity of Fostering STEM Educational Competency for Students The results of this survey aim to answer the second question: What does the content of STEM education competency building for students majoring in primary education include? We used the student questionnaire—question 2 to investigate the necessity of fostering STEM educational competencies. The results of the survey are shown in Fig. 1: The survey results shown in Fig. 1 show that Students appreciate the need for content that fosters STEM educational competencies. In particular, students are especially interested in the STEM teaching plan design module and the STEM teaching plan assessment and adjustment module. Up to 72.3% of students think that fostering the STEM teaching plan design module is necessary or higher. With the assessment module and adjustment of the STEM teaching plan, 63.9% of students think that it is necessary to foster this module from the necessary level or higher. The results of the in-depth interview are consistent with the survey results by questionnaire, because in fact, the competency to design STEM teaching plans and the competency to evaluate and adjust STEM teaching plans is at a low level. Most of the respondents said that

Fig. 1 STEM educational competency fostering content for students

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when implementing STEM education, they find it very difficult to find ideas to design for STEM education topics for students. Most of the students surveyed admitted that evaluating and adjusting the STEM teaching plan they also paid little attention to this content. Sometimes, students in Vietnam often only focus on forming the knowledge contained in their lessons, which leads to the phenomenon of trying to teach all the knowledge in the lesson to avoid exceeding the allotted time. This results in them not paying much attention to the assessment and adjustment of the plan after the lesson.

6.3 The Current Situation of Assessment on the Method of Organizing STEM Education Competency Building for Students The results of this survey aim to answer the last question: Which teaching method should be used in fostering STEM educational competencies for students majoring in Primary Education? Through consulting experts, the results show that in some primary education student training activities of universities, a number of methods can be used to foster STEM education competencies for students. Students such as: researching the theory of STEM education, watching videos illustrating STEM lessons/topics, learning experiences in STEM topics, practicing STEM topic design, experiencing teaching STEM topics, design and discuss, discuss with experts. According to the results of the in-depth interviews, at universities, almost all students only study the theory of STEM education. Some faculty members of some schools have occasionally organized for students to watch demonstration videos on STEM lessons/topics, learning experiences in STEM topics, and practice designing STEM education topics. Some organizational methods such as the designed STEM subject teaching experience and seminars and discussions with experts are almost never used. The lecturers also discussed further because the training program for the modules related to basic knowledge and the modules on the organization of teaching subjects in primary school takes up most of the training time, so there are few opportunities. Organized methodically on fostering STEM educational competency for students. In addition, STEM education is also one of the relatively new contents for primary schools in Vietnam, so the competency building of STEM education for final year students has been implemented in schools, but there is no systematic and thorough organization. Simultaneously with the survey on the methods teachers have used to foster STEM education competency for students, we also conducted a survey through questionnaires—question 3 to learn about which methods are needed. Used to foster STEM education competency for students to bring about positive fostering results. The results of the survey are shown in Fig. 2.

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Fig. 2 Evaluation of the methods of fostering educational STEM competency

The results of Fig. 2 show that Students are not interested in purely theoretical research activities. Only 16.7% of students think that it is necessary to organize theoretical research activities on STEM education. Most of the interviewed students said that this activity can cause boredom when studying in students. Watching videos illustrating STEM lessons has a positive effect on students, up to 75.0% of students said that this method can be effective when fostering STEM educational competencies for students. The learning experience in STEM topics is the activity that receives the most positive reviews, with up to 97.2% of students saying that this way of organization will bring the most positive results. There are 63.9% of students’ opinions that organizing for students to practice designing STEM educational topics for students is a positive result, organizing STEM theme design practice is an activity. difficult for students. However, students are more interested in this activity thanks to working in groups. The STEM topic teaching practice method is evaluated as an effective activity to develop STEM educational competency with 58.3% of students agreeing with the use of this method. Finally, discussions with experts on STEM education received great attention from students with 50% of students saying that this method can be used to bring about positive results in competency building. STEM education for students.

7 Discussion Regarding the first research issue, it is to what extent does the STEM education competency of final year primary education students at pedagogical universities in Vietnam show up? The results show that the STEM educational competencies of final year primary education students at pedagogical universities in Vietnam perform at a moderate level. Students do not have many diverse and interesting activities to

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experience with the organization of STEM education. This is understandable because in the student training program of some universities, there are not many modules and activities to develop STEM educational competency for students. The competency to evaluate and adjust the teaching plan of students is very low, which means that the assessment of students’ STEM learning outcomes has not been done well. The design of students’ STEM teaching plans is also a weak aspect of the STEM educational competency of primary education students in pedagogical universities in Vietnam. Students’ cognitive competency for STEM education is relatively high. This is also understandable because of the fact that pedagogical universities in Vietnam still focus on providing and forming subject knowledge for students without many practical application activities. This is influenced by the content approach that education programs in Vietnam used to use in the past. Since the implementation of educational curricula in Vietnam has been subject to content for a long time, lecturers in universities are still affected by this approach even though since 2018, educational curricula in Vietnam have approached in the direction of developing learners’ competency. Because of that, the competency to implement STEM teaching plans is only at an average level. This also explains that, although students majoring in primary education at pedagogical universities in Vietnam are still weak in proposing, finding ideas and designing STEM educational activities, when they are guided lead, or build a STEM education topic, they have the ability to be flexible and agile in organizing those activities in practice. This is also one of the strengths of pedagogical students in Vietnam. The survey results also show that primary education students in different regions also have different levels of STEM education competency. Regarding the second research problem: What does the content of fostering STEM educational competency for students majoring in Primary Education include? The survey results show that students appreciate the importance of content that fosters STEM educational competencies. In which, students are especially interested in the STEM theme design module. This is consistent with the low level of students’ ability to design STEM teaching plans. Because it is clear that the search for ideas and the design of STEM activities is limited, students are especially interested in the content of this topic design, so that through training on this topic, students can competent teachers design better STEM teaching plans. The results also show that students are less interested in the STEM educational theory module. Most of the students said that learning about the theory of STEM education makes them boring, so it is necessary to organize for them to briefly learn about the most important issues about the theory of STEM education. Students want more time to learn about the design of a STEM activity plan and delve into the assessment and adjustment of the STEM teaching plan to perfect their STEM education competencies. The third issue: Which teaching method should be used in fostering STEM educational competency for students majoring in Primary Education? The results show that in practice, teachers at pedagogical universities in Vietnam have not used these methods in a variety of ways. Most of the time, when organizing STEM education competency building for students, lecturers only stop at the organizing method for students to study purely theoretically. At the same time, through the survey results, students are not interested in this method of organization. It is for this reason that

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elementary students in Vietnam are not interested in learning about STEM education. The survey results show that experiential learning in STEM topics is the activity that receives the most positive reviews. Students enjoy this method of learning. They believe that having hands-on experience in STEM topics will help them better understand how to organize STEM education for students than if they only understand the theory of STEM education. At the same time, the survey results also show that the practice of designing STEM topics is a difficult activity for students. However, they still expressed a desire to learn through this method so that they will apply their knowledge in practice and help them develop their competency to design their STEM educational activity plan. Practice teaching STEM topics is considered an effective activity to develop STEM educational competency. Discussions with experts on STEM education receive great attention from students, they believe that participating in discussions with experts will help them have a deeper understanding of STEM education and help them answer questions solve the problems they are facing.

8 Conclusion STEM education has been receiving great attention from the Vietnamese education community. In the current period, Vietnam’s education is undergoing renovation according to the goal of developing learners’ competency. Accordingly, STEM education is considered as one of the approaches to form and develop important competencies of modern people in the twenty-first century. Therefore, the issue of fostering STEM educational competencies for future teachers when they are studying in pedagogical universities is very necessary. Currently, although pedagogical universities have paid attention to training STEM educational competencies for students majoring in primary education. However, it can be said that it does not meet the requirements of the practical context. These future teachers still lack some component competencies such as designing STEM learning topics for elementary students, organizing STEM learning activities for elementary students, linking STEM topics with practical context close to elementary students. From this study, there are some recommendations proposed to Vietnam’s pedagogical universities, namely, it is necessary to develop some specialized modules to train STEM educational competencies for students. In which, it is necessary to focus on training contents on designing STEM learning topics for elementary students. Connect STEM knowledge and skills to real-life problems. In addition, the process of training STEM educational competency for students needs to actively apply a learning strategy with practical experiences to ensure that upon graduation, students have the competency to conduct teaching STEM topics for elementary students. Future studies on STEM education should focus on assessing the STEM educational competency of primary school teachers. From there, identify a training program for primary school teachers to supplement the knowledge and skills that are lacking in STEM education. At the same time, it is necessary to research and develop STEM

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educational programs and content for students from grades 1–12 as an independent subject or as an educational topic with complementary competencies for students.

References 1. Banks, F. & Barlex, D. (2014). Teaching STEM in the Secondary School. Helping teachers meet the challenge. Chapter 10, New York: Routledge. 2. Bien, N. V. et al. (2019). STEM education in middle school. Vietnam Education Publishing House One Member Limited Liability Company, Hanoi. 3. Françoise D. L. D., & Winterton, J. (2007). What Is Competence? Human Resource Development International. 4. Gonzales, A., Jones, D., & Ruiz, A. (2014). Toward achievement in the “Knowledge Economy” of the 21st Century: Preparing students through T-STEM academies. Research in Higher Education Journal, 25, 1–14. 5. Hung, V. X. (2016). On the system of teaching competence of teachers in vocational education institutions according to the implementation competency approach. Journal of Vocational Science, 30. 6. Khuyen, N. T. T. et al. (2020). Measuring teachers’ perceptions to sustain STEM education development. Sustainability (Switzerland), 12(4). 7. Linh, N. Q., Suong, H. T. H., & Khoa, C. T. (2017). STEM contents in pre-service teacher curriculum: Case study at physics faculty. International Conference for Science Educators and Teachers (ISET). pp. ISBN 978–0–73541615–4; ISSN 0094–243X, P030071–1 to P030071–8), Bangkok: Proceedings of the 5th International, 2017. 8. Linh, N. Q., & Phuong, H. T. (2019). STEM education in the new general education program. TNU Journal of Science and Technology. ISSN: 1859–2171 e-ISSN: 2615–9562. 9. Ministry of Education and Training [MOET]. School Curriculum 2018. December 26, 2018. 10. Ministry of Education and Training [MOET]. Circular no 3089/2020/TT-BGDÐT, Vietnam. 11. Song, M. (2017). Teaching integrated STEM in Korea: Structure of teacher competence. LUMAT-B: International Journal on Math, Science and Technology Education, 2(4), 61–72. 12. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang. (2016). Towards smart factory for industry 4.0: A self-organized multi-agent system with big data base feedback and coordination. Computer Networks, 101, 158–168.

Blockchain Based E-Medical Data Storage for Privacy Protection Suja A. Alex, Noor Zaman Jhanjhi, and Sayan Kumar Ray

Abstract Electronic Medical Data (E-Medical Data) is sensitive and the privacy should be preserved. E-Medical Data is easily stolen, altered, or even deleted entirely. Accordingly, the healthcare organizations must guarantee that their medical data is treated confidential, secure, and private. If the situation happens like medical data cannot be logged or retrieved reliably, which delays treatment progress and even endangers the patient’s life. Conventional method of medical data storage led to threating of data by the attackers. Many medical applications face security problems like data stealing. Blockchain technology provides a solution to the security issue in many applications. As, the Blockchain features such as decentralization, cryptography-based security, immutability, and consensus algorithms open a solution to store e-medical data in a secure way with blocks and shared key. Our work highlights the decentralized E-medical data storage with consensus algorithms and its performance. Keywords Medical data · Blockchain · Consensus algorithm · Security attacks

1 Introduction Electronic patient data management system stores data from the patients such as implantable sensors to monitor chemotherapy response and glucose level [1]. All the medical applications store the patient’s diagnosis data for the treatment process. As per the statistics in 2016, the United States filed 17,000 malpractice cases [2]. It S. A. Alex (B) St. Xavier’s Catholic College of Engineering, Nagercoil, India e-mail: [email protected] N. Z. Jhanjhi (B) · S. K. Ray School of Computer Science, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] S. K. Ray e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_10

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is burden to prove the medical malpractice [3]. The electronic patient data can be modified or deleted by the defendant. The security of medical data in centralized data management systems is difficult to verify. The hacker modifies or deletes the data by getting appropriate permissions. Whenever the manager requests the database administrator to modify or delete the data, the action is performed only after the permission is granted by the database administrator. The centralized security is not suitable for Internet of Medical Things network [4]. Internet of Things (IoT) technology impacts a great role in many areas such as social, environmental, and economic. The concept of smart homes has recently emerged by including different kinds of devices on the Internet. Chong et al. developed an approach to smart homes. In this study the client/server unit provides a convenient, and easy option for controlling the smart home [5]. Soliman et al. recommended a solution for smart home development using IoT and Cloud which controls several sensors [6]. Smart grid is a digital communication technology-based electricity supply network that notices and responds to local changes in usage. Karnouskos and Holanda emphasized the smart grid-based solution. The smart infrastructure boosts the energy efficiency [7]. Yu et al. researched the smart grid architecture and its key technologies [8]. Internet of Medical Things (IoMT) is popular to provide solutions to healthcare organization in which all the medical devices are connected on-line. Istepanian et al. emphasized IoMT for glucose monitoring from diabetes data storage. The notifications are shared to mobile for information updates [9]. Ukil et al. coined the importance of IoT for healthcare researchers. A methodology is proposed for healthcare analysis that senses heart attack and sends notification [10]. The IoT concept is emerging successfully to provide support to recent industrial requirements. Perera et al. measured several resources and techniques that focused Context-Aware computing theories, evaluation framework, and communication mediums [11]. Qiu et al. presented a deep information with public logistics. Supply Hub Industrial Park (SHIP) used to share the information in real-time. It provides distributed physical devices and functions effectively [12]. Climate-Smart Agriculture (CSA) is a method that transforms toward agricultural development under climatic change situation. Zhao et al. reviewed various uses of doing agricultural tasks with greenhouse effect. This automation process incorporating the concepts of IoT technology. The authors use information networks. Hence, Remote Monitoring System (RMS) is proposed [13]. Bandyopadhyay et al. proposed a framework using IoT that helps the farmers to acquire information about crops delivery to customers [14].

2 Related Work Blockchain is a distributed peer-to-peer secure network in which the data is stored in terms of blocks and linked together like chain using appropriate hash values. The best example is Bitcoin cryptocurrency [15]. The countries including Vietnam,

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Bangladesh, and China invest in Blockchain technology and develop its own blockchains. Blockchain technology powers several IoT devices to promote a smart healthy city. The blockchain is also popularly used in smart logistics, transportation [16], healthcare applications [17], air quality monitoring [18], and societal applications [19]. The application of Blockchain technology evolves from web app development [20] to Artificial Intelligence [21]. Blockchain technology helps to preserve security by using electronic health records (EHR) and Personal health records (PHR). These frameworks contain patient data. EHR stores data about a patient of many hospitals which is controlled by the environment [22]. The healthcare field uses blockchain technology that ensures decentralized method of storing medical and distributing healthcare data. Omar et al. 2017 designed a system that stores medical data on federation blockchain [23] that provides decryption key to the data owner. Dubovitskaya et al. 2017 developed healthcare records distribution framework based on blockchain [24]. It stores only medical data in a cloud server and issues the decryption key to the data owner. Yue et al., 2016 designed a healthcare data gateway architecture that stores the data in a private blockchain cloud and provides the decryption key to the receiver [25]. Kannan et al. [26] developed GemOS that combines the local databases into a blockchain. Fan et al., 2018 developed MedBlock to store patient data in blockchain [27]. Xia et al. 2017 developed a blockchain-enabled medical data protection to store the medical records in cloud repositories and provides security using blockchain [28]. Vyas et al., 2019 proposed an integration approach that combines machine learning and permissioned blockchain in healthcare [29]. It helps to perform early prediction of disease. Scalability is the issue in this integrated approach. Griggs et al., 2018 used a healthcare blockchain of permissioned kind [30]. It uses smart contracts for patient monitoring remotely on Ethereum blockchain. The transactions are traceable, available, and speed. However, Ethereum protocol not addressed authentication. Blockchain solutions are focused for (1) (2) (3) (4)

Secure storage of patient identification information Dealing medical device supply chain process Data monetization Fraud detection on medical data.

3 Materials and Methods This section discusses various consensus algorithms, datasets used for secure data storage and proposed system.

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3.1 Consensus Algorithms The security of public and permissionless blockchain depends on Proof-of-Work (PoW) consensus algorithms. Based consensus algorithms are SHA256 [1], Ethash [31], Scrypt [29] and Equihash [30]. SHA256 is a hash function which is applied in Secure Hash Function—2 (SHA-2). Ethash is used in Ethereum blockchain. It is a variant of Dagger-Hshimoto hash function and it takes more memory space. Scrypt is an encryption mechanism that takes more memory access when compared to SHA256. The block time of Scrypt is higher than SHA256. Equihash is an improved version of Wagner’s algorithm. It takes more memory for creating a proof. However, the verification can be done instantly.

3.2 Dataset The proposed system uses various datasets such as Pima Indian Diabetic Dataset [32], Heart Disease Dataset [32], and Mammography Dataset [32]. Table 1 displays the details of three datasets.

3.3 Blockchain-Based Medical Data Storage The proposed method includes developing an application that authenticates the client using MetaMask account followed by medical data storage. The workflow of the proposed method is shown in Fig. 1. The client can be a doctor who stores the patient data. When many hospitals are connected through the blockchain, the patient data is shared between doctors of different hospitals for further analysis.

4 Experimental Results This section discusses various performance metrics and the results comparison on various Proof-of-Work (PoW)-based consensus protocol. Table 1 Details of medical datasets

Dataset

Number of features

Number of instances

Heart Disease

14

303

Pima Indian Diabetic

9

768

Mammography

5

11,183

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Fig. 1 Workflow of Ethereum Blockchain-based medical data storage

4.1 Experimental Setup The experiment is carried out in Windows 11 machine, and the smart contract is created using Remix IDE environment. Various consensus algorithms were selected are SHA256, Ethash, Scrypt, and Equihash. The experiment involves various number of transactions such as 100, 200, 300, 400, and 500. Smart contract is created in all the consensus algorithms and evaluated its performance on various evaluation metrics.

4.2 Evaluation Metrics The proposed method is evaluated using Transactions Per Second (TPS), Block Time (BT), and Transaction Fee (TF). Transaction per second represents counting the number of transactions completed per second [33]. It is mostly used for evaluating the speed of system or network that involves cryptocurrencies. The system is fast when it executes more transactions per second. It is a important parameter to measure the speed of blockchain network. TPS of blockchain network depends on consensus algorithm. All the transactions are executed in terms of blocks. Block time is the time engaged for block creation for each transaction. After the fresh block is created, it is further included in existing blockchain [34]. This parameter affects the latency of blockchain network. Transaction fee is a fee that is given to the transaction miners for block verification of a transaction in the blockchain network [35]. It is like an incentive to the miners for processing a transaction. During the mining process, the blocks are created for a transaction.

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Fig. 2 Block time analysis

Fig. 3 Transactions per second analysis

4.3 Results Analysis The performance of blockchain-based medical data storage system is analyzed using Transactions Per Second (TPS), Block Time (BT), and Transaction Fee (TF). Fig. 2 shows the block time of all three datasets. It infers that the block time increases when the number of instances in a dataset increases. Figure 3 expresses the Transactions Per Second details of chosen consensus algorithms. Figure 4 displays the average block time of all consensus algorithms of three datasets. However, Ethash has generated the proof with 1.83 minutes on average. The transaction fee of set of transactions 100, 200, 300, 400, and 500 are displayed in Fig. 5.

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Fig. 4 Average block time analysis

Fig. 5 Impact of transactions versus transaction fee

5 Conclusion In this work, medical data is stored in blockchain network. The experiment is carried out on various blockchain networks with PoW-based consensus algorithm such as SHA256, Ethash, Scrypt, and Equihash. The datasets Pima Indian Diabetic dataset, Heart Disease dataset, and Mammography dataset are stored in blockchain. The performance of storage in blockchain is also evaluated. This work can be extended to proof-of-stake (PoS)-based consensus algorithms.

References 1. Shi, Y., Peng, Y., Kou, G., & Chen, Z. (2007). Introduction to data mining techniques via multiple criteria optimization approaches and applications. In Research and Trends in Data Mining Technologies and Applications, IGI Global, pp. 242–275. 2. Tian, H., He, J., & Ding, Y. (2019). Medical data management on blockchain with privacy. Journal of Medical Systems, 43, 1–6. 3. Nadin, M. (2018). Redefining medicine from an anticipatory perspective. Progress in Biophysics and Molecular Biology, 140, 21–40.

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A Study on Different Fuzzy Image Enhancement Techniques Lalit Kumar Narayan and Virendra Prasad Vishwakarma

Abstract Modern medical science has seen a revolution in medical image processing. We would all be able to diagnose and treat patients without side effects. Medical imaging allows doctors to see patients without opening them. Medical imaging allows us to learn more about human neurobiology and human behavior. Brain imaging is used to study why some people become addicted to cocaine over time. Medical imaging combines biology, chemistry, and physics. The technology created can be used in many other fields. This article explains how medical imaging can be improved in the frequency and time domains. Contrast enhancement is performed using the local transform histogram method. The images are then enhanced using Fuzzy-Neural techniques. Fuzzy logic and fuzzy set are very good at dealing with multiple uncertainties. Recent research has focused on the ability of fuzzy theory to enhance low-contrast images and fuzzy technique and better approach for new research. Keywords Fuzzy understanding · Histogram evaluation · Leukemia detection · Genetic algorithm · Fuzzy logic

1 Introduction Lots of applications such as medical image analysis, satellite photo evaluation, remote sensing, equipment vision, automated navigation, as well as dynamic as well as traffic scene evaluation need high-resolution photos that preserve info. A high comparison photo is tough to attain since we cannot manage the tape-taping problems. As an example, lots of videotaped photos are fairly negative because of bad illumination, bad shutter rate as well as aperture dimension, as well as non-linear mapping. Over L. K. Narayan (B) · V. P. Vishwakarma University School of Information Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi, India e-mail: [email protected] Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_11

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the previous years, there have been lots of spatial as well as regularity domain name methods that enhance photo comparison. Fuzzy concept, initially presented by Zadeh, was reached various other areas, consisting of photo-refining, information design, as well as manage system make. Fuzzy concept can surely take care of unpredictability. This is the bottom line of fuzzy concept efficiently. Lots of scientists are functioning to create fuzzy photo-refining concept. This work goal is to verify that fuzzy reasoning can surely be utilized to enhance differentiation. Worldwide photo adjustment for Grayscale worth. This establishment doesn’t impact where the factors lie. Nevertheless, we believe that the factors in the photo are not separated, yet can surely be linked to various other bordering factors. A picture improvement formula needs to make complete use of the appropriate info in the regional setting. A picture improvement formula needs to likewise take into consideration obscuring as a function of photo unpredictability. To build much far better use fuzzy info as well as stats regarding the bordering area of a picture, we present fuzzy entropy into the photo improvement formula. This makes our formula more suitable with the fact of photo information refining. The 2nd action is to present the standard human qualities considered as a covering up the result as well as suggest a dimension work that functions greatest to determine the level of alter of the grey range worth of the photo pixels. This allows us to utilize the physical functions of the individual.

1.1 Objective This article reviews fuzzy grey-level contrast that is based on fuzzy logic to improve low-contrast images. The techniques for enhancing contrast are under and over enhancement. Through the use of non-linear membership functions in fuzzy set theory, the drawback of under and over enhancement of images could be corrected.

1.2 Motivation The purpose of this paper is to present a fuzzy image enhancement algorithms, which maps elements from the pixels to fuzzy plane as well as to transformed plane using the fuzzy techniques and provides a better strategy for new research.

2 Literature Reviews Wei and Lidong [1] suggested a feature-based limit choice criterion for histogram segmentation to execute bi-HE (warm mapping in low-contrast pictures). Its objective is to enhance low-contrast pictures by utilizing an ideal limit that sustains all

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efficiency metrics similarly, rather than concentrating on simply a couple of metrics. The highlight of the Bi-HE technique is its ideal efficiency for all specifications. Hanmandluet et al. [2] designed a fuzzy concept formula to improve sporadic pictures. Specification optimization is carried out utilizing the Microbial Foraging Formula (BFA). The formula functions, however the intricacy of BFA makes it challenging to utilize. Sheet and others [3] provided a customized BPDHE formula and utilized fuzzy histogram for histogram smoothing. This formula benefits reduced vibrant vary pictures however has bad efficiency for complete vibrant vary pictures. Çelik [4] produced a two-dimensional histogram to examine the contextual residential or commercial homes of pixels. Pixels of the exact same dimension are scaled to the exact same dimension despite their place. This might impact the high quality of the bigger pictures. Likewise, 2-D histograms can be really complicated, producing them computationally ineffective. Celik [5] likewise provided an optimization formula called Spatial Entropy-Based Comparison Contrast in Distinct Sine Change (SECEDCT). This formula utilizes the spatial entropy in the 2-D histogram to improve the regional comparison, while the worldwide comparison improvement is carried out by DCT. Lee et al. [6] suggested a split 2-D histogram. This is done by enhancing the comparison of current grey degrees. Huang et al. [7] emphasized that this formula is chosen with level of sensitivity, which might impact efficiency. Abdoli and others [8] utilized Gaussian mix designs to approximate the histogram and prolonged the private elements to utilize the entire characteristics. Wei et al. [9] suggested an optimization formula that makes the most of entropy by integrating non-zero containers. Saturation is feasible when grey degrees are decreased. Fu et al. [10] proposed that the histogram was changed with a sigmoid work for worldwide comparison improvement and DCT for regional comparison improvement. Singh et al. [11] described that this formula might not offer considerable enhancement on smooth surface areas. Chen and Beghdadi [12] provided a distinction optimization formula based upon multiscale Retinex design. Worldwide mapping was carried out utilizing gamma adjustment and spatial improvement with a customized Retinex filter. Fu et al. [13] proposed that this formula has lots of restrictions, so it might not help all pictures. Liang et al. [14] suggested a technique for picture illumination dimension utilizing the service of non-linear equations of duplicated distributions. Although the formula offers great comparison, it can result in boring shades because of excessive light.

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2.1 Reviews on Leukemia Cancer Classification Using Fuzzy Support Vector Machine Cancer cells are the 2nd prominent reason for fatality around the world. Leukemia is a kind of cancer cells that impacts the blood and blood-forming cells. Kids under the age of 15 have a high danger of establishing leukemia. Han and Kamber [15] objective is that category is the procedure of organizing items into predefined classifications. This various from combination, which divides points however doesn’t different them. The products in the classification are split into a number of classifications. A design is produced that discusses the guidelines for splitting programs into various classifications. This design can be utilized to designate a lesson to a defined course. Honeine [16] specifies multi-classification as producing courses from several information collections. Changing a multi-class issue into a two-class issue is a typical method to refix multi-class category issues. Fuzzy Assistance Vector Device, designed by Abe [17], TakuyaInoue, and Shigeo Abe, suggests fuzzy subscription to define the unidentified area issue in the Abe SVM formula. The FSVM technique can likewise decrease outliers in information category. Each dataset is designated a subscription degree that shows the payment of the information to every course. Nimesh et al. [18] suggested an automatic technique for leukemia discovery. Experts take a look at the micrographs to identify if there’s a medical diagnosis of leukemia. This procedure takes a great deal of time and needs a great deal of ability. These restrictions are conquered by an automatic leukemia discovery gadget. It after that essences appropriate components from the pictures and uses filtering system methods. Category is done utilizing SVM. The system was evaluated utilizing a picture dataset. 93.57% precision was accomplished. The program was effectively executed in MATLAB. Beatriz et al. [19] suggested a DNA microarray category technique. The suggested technique utilizes a swarm knowledge formula to choose functions to determine the very best establish of genetics to discuss the illness. A subset of genetics was utilized to educate various ANNs. 4 datasets were utilized to examine the credibility of the suggested design and analyze the hereditary correlation for illness category. Himaliet et al. [20] review techniques for spotting leukemia. Lots of picture refining methods can be utilized to spot red blood cells and premature cells. Anemia, Leukemia, and jungle fever can all be triggered by another problem, such as vitamin B12 shortage or anemia. It can be utilized to identify the problem. They examine objectives to matter and determine cells afflicted by leukemia. Discovery of premature blast cells assists to identify leukemia and identify whether it’s persistent or malignant. There are lots of methods to determine fully grown cells. These consist of histogram scaling and direct contrast extending. Various other morphological techniques are zoning, zoning disintegration, zoning, growth, and disintegration. K means watershed alter. Histogram estimation and direct contrast are precise at 72, 73.7, and 97.8%, specifically.

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Jyoti Rawat and associates [21] suggested a brand-new technique to distinguish ALL (severe lymphoblastic leukemia) lymphoblast cells from healthy and balanced lymphocytes. Initially, this procedure divides leukocytes from various other blood cells. After that the lymphocytes are launched. A brand-new computer-aided medical diagnosis (CAD) system was designed to spot hematological illness such as leukemia cancer cells based upon gray-level reoccurring matrices (GLCM) and shape-based functions. To identify the existence of lymphoblast (leukemia) cells, the acquired aspects are split into 2 classifications, automated assistance vector device. GLCM structure functions with function vector size 13 exposed a category precision of 86.7% for cytoplasm, 72.4% for nucleus, and 56.1% for suggested form assistance. Category accuracies were 56.1%, 72.4%, and 72.4% for 11 vector functions in both lymphocyte areas, specifically. The integrated structure form function has a category precision of 89.8% and the vector size function is 37, which is much far better compared to one. Tejashriet et al. [22] suggested a technique to spot leukemia, a kind of youth cancer cells. The development price of severe lymphoblastic leukemia (ALL) is a lot much faster compared to others, so it ought to be spotted very early. Traditional methods such as fluorescence in situ hybridization, immune phenotyping, and cytogenetic evaluation (FISH) are sluggish. The suggested technique allows quick discovery since it utilizes automated category based upon the Otsu technique. Spontaneous differentiation plays an essential function in the medical diagnosis of leukemia. The medical diagnosis of leukemia likewise depends upon the outcomes of physical qualities. A shape trademark is utilized to essence the function. Shape trademark outcomes are utilized for essence functions. Joshi et al. [23] designed a technique for the splitting up and seclusion of leukocyte. They utilized comparison improvement and histogram estimation to process really great blood pictures. They likewise utilized Otsu limit segmentation to essence leukocyte. KNN clustering utilized the acquired functions to categorize blood pictures into blasts and typical cells. The technique was evaluated utilizing 108 pictures from peripheral blood smears acquired from the neighborhood available dataset. Phuzu and others. Mohapatra et al. [24] suggested a technique for leukocyte category utilizing tiny imaging. This likewise divides the nuclei from the cytoplasm. They evaluated various category designs and after that drawn out structure, form, and shade functions to identify the very best design for leukemia category. They evaluated several classification developers and utilized 368 pictures. They discovered that SVMs with Gaussian Radial Basis bit carry out much far better compared to various other filtering systems in regard to category precision. Mohapatraet et al. [25] described that it’s based upon the mix of the color design to differentiate leukemia. A two-step shade splitting up technique based upon fuzzy reasoning was utilized to divide leukocytes from various other blood cells. The shape trademark, form, structure, and fractal measurement were drawn out by the writers. Singhalet et al. [26] produced a formula to spot ALL utilizing regional binary patterns (LBP) and geometric structure functions. The suggested design was based upon a little dataset of 368 pictures for function removal. These functions are then fed to SVM for binary category (Table 1).

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Table 1 Summary of some best papers related to fuzzy image enhancement S.no

Author

Contribution

Limitation

1

Fu et al. [27]

Algorithm that performs both local and global contrast enhancement. The histogram is altered using an sigmoid function to perform global enhancement. It also employed DCT to enhance local contrast

Low-contrast images by using an optimal limit that supports every efficiency measure in the same way instead of focusing on a few metrics

2

Liang et al. [28]

Method to determine the amount of illumination in an image by working out the non-linear equation for diffusion. The algorithm for enhancing contrast is based on fuzzy information from the pixels

The algorithm provides excellent contrast, but it could cause discoloration due to an over-increase in brightness

3

Balasubramaniam Jayaram [29]

The key feature that is unique to fuzzy theories is that it has the ability to manage uncertainty with ease There are many researchers trying to create the theories of fuzzy processing of images. This research is a way to prove that the applying fuzzy logic techniques will result in more effective enhancement of contrast

Techniques are low-cost solutions to improve the visual appeal of images for a human being

4

Shin and Park [30]

Introduces fuzzy entropy and makes full use of neighborhood information fuzzy information and human visual traits. To improve an image, this paper first performs the reasonable fuzzy-3 division of its histogram into dark region, the intermediate region, and the bright region

It isn’t well established the fact that Fuzzy set of logic or fuzzy is moderate in handling a variety of uncertainties

3 Conclusion We suggested a distinction optimization formula for FDHE and a prolonged variation of FCCE. We suggested a fuzzy histogram to effectively spot the strength degree distinction in the pixel location. FDHE enhances strength degree comparison however doesn’t alter the all-natural qualities of pictures. Leukemia (or unusual leukocyte) is a blood illness that happens in great deals and can be seen rapidly. This implies that the WBC can’t carry out its responsibilities correctly. Medical diagnosis and discovery of leukemia in the beginning is performed by picture evaluation. Fuzzy reasoning and hereditary formula were utilized in the advancement. The efficiency of the leukemia discovery formula was determined utilizing FAR, FRR, and precision specifications. Range approximate is 0.0021263, FRR is 0.00041855, and typical precision is 99.7455.

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4 Future Scope In this review, various methods used for image enhancement using fuzzy logic are done and also to identify the outcomes and shortcomings of the earlier works. To overcome the limitations of existing techniques, a new technique based on morphological enhancement using fuzzy logic will be proposed in the near future.

References 1. Wei, Z., Lidong, H., & Jun, W. (2015). Combination of contrast limited adaptive histogram equalization and discrete wavelet transform for image enhancement. 9(3), 226–235. 2. Hanmandlu, M., Verma, O. P., Kumar, N. K., & Kulkarni, M. (2009). A novel optimal fuzzy system for color image enhancement using bacterial foraging. IEEE Transactions on Instrumentation and Measurement., 58(8), 2867–2879. 3. Sheet, D., Garud, H., Surveer, A., & Mahadevappa, M. (2010). Brightness preserving dynamic fuzzy histogram equalization. IEEE Transactions on Consumer Electronics, 56(4), 2475–2480. 4. Ceilik, T., & Tjahjadi, T. (2011). Contextual and variational contrast enhancement. IEEE Transactions on Image Processing, 20(12), 3431–3441. 5. Celik, T. (2012). Two-dimensional histogram equality and contrast enhancement. Pattern Recognition, 45, 3810–3824. 6. Lee, C., Lee, C., & Kim, C.-S. (2013). Contrast enhancement based on layered different representation of 2D histograms. IEEE Transactions on Image Processing, 22(12), 5372–5384. 7. Huang, S. C., Cheng, F. C., & Chiu, Y. S. (2013). Efficient contrast enhanced using adaptive gamma correction with weighting distribution. IEEE Transactions on Image Processing, 22(3), 1032–1041. 8. Bdoli, M. A., Sarikhani, H., Ghanbari, M., & Brault, P. (2015). Gaussian model-based contrast enhancement. IET Image Processing 9(7), 569–577 9. Wei, Z., Lidong, H., Jun, W., & Zebin, S. (2015). Entropy maximisation histogram mod scheme for image enhancement. IET Image Processing, 9(3), 226–235. 10. Fu, X., Wang, J., Zeng, D., Huang, Y., & Ding, X. (2015). Remote sensing image enhancement using regularized histogram equalization and DCT. IEEE Geoscience and Remote Sensing Letters, 12(11), 2301–2305. 11. Singh, K., & Vishwakarma, D. K., along with Walia, G. S., Kapoor, R. (2016). Contrast enhancement through texture region based histogram equalization. Journal of Modern Optics. 12. Chen, S., & Beghdadi, A. (2010). Natural enhancement of color image. EURASIP Journal and Video Processing, 2010, 1–19. 13. Fu, X., LiWang, M., Huang, Y., Zhang, X. P., & Ding, X. (2014). A novel retinex based method for image enhancement with illumination adjustment. In IEEE international conference on acoustic, speech and signal processing, Florence. 14. Liang, Z., & Liu, W. (2016). Contrast Enhancement using nonlinear diffusion filtering. IEEE Transactions on Image Processing, 25(2), 673–686. 15. Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and Techniques. Waltham, Morgan Kaufmann USA. 16. Honeine, P., Noumir, Z., & Richard, C. (2013). Signal Process 93, 1013–26. 17. Abe, S., & Inoue, T. (2020). European symposium on artificial neural networks, (Bruges) ESANN. Belgium. 18. Nimesh, S., et al. (2015). Automated leukaemia detection using microscopic images (vol. 58, pp. 635–642). Elsevier. 19. Garro, B. A., et al. (2016). Classification of DNA microarrays using artificial neural networks and abc algorithm. Applied Soft Computing, 38.

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20. Himali, P. (2015). Leukemia detection using digital image processing techniques. International Journal of Applied Information Systems, 10(1). 21. Rawat, J. (2015). Computer aided diagnosis system for the detection of leukemia using microscopic images (vol. 70, pp. 748–756). Elsevier. 22. Tejashree, G., et.al. (2015). Blood microscopic image segregation and acute leukemia detection. International Journal of Emerging Research in Management and Technology, 4(9). 23. Joshi, M. D. (2013). International Journal of Emerging Trends and Technology in Computer Science (IJETTCS), 2, 147–151. 24. Mohapatra, S., Patra, D., & Satpathy, S. (2014). An ensemble classification system for the early diagnosis of acute lymphoblastic Leukemia in blood microscopy images. Neural Computing and Applications, 24, 1887–1904 25. Putzu, L., Caocci, G., & Di Ruberto, C. (2014). Leukocyte classification using image processing techniques for leukaemia detection Artif. Artificial Intelligence in Medicine, 62, 179–191. 26. Singh, P., & Singh, V. (2014). A binary pattern to detect acute lymphoblastic Lukemia. Kanpur, India. 27. Fu, X., Wang, J., Zeng, D., Huang, Y., & Ding, X. (2015). Remote sensing image enhancement using regularized-histogram equalization and DCT. IEEE Geoscience and Remote Sensing Letters, 12(11), 2301–2305. 28. Liang, Z., Liu, W., & Yao, R. (2016). Contrast enhancement by nonlinear diffusion filtering. IEEE Transactions on Image Processing, 25(2), 673–686. 29. Jayaram, B., Kakarla, V. V. D. L., Narayana, K., & Vetrivel, V. (2011). Fuzzy inference system based contrast enhancement. EUSFLATLFA Aix-les-Bains, France. 30. Shin, J., & Park, R. H. (2015). Histogram-based locality-preserving contrast enhancement. IEEE Signal Processing Letters, 22(9), 1293–1296.

A Review on Different Image Enhancement Techniques Lalit Kumar Narayan and Virendra Prasad Vishwakarma

Abstract Image enhancement is among the most significant issues encountered when it comes to image processing. This includes medical image enhancement, underwater image enhancement, also known as colour dusty images. The aim for image enhancement is to modify images so that the final image is superior to the original image to be used in the specific application. The digital image enhancement technique provides many possibilities to improve the high quality of photos. Making the right choice between different types of techniques is vital. This article will give an overview and an analysis of different techniques used to enhance image. Image enhancement plays a crucial function in applications that use vision. In recent times, a lot of work has been done in the area that deals with image enhancement. There have been a variety of techniques proposed in the past to improve digital images. In this paper, a review is presented on different methods of image enhancement. Keywords Digital image processing · Image fusion · Histogram equalization · Image enhancement

1 Introduction I am using digital image enhancement techniques to improve low image quality and better incorporate image processing. In simple language, digital image processing can be simply defined as processing of an image which is digital in nature by using digital computer. In digital image processing, we apply different types of operations on an image so that it becomes more appropriate for viewing. There are two methods: 1. Spatial domain methods: [1] in this method, the operation is carried out directly on the image pixels, which leads to its turn an increase in contrast. Frequency domain L. K. Narayan (B) · V. P. Vishwakarma University School of Information Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi, India e-mail: [email protected] Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_12

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methods [2]: in this method, the operation is used on the fierce conversion of the corresponding image. The article deals with spatial domain techniques, different types of interference and filters applied to interference. The assessment of functions is carried out in relation to the frequency of the frequency field in order to improve image quality. By using this method, we can improve the corresponding image quality by making changes to the transformation coefficient functions. Real-time solutions are implemented in the spatial subject due to the fact it is very simple, clean to interpret, and fundamentally, the range of complexity is very low. Filtering is a method that serves as a means of disposing of the noise from a photograph real-time answers are implemented inside the spatial field due to the fact it’s miles very simple, clean to interpret, and essentially, the variety of complexity could be very low [3]. Coherence and intangible factors are the two foremost criteria that are lacking within the space subject. The evaluation of capabilities is done in terms of the frequency of the frequency field with a purpose to enhance photograph fine [4]. The Fourier transform of the photo works inside the shape of a discreet cosine and a sine by way of the usage of this technique, we can enhance the corresponding image high-quality with the aid of making changes to the transformation coefficient capabilities [5]. The advantages of enhancing the image of the frequency area include a low calculation complexity, the management of the image coefficients and the use of an improved version of the area feature [6]. The main drawback of this approach is that it can’t create a clear picture of the historical past. This does not improve all components of the picture. It may only be awareness on person components [7]. The removal of the noise of a photo performs a vital function, and it’s by far one of the maximum essential tasks in applications which include the scientific discipline, wherein noise -unfastened photos lead to the detection of minimal mistakes. Filtering is a way that serves as a method of getting rid of the noise from a photograph [8]. The thing deals with spatial domain strategies, one-of-a-kind forms of interference and filters applied to interference [9].

1.1 Objective It makes the image clearer for people to see, removes noise and blur, increases contrast and shows more detail. This paper fulfils the basic objective of image enhancement that image should be improved for better human perception. Examples of development activities include these. The purpose of each site determines how the development strategy is implemented.

1.2 Motivation The motivation of this paper is to arrange and survey the Picture Handling Strategies and the different strategies applied to the picture. As sometimes most pictures suffer

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from high noise, weak contrast. In this paper, our main focus is to review different methods for removing the noise from an image, improve the contrast of an image, improve the brightness of an image, increase the resolution of an image. We additionally utilized various channels to see which channel turns out best for eliminating specific commotions.

2 Different Image Enhancement Techniques Image enhancement can be regarded to be one of the primary methods used to analyse images. The purpose of contrast enhancement is to increase the quality of an image so that it can be more appropriate for the particular application. As of today, many enhancement techniques have been suggested for various applications. The effort has been made to improve the quality of enhancement results, while reducing the processing complexity and memory consumption.

2.1 Enhancement Techniques The pixels in an image are utilized for the implementation of spatial method. Pixels in photographs are subjected to direct operations. The purpose of this method is to enhance the image’s clarity of information [10].

2.2 Gray Level Transformation One of the most important aspects of gray-level image improvement is that, in this technique the gray-level image enhancement techniques are directly applied on the particular pixel of an image. One of the most important aspects of gray-level image enhancement is that it is carried out directly on a specific pixel in an image when using this technique [11]. The value of each individual pixel in the processed image is based on the original value of the pixel. Numerous researchers such as Umar Farooq have created a unique approach to image enhancement with infrared photos.

2.3 Contrast Stretching Intensity difference is described as the contrast among adjacent pixels. In some cases, photograph best can be improved via increasing its contrast. Contrast in simple words can be described as difference between highest and lowest pixel intensity value of an image [12].

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2.4 Threshold Transformation When the image needs to be segmented, door conversion is used. The background and a portion of the image are separated as desired. It is basically a process of creating black and white image from gray scale image by setting exactly those pixels to white which are greater than the particular threshold and setting exactly those pixels to black which are lower than the particular threshold [13].

2.5 WF Wavelet Filtering WT is a set of mathematical modification functions known as a wave function that can be used to possibly evaluate or display a signal. It is also a Fourier transform method. WT is utilized for the multidimensional evaluation of features or signals through operations like scaling and translation that lead us, in addition to describing nearby capabilities, i.e. adjusting the nearby development factors of alerts in domains of time and frequency. Again, the above statement accurately describes a lighting environment in which the strength of light or unevenness of light is the primary factor the human eye can depend on. Consequently, a single factor, such as the mirrored image coefficient, that reveals the underlying information or statistics of any is preserved [14].

2.6 Retinex Methods This theory, developed on the ground and McCann, deals with the perception of colours from the factor of view of the human eye and the restart of the range of the colours. The purpose of this technique is to decide the mirrored image of a photo by way of disposing of the impact of mild from the unique image. In step with idea, the human eye receives facts in a selected manner beneath exclusive lights conditions, i.e. while mild touches and is pondered on an item, the human eye can understand that item. Possible again, the above assertion in reality describes a light’s surroundings wherein the principle aspect that the human eye can be dependent on isn’t always light, however the strength of mild or the unevenness of light. Consequently, a unmarried factor that displays the underlying information or statistics of any. The object, just like the mirrored image coefficient, is preserved. It is based totally on the abovementioned model, the image may be expressed as follows: it’s far the fabricated from the reflection component and the light component [14, 15].

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3 Literature Reviews Muhammad Aamir [16] expands a fusion-based totally enhancement method with the usage of CRM and Dehazing concept, that is an effective natural technique for enhancing low-mild photograph maintenance, however it particularly relies upon one-of-a-kind publicity map evaluation strategies, and this approach is best used for a particular camera. Even though it has no longer been tested for the response model and different fashions both, it affords the right refinement method and gives the favoured result, with a few quick facets, for example, via increasing the ultra-vibrant a part of the unique picture sends. Arafat Ali Rabab [17] proposed an algorithm for improvement of the low-light image primarily based on fusion, which consists of two main stages, first, the assessment of the light channel primarily based on fusion, and second, improvement. Every other refinement set of rules to enhance the sharpness of the image is to begin with advanced image, in order that it can perform refinement of the image, however there may be no development in local evaluation, accompanying this. Ronggang Wang [18] proposed that frame is combining the camera’s response version and the conventional retina version. Primarily based on them, it offers a unique digital camera response model that’s capable of lowering the average RMSE so as of importance, and solves this trouble quick exposure map estimation. Arathy [19] right here uses vague thoughts. Its equations and functions are taken into consideration in a blurred field. Triangle membership feature, sigmoid operators are used to move from the actual global to the summary worldwide of mathematical concepts. The rebalancing of the go out membership feature helped save you interruptions from the image histogram. Even though he is able to improve images, it seems to have lengthy past via severe refinement. Seonhee Park [20] proposed that this technique used the idea of PCA (primary aspect evaluation), which simplifies the evaluation of atmospheric light by the use of the fog patch function analysed by means of the principle factor evaluation, so it could integrate the two techniques. The concept of PCA and hazing is to get an enlarged picture. Therefore, this technique is capable of attaining the suitable result and progressed photo even in low-mild environment. Shih-Yu Huang [21] afford an effective CED algorithm that complements assessment and removes noise for nighttime snapshots. The Retinex-based adaptive clear out is carried out on three scales to significantly enhance assessment and brightness. It well known to show the spectral properties of colourants for statistics processing to attain suitable outcomes. But even though it gives the suitable development end result, it is possible to lose facts. Dengyin Zhang [22] proposed a photo distortion version of small simple but physically actual light (LIDM) derived from the atmospheric diffusion model. Growth right here, the improvement of the answer is performed way to the use of a linked enlargement strategy within the wave location. The attachment is connected in high repetition sections and with a photo of the information. Anyhow, what makes it

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special from the other paper is that it uses the most substantial processes, specifically DWT (Discrete Wavelet Transforms) and SWT (desk bound wave). Sreejith [23] proposed it’s miles a synthesis-based totally approach that makes use of a brand new concept evolved to enhance images based totally on fuzzy good judgment with a method, which enhances a excessive-assessment photo. It became executed successfully, however for some images it appears to magnify a sure a part of the image. Gopalan, Sasi and Arathy, [24] “Progressed overall performance Detector Watermark image development using Filters” in 2018 discusses the effect of a few strategies to manage the value of watermark image reputation. The real yet brittle watermark method for ambush complicated heavy infection JPEG or thawing may be planned, as a result decreasing the extent of waterfall technique discovered inside the image waterfall attack. Their revelations lessen to increase in fee photo distortion in the usage of United States, Laplacian or channel. There are watercolours on 1000 photographs for the take a look at facts set and they are checked while they are published or published. A distorted photo changed into advanced the use of inhibition of separation, Laplacian and external canal deconvolution. The cost of revealing the preceding watermark become then assessed and improved broke down. Seung-received Jung, Jae-Yun Jeong and Sung-JeaKo, “improving Stereo photograph Accuracy using Binoculars—tremendous difference” in 2018 pictures proposed to feature every other approximate sharpness to the sound machine. The sound device is a useful response to reduce the development hassle of improving photographs accuracy. Arranging the interior of the car with restraints where the path of movement can be determined is more vital to suppress pointless will increase in light price. In addition, the strict consciousness of the BJND version is considered with the aid of the approach of contrasting the accuracy of sound gadget planning. Yue-cheng Li [25] proposed “enhancing Multidimensional image based on Human Nature’s visual gadget,” which will use and display LIP (picture logarithmic variation) display in 2018. Attribute shape seen human (HVS) helps multi-scale computational restore. At that point, a extraordinary level of increase in the adaptability of the smooth JND (simply seen variations, JND) of the exact device of people was proposed and used as a machine to reveal the implementation of repairs techniques. Their algorithm gives better results than other algorithms (Table 1).

4 Conclusion After reviewing exclusive papers and specific methods, it may be determined that there are basically broad classes for low-mild photograph improvement strategies, namely, Pre-Enhancement and post-Enhancement. Pre-improvement is a tradition of strategies that we observe in our self-improvement method earlier than taking any photo, and submit-development is a way of life of techniques which might be implemented after a photo is taken. There are extraordinary methods to enhance the image in low light that fall into these classes. It changed into found that every approach

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Table 1 Summary of some best papers related to image enhancement method SI.No Authors

Findings

Limitation

1

Rahman et al. Machine for New fusion is [16] low-light applied picture Improvement with variety of constancy and detail control in premier light conditions

Contribution

Technique

However, in relation to photographs with a lot of shining and dark areas, the improvement algorithm favours the intense part overly

Doesn’t produce a lot of attention-grabbing effects in various scenarios

2

Sandoub et al. A low-mild [17] picture upgrade approach in mild of superb channelpast and maximum variety channel

The image’s overall contrast can be raised

It will increase the noise when the image has areas of low intensity

3

Ying et al. [18]

Whether this method uses specific or constant digicam parameters, the overall output is enhanced. Additionally, it is unable to identify various image capabilities and may not be able to configure all of the scene information, thus occasionally, over-enhancement is necessary

A limited enhancement of contrast because of local contrast enhancement

4

Arathy and A brand-new Fuzzification SasiGopalana mathematical [19] version in photograph enhancement hassle

Low-light picture enhancement algorithm is done using image fusion

A low-gentle Fusion-based picture low-light Improvement enhancement for the utilization of digicam reaction model

Very helpful for Quite complex enhancement, although occasionally provides excessive improvement (continued)

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Table 1 (continued) SI.No Authors

Contribution

Technique

Findings

Limitation

5

Kim et al. [20]

Photo dehazing and improvement of the utilization of most compelling thing investigation and altered murkiness capacities

PCA-based fog removal

Give pictures to various picture Handling bundles in dimness and periodic light situation

Can merely be low use for linear stretching

6

Priyanka et al. [21]

Principal component analysis was used for the improvement of low-light images

Adaptive filters were used along with the PCA for Retinex-based methods

It gives a powerful upgrade picture even in more hazier picture as in evening time picture. Anyway some data might get misfortune in this technique

This method is ideal for visual observation, particularly when images are of high contrast, the best effects for radiographic and thermal images

7

Gu et al. [22]

A low-light Poor light picture model used improvement strategy in view of picture debasement model and unadulterated pixel proportion earlier

It provides a better Can’t return good way for image sound effects then Enhancement it needs to improve using inverted low-light image enhancement model

8

Sreejith and Sarath [23]

Picture upgrade utilizing fluffy rationale

This Strategy upgrades the pictures anyway this goes through the over improvement some of the time

9

Gopalan and Arathy [24]

Another PCA numerical model in picture upgrade issue

Homomorphic filtering is done along with fuzzy logic

Fuzzy logic need to enhancement techniques are a way to enhance image quality. Imaging

Extremely helpful PCA techniques for improvement are time taking to yet some time it implement gives over upgrade (continued)

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Table 1 (continued) SI.No Authors

Contribution

10

Multi-scale in LIP picture improvement

Zhang et al. [25]

Technique

Findings

Limitation

JND (Simply Noticeable Contrasts, JND) of a reasonable arrangement of people transformed into proposed and utilized as a contraption to notice the execution of fix procedures

According to lip techniques enhancement and filtering requirements

has its very own benefits and drawbacks. This is, a few methods are over-subtle, a few strategies are exceptionally complex and a few methods also are unexpected in their consequences, however they’re financially high priced in computing and obtaining any image for a data set. As a result, in preference to the use of one approach with all its shortcomings, we are able to finish that if we are able to integrate two or more techniques and create a fusion primarily based methods. One method turns into an advantage over the other approach’s shortcomings and can offer the proper result. This will be beneficial in improving the image in low mild, but additionally facilitates to preserve the main goal of improving the image in low light by removing all the hidden details from the image low light. The main purpose of low-light image enhancement is to increase the image contrast so that pictures become more suitable for viewing as well used in various different areas of applications. It should also be ensured that images show good quality visual perception for humans.

5 Future Scope The destiny of photo processing will involve looking space for adding wise life. Moreover, advances in photograph processing programs are integrated into the advent of brand-new clever digital species by means of researchers from all over the global. In some years, the creation of photo processing and associated technologies will lead to the arrival of millions upon millions of robots a good way to adjust international governance. In the coming years, a lot of work can be done in image enhancement using machine learning and deep learning algorithms. Various different techniques are reviewed in this paper for enhancement of low-light images and different methods like image fusion method, defogging method and machine learning methods can be used in future for better results of image enhancement.

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References 1. Wang, W., Wu, X., Yuan, X., & Gao, Z. (2020). An experiment-based review of low-light image enhancement methods. IEEE Access, 8, 87884–87917. https://doi.org/10.1109/ACCESS.2020. 2992749 2. Chen, S. D., & Ramli, R. (2003). Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics, 49(4), 1310–1319. 3. Park, S., Kim K., Yu, S., & Paik, J. (2018). Contrast enhancement for low-light image enhancement: A survey. IEIE Transactions on Smart Processing Computing, 7(1), 36–48. 4. Hu, H., & Ni, G. (2010). Colour image enhancement based on the improved retinex. In Proceedings of the international conference on multimedia technology, pp. 1–4. 5. Li, L., Sun, S., & Xia, C. (2014) Survey of histogram equalization technology. Computer Systems Applications, 23(3), 1–8 6. Lee, H. -G., Yang, S., Sim, J. -Y. (2015). Colour preserving contrast enhancement for low light level images based on retinex. In Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 884–887. 7. Land, E. H., McCann, J. J. (1971). Lightness and Retinex theory. The Journal of the Optical Society, 61(1), 1–11. 8. Kim, Y.-T. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transaction on Consumer Electronics, 43(1), 1–8. 9. Jobson, D. J., Rahman, Z., & Woodell, G. A. (2002). A multiscaleretinex for bridging the gap between colour images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7), pp. 965–976. 10. Wang, M., Tian, Z., Gui, W., Zhang, X., & Wang, W. (2020). Low-light image enhancement based on nonsubsampledshearlet transform. IEEE Access, 8, 63162–63174 11. Gu, Z., Li, F., Fang, F., & Zhang, G. (2019). A novel retinex-based fractionalordervariational model for images with severely low light. IEEE Transactions on Image Processing, 29, pp. 3239–3253. https://doi.org/10.1109/TIP.2019.2958144 12. Wang, Y., Chen, Q., & Zhang, B. (1999). Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transaction on Consumer Electronics, 45, 68–75. 13. Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In Graphics gems (pp.474-485). Elsevier. ISBN: 0-12-336155-9 14. Loza, D. B., & Achim, A. (2013). Automatic contrast enhancement of low-light images based on local statistics of wavelet coef_cients. In Proceedings of the IEEE international conference on image processing, pp. 3553–3556. 15. Park, S., Yu, S., Moon, B., Ko, S., Paik, J. (2017). Low-light image enhancement using variational optimization-based Retinex model. IEEE Transactions on Consumer Electronics, 63(2), pp. 178–184. 16. Rahman, Z., Aamir, M., Pu, Y.-F., Ullah, F., Dai, Q. (2018). A smart system for low-light image enhancement with color constancy and detail manipulation in complex light environments symmetry, 10, 718. https://doi.org/10.3390/sym10120718. 17. Sandoub, G., Atta, R., Ali, H. A., Abdel-Kader, R. F. (2021). A low-light image enhancement method based on bright channel prior and maximum colour channel Department of Electrical Engineering. Faculty of Engineering, Port Said University, Port Said, Egypt, February 2021 IET Image Process 15, 1759–1772 18. Ying, Z., Li, G., Ren, Y., Wang, R., & Wang, W. (2017). A new low-light image enhancement algorithm using camera response model. IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, 3015–3022. https://doi.org/10.1109/ICCVW.2017.356 19. Gopalan, S., Arathy, S. (2015). A new mathematical model in image enhancement problem. Procedia Computer Science, 46, 1786–1793. 20. Kim, M., Yu, S., Park, S., Lee, S., & Paik, J. (2018). Image dehazing and enhancement using principal component analysis and modified haze features. Applied Science, 8, 1321. https://doi. org/10.3390/app8081321

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Cryptocurrency and Application of Blockchain Technology: An Innovative Perspective Chetan Trivedi and Sunil Kumar

Abstract Cryptocurrency has been a moving point over the course of the last 10– 15 years due to the increasing demand of Digital Currency many people are using it which makes it a useful investment. Efficiency, adaptability and data dense qualities are determined by its unique design and technological innovation. The technology used for cryptocurrency is Blockchain. Moreover, this paper deals with systematic interaction between Block Chain and cryptocurrency. The present work shows and summarizes the interplay connection of two key ideas in today’s digitalized society both cryptocurrency and blockchain are at the forefront of technical study, and this article focuses on their most current applications and advances. The main aim of this study is to investigate cryptocurrencies and its legal status in India, as well as suggesting ways to regulate cryptocurrency. This paper also deals with primary source of data for analysing and to generate result about the awareness of digital currency and the requirement of regulation. Keywords Cryptocurrency · Cryptographic · Blockchain · Digital currency · Technology · Legal perspective

1 Introduction The Bartar framework, which was very famous in antiquated times, has been supplanted by money. As time elapsed, the headway of money started as a need. Another period of money has started because of innovative headways as computerized cash. Cryptocurrency is a new peculiarity that is getting critical consideration. From one viewpoint, it is based on a fresh out of the box new innovation whose maximum capacity presently can’t seem to be understood. Then again, in the ongoing structure, it satisfies comparable capacities as other, more conventional resources. C. Trivedi (B) · S. Kumar Chandigarh University, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_13

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The rise of digital money sets off the arrangement of monetary relations, where the trading of resources happens without including concentrated monetary establishments (specifically-banks) or different individuals. Cryptographic cash is difficult for the state with the most irksome tasks, rising up due to the prerequisite of legitimate rule of law, which emerges and makes social relations and to harmonize the interest of various accomplices. Focusing towards the security of the state and society, the main aim of state towards the economy is to build a modernized economy, which is not possible without the Intervention of Blockchain technology. The digitalization and innovative movement of the cutting-edge world has incited the assortment, investigations and executions of Enormous Information examination, which have been implanted into each part of day-to-day existence and advancing quickly [1]. (IOT) [2] are changing the association and correspondence framework, adjusting the method of calculation and information stockpiling, while information mining procedures, AI, and Artificial Intelligence [3] are upsetting information extraction, critical thinking, navigation and activity advancement. These Enormous Information scientific advancements are not only the moving focal points of investigations and executions, yet additionally the potential arrangements and driving procedures for all parts of human existence, for example, infection expectation [4], medical services [5, 6] and so forth. For example, the MapReduce programming system [7] as large information investigation process combination has given a huge worldview to both industry and the scholarly community. As a scrambled advanced money, digital currencies are worked in a framework which is not possible without the Blockchain technology and can’t be emerged. Organization fulfils the 5 V’s component of huge Information that is ‘volume, variety, velocity, veracity and value’ [8]. Accordingly, it fulfils the purpose in a decent manner which assists in Large Information investigation. Other Huge Information examines the hold of keys for the unrest and improvement of cryptographic forms of money, which makes cryptographic money a really encouraging with other options. Additionally, Huge Information examination can likewise help financial backers and engineers to go with better choices and defeat its foundation constraints. Advances in hidden cryptographic forms of money have demonstrated its pertinence in a wider scope. This enhanced the speed of digitalization process and expanded the enormous information which the organization is to examine. In a nutshell, there are common advantages for double-dealing while considering the cooperation’s between Huge Information and digital money and the possibilities stay unlimited. The paper straightforwardly centres around the cooperation between Blockchain and digital money, which are two critical ideas that have been completely researched independently. We expect to introduce an exhaustive examination of their assembly and a methodical survey of late improvements for all partners. This paper is both scholar and modern amicable for partners who try to acquire a superior comprehension of the cooperation between blockchain and digital currency or intend to investigate its future possibilities.

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2 Cryptocurrency Overview Though the possibility of electronic money follows as far as possible back to the last of the 1980s, Bitcoin, shipped off in 2009 by unknown. Engineer Satoshi Nakamoto is the chief compelling decentralized computerized cash [9]. Thus, a cryptographic cash is a digital/virtual monetary system which limits the standards and giving clients a virtual portion to work and items are freed from a central trusted agency. Digital forms of money depend upon the transfer of information, for utilizing cryptographic money is to ensure and to be certified. Bitcoin took the high-level coin market beyond anyone’s expectations, and decentralizing the money and freeing it from moderate influence structures is the reason for growth of Digital Currency. Taking everything into account, individuals and associations execute the coin electronically on a disseminated association. It got wide responses in beginning of 2011, different names that is altcoins—A general name for leftover computerized types of cash which is post-Bitcoin. Another type of digital cash which got it name Litecoin and was conveyed in 2011. Its Procurement and its accomplishment in participating to become the most important computerized currency market. After Litecoin changed Bitcoin’s show, accelerating with the it would be more appropriate for regular trades. Ripple, sent off in 2013, is familiar with a through and through phenomenal model with that used by Bitcoin [10]. Another prominent coin in the extraordinary chain of computerized cash Peercoin, which uses a dynamic imaginative improvement to get and uphold its cash [11]. Peercoin joins the Proof of Work development used by Bitcoin and Litecoin close by its own framework, Proof of stake, to use a combination network security instrument. In August 2014 a new form of cryptocurrency NuShares/NuBits have emerged, which exist on twofold money model system [12].

3 Blockchain Blockchain is a subset or kind of purportedly distributed ledger technology circulated record innovation called as distributed ledger technology (‘DLT’). DLT is a technology to manage the information recording and sharing across several data storage (otherwise called records), which consist of precise comparative information records and which remained mindful of and obliged Computer servers, i.e. focuses [13]. The encryption methodology is used in the structures of Blockchain known as cryptography and usages (in ton) express mathematical computations, to make and avow a productively making information structure—to which data ought to be added and from which existing data can’t be wiped out—that shows up as a chain of ‘trade blocks’, what limits as a coursed record.

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3.1 How a Blockchain Functions: The Rudiments In direct terms, the blockchain may be compared as a data transmission system inclusion to this informational collection is began by one people like association centres, creates a new data block containing wide information. The creation of new block is then conveyed to each specific party and associating in a mixed construction (utilizing cryptography) with the objective that the trade nuances are not uncovered [14]. Those in the association (i.e. the other association centre points) overall choose the block’s authenticity according to a pre-described algorithmic endorsement method, for the most part suggested as an ‘understanding part’. Once supported, the blockchain is updated with a new ‘block’ which effectively updates the trading record that is sent across the connection [15]. This system can be used for enormous exchange that can be applied to any resource which can be watched out by an electronic plan. The benefits of blockchain innovation are to permit the work on implementation of a broad display of trades that require consistently the intermediation of an untouchable. Fundamentally, blockchain is tied in with decentralizing trust and empowering decentralized confirmation of exchanges. Basically, it permits to remove the ‘middleman’. By and large this will probably prompt productivity gains. In any case, it is critical to highlight that it might likewise open cooperating gatherings to specific dangers that were recently overseen by these mediators, that the use of cryptographic ledger advancement may create the latest liquidity risk. By and large, apparently when a middle person goes about as a support against critical dangers, like foundational risk, blockchain innovation can’t just supplant him, as a safeguard against major threats, such as systemic risk, it cannot replace blockchain technology. For example, the Bank for International Settlements (‘BIS’), in 2017 as per this report named Distributed record innovation within instalment, getting and settlement [16], especially reception free from blockchain advancement could introduce new liquidity bets. Additionally, overall, it appears to be that when a middle person capacity as a cushion against significant dangers, for example, fundamental gamble, he can’t just be supplanted by blockchain innovation.

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Transaction in Blockchain

Transaction request and authentication.

A block is constructed to reflect that transaction.

Every node receives the block (participant in the network)

The transaction is validated by nodes.

The Transaction is complete

The update is sent throughout the network.

The block has been added to the current blockchain.

Proof of work is rewarded to nodes, generally in bitcoin

4 Legal Perspective The years 2013–2017 might be considered the beginning of the cryptocurrency movement in India. In 2013, the RBI issued a public alert regarding cryptocurrencies. The Reserve Bank of India (RBI) has also stated that it closely monitors all developments pertaining to cryptocurrencies, including Bitcoins (very popular one) and other cryptocurrencies (Altcoins-An Altcoin is an alternative digital currency to Bitcoin). In February 2017, the RBI issued another caution to the public, and in the fourth quarter of 2017, the RBI issued an explicit warning that ‘virtual currencies/cryptocurrencies are not legal money in India’. The Committee appointed by the Finance Ministry drafted a bill on cryptocurrencies in April 2018 but ‘was not in favour of ban’. In March 2020, the India’s Supreme Court dealt a blow to the Reserve Bank of India by lifting the prohibition on cryptocurrencies enforced by the RBI. It is likewise pertinent to take note of the Committee [17] in 2019, drove the acquittance to boycott Virtual Currency, the Committee shows its interests in regard to the swelling of Virtual Currency in its report and expressed that essentially all Virtual Currencies are given a broad with gigantic quantities of individuals in India putting resources into them. According to the study, ‘Every one of these digital forms of money have been made by nonsovereigns and are in this sense completely confidential undertakings and there is no fundamental natural worth of these confidential digital forms of money because of which they miss the mark on the qualities of a cash.’ The ‘Cryptocurrency and Regulation of Official Digital Currency Bill, 2021’ (the ‘Bill’) is a current bill presented in Lower House. According to a Lower Sabha statement on 23-11-2021, it is stated ‘to make a facilitative system for production of the authority computerized money to be given by the Reserve Bank of India. The Bill additionally tries to preclude all confidential digital currencies in India; be

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that as it may, it considers specific exemptions for advance the basic innovation of cryptographic money and its purposes’. The proposed Bill may preferably present a degree of consistency of understanding and bring the different government organizations required onto a similar page while likewise giving security and managing the generally unregulated business sectors and forestall its abuse. In India, the Crypto exchanging stages are seeing a significant leap in volumes. According to a new report [14], WazirX, India’s greatest Cryptocurrency trade enrolled a yearly exchange of more than $43 billion Cryptocurrency exchange venues in India are seeing a significant increase in volume. In the event that is appropriately directed, the Government can burden the income produced, which can be a mutually beneficial arrangement for both the Government as well as financial backers. It can be concluded from the above discussion that the journey of cryptocurrency is not too long in India but it has seen many ups and downs in this short span. The banning of cryptocurrencies bill in 2019 and Supreme Court verdict in 2020 is the key issues. Cryptocurrencies have a high potential and recently after union budget of 2022–2023 (presented on 1st February 2022), Indians have once again started talking about it. It will be very interesting to see that after 30% tax impositions, how investors react about cryptocurrencies in India. The launch and features of RBI’s-future digital currency will also be very important. After the union budget 2022–2023, investors are started saying that India is following China by giving sole authority to RBI to launch and promote digital currencies. If government of India will present fresh bill on cryptocurrency, it will be very interesting to see the nature and regulations of it. Apart from all the facts and predictions, one thing is clear that cryptocurrencies (and hence Blockchain) will be the matter of discussion in upcoming years and this article may be useful as a reference for further research and studies in the said regard.

5 Data Analysis and Conclusion This research was carried out in May 2022 to gather information on several facets of cryptocurrencies. The goal of the study was to determine the prevalence of cryptocurrency use in order to have a clear-cut view. It investigated to know the view of about cryptocurrency in India and how frequently they are using it. In addition, In addition, the review looked at the members’ confidence in managing digital currency in a time when such virtual money isn’t entirely controlled and directed. The report also looked into the participants’ predictions for the future of digital money. The research consisted of ten questions that were expected to be answered in a short span of time (5 min). A Google sheet survey is used to collect the data. All the questionnaires have been shown in Tables 1, 2, 3, 4, 5, 6, 7, 8 and 9.

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Table 1 Do you think business is possible without currency? Valid

Frequency

Percent

Yes

12

Valid percent 24.0

Cumulative percent 24.0

No

18

36.0

36.0

60.0

Maybe

20

40.0

40.0

100.0

Total

50

100.0

100.0

24.0

Table 2 Which currency is important in our day-to-day life? Frequency Valid

Percent

Valid percent

Cumulative percent

Paper currency

27

54.0

54.0

54.0

Paperless currency

23

46.0

46.0

100.0

Total

50

100.0

100.0

Table 3 What type of transaction you will prefer? Frequency Valid

Percent

Valid percent

Cumulative percent

Paperless currency

11

22.0

22.0

22.0

Paper currency

11

22.0

22.0

44.0

Both

28

56.0

56.0

100.0

Total

50

100.0

100.0

Table 4 Do you think the digital currency will replace Paper currency in future? Frequency Valid

Percent

Valid percent

Cumulative percent

Yes

42

84.0

84.0

No

4

8.0

8.0

92.0

Maybe

4

8.0

8.0

100.0

50

100.0

100.0

Total

84.0

Table 5 Are you aware regarding cryptocurrency? Frequency Valid

Percent

Valid percent

Cumulative percent

Yes

48

96.0

96.0

96.0

No

2

4.0

4.0

100.0

50

100.0

100.0

Total

Table 6 What will be the position of cryptocurrency in upcoming years? Frequency Valid

Decline

Percent

Valid percent

Cumulative percent

7

14.0

14.0

14.0

Remain the same

14

28.0

28.0

42.0

Grow substantially

29

58.0

58.0

100.0

Total

50

100.0

100.0

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Table 7 Do you think it is the right time to regulate the cryptocurrency? Frequency Valid

Yes

Percent

Valid percent

30

60.0

60.0

No

Cumulative percent 60.0

7

14.0

14.0

74.0

Maybe

13

26.0

26.0

100.0

Total

50

100.0

100.0

Table 8 Cryptocurrency will affect the economy? Valid

Yes

Frequency

Percent

Valid percent

Cumulative percent

50

100.0

100.0

100.0

Table 9 Do you think that RBI should regulate the cryptocurrency? Frequency Valid

Yes No

35

Percent 70.0

Valid percent 70.0

Cumulative percent 70.0

1

2.0

2.0

72.0

Maybe

14

28.0

28.0

100.0

Total

50

100.0

100.0

6 Conclusion and Suggestion The readers will have seen that our outline and appraisal of the administrative structure primarily connects with digital forms of money. This has been done on purpose. As previously mentioned, and proved all through this exploration, blockchain is innovation technology that allows a cryptocurrency to function. The extent of blockchain is, nonetheless, a lot more extensive than that of digital forms of money. It seems to be utilized in an enormous area (like business, trade, service, hospital care and governance), given promising outcome, like a connection with a security oath, the collecting of offers bonds and various resources, the operation of land registration offices, and so on. Consequently, it would be excessively obtuse to relate blockchain with illegal tax avoidance, psychological oppressor funding or tax avoidance. It is simply innovation, which isn’t intended to launder cash, work with fear monger funding or sidestep burdens, and has various applications all through the entire legal economy. It wouldn’t be wise to place future advancements in such manner somewhere near accepting blockchain and fin-tech examining its use cause to irksome necessities, just in light of one of the applications utilizing blockchain innovation, digital forms of money, is utilized illegally by some. As a matter of fact, cryptographic forms of money are the main notable technology giving blockchain innovation into the limelight, yet these days blockchain has plainly grown out of the setting of digital currencies.

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With the above analysis, it can be suggested that now the cryptocurrency should be regulated by proper or strict laws. It can be done in manner by passing the law from parliament, so the rules and regulation can be maintained. Here the role of government is also important for creating awareness programme for the citizen and proper information should be shared with all the citizens. RBI also should take initiative step for its regulation as it is the central Bank of India by providing reliable information to the public at large and the ways to regulate it.

References 1. Hwang, K., & Chen, M. (2017). Big-data analytics for cloud, IoT and cognitive computing. Wiley. 2. Morgan, J. (2014). A Simple Explanation of the Internet of Things. https://www.forbes.com/ sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-unders tand/2a28a25b1d09 3. Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23, 368–375. 4. Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 5, 8869–8879. 5. Chen, M., Yang, J., Hao, Y., Mao, S., & Hwang, K. (2017). A 5G cognitive system for healthcare. Big Data and Cognitive Computing, 1, 2. 6. Chen, M., Li, W., Hao, Y., Qian, Y., & Humar, I. (2018). Edge cognitive computing based smart healthcare system. Future Generation Computer Systems, 86, 403–411. 7. Ramírez-Gallego, S., Fernández, A., García, S., Chen, M., & Herrera, F. (2018). Big data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Information Fusion, 42, 51–61. 8. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. 9. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bit coin.pdf 10. Schwartz, D., Youngs, N., & Britto, A. The ripple protocol consensus algorithm. Ripple Labs Inc. 11. Nadal, S., & King, S. (2012). PP coin: peer-to-peer crypto-currency with proof-of-stake. http:/ /www.peercoin.net/assets/paper/peercoin-paper.pdf 12. Jordan, L. https://nubits.com/sites/default/files/assets/nu-whitepaper-23_sept_2014-en.pdf 13. Federal Law 259-FZ. On digital financial assets, cryptocurrency and making changes to certain legislative acts of the russian federation. Retrieved April 01, 2022, from http://www.consul tant.ru/document/cons_doc_LAW_358753/ 14. World Bank Group, Natarajan, H., Krause, S., & Gradstein, H. (2017). Distributed ledger technology (DLT) and blockchain. FinTech note, no. 1. Washington, D.C. http://docume nts.worldbank.org/curated/en/177911513714062215/pdf/122140-WP-PUBLIC-DistributedL edger-Technology-and-Blockchain-Fintech-Notes.pdf, 1. 15. CPMI. (2015). Digital currencies. https://www.bis.org/cpmi/publ/d137.pdf, 5 16. CPMI. (2017). Distributed ledger technology in payment, clearing and settlement—An analytical framework. https://www.bis.org/cpmi/publ/d157.pdf 17. Coindesk. https://www.coindesk.com/markets/2021/12/16/indian-crypto-exchange-wazirxstrading-volume-jumps-to-over-43b-in-2021/ 18. Microsoft. What Is Cloud Computing? A Beginner’s Guide. (2018). https://azure.microsoft. com/en-us/overview/what-is-cloud-computing/

Efficient Cluster-Based Routing Protocol in VANET Hafsah Ikram, Inbasat Fiza, Humaira Ashraf, Sayan Kumar Ray, and Farzeen Ashfaq

Abstract The recent advancements in technology have shifted the focus toward wireless sensor technology. Vehicular Adhoc Networks (VANET) is a wireless network of vehicles that communicate with each other using different routing protocols. Various protocols have been proposed for this purpose, out of which clusteringbased protocols have been in the current focus of research. The clustering-based protocols proposed so far have primarily emphasized on the packet delivery ratio (PDR), throughput, transmission delay, and stability. The energy consumption factor of vehicles in the network is significantly ignored. The goal of the proposed approach is to reduce the energy consumption of vehicles in VANET. For this purpose, an efficient clustering-based framework is proposed, which includes an efficient cluster head selection procedure and routing protocol along with cluster formation and merging procedures, using which the energy consumption of the vehicles will be significantly reduced. Keywords Efficient routing protocol · Routing protocol · Routing protocol in VANET · Efficient routing protocol in VANET · Suitability factor · Cluster formation

H. Ikram (B) · I. Fiza · H. Ashraf Department of Computer Science & Software Engineering Faculty of Basic and Applied Sciences International Islamic University Islamabad, Islamabad, Pakistan e-mail: [email protected] I. Fiza e-mail: [email protected] H. Ashraf e-mail: [email protected] S. K. Ray · F. Ashfaq School of Computer Science (SCS), Taylor’s University, Subang Jaya 47500, Tanjung, Malaysia e-mail: [email protected] F. Ashfaq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_14

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1 Introduction VANET is a P2P ad hoc network system. It is made up of groups of mobile or stationary vehicles linked together via a wireless network. VANET is one of the main types of mobile ad hoc networks (MANETs). From the high level perspective, they are the same. It is characterized as a network that lacks infrastructure and centralized administration. Intelligent vehicle ad hoc networking (IVANET) is a smart technique to use automotive networking. It aims to help with vehicle safety, traffic monitoring, accident prevention, and a variety of other uses. Clustering in VANET is implemented to provide rapid and efficient communication among vehicles by ensuring network stability. In a distributed way, the protocol separates the nodes of the ad hoc network into a number of overlapping or disjoint clusters. The communication among various nodes within the cluster or with other clusters is managed by cluster head. For cluster-based routing in VANET, multiple routing algorithms were proposed. It is found that the existing literature review primarily focused on packet delivery ratio [1–4], throughput [2–5], transmission delay [1, 3, 4, 6], network stability [1, 3, 6, 7], energy consumption [5], and network overhead [1, 3]. However, the existing techniques have overlooked high bandwidth consumption [1], time overhead which leads to performance degradation [1, 2], energy consumption [3, 4, 7] and security factor being ignored [4], and high complexity algorithms [3–5] which also leads to high energy consumption. This research proposed an efficient routing framework that consists of a cluster formation procedure, cluster head selection procedure, a modified cluster merging, and a modified routing protocol. This scheme considers improving communication overhead, stability of the network, and reducing the energy consumption; for this purpose, a new Suitability Factor (SF) is introduced for each vehicle along with calculating its energy level value E. The SF is calculated based on avg. LLT of that vehicle with other vehicles, its neighborhood degree (NHD), energy level, and directional distance. Whenever a vehicle joins a cluster, it will send a join request to the cluster head of that respective cluster. Then the CH will calculate its SF and based on this SF and E value, the new vehicle will either given the status of CM or CH of that cluster. If the SF and E values of new vehicle are greater than those of current CH and E level also surpasses the threshold energy level value, then this vehicle will become the new CH and will broadcast itself to the whole network. In the modified cluster merging procedure, originally proposed by [3], if two clusters come into vicinity of each other such that the SF and E values of one of the cluster’s CH are greater than those of second cluster’s CH, then the second cluster will send merge request to first cluster. After acceptance, all the CMs of second cluster will become CMs of first cluster and the CH of the first cluster will become the new CH, i.e., the one with larger SF and E values. During communication in the network, a modified routing protocol [8] is proposed. If the message sending vehicle itself is not a CH, it will send the packet to its CH. The CH will check for destination in its CMs. If it is found, the packet will be forwarded to the destination. However, if the

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destination is not within the cluster, the current CH will look for other CHs in its neighbor. If within a certain time, neighbor CH is found, current CH will forward the message packet to it and now this CH will look for destination in its members or else forward the packet to neighbor CHs. But if a neighbor CH is not found within a predefined time limit, the CH will generate an error message and will not receive any new messages. This document is divided in the following sections; a short study of the clusteringbased routing protocols suggested in the literature is given in Sect. 2, the methodology is presented in Sect. 3, Sect. 4 describes the simulation model, results are presented in Sect. 5, and the conclusion in Sect. 6.

2 Related Work There are numerous routing protocols proposed to reinforce the routing operation in VANET. This section focuses on some of the clustering strategies and cluster-based routing protocols. Kadhim et al. [1] proposed an efficient routing-based protocol that is based on the stability of cluster head and proposed a gateway. This technique focuses on improving path stability, PDR, transmission delay, network stability, and reducing network overhead. The results show that their approach performs better than LRCA, PASRP, and CVoEG. Khayat et al. [7] developed a new clustering methodology for cluster head selection, in which the weight is calculated for each node. The three essential parameters for weighted formula are trust, distance, and velocity. The trust for each node in this technique is a combination of direct and indirect trust values. As the cluster head, the vehicle with the highest probability will be chosen. As a result, the vehicle with the shortest distance, most trust, and velocity has a better chance of being chosen as the cluster head. The simulation looked at how each weighted parameter affected clustering and cluster head selection. Aradhana Behura et al. [5] proposed that Giraffe kicking optimization (GKO) is a nature-inspired method that helps to awaken the least number of sensor nodes while improving throughput and network longevity. This hybrid C-means-based GKO method for VANET minimizes excessive energy usage caused by redundant sensor nodes. The results showed that GKO had the best outcomes when compared to other strategies such as GA, cuckoo search, and DE. Furthermore, the GKO method is effective for extending the lifetime of the vehicle network while preserving coverage. Khalid Kandili et al. [2] for VANET, proposed a new clustering-based routing protocol that combines a modified K-Means method with Continuous Hopfield Network and Maximum Stable Set Problem (KMRP). The proposed technique avoided arbitrarily selecting the initial cluster head and cluster. A link dependability model is also used to assign vehicles to clusters. Cluster heads are chosen depending on their free buffer space, speed, and node degree. According to the findings, KMRP decreases traffic congestion and collisions, resulting in a significant

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boost in throughput. At high density and mobility, KMRP provides a fast algorithm that decreases end-to-end delay and gives better PDR than other schemes, and it is important to extend the lifetime of the vehicle network while preserving coverage. Katiyar et al. [3] proposed an efficient multi-hop clustering (CH selection and cluster merging) algorithm using which the vehicles can select and follow the most suitable target vehicle from their one-hop neighbors. This algorithm contributes in strengthening stability of network, and improves the data transmission performance in terms of PDR, throughput and normalized routing overhead (NRO). The results show that in the proposed AMC algorithm, the 10% to 30% improvement is recorded in average CH duration, 10% to 15% in CM duration, 10% to 40% in CH changes, and significant improvement in PDR with max 78% in 300m range, NRO with max 7 in 100m range, and throughput with max 82kbps in 300m range. Bakkour et al. [6] proposed a clustering-based machine learning solution with selfstabilization mechanism, for delay-sensitive applications in VANET. The proposed scheme deals with data sharing delay, ensures high data availability, and reduces packet loss in multi-hop VANET architecture. The results show that the stability of the network is improved, and the average transmission delay of 100 vehicles/km is ~500 ms, PDR ~85%, and information coverage ~87%. Darabkh et al. [4] proposed a dual-phase routing protocol using fog computing and software-defined vehicular network, along with clustering technique. The proposed protocol lessens the long-distance communications in each cluster and provides an efficient mechanism for control overhead reduction. The results show that the proposed algorithm gives impressive results when compared to IDVR, VDLA, IRTIV, GPCR, CORA, MoZo, BRAVE, and CBDRP with a packet delivery ratio (PDR) of 90%, increased throughput with a max of 180.27 kbps, reduced end-to-end delay with max of 0.5 s, and a decrease in no. of control messages (Table 1). Table 1 Summary of literature References Aim

Objective

Limitations

[1]

– To improve network overhead, average end to end delay, path stability, cluster head stability, and packet delivery ratio

– The clustering strategy requires a large number of messages to be exchanged frequently, resulting in increased overhead and bandwidth consumption

A new clustering formation approach to build optimal paths

(continued)

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Table 1 (continued) References Aim

Objective

Limitations

[7]

An efficient clustering algorithm based on a weighted formula for calculating the probability of cluster head selection

– To ensure the stability of the network

– Energy consumption factor not considered for CH selection

[5]

Hybrid C-means Giraffe optimization technique with a multi-fitness function used to reach efficient routing enactment in VANET

– To improve the energy consumption, jitter, throughput and probability of sensor node redistribution

– High Complexity

[2]

A new clustering-based routing – To reduce traffic protocol based on a weighted congestion and formula that combines a modified providing a K-Means algorithm with a new significant clustering algorithm for increase in determining the likelihood of throughput – To act better in cluster head selection terms of the packet delivery ratio

[3]

An effective cluster building process that assists the vehicle in selecting and following the most appropriate target vehicle among one-hop neighbors

– Contributes to – High complexity strengthening – Energy consumption stability (CH and factor not considered – PDR not very better CM duration) with max 78% – To increase the data transmission performance (in terms of PDR, throughput and normalized routing overhead NRO)

[6]

To establish robust and reliable communication between nodes using machine learning and clustering

– To reduce transmission delay and packet collision (high data availability) – Extend the data coverage – Improve the stability of clusters

– Performance decreases with high density

– CH being the central element can cause additional time of data fusion and aggregation for data sharing applications – Direction of vehicles not considered while building clusters – Stability and PDR (max 85%) can further be optimized (continued)

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Table 1 (continued) References Aim [4]

Objective

To discover the most reliable – To increase PDR route from the source to the and throughput – To reduce destination at the shortest end-to-end delay possible time by combining SDN, – To minimize the fog computing, and clustering no. of control messages by reducing control overhead

Limitations – High complexity – Power consumption factor not considered – Security factor not considered as fog/ cloud computing is being used

3 Proposed Efficient Routing Scheme for Cluster-Based Routing in VANET This section comprises the clustering formation and merging strategies, the algorithm for cluster head election, the routing protocol, and the related concepts used in our framework. Due to the high mobility of the vehicles, clusters may be restructured often, resulting in significant communication overhead, poor stability, and higher energy consumption than usual. These parameters are taken into account by our proposed technique and hence this provides an efficient scheme for routing. This proposed architecture contains four phases: The first phase is Cluster Formation, which is based on certain factors such as average link lifetime of a vehicle, its energy level, its neighborhood degree, and predefined threshold directional distance. The second phase is Cluster Head Selection: the vehicle elected as CH will be the most predominant vehicle among all with maximum energy, avg. link lifetime, and neighborhood degree. The third phase is Cluster Merging: which describes the scenario of merging two or more than two neighbor clusters and selecting a single cluster head. The fourth phase is routing of the packet from source to destination (Table 2).

Table 2 Commonly used abbreviations

Abbreviation

Definition

VANET

Vehicular ad hoc network

P2P

Peer to peer

CH

Cluster head

CM

Cluster member

SF

Suitability factor

LLT

Link life time (continued)

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Abbreviation

Definition

NHD

Neighborhood degree

E

Energy consumption

DTH

Threshold directional distance

ETH

Threshold energy level

PDR

Packet delivery ratio

NRO

Normalized routing overhead

Thtime

Wait-time limit

Suitability Factor The clustering procedure calculates the suitability factor for all the vehicles which includes four factors, namely, neighborhood degree of a vehicle i, its average link lifetime, its directional distance, and energy consumption value. The SF of a vehicle i can be calculated using Eq. (1), as follows: SFi = AvgLLTi + NHDi + Di + E i

(1)

Link Life Time The time period for which two vehicles are within the communication range of each other is defined as link life time of that vehicle. It is an important parameter for any sort of network. Accurate knowledge of average link lifetime of vehicles improves the routing performance, and hence achieves the desired network performance. The average LLT of vehicle i can be calculated by doing sum of the LLT of vehicle i with vehicle j, as in Eq. (2) [9]. AvgLLTi =

n 

LLT(i, j)

(2)

j=1

Here, j represents all the vehicles with which vehicle i has maintained a link at some time. Now for calculating LLT of vehicle I with vehicle j, we will check if both the vehicles are within the DTH of each other. For this, we will calculate the distance covered by both vehicles i and j, as Si = Vi ∗ t (i ) and S j = V j ∗ t (ii)

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Then, we will calculate the relative distance between both of the vehicles during time t. S = |S i − S j | (iii ) Now we will compare this distance S with D, if S is less than DTH then t becomes LLT of vehicle i and vehicle j, because both of the vehicles maintained the link with each other for time t. It can be represented as If DTH > S Then LLT(i,j) = t Neighborhood Degree A vehicle’s neighborhood degree is defined by number of vehicles that are in the direct communication range of that vehicle within the threshold directional distance with that vehicle [10–13]. To calculate the NHD of vehicle I, we will investigate that if there is any vehicle j in the DTH of vehicle i. If there is any vehicle j present in the DTH of vehicle i then update the NHD table of vehicle i by incrementing its NHD value by one. After time t, it will again check that whether this vehicle j is still in its DTH , if it is not then decrement the NHD value by one from NHD table of vehicle i. If DTH >= S(i, j ) Then NHD = NHD + 1 Else NHD = NHD − 1 Energy level Energy level is the amount of energy which is held by a sensor in the vehicle. This energy is consumed by the vehicle in order to route a packet. The energy level is necessary to calculate if that vehicle is a candidate for cluster head selection. A threshold for energy level is also defined in order to select the most efficient energy-consuming vehicle as a CH. Directional Distance Directional distance is the distance of vehicle A with Vehicle B in the direction of vehicle A. Our proposed system uses a predefined threshold directional distance value which is compared with every vehicle’s directional distance.

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Phase 1: Cluster Formation Procedure To form a cluster of vehicles in the network, a set of vehicles with their calculated avg. link lifetime, neighborhood degree, their energy level, threshold directional distance, and energy level values are required. At first, when a vehicle is not a part of any cluster, it will broadcast a HELLO message in the network to calculate its NHD (Line 1–5). After that, each vehicle will calculate its suitability factor SF as in Eq. (1) (Line 8–9). Based on SF and E values, a vehicle will obtain either CM state or CH state. Every vehicle V will check if one of the neighbors of vehicle V is a CH, and whether the SF and E values of CH are more than those of vehicle V. In this case, V will send a JOIN_REQUEST to that CH (Line 11–15). If the JOIN_ REQUEST is accepted, V will become CM of that cluster with that neighbor as its CH (Line 16–18). But in case, if SF and the energy level of vehicle V is more than that of its neighbor I, V will become CH and broadcast itself as CH to the whole network (Line 20–23).

Phase 2: Cluster Head Selection We introduced a new suitability factor SF, as discussed above, to select the most suitable CH for a cluster, based on its link lifetime, NHD, its directional distance

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compared to a threshold, its energy level (Eq. (1)), along with the comparison of this consumed energy level with a predefined threshold. This CH selection condition can be mathematically presented as below in Eq. (3). (LLT(i ) > LLT( j ))&&(NHD(i ) > NHD( j ))&&(D(i ) > D( j ))&& ((E(i ) > E( j ))&&E(i ) > Threshold))

(3)

Here, LLT(i) represents the link life time of vehicle i which is joining a cluster. After sending the join request to the CH vehicle j, the vehicle j will compare the LLT, NHD, D, and E values of vehicle i with the respective values of itself, along with comparing the E value of incoming vehicle i with the predefined threshold energy value. If the condition presented in Eq. (3) is satisfied, then vehicle will become new CH of that cluster. To select CH of a cluster, the values of our introduced SF of all k number of vehicles of that cluster are required, along with a predefined threshold energy level E TH value. So in a cluster C, the SF and E TH of every vehicle j will be compared with every other vehicle i present in C (Line 1–4). The vehicle with highest SF and E TH value will be selected as CH of that cluster. This process will be repeated until a CH is selected for every cluster.

Phase 3: Cluster Merging Procedure Two clusters can merge together to form a new larger cluster, if they are neighbors and they satisfy the cluster merging condition. The cluster merging algorithm originally proposed by [3] takes two clusters as input along with the SF and E values of their CHs. According to algorithm, if clusters 1 and 2 are neighbors (Line 1), and if the SF and E values of second cluster’s CH are greater than SF and E values of first cluster’s CH (Line 2), then first cluster will send MERGE_REQUEST to second cluster (Line 3). If the merging request is accepted, CH of second cluster will become new CH of both clusters and the CH of first cluster will become a CM (Line 4–6). Along with

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this, the CMs of first cluster will also update the id of their CH to make CH.C2 as their new CH (Line 7, 8). In this way, both the clusters will merge together.

Modified Density-Connected Cluster-Based Routing Protocol For reliable and efficient routing in a VANET network, cluster-based structure is the best solution as it can deliver a message from source to destination in the most efficient way and is very reliable. There are multiple proposed routing protocols in the literature, based on clustering. The routing protocol we are using in our network is a slight modification of the protocol proposed by [8]. In the proposed routing algorithm, when a sender vehicle (source node which is forwarding a message to some receiver at destination) will send a message, the routing protocol will first check whether the sending node itself is a CH or not. If it is not a CH, the message packet will be forwarded to its CH (Line 1, 2). If the sender is a CH, then the protocol will check if the receiver is its CM. If true, the message packet will be delivered to the receiver (Line 4, 5). In case, the destination vehicle is not a part of that cluster, the CH will look for other CHs in its neighbor for a certain time limit (Thtime). If it founds a CH in its neighborhood in this time, it will forward the packet to that CH to look for destination in its members or else in its neighbor clusters (Line 7–10). But if no neighbor CH is found in the given time limit, it will generate an error message to notify the sender that the message is not delivered and will not accept any new packets for a certain time (Line 12).

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4 Simulation Model For the experimental evaluation of our scheme, simulations are performed using MATLAB on a PC with RAM 8 GB and Intel processor with core i3 on Windows 10 operating system. The following scenarios are used in the simulation. Scenario 1: In the first scenario, total number of nodes (vehicles) considered are ten, the threshold directional distance DTH value is 2 km and the energy threshold E TH is 120 J. And during routing between nodes, the wait time limit Thtime is 10 s. Scenario 2: In the second scenario, total number of nodes (vehicles) considered are twenty, the threshold directional distance DTH value is 5 km and the energy threshold E TH is 115 J. And during routing between nodes, the wait time limit Thtime is 15 s. Scenario 3: In the third scenario, total number of nodes (vehicles) considered are hundred, the threshold directional distance DTH value is 10 km and the energy threshold E TH is 115 J. And during routing between nodes, the wait time limit Thtime is 18 s.

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5 Results The simulation results of scenario 1 are shown in Figs. 1, 2, and 3. The total number of nodes, their speed, cluster re-calculation time, and the size of field are taken in the form of input from the user. Figure 1 shows the initial state of cluster when all the ten nodes/vehicles are not a part of any cluster yet. Initially, all the nodes are marked as blue. After time t, nodes have changed their position and now they are moving in different directions with different speeds and energy, but they are not a part of any cluster yet, as shown in Fig. 2. Now, when the vehicles are moving in separate directions, clusters will be formed of vehicles within a certain range of each other, shown in Fig. 3. The CHs are marked red while all the other nodes are marked as blue. Here, the vehicle with the best SF will be calculated as CH of that cluster, whereas the vehicles which are not in the communication range of a CH will not be a part of any cluster. The clusters will keep getting updated as the nodes change their positions in the network, and CH may also change during the new cluster formation/ upgradation. Fig. 1 Initial state of field/ network

Fig. 2 Network state after time t

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Fig. 3 Network state after cluster formation

Fig. 4 Initial state of network

Fig. 5 Network after time t

The simulation results of scenario 2 are shown in Figs. 4, 5, 6, and 7. The total number of nodes, their speed, cluster re-calculation time, and the size of field are taken in the form of input from the user. Figure 4 shows the initial state of cluster when all the twenty nodes/vehicles are not a part of any cluster yet. Initially, all the nodes are marked as blue. After time t, nodes have changed their position and now

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they are moving in different directions with different speeds and energy, but they are not a part of any cluster yet, as shown in Fig. 5. Now, when the vehicles are moving in separate directions, clusters will be formed of vehicles within a certain range of each other, shown in Fig. 6. The CHs are marked red while all the other nodes are marked as blue. Here, the vehicle with the best SF will be calculated as CH of that cluster, whereas the vehicles which are not in the communication range of a CH will not be a part of any cluster. The clusters will keep getting updated as the nodes change their positions in the network, and CH may also change during the new cluster formation/ upgradation. This change can be seen in Fig. 7. The simulation results of scenario 3 are shown in Figs. 8, 9, 10, and 11. The total number of nodes, their speed, cluster re-calculation time, and the size of field are taken in the form of input from the user. Figure 8 shows the initial state of cluster when all the hundred number of nodes/vehicles are not a part of any cluster yet. Initially, all the nodes are marked as blue. After time t, nodes have changed their position and now they are moving in different directions with different speeds and energy, but they are not a part of any cluster yet, as shown in Fig. 9. Now, when the vehicles are moving in separate directions, clusters will be formed of vehicles within Fig. 6 Clusters formation

Fig. 7 After cluster upgradation (new CHs formed)

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Fig. 8 Initial state of the network

Fig. 9 Network after time t

a certain range of each other, shown in Fig. 10. The CHs are marked red while all the other nodes are marked as blue. Here, the vehicle with the best SF will be calculated as CH of that cluster, whereas the vehicles which are not in the communication range of a CH will not be a part of any cluster. The clusters will keep getting updated as the nodes change their positions in the network, and CH may also change during the new cluster formation/upgradation. This change can be seen in Fig. 11.

Efficient Cluster-Based Routing Protocol in VANET

181

Fig. 10 Network after cluster formation

Fig. 11 Clusters after time t

6 Conclusion We proposed a novel scheme in this study to reduce the energy consumption of vehicles in VANET. The suggested framework aims to reduce vehicle energy consumption in VANET. An effective clustering-based framework is provided for this purpose, which comprises an efficient cluster head selection technique and routing protocol, as well as cluster creation and merging operations, which would greatly lower the energy consumption of the vehicles. We demonstrated that the suggested framework outperforms the conventional schemes using comprehensive simulation results.

References 1. Al-Shaibany, A. J. K. (2021). Stability-delay efficient cluster-based routing protocol for VANET Karbala. International Journal of Molecular Sciences, 7(3). https://doi.org/10.33640/2405609X.3118

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2. Kandali, K., Bennis, L., & Bennis, H. (2021). A new hybrid routing protocol using a modified K-means clustering algorithm and continuous hopfield network for VANET. IEEE Access, 9, 47169–47183. 3. Katiyar, A., Singh, D., & Yadav, R. S. (2022). Advanced multi-hop clustering (AMC) in vehicular ad-hoc network. wireless Network, 28(1), 45–68. 4. Darabkh, K. A., Alkhader, B. Z., Ala’F, K., Jubair, F., & Abdel-Majeed, M. (2022). ICDRPF-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network. Vehicular Communications 100453. 5. Behura, A., Srinivas, M., & Kabat, M. R. (2022). Giraffe kicking optimization algorithm provides efficient routing mechanism in the field of vehicular ad hoc networks. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03519-9 6. Bakkoury, S. O. S. B. Z. New machine learning solution based on clustering for delay-sensitive application in VANET. 7. Khayat, G., Mavromoustakis, C. X., Mastorakis, G., Batalla, J. M., Maalouf, H., & Pallis, E. (2020). VANET clustering based on weighted trusted cluster head selection. International Wireless Communications and Mobile Computing (IWCMC), 2020, 623–628. 8. Ram, A., & Mishra, M. K. (2020). Density-connected cluster-based routing protocol in vehicular ad hoc networks. Annals of Telecommunications, 75(7), 319–332. 9. Shelly, S., & Babu, A. V. (2017). Link residual lifetime-based next hop selection scheme for vehicular ad hoc networks. EURASIP Journal on Wireless Communications and Networking, 2017(1), 23. https://doi.org/10.1186/s13638-017-0810-x 10. Rawashdeh, Z. Y., & Mahmud, S. M. (2012). A novel algorithm to form stable clusters in vehicular ad hoc networks on highways. EURASIP Journal on Wireless Communications and Networking, 2012(1), 15. https://doi.org/10.1186/1687-1499-2012-15 11. Pal, S., Jhanjhi, N. Z., Abdulbaqi, A. S., Akila, D., Almazroi, A. A., & Alsubaei, F. S. (2023). A hybrid edge-cloud system for networking service components optimization using the internet of things. Electronics, 12(3), 649. 12. Humayun, M., Ashfaq, F., Jhanjhi, N. Z., & Alsadun, M. K. (2022). Traffic management: Multi-scale vehicle detection in varying weather conditions using yolov4 and spatial pyramid pooling network. Electronics, 11(17), 2748. 13. AlZain, M. A. A secure multi-factor authentication protocol for healthcare services using cloud-based SDN.

Type II Exponentiated Class of Distributions: The Inverse Generalized Gamma Model Case Salah H. Abid and Jumana A. Altawil

Abstract We display here a new class of probability models named Type II exponentiated. This class plays a leading turn in creating more pliable distributions. It employs the distribution function formula of the smallest order statistic instead of the formula of the greatest order statistic function in generating distributions as in the class of Gupta et al. The Type II Exponentiated Inverse Generalized Gamma Distribution (Type II EIGGD) is applied here to spell the model origin. Some properties of Type II EIGGD are derived. Keywords Exponentiated class · Inverse generalized gamma model · Stress-strength · Entropy

1 Introduction Gupta et al. in 1998 [1] proposed the exponentiated class of distributions as, F(x) = [G(x)]k where G(x) is the baseline distribution function, and k is a positive real number. Lots of work have been carried out on this idea. The focus here will be on recent literature only due to the large number of scientific research related to the topic. Pu et al. in 2016 [2] studied the generalized modified Weibull (GEMW) distribution, which contains many models. Mathematical properties of this distribution are presented. Maximum likelihood estimation mechanism is used to estimate the model parameters with real data sets. The Exponentiated T-X family of distributions is introduced by Ahmad et al. in 2019 [3]. Some properties of a special submodel, exponentiated exponential-Weibull are studied in detail. An empirical study is conducted to evaluate the performances of the maximum likelihood estimators of the model parameters. Oluyedea et al. in 2020 [4] proposed a generalized family of distributions named the exponentiated generalized power series (EGPS) family of distributions and studied its sub-model, the exponentiated generalized logarithmic S. H. Abid (B) · J. A. Altawil Mathematics Department, College of Education, University of Mustansiriyah, Bagdad, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_15

183

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S. H. Abid and J. A. Altawil

(EGL) class of distributions. Some properties of the new EGPS and EGL distributions are derived. They used the method of maximum likelihood to estimate the parameters of this new family of distributions. Some properties of exponentiated generalized Gompertz-Makeham distribution are derived by [5]. The model parameter estimation is derived via maximum likelihood estimate method. Abid and Kadhim in 2021 [6] presented Doubly Truncated Exponentiated inverse Gamma distribution (EIGD). Chipepa in 2022 proposed [7] the Exponentiated Half Logistic-Generalized-G Power Series (EHL-GGPS) distribution. Several mathematical properties of the EHL-GGPS distribution are derived. A simulation study for selected parameter values is presented to examine the consistency of the maximum likelihood estimates. In 2022, Abid and Jani [8] presented two doubly truncated generalized distributions with a lot of properties. The proposed class for generating new distributions will have the cumulative distribution function (cdf), F(x) = 1 − [1 − G(x)]k

(1)

And the probability density function (pdf), f (x) =

∂ F(x) = kg(x)[1 − G(x)]k−1 ∂x

(2)

The proposed class of distributions will be called type II exponentiated class. ( ) β −1 1 β/λθ x −(θ +1) e−(x /λ) and G(x, λ, θ, β) = Assume that g(x, λ, θ, β) = Γ(θ/β) ) ( β Γ βθ ,(x −1 /λ) (x > 0) are pdf and cdf of Inverse Generalized Gamma random variΓ(θ/β) able, respectively. The cdf and the pdf of Type II EIGGD based on (1) and (2) are, ( ( )β ) ⎤k Γ θ/β, x −1 /λ ⎦ ,x >0 F(x) = 1 − ⎣1 − Γ(θ/β) ⎡

(

f (x) =

β k Γ(θ/β) λθ

x >0

)

(3)

( ( −1 )β ) ⎤k−1 Γ θ/β, x /λ −1 ⎦ , x −(θ +1) e−(x /λ) ⎣1 − Γ(θ/β) ⎡

β

(4)

where Γ(α) is the ordinary Gamma function, γ (α, βx) is the lower incom{ βx plete Gamma function such that γ (α, βx) = 0 t α−1 e−t dt and Γ(α, β x) = { β x α−1 −t e dt = Γ(α) − γ (α, βx) is the upper incomplete Gamma function. ∞t So, the reliability and hazard rate functions are respectively

Type II Exponentiated Class of Distributions: The Inverse Generalized …

( ( )β ) ⎤k Γ θ/β, x −1 /λ ⎦ R(x) = 1 − F(x) = ⎣1 − Γ(θ/β)

185

⎡

( ) ( ) β β −1 −1 k λβθ x −(θ +1) e−(x /λ) k λβθ x −(θ +1) e−(x /λ) ) ( ( λ(x) = ( )( = ( ) ) β ) Γ θ/β,(x −1 /λ) −1 /λ β γ θ/β, x θ Γ β 1− Γ(θ/β)

(5)

(6)

The rth raw moment can be derived as follows, ( ) E xr =

{∞

{∞ x f (x)d x = r

0

x 0

r

Γ

k ( ) θ β

(

) β x −(θ +1) λθ

( ( −1 )β ) ⎤k−1 Γ θ/β, xλ ( −1 )β ⎥ ⎢ − xλ ⎥ dx ⎢1 − e ⎦ ⎣ Γ(θ/β) ⎡

( ){∞ k β = x −(θ−r +1) Γ(θ/β) λθ 0

( ( −1 )β ) ⎤k−1 Γ θ/β, x /λ β −1 ⎦ dx e−(x /λ) ⎣1 − Γ(θ/β) ⎡

( ){∞ β k = x −(θ−r +1) Γ(θ/β) λθ 0

( ( −1 )β ) ⎤k−1 Γ θ/β, x /λ β Γ(θ/β) −1 ⎦ dx − e−(x /λ) ⎣ Γ(θ/β) Γ(θ/β) ⎡

=

( ){∞ β k x −(θ−r +1) Γ(θ/β) λθ 0

( ( −1 )β ) ⎤k−1 Γ(θ/β) − Γ θ/β, x /λ −1 ⎦ dx e−(x /λ) ⎣ Γ(θ/β) ⎡

β

( ( ) ( ) ( )β ) = Now since, Γ s, ϒ¯ + γ s, ϒ¯ = Γ(s) → Γ(θ/β) − Γ θ/β, x −1 /λ ( ( ) ) β γ βθ , x −1 /λ we get,

186

S. H. Abid and J. A. Altawil

( ) E xr =

){∞

(

β k Γ(θ/β) λθ

0

Now since, (1 − z)b =

⎡ ( ( −1 )β ) ⎤k−1 γ θ/β, x /λ β −1 ⎦ d x, x −(θ −r +1) e−(x /λ) ⎣ Γ(θ/β)

Σ∞ w=0

(1 − z)−k =

(−1)w Γ(b+1) zw w! Γ(b−w+1)

with |z| < 1, b > 0 and

∞ Σ Γ(k + j ) j z with |z| < 1, k > 0 j!Γ(k) j=0

[

[ ( ( ( ( )β ) ]k−1 ( )β ) )]k−1 γ θ/β, x −1 /λ γ θ/β, x −1 /λ ( ) 1 − 1 − = Γ(θ/β) Γ dp [ ( ( ( ( ( ( −1 )β ) )]k−1 )β ) )3 γ θ/β, x /λ Σ∞ (−1)w Γ(k) Σ∞ (−1)3 Γ(w+1) γ θ/β, x −1 /λ 0, then 1 − 1 − = w=0 w!Γ(k−w) 3=0 3!Γ(w−3+1) Γ(θ/β) Γ(θ/β)

So that for If k − 1 >

we get three formulas.

[

If k − 1 < 0, then

( 1− 1−

[

If k − 1 = 0, then

(

( ( )β ) )]k−1 γ θ/β, x −1 /λ Γ(θ/β) ( )β ) )]k−1 γ θ/β, x −1 /λ

=

Γ(θ/β)

Γ(k−1+ j ) j=0 j!Γ(k−1)

[

(

1− 1−

Σ∞

(

= 1− 1−

(

Σ∞

(−1)w Γ( j+1) w=0 w!Γ( j−w+1)

( ( )β ) )]1−1 γ θ/β, x −1 /λ Γ(θ/β)

( ( )β ) )w γ θ/β, x −1 /λ Γ(θ/β)

=1

So, we have the following three cases, Case one: for k − 1 > 0 ( ) E xr =

( ){∞ ( −1 )β ∞ ∞ Σ (−1)w Γ(k) Σ x β k (−1)3 Γ(w + 1) −(θ −r +1) − λ x e Γ(θ/β) λθ w!Γ(k − w) 3=0 3!Γ(w − 3 + 1) w=0 0

⎛ ( ( )β ) ⎞3 γ θ/β, x −1 /λ ⎝ ⎠ dx Γ(θ/β)

( ) ∞ {∞ ( −1 )β ∞ x Γ(k + 1) β Σ (−1)w Σ (−1)3 Γ(w + 1) −(θ −r +1) − λ = x e θ Γ(θ/β) λ w=0 w!Γ(k − w) 3=0 3!Γ(w − 3 + 1) 0

⎛ ( ( )β ) ⎞3 γ θ/β, x −1 /λ ⎠ dx ⎝ Γ(θ/β) Let y =

(

x −1 λ

)β

→

x −1 λ

=

√ β

y→x=

1 √ λβ y

→ dx =

−1 − β1 −1 y dy, then λβ

( ) ∞ )−(θ −r +1) {0 ( ∞ ( r ) Γ(k + 1) β Σ 1 (−1)w Σ (−1)3 Γ(w + 1) E x = √ Γ(θ/β) λθ w=0 w!Γ(k − w) 3=0 3!Γ(w − 3 + 1) λβ y e−y

(

γ (θ/β, y) Γ(θ/β)

)3

∞

−1 − β1 −1 y dy λβ

Type II Exponentiated Class of Distributions: The Inverse Generalized …

187

{ [ (θ −r ) ] ∞ ∞ Γ(k + 1) Σ (−1)w Σ (−1)3 Γ(w + 1) −1 = y β Γ(θ/β)λr w=0 w!Γ(k − w) 3=0 3!Γ(w − 3 + 1) ∞

e−y d

(

γ (θ/β, y) Γ(θ/β)

By using

∞ {

0

)3

dy

y α+r−1 e−y (γ (α, y))m dy

=

I (α + r, m)

0

=

α −m Γ(r + α(m + 1))FA(m) (r +

FA(m) is the Lauricella function of type ( ) ) (−1)3 Γ(w+1) 1 I (θ −r ,3 3!Γ(w−3+1) (Γ(θ/β))3 β

α(m + 1); α, . . . α; α + 1, . . . α + 1; −1, . . . , −1), where,

A, then, Γ(k+1) Σ∞ (−1)w Σ∞ E(x r ) = Γ(θ/β)λ r w=0 w!Γ(k−w) 3=0 Case two: for k − 1 < 0.

( −1 )β ( )Σ {∞ ∞ ∞ β k Γ(k − 1 + j ) Σ (−1)w Γ( j + 1) − xλ −(θ −r +1) x e Γ(θ/β) λθ j!Γ(k − 1) w!Γ( j − w + 1) w=0 j=0 0 ⎛ ( ( )β ) ⎞w −1 γ θ/β, x /λ ⎟ ⎜ ⎟ dx ⎜ ⎠ ⎝ Γ(θ/β)

( ) E xr =

( )β Again Let y = x −1 /λ →

x −1 λ

=

√ β

y→x=

1 √ λβ y

→ dx =

−1 − β1 −1 y dy, then λβ

( )Σ )−(θ −r +1) {0 ( ∞ ∞ 1 β Γ(k − 1 + j ) Σ (−1)w Γ( j + 1) k √ Γ(θ/β) λθ j=0 j!Γ(k − 1) w=0 w!Γ( j − w + 1) λβ y ∞ ( )w −1 − β1 −1 −y γ (θ/β, y) y e dy Γ(θ/β) λβ {∞ [ (θ −r ) ] ∞ ∞ Σ Γ(k − 1 + j ) Σ (−1)w Γ( j + 1) k −1 y β = Γ(θ/β)λr j=0 j!Γ(k − 1) w=0 w!Γ( j − w + 1) 0 ( )w γ y) (θ/β, e−y dy Γ(θ/β)

( ) E xr =

Again by using

∞ {

y α+r−1 e−y (γ (α, y)) j dy = I (α + r, j ) = α − j Γ(r + α( j + 1))F ( j ) A (r +

0

F ( j) A is the Lauricella function of type ( ) (−1)w Γ( j+1) (θ −r ) 1 , w w I w!Γ( j−w+1) (Γ(θ/β)) β

α( j + 1); α, . . . α; α + 1, . . . α + 1; −1, . . . , −1) where,

A, then Σ∞ Γ(k−1+ j) Σ∞ k E(x r ) = Γ(θ/β)λ r j=0 j!Γ(k−1) w=0 Case three: for k − 1 = 0 → k = 1

188

S. H. Abid and J. A. Altawil

( ) E xr =

{∞

{∞ x f (x)d x = r

( ) β β k −1 x −(θ+1) e−(x /λ) x Γ(θ/β) λθ r

0

0

( ( )β ) ⎤k−1 Γ θ/β, x −1 /λ ⎦ dx ⎣1 − Γ(θ/β) ⎡

=

xr 0

⎡

)

(

{∞

β 1 x Γ(θ/β) λθ

( −1 )β x −(θ +1) − λ ⎣

1−

e

Γ

(

θ , β

(

x −1 /λ

)β ) ⎤1−1

Γ(θ/β)

⎦

( ){∞ β β 1 −1 dx = x −(θ −r +1) e−(x /λ) d x Γ(θ/β) λθ 0

(

Again Let y = −1 y λβ

− β1

= Γ

(

(θ −r ) β

−1

dy, then ( ) {0 (

1 Γ(θ/β) )

β λθ

∞

λ

x −1 /λ

1 √ β

)β

→ x −1 /λ =

)−(θ −r +1) y

√ β

y → x =

e−y −1 y − β −1 dy = λβ 1

1 Γ(θ/β)λr

{∞ 0

1 √ λβ y [

y

(θ −r ) β

→ dx = ] −1 −y

e

dy =

. Expansion formula for the rth raw moment functions of Type II EIGGD is given

Γ(θ/β)λr

by ⎧ ( ) Γ(k+1) Σ∞ (−1)w Σ∞ (−1)3 Γ(w+1) (θ −r ) 1 ⎪ ⎪ w=0 w!Γ(k−w) 3=0 3!Γ(w−3+1) (Γ(θ/β))3 I ⎪ β ,3 ,k − 1 > 0 Γ(θ/β)λr ⎪ ( ) Σ∞ Γ(k−1+ j ) Σ∞ (−1)w Γ( j+1) ( ) ⎨ (θ −r ) 1 k ,w ,k − 1 < 0 w I E X r = Γ(θ/β)λr w=0 j=0 j!Γ(k−1) β w!Γ( j−w+1) (Γ(θ/β)) ( ) ⎪ ⎪ ) ⎪ Γ (θ −r ⎪ β ⎩ Γ(θ/β)λr , k − 1 = 0

(7)

2 Shannon and Relative Entropies 2.1 The Shannon Entropy The Shannon entropy [9] of a continuous random variable with PDF (4) is defined by Shannon as H = E(−ln f (x)). So, ⎛ H = ln⎝ +E

((

λθ Γ

( )⎞ θ β

kβ x −1 /λ

⎠ + (θ + 1)E(ln(X ))

)β )

− (k − 1)E

Type II Exponentiated Class of Distributions: The Inverse Generalized …

⎛ ⎛ ⎝ln⎝1 −

Let

I1 = (θ + 1)E(ln(X )), I2 = E

((

Γ

(

d , p

(

x −1 /λ

)β ) ⎞⎞

Γ(θ/β)

)β x −1 /λ

)

189

⎠⎠

(8)

( ( , I3 = −(k − 1)E ln 1 −

( ( )β ) )) Γ θ/β, x −1 /λ Γ(θ/β)

for ( ){∞ β (θ + 1)k β −1 I1 = (θ + 1) lnx f (x)d x = (lnx)x −(θ +1) e−(x /λ) θ Γ(θ/β) λ {∞ 0

0

( ⎧ )β ) ⎫k−1 ( ⎨ ⎬ Γ θ/β, x −1 /λ dx 1− ⎩ ⎭ Γ(θ/β)

( ( ⎡ )β ) ⎤k−1 ( ){∞ Γ(θ/β) − Γ βθ , x −1 /λ β (θ + 1)k β −1 ⎦ dx I1 = lnx x −(θ +1) e−(x /λ) ⎣ Γ(θ/β) λθ Γ(θ/β) 0

( ( ) ( ) ( )β ) = Now since, Γ s, ϒ¯ + γ s, ϒ¯ = Γ(s) → Γ(θ/β) − Γ θ/β, x −1 /λ ( ( −1 )β ) γ θ/β, x /λ we get, (

I1 =

(θ + 1)k β Γ(θ/β) λθ [

){∞ 0

⎡ ( ( −1 )β ) ⎤k−1 γ θ/β, x /λ β −1 ⎦ dx lnx x −(θ +1) e−(x /λ) ⎣ Γ(θ/β)

( ( )β ) ]k−1 γ θ/β, x −1 /λ

So that for

Γ(θ/β)

[ =

( 1− 1−

) ( β )]k−1 γ θ/β,(x −1 /λ) Γ(θ/β)

we get three cases,

Case one: for k − 1 > 0 ( ) ∞ ∞ (θ + 1)k β Σ (−1)u Γ(k) Σ (−1)s Γ(u + 1) Γ(θ/β) λθ u=0 u!Γ(k − u) s=0 s!Γ(u − s + 1) ⎧ ( ( −1 )β ) ⎫s {∞ ⎨ ⎬ γ θ/β, x /λ β −1 dx (lnx)x −(θ +1) e−(x /λ) ⎩ ⎭ Γ(θ/β)

I1 =

0

( )β Let y = x −1 /λ → (θ + 1)k I1 = Γ(θ/β)

(

β λθ

)Σ ∞ u=0

x −1 λ

=

√ β

y→x=

1 √ λβ y

→ dx =

−1 − β1 −1 y dy, then λβ

]( )−(θ+1) {0 [ ∞ 1 1 (−1)u Σ (−1)s Γ(u + 1) ln √ √ u!Γ(k − u) s!Γ(u − s + 1) λβ y λβ y s=0

∞

190

S. H. Abid and J. A. Altawil

e−y

{

} γ (θ/β, y) s −1 − β1 −1 y dy Γ(θ/β) λβ ∞

∞

(θ + 1)Γ(k + 1) Σ (−1)u Σ (−1)s Γ(u + 1) I1 = Γ(θ/β) u!Γ(k − u) s=0 s!Γ(u − s + 1) u=0 ( )s θ γ (θ/β, y) dy e−y y β −1 Γ(θ/β)

) {∞ ( 1 −lnλ − lny β 0

( )s ∞ ∞ { { θ (θ/β,y) I11 = −lnλ e−y y β −1 γΓ(θ/β) dy, By using y α+r −1 e−y (γ (α, y))m dy = 0

0

I (α + r, m) = α −m Γ(r + α(m + 1))FA(m) (r + α(m + 1); α, . . . α; α + 1, . . . α + 1; −1, . . . , −1), where, FA(m) is the Lauricella function of type A, then, ( )s ( ) ∞ { θ −lnλ θ −1 −y β −1 γ (θ/β,y) I11 = (Γ(θ/β)) , s , I = lnye y dy. By using s I 12 β β Γ(θ/β) 0

incomplete gamma function −1 β

∞ {

θ

lnye−y y β −1

(

0

γ (θ/β,y) Γ(θ/β)

y θ/β Σ∞ (−y)m m=0 (θ/β+m)m! Γ(θ/β)

= )s

y( β ) Σ∞ (−y)m m=0 (θ/β+m)m! , Γ βθ θ

we get I12 =

dy, By application of an equation in

Sect. 0.314 of [10] for(Σpower series ) raised to Σ∞power, we mobtain for ∞ m u = , where any u positive integer m=0 am (βx) m=0 C u,m (βx) the coefficient Cu,m ( f or m = 1, 2, . . . )satisfy the recurrence relation Cu,m = Σ (−1) p , we get. (ma0 )−1 mp=1 (up − m + p)a p Cu,m− p , Cu,0 = a0 u anda p = (α+ p) p! (

then I12 =

Σ∞

− m=0 Cs,m β(Γ(θ/β))s

(

)s ( θ/β Σ )s m y θ/β Σ∞ y (y)θ s/β Σ∞ ∞ m ( (−1) ) y m = Γ(θ/β) = (Γ(θ/β)) s m=0 θ m=0 am y m=0 Γ(θ/β) +m m! β ( ) ∞ θ [1+s] { { +m−1 ∞ s−1 e−mx (lnx) = m −s Γ(s){ψ(s) − ln(m)} lnye−y y β dy 0 x 0 ( ) θ[1+s] Γ β +m ∞ θ [1+s] 1 m=0 s,m β(Γ(θ/β))s β

y θ/β Σ∞ (−y)m m=0 (θ/β+m)m! Γ(θ/β)

= −(θ +1)Γ(k+1)lnλ Σ∞

)s

=

, since

−

Σ

, then

ψ

(

) +m ,

then

I

(−1)s Γ(u+1) 1 s=0 s!Γ(u−s+1) (Γ(θ/β))s I (θ/β, s) Γ(θ/β) ( ) ( Γ θ [1+s] β +m (−1)u Σ∞ (−1)s Γ(u+1) Σ∞ (θ +1)Γ(k+1) Σ∞ θ [1+s] u=0 u!Γ(k−u) s=0 s!Γ(u−s+1) m=0 C s,m β(Γ(θ/β))s ψ Γ(θ/β) β

)

I12

C

(−1)u u=0 u!Γ(k−u)

Σ∞

Cs,my m ,

Case two: for k(− 1 ) 0 ⎪ 3 ⎨ Γ(θ/β) w=0 w!Γ(k−w) 3=0 3!Γ(w−3+1) (Γ(θ/β)) ( β ) Σ∞ Γ(k−1+ j ) Σ∞ (−1)w Γ( j+1) +β] [θ k 1 = ,w ,k − 1 < 0 j=0 j!Γ(k−1) w=0 w!Γ( j−w+1) (Γ(θ/β))w I Γ(θ/β) β ⎪ ⎪ ⎩ θ/β, k − 1 = 0 ( ( )β ) ⎞⎞ Γ θ/β, x −1 /λ ⎠⎠ For I3 = −(k − 1)E ⎝ln⎝1 − Γ(θ/β) ( ⎛ ⎛ ( −1 )β ) ⎞⎞ Γ θ/β, x /λ Γ(θ/β) ⎠⎠ = −(k − 1)E ⎝ln⎝ − Γ(θ/β) Γ(θ/β) ( ⎛ ⎛ ( )β ) ⎞⎞ Γ(θ/β) − Γ θ/β, x −1 /λ ⎠⎠, = −(k − 1)E ⎝ln⎝ Γ(θ/β) ⎛ ⎛

⎛ ⎛ ( )β ) ⎞⎞ ( γ θ/β, x −1 /λ ⎟⎟ ⎜ ⎜ ⎟⎟. ⎜ Γ s, ϒ¯ + γ s, ϒ¯ = Γ(s), then I4 = −(k − 1)E ⎜ ⎠⎠ ⎝ln⎝ Γ(θ/β) (

Since

)

(

)

( )β ) ⎞ γ θ/β, x −1 /λ

⎛

⎛ ( ln(1 − x)

= ⎛

−

Σ∞

1⎜ n=0 n ⎝1 −

Σ xn − ∞ n=0 n , we get ( ) β ) ⎞n γ θ/β, x −1 /λ

⎜ ln⎝

⎟ ⎠

Γ(θ/β)

⎟ ⎠

using

( ( ) β ) ⎞⎞ γ θ/β, x −1 /λ Γ(θ/β)

⎟⎟ ⎠⎠

=

−

Σ∞

1 Σ∞ (−1)s Γ(n+1) ⎜ n=0 n s=0 s!Γ(n−s+1) ⎝

( ) β ) ⎞s γ θ/β, x −1 /λ

=

⎛ (

(

Γ(θ/β)

=

⎛

⎜ ⎜ ln⎝1 − ⎝1 −

By

Γ(θ/β)

⎟ ⎠

=

192

S. H. Abid and J. A. Altawil (( ⎛ ) ( )β )v ⎞s −1 β (( x −1 /λ )β ) βθ − x Σ∞ ⎟ λ (−1)1+s Γ(n+1) ⎜ −1 e ⎠ s=0 ns!Γ(n−s+1) ⎝ x /λ v=0 (θ/β+v)!

Σ∞ Σ∞ n=0

[

(

x

−1

)θ

/λ e

( −1 )β ∞ Σ − xλ v=0

)βv ]s ∞ ∞ Σ Σ x −1 /λ λ−βv1 −···−βvs x −βv1 −···−βvs ( ) ( ) = ··· θ (θ/β + v)! + v1 ! . . . θ + vs ! v =0 v =0 (

1

λ

s

x

e

∞ Σ

···

v1 =0

e−s (x let e

(

x −1 λ

)β

=

Σ∞ q=0

(

−s (x −1 /λ)

β

−θ s −θ s −s (x −1 /λ)

=

−s

, then

−1

/λ)

β

β

∞ Σ

λ−θ s−βv 1 −···−βvs (θ/β + v1 )! . . . (θ/β + vs )! v =0 s

β

x −θs−βv 1 −···−βvs

) β q

q!

, then

( ( )β ) ⎞⎞ ∞ Σ ∞ ∞ Σ Γ θ/β , x −1 /λ Σ (−1)1+s+q Γ(n + 1) ( s )q ⎠⎠ = −(k − 1) − (k − 1)E ⎝ln⎝1 − Γ(θ/β) nq!s!Γ(n − s + 1) λβ ⎛ ⎛

n=0 s=0 q=0

∞ Σ v1 =0

...

∞ Σ vs =0

λ−θ s−βv1 −...−βvs (θ/β + v1 )! . . . (θ/β + vs )!

) ( E X −θ s−βv1 −...−βvs −βq

) ( E X −θs−βv 1 −···−βvs −βq ⎧ Σ∞ (−1)w Σ∞ (−1)3 Γ(w+1) 1 ( ) Γ(k+1) ⎪ w=0 3=0 3!Γ(w−3+1) ( ( θ ))3 θ w!Γ(k−w) ⎪ −θ s−βv −···−βv −βq s 1 λ Γ ⎪ Γ β ⎪ β ( ) ⎪ ⎪ ⎪ [θ +θ s+βv 1 +···+βvs +βq ] , 3 , k − 1 > 0 ⎪ I ⎪ β ⎪ ⎨ Σ∞ Γ(k−1+ j ) Σ∞ (−1)w Γ( j+1) k ( ) ( ( 1 ))w = Γ θ λ−θ s−βv1 −···−βvs −βq j=0 j!Γ(k−1) w=0 w!Γ( j−w+1) Γ βθ β ⎪ ( ) ⎪ ⎪ θ +θ s+βv +···+βv +βq ⎪ ], w , k − 1 < 0 s 1 ⎪ I [ ⎪ β ⎪ ) ( ⎪ ⎪ θ +θ s+βv +···+βv +βq ] s 1 ⎪ Γ [ ⎩ β ,k − 1 = 0 Γ(θ/β)λ−θ s−βv1 −···−βvs −βq Substituting I1 , I2 , and I3 in Eq. (8), we get the Shannon entropy of the Type II EIGGD.

2.2 The Relative Entropy The relative entropy of the Type II EIGGD that can be obtained from ) ( ∞ { d x, suchthat f 1 (x) is the pdf in (4) with parameter (λ1 , θ1 , β1 , k1 ) f 1 (x)ln ff21 (x) (x) 0

and f 2 (x) is the pdf with parameter s(λ2 , θ2 , β2 , k2 ).

Type II Exponentiated Class of Distributions: The Inverse Generalized …

⎛

193

⎞

β1 k1 (( )β1 ) ⎜ Γ(θ1 /β1 ) λ1 θ1 ⎟ ( ) ⎠ + {(θ2 + 1) − (θ1 + 1)}E(lnX ) − E x −1 /λ1 DKL (F1 ||F2 ) = ln⎝ β2 k2 Γ(θ2 /β2 ) λ2 θ2

+E

((

x −1 /λ2

)β2 )

( )β1 ) ⎞⎞ ( Γ θ1 /β1 , x −1 /λ1 ⎟⎟ ⎜ ⎜ ⎟⎟ ⎜ + (k 1 − 1)E ⎜ ⎠⎠ ⎝ln⎝1 − Γ(θ1 /β1 ) ⎛ ⎛

( )β2 ) ⎞⎞ ( Γ θ2 /β2 , x −1 /λ2 ⎟⎟ ⎜ ⎜ ⎜ ⎟⎟ − (k2 − 1)E ⎜ ⎠⎠ ⎝ln⎝1 − Γ(θ2 /β2 ) ⎛ ⎛

(9)

By the same arguments of the Shannon entropy derivations, we can )easily write ⎛ ⎛ ( ( −1 )β1 ⎞⎞ (( ) ) (( ) ) X Γ θ1 /β1 , λ −1 β1 −1 β2 1 ⎠⎠. , E Xλ2 , E(ln(X )), and E ⎝ln⎝1 − E Xλ1 Γ(θ1 /β1 ) ⎛( E⎝

X −1 λ1

(( E

) ⎧ ( w Σ∞ Γ k1 +1 Σ∞ (−1) ⎪ ) ( ( ) ⎪ ⎪ w=0 w!Γ k −w 3=0 Γ θ /β ⎪ 1 1 1 ⎨ 1 ( ) ⎠= Σ∞ Γ k1 −1+ j Σ∞ k 1 ) ( ⎪ w=0 j=0 j!Γ(k−1) ⎪ ⎪ Γ θ1 /β1 ⎪ ⎩

)β ⎞

X −1 λ2

)β2 ) =

⎧ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎩

Γ(k1 +1) Γ(θ1 /β1 ) k1 Γ(θ1 /β1 )

(

(

λ1 λ2

)β2 Σ ∞

(−1)w w=0 w!Γ(k1 −w)

(−1)3 Γ(w+1) 3!Γ(w−3+1) (−1)w Γ( j+1) w!Γ( j−w+1)

([ ] ) θ1 +β1 1 , 3 , k1 − 1 > 0 ))3 I ( ( β 1 Γ θ1 /β1 ([ ) ] θ1 +β1 ( ( 1 ))w I , w , k1 − 1 < 0 β Γ θ1 /β1 1

θ1 /β1 , k1 − 1 = 0 Σ∞ (−1)3 Γ(w+1)

1 3=0 3!Γ(w−3+1) (Γ(θ1 /β1 ))3 Γ(k1 −1+ j ) Σ∞ (−1)w Γ( j+1) 1 j=0 j!Γ(k1 −1) w=0 w!Γ( j−w+1) (Γ(θ1 /β1 ))w ) ( θ +β Γ [ 1β 2 ] ( λ )β2 1 1 , k1 − 1 = 0 Γ(θ1 /β1 ) λ2

)β2 Σ ∞ λ1

λ2

(

) , k1 − 1 > 0 ) 2] I [θ1β+β , w , k1 − 1 < 0 1

I

(

[θ1 +β2 ] ,3 β1

( ) ( ) Γ (k +1) ⎧ u Σ∞ (−1)s Γ(u+1) −Γ k1 +1 lnλ1 Σ∞ θ (−1) ⎪ ( ( ) ) ( ( 1 ))s I 1 , s − ( 1 ) ⎪ u=0 s=0 β s!Γ(u−s+1) ⎪ θ θ u!Γ k −u θ 1 ⎪ 1 ⎪ Γ β1 Γ β1 Γ β1 ⎪ ⎪ 1 1 1) ⎪ ( ⎪ ⎪ θ [1+s] ⎪ ⎪ +m Γ 1β ( ) ⎪ u Σ∞ (−1)s Γ(u+1) Σ∞ Σ∞ ⎪ θ1 [1+s] 1 (−1) ⎪ ⎪ ))s ψ ( ( + m , k1 − 1 > 0 ⎪ u=0 u!Γ(k−u) s=0 s!Γ(u−s+1) m=0 C s,m β1 ⎪ θ ⎪ ⎪ β1 Γ β1 ⎪ ⎪ 1 ⎨ ( ) ( ) −k(1 lnλ)1 Σ∞ Γ k1(−1+ j) Σ∞ (−1)s Γ( j+1) θ1 1 E(ln(X )) = (k1 ) s=0 s!Γ( j−s+1) ( ( θ ))s I β1 , s − j=0 j!Γ k −1 ⎪ θ1 θ ⎪ 1 1 ⎪ Γ β Γ β1 Γ β ⎪ ⎪ 1 1 1 ) ⎪ ( ⎪ ⎪ θ [1+s] ⎪ ( ) ⎪ +m Γ 1β ( ) ⎪ s Σ Σ Σ ⎪ Γ k −1+ j θ [1+s] Γ( j+1) (−1) 1 ∞ ∞ C ⎪ ∞ 1( 1 ) ⎪ + m , k1 − 1 < 0 ⎪ s=0 s!Γ( j−s+1) m=0 s,m ( ( θ ))s ψ β1 ⎪ ⎪ j=0 j!Γ k1 −1 ⎪ Γ β1 ⎪ ⎪ 1 ⎪ ( ) ⎪ θ ⎩ −lnλ1 + β1 ψ β1 , k1 − 1 = 0

⎛ ⎛ ⎜ ⎜ ⎜ E⎜ ⎝ln⎝1 −

(

Γ

1

θ1 , β1

Γ

(

1

X −1 λ1

( ) θ1 β1

)β1 ) ⎞⎞

⎟⎟ ⎟⎟ = ⎠⎠

194

S. H. Abid and J. A. Altawil

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨

( )q Σ ∞ (−1)1+s+q Γ(n+1) s β1 q=0 nq!s!Γ(n−s+1) v1 =0 . . . λ1 Σ∞ λ1 −θ1 s−β1 v1 −···−β1 vs vs =0 (θ1 /β1 +v1 )!...(θ1 /β1 +vs )! Σ∞ Σ∞ (−1)3 Γ(w+1) Γ(k1 +1) (−1)w 1 ( ) θ1 w=0 w!Γ(k1 −w) 3=0 3!Γ(w−3+1) ( ( θ1 ))3 Γ β λ1 −θ1 s−β1 v1 −···−β1 vs −β1 q Γ Σ∞ Σ∞ Σ∞ n=0

s=0

(

) θ +θ s+β v +···+β v +β q I [ 1 1 1 1β1 1 s 1 ] , 3 , k1 − 1 > 0 Σ∞ Σ∞ Σ∞ (−1)1+s+q Γ(n+1) ( s )q

1

n=0

s=0

q=0 nq!s!Γ(n−s+1)

λ

β1

β1

1 Σ∞ Σ λ1 −θ1 s−β1 v1 −···−β1 vs ⎪ ··· ∞ ⎪ =0 =0 v v /β +v /β +v (θ )!...(θ 1 s 1 1 1 1 s )! ⎪ Σ∞ Γ(k11−1+ ⎪ j ) Σ∞ (−1)w Γ( j+1) ( ( 1 )) k1 ⎪ ( ) ⎪ w θ ⎪ j=0 j!Γ(k1 −1) w=0 w!Γ( j−w+1) θ ⎪ Γ β1 λ1 −θ1 s−β1 v1 −···−β1 vs −β1 q Γ β1 ⎪ 1 1 ( ) ⎪ ⎪ θ +θ s+β v +···+β v +β q ⎪ ⎪ I [ 1 1 1 1β1 1 s 1 ] , w , k1 − 1 < 0 ⎪ ⎪ ⎪ ⎪ Σ∞ Σ∞ Σ∞ (−1)1+s+q Γ(n+1) ( s )q ⎪ ⎪ ⎪ n=0 s=0 q=0 nq!s!Γ(n−s+1) ⎪ λ1 β1 ⎪ ( ) ⎪ [θ1 +θ1 s+β1 v1 +···+β1 vs +β1 q ] ⎪ Γ Σ Σ −θ s−β v −···−β v ⎪ s 1 1 1 β1 1 λ1 ⎩ ∞ ··· ∞ ,k − 1 = 0

v1 =0

vs =0 (θ1 /β1 +v1 )!...(θ1 /β1 +vs )! Γ(θ1 /β1 )λ1 −θ1 s−β1 v1 −···−β1 vs −β1 q

⎛ ⎛

(

let I 4 = E ⎝ln⎝1 − ⎛ ⎛ E ⎝ln⎝

Γ

( −1 )β2 ) ⎞⎞ , Xλ 2 ⎠⎠ ( ) θ Γ β2

θ2 β2

⎛ ⎛ =

2 /β2 ) E ⎝ln⎝ Γ(θ Γ(θ2 /β2 )

−

1

( ( −1 )β2 ) ⎞⎞ Γ θ2 /β2 , Xλ 2

Γ(θ2 /β2 )

⎠⎠ =

2

( ( −1 )β2 ) ⎞⎞ Γ(θ2 /β2 )−Γ θ2 /β2 , Xλ

⎠⎠,

2

Γ(θ2 /β2 )

) )) ( ( ( β ( ) ( ) γ θ2 /β2 ,( X −1/λ2 ) 2 ¯ ¯ Since Γ s, ϒ + γ s, ϒ = Γ(s), then I4 = E ln . By Γ(θ2 /β2 ) Σ∞ x n using ln(1 − x) = − )n=0 n , we get ( ⎛

⎜ ln⎜ ⎝

( )β 2 γ θ2 /β2 , x −1 /λ2 Γ(θ2 /β2 )

⎞ ⎟ ⎟ ⎠

( ( )β ) ⎞⎞ 2 γ θ2 /β2 , x −1 /λ2 ⎜ ⎜ ⎟⎟ ⎜1 − ⎟⎟ 1 − = ln⎜ ⎝ ⎝ ⎠⎠ Γ(θ /β ) ⎛

⎛

2

⎛

(

2

(

γ θ2 /β2 , x −1 /λ2 ∞ Σ 1⎜ ⎜1 − =− ⎝ n Γ(θ2 /β2 )

)β ) ⎞n 2

⎟ ⎟ ⎠

n=0

⎛ (

γ ∞ ∞ Σ 1 Σ (−1)s Γ(n + 1) ⎜ ⎜ =− n s!Γ(n − s + 1) ⎝ n=0

s=0

(

θ2 −1 β2 , x /λ2 ( ) θ Γ β2 2

)β ) ⎞s 2

⎟ ⎟ ⎠

(( ⎛ )β )v ⎞s 2 ) ( θ (( x −1 /λ2 ∞ ∞ Σ ∞ )β ) β2 − x −1 /λ β2 Σ Σ ⎟ (−1)1+s Γ(n + 1) ⎜ 2 2 2 −1 ⎟ ⎜ x /λ2 = e ns!Γ(n − s + 1) ⎝ (θ2 /β2 + v)! ⎠ n=0 s=0 v=0

then

[ (

x −1 /λ2

)θ2

e−(x

−1

/λ2 )

β2

Σ∞ (x −1 /λ2 )β2 v v=0 (θ2 /β2 +v)!

]s

=

Type II Exponentiated Class of Distributions: The Inverse Generalized … ∞ Σ

···

v1 =0

=

∞ Σ

let e−s (x

−1

/λ2 )

∞ Σ β2 λ2 −β2 v1 +···−β2 vs x −β 2 v1 −···−β2 vs −1 λ2 −θ 2 s x −θ 2 s e−s (x /λ2 ) (θ2 /β2 + v1 )! . . . (θ2 /β2 + vs )! v =0 s

···

v1 =0

195

∞ Σ

β2 λ2 −θ 2 s−β2 v1 −···−β2 vs −1 e−s (x /λ2 ) x −θ 2 s−β2 v1 −···−β2 vs /β + v /β + v . . . (θ )! (θ )! 2 2 1 2 2 s v =0 s

β2

=

Σ∞

) ( β q −s (x −1 /λ2 ) 2

q=0

q!

( ( , then E ln 1 −

Γ

(

θ2 β2

β2

,(x −1 /λ2 )

) ))

Γ(θ2 /β2 )

=

( )q Σ ∞ ∞ Σ ∞ ∞ Σ ∞ Σ Σ s λ2 −θ 2 s−β2 v 1 −···−β2 vs (−1)1+s+q Γ(n + 1) · · · nq!s!Γ(n − s + 1) λ2 β2 /β + v1 )! . . . (θ2 /β2 + vs )! (θ n=0 s=0 q=0 v1 =0 vs =0 2 2 ) ( E X −θ 2 s−β2 v 1 −···−β2 vs −β2 q

) ( where, E X −θ 2 s−β2 v1 −···−β2 vs −β2 q ⎧ Σ∞ Σ∞ (−1)3 Γ(w+1) Γ(k1 +1) (−1)w 1 ( ) ⎪ θ1 w=0 3=0 3!Γ(w−3+1) ( ( θ1 ))3 −θ 2 s−β2 v 1 −···−β2 vs −β2 q w!Γ(k −w) ⎪ 1 ⎪ Γ β1 λ1 Γ β ⎪ 1 ( ) ⎪ ⎪ ⎪ [θ1 +θ2 s+β2 v1 +···+β2 vs +β2 q ] , 3 , k − 1 > 0 ⎪ I ⎪ 1 β ⎪ ⎨ Σ1∞ Γ(k1 −1+ j ) Σ∞ (−1)w Γ( j+1) k1 ( ) ( ( 1 ))w = Γ θ1 λ1 −θ 2 s−β2 v1 −···−β2 vs −β2 q j=0 j!Γ(k1 −1) w=0 w!Γ( j−w+1) θ Γ β1 β1 ⎪ 1 ( ) ⎪ ⎪ θ1 +θ2 s+β2 v 1 +···+β2 vs +β2 q ] [ ⎪ ⎪ I , w , k − 1 < 0 ⎪ 1 β ⎪ 1 ) ( ⎪ ⎪ θ +θ s+β v +···+β2 vs +β2 q ] ⎪ Γ [ 1 2 2 1β ⎩ 1 , k1 − 1 = 0 Γ(θ /β )λ −θ 2 s−β2 v1 −···−β2 vs −β2 q 1

1

1

By substituting the above results in Eq. (9), we get the relative entropy of the Type II EIGGD.

3 Stress-Strength Reliability Model We consider the stress-strength problem in which a unit of strength X is subjected to stress Y following, respectively, Type II EIGG (λ, θ, β, k) and Type II EIGG (λ1 , θ1 , β1 , k1 ), then the stress-strength model of type II EINGGD is, { R = P(Y < X ) =

∞ 0

[

( )β ) ⎤k1 ( Γ θ1 /β1 , X −1 /λ1 1 ⎦ f X (x)FY (x)d x = 1 − E ⎣1 − Γ(θ1 /β1 )

) ]k1 ( β Γ θ1 /β1 ,( X −1 /λ1 ) 1

Let I5 = E 1 − Γ(θ1 /β1 ) ( ) ]k1 [ β Γ(θ1 /β1 )−Γ θ1 /β1 ,( X −1 /λ1 ) 1 , E Γ(θ1 /β1 )

⎡

[ = E

Γ(θ1 /β1 ) Γ(θ1 /β1 )

−

) ]k1 ( β Γ θ1 /β1 ,( X −1 /λ1 ) 1 Γ(θ1 /β1 )

=

196

S. H. Abid and J. A. Altawil

( ) ( ) Since Γ s, ϒ¯ + γ s, ϒ¯ = Γ(s), then ) ]k 1 ) )]k1 ( [ ( [ ( β β γ θ1 /β1 ,( X −1 /λ1 ) 1 γ θ1 /β1 ,( X −1 /λ1 ) 1 E = E 1− 1− By using Γ(θ1 /β1 ) Γ(θ1 /β1 ) Σ s (−1) Γ(b+1) s (1 − z)b = ∞ s=0 s! Γ(b−1+s) z , ( ⎡ ⎛ )β ) ⎞⎤k1 ( γ θ1 /β1 , X −1 /λ1 1 ⎠⎦ ⎣1 − ⎝1 − Γ(θ1 /β1 ) ∞ Σ (−1)s

∞

Γ(k1 + 1) Σ (−1)u Γ(s + 1) s! Γ(k1 − s + 1) u=0 u!Γ(s − u + 1) s=0 ⎛ ( ( )β ) ⎞u γ θ1 /β1 , X −1 /λ1 1 ⎠ ⎝ Γ(θ1 /β1 )

we get =

⎞u ⎛( ( ( ⎛ ( ( −1 )β1 ) βθ11 )β ) ⎞u )β1 )q ( −1 ∞ X γ θ1 /β1 , X −1 /λ1 1 − X /λ /λ Σ 1 1 ⎟ ⎜ ⎟ ⎠ =⎜ ⎝ ⎠ ⎝ Γ(θ1 /β1 ) Γ(θ1 /β1 ) /β + q)q! (θ 1 1 q=0 =

∞ Σ

···

q1 =0

∞ Σ

q1 +···+qu

(−1)

qu =0

λ1 −θ 1 u−β1 q1 −···−β1 qu x −θ 1 u−β1 q1 −···−β1 qu ( ) ( ) θ1 θ1 . . . + q + q 1 u q1 ! . . . qu ! β1 β1

(

1 Γ(θ1 /β1 )

)u

( )β ) ⎞⎤k1 ( γ θ1 /β1 , X −1 /λ1 1 ⎠⎦ E ⎣1 − ⎝1 − Γ(θ1 /β1 ) ⎛

⎡

=

∞ Σ (−1)s

s!

s=0 ∞ Σ

···

q1 =0

∞

Γ(k1 + 1) Σ (−1)u Γ(s + 1) Γ(k1 − s + 1) u=0 u!Γ(s − u + 1)

∞ Σ (−1)q1 +···+qu λ1 −θ 1 u−β1 q1 −···−β1 qu E(X −θ 1 u−β1 q1 −···−β1 qu ) Γ u (θ1 /β1 ) (θ1 /β1 + q1 ) . . . (θ1 /β1 + qu )q1 ! . . . qu ! q =0 u

where ) ( E X −θ 1 u−β1 q1 −···−β1 qu ( ) ⎧ Σ∞ [θ +θ1 u+β1 q1 +···+β1 qu ] (−1)w Σ∞ (−1)3 Γ(w+1) 1 ( ) Γ(k+1) ,3 ,k − 1 > 0 ⎪ w=0 w!Γ(k−w) 3=0 3!Γ(w−3+1) ( ( θ ))3 I ⎪ β θ −θ u−β q −···−β1 qu ⎪ Γ β ⎪ Γ β λ 1 11 ⎪ ( ) ⎪ Σ∞ Γ(k−1+ j ) Σ∞ (−1)w Γ( j+1) ⎨ k ( ) ( ( 1 ))w I [θ +θ1 u+β1 q1 +···+β1 qu ] , w , k − 1 < 0 j=0 j!Γ(k−1) w=0 w!Γ( j−w+1) β = Γ βθ λ−θ 1 u−β1 q1 −···−β1 qu Γ βθ ⎪ ( ) ⎪ θ+θ u+β1 q1 +···+β1 qu ] ⎪ ⎪ Γ [ 1 ⎪ β ⎪ ( ) ,k − 1 = 0 ⎩ θ −θ u−β q −···−β q Γ

β

λ

1

1 1

1 u

Then, we have the stress-strength reliability model for Type II EIGGD as follows, R =1−

∞ ∞ ∞ Σ (−1)s Γ(k1 + 1) Σ (−1)u Γ(s + 1) Σ s! Γ(k1 − s + 1) u!Γ(s − u + 1) s=0

u=0

q1 =0

Type II Exponentiated Class of Distributions: The Inverse Generalized … ···

197

∞ Σ (−1)q1 +···+qu λ1 −θ 1 u−β1 q1 −···−β1 qu E(X −θ 1 u−β1 q1 −···−β1 qu ) Γ u (θ1 /β1 ) (θ1 /β1 + q1 ) . . . (θ1 /β1 + qu )q1 ! . . . qu !

qu =0

=1−

∞ ∞ ∞ Σ (−1)s Γ(k1 + 1) Σ (−1)u Γ(s + 1) Σ s! Γ(k1 − s + 1) u!Γ(s − u + 1) s=0

q1 =0

u=0

∞ Σ λ1 −θ 1 u−β1 q1 −···−β1 qu (−1)q1 +···+qu ··· Γ u (θ1 /β1 ) (θ1 /β1 + q1 ) . . . (θ1 /β1 + qu )q1 ! . . . qu !

.

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩

qu =0 Γ

Γ

Γ(k+1)

( ) θ β

λ−θ 1 u−β1 q1 −···−β1 qu

k λ−θ 1 u−β1 q1 −···−β1 qu

( ) θ β

Σ∞

(−1)w w=0 w!Γ(k−w)

Σ∞

Σ∞

(−1)3 Γ(w+1) 1 3=0 3!Γ(w−3+1) ( ( θ ))3 Γ β

(

) ,k − 1 > 0 ( ) [θ +θ1 u+β1 q1 +···+β1 qu ] I ,w ,k − 1 < 0 β

I

Γ(k−1+ j ) Σ∞ (−1)w Γ( j+1) ( ( 1 )) w j=0 j!Γ(k−1) w=0 w!Γ( j−w+1) Γ βθ ) ( θ +θ1 u+β1 q1 +···+β1 qu ] [ Γ β ( ) ,k − 1 = Γ βθ λ−θ 1 u−β1 q1 −···−β1 qu

[θ +θ1 u+β1 q1 +···+β1 qu ] ,3 β

(10)

0

4 Conclusion In this paper, we presented a novel model named Type II Exponentiated class of distributions. Type II EIGGD used as sub-model for application. Forms for main properties of this distribution, which include the rth moment, Shannon and Relative Entropies, have been derived. Finally, the form of stress-strength reliability model has been presented with completely different parameters.

References 1. Gupta, C., Gupta, L., & Gupta, D. (1998). Modeling failure time data by Lehman alternatives. Communications in Statistics Theory and Methods, 27, 887–904. 2. Pu, S., Oluyede, B., Qiu, Y., & Linder, D. (2016). A Generalized class of exponentiated models Weibull distribution with applications. Journal of Data Science 14, 585–614. 3. Ahmad, Z., Ampadu, C., Hamedani, G., Jamal, F., & Nasir, M. (2019).The new exponentiated T-X class of distributions: Properties, characterizations and application. Pak.j.stat.oper.res. XV(IV), 941–962. 4. Oluyedea, B., Mashabeb, B., Fagbamigbec, A., Makubateb, B., & Wandukua, D. (2020). The exponentiated generalized power series family of distributions: Theory, properties and applications. Heliyon, 6(e04653), 1–16. 5. Olosunde, A., & Adekoya T. (2020).On some properties of exponentiated generalized Gompertz-Makeham distribution. Indonesian Journal of Statistics and Applications, 4(1), 22–38. 6. Abid, S., & Kadhim, F. (2021). Doubly truncated exponentiated inverted gamma distribution. Journal of Physics: Conference Series, 1999(1), 012098. 7. Chipepa, F., Chamunorwa, S., Oluyede, B., Makubate, B., & Zidana, C. (2022). The exponentiated half logistic-generalized-G power series class of distributions: Properties and applications. Journal of Probability and Statistical Science, 20(1), 21–40. 8. Abid, S., & Jani, H. (2022).Two doubly truncated generalized distributions: Some properties. AIP Conference Proceedings, 2398, 060033. 9. Kullback, S., & Leibler, R. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86. https://doi.org/10.1214/aoms/1177729694. MR 39968

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10. Gradshteyn, S., & Ryzhik, M. (2000). Table of integrals, series, and products (6th ed.). Academic Press. 11. Shannon, E. (1948). A mathematical theory of communication. Bell System Technical Journal 27, 379–432.

A Comparative Study of Masi Stock Exchange Index Prediction Using Nonlinear Setar, MS-AR and Artificial Neurones Network Models Saoudi Youness, Falloul Moulay Mehdi, Hachimi Hanaa, and Razouk Ayoub

Abstract The aim of this paper is to examine the effectiveness of three nonlinear econometric prediction models on the Casablanca stock exchange’s MASI index: the Self-Exciting Threshold Autoregressive (SETAR), the Markov Switching Autoregressive Model (MS-AR) and the Artificial Neural Network (ANN) model. The time frame under investigation is from January 1, 2002, to September 20, 2018. Nonlinearity tests are used to confirm the hypotheses of the study. Schwartz selection criteria were also used to select the optimal delay. To choose the best prediction model, the Mean Absolute Error Criterion, the Root Mean Square Error Criterion and the Mean Absolute Percentage Error Criterion were used. The results of applying SETAR, MSAR and ANN models showed that the neural network model is the most optimal. This model is followed by the Markovian model MS-AR since it has given better results than the SETAR model. These results can be beneficial for financial market traders to make good decisions regarding allocative portfolio and asset management strategies. Keywords SETAR · MS-AR · ANN · MASI Index · Forecast

1 Introduction Over the past 20 years, interest in nonlinear models of time series has evolved significantly. The presence of nonlinearity in financial time mean series has important consequences especially with regard to propriety of the low efficiency of financial markets. The Threshold AutoRegressive (TAR) model is proposed by Tong [1] and Tong and Lim [2]. Some of the most well-known nonlinear models include S. Youness (B) · F. M. Mehdi · H. Hanaa Systems Engineering Laboratory USMS, Beni Mellal, Morocco e-mail: [email protected] F. M. Mehdi · R. Ayoub Economics and Management Laboratory USMS, Beni Mellal, Morocco © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_16

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Hamilton’s Markov Switching Autoregressive Model and the Self Exciting Threshold AutoRegressive model. These three models are different from conventional linear econometric models in that they assume that time series may behave differently under various regimes. Tong studied the SETAR model (Self-Excited Threshold Autoregressive Model) [4]. In this model, the change in regime is controlled by a piecewise function of the values in the time series itself. Amiri [5], who demonstrated the power of nonlinear models by contrasting the performance of the Markovian autoregressive switching model (MS-AR) and linear model forecasting, is one of many studies that have been carried out to evaluate the precision of financial and economic time series forecasting and modeling. Wen-Yi Peng, Ching-Wu Chu [6] compared the performance of univariate methods for forecasting. This study showed that nonlinear models allowed better modeling of macroeconomic series than linear models. The artificial neural network (ANN) is a prediction method based on mathematical models of the brain, and it allows to give complex nonlinear relationships between response variables and predictors, the prediction through the ANN is classified among the second generation of prediction models as stated in Zang [7]. This paper is structured as follows: Beginning with an introduction and the objective of the paper, the properties and econometric tests are studied in the second section, the third section explained the estimation of models, namely, SETAR, MSAR and ANN. The last section is devoted to the comparative approach between these three models in order to choose the most optimal model for forecasting.

1.1 Objective of the Paper The objective of this paper is to give and test the forecasting efficiency between two nonlinear econometric models, namely, SETAR, MS-AR and the neural network model—ANN in order to choose the best-performing model.

2 Methodology The purpose is to study the methods and tests used to test and model the series of the MASI index following the SETAR, the MS-AR and the ANN models and choose the most optimal forecasting model.

A Comparative Study of Masi Stock Exchange Index Prediction Using …

201

3 Nonlinear Model Estimates 3.1 Properties and Statistical Tests This section contains descriptive statistics for the daily data for the period from January 1, 2002, to September 20, 2018. These statistics of MASI include: – The standard deviation, mean, – The Kurtosis, Skewness and the Jarque Berra, – The econometric tests of stationarity, homoscedasticity, linearity and stability. 3.1.1

Statistical Properties

The data used in this paper consist of the daily MASI index downloaded from the Casablanca stock exchange website, covering a historical period that extends from 01 January 2002 to 28 September 2018 with a number of 4176 observations. Figure 1 describes the evolution of the MASI series on a sample of 4167 observations. This series is transformed into logarithmic difference to account for nonstationarity in variance (Table 1). The J.B. statistic shows that the normality null hypothesis is rejected, and furthermore, the series of MASI yield is leptokurtic. The series of yields is spread to the left, as indicated by the negative skewness coefficient. This asymmetry might indicate that the series isn’t linear. The Jarque and Bera test, whose P-value is less than

Fig. 1 MASI daily (in level and yields)

Table 1 Descriptive statistics on the yield series Series

T

Average

Standard deviation

Skewness

Kurtosis

J.B

MASI

4176

0,000,272

0,000,282

−0,414,859

9,809,579

8,186, 271

T is the number of observations, J.B is the statistic of Jarque and Bera

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Table 2 Phillips-perron test Logarithm series

Returns

Series

Model

Delays

State. PP

Model

Delays

State. PP

MASI

1

4

−1,76

1

9

−48,05

Model 1: model without constant or trend, The stat

Table 3 Homoscedasticity test

Series

Q

TR2

MASI

39,38a

465,83b

Q is the Breush Pagan statistic, TR2 of White’s test. a and b Rejection of the null hypothesis of homoscedasticity at the respective thresholds of 1 and 5%

5% in relation to the Jarque and Bera statistic, confirms the non-normality of the distribution of MASI yields.

3.1.2

Statistical Tests: Stationarity, Homoscedasticity, Linearity and Stability

Stationarity Test: Phillipe Perron T is the number of observations in the series and T 1/4 is the typically used delay (Table 2). The t-DF statistic’s value, PP, should be compared to the critical values. At the 5% threshold, the results for model 1, model 2 and model 3 were 1.95, −2.86 and 3.41, respectively. The Philips-Perron test results demonstrate stationarity for the series in the first difference and the presence of stationarity in the MASI series in level.

Homoscedasticity Test: Breush Pagan and De White The results of the estimates from the application of the PG and White homoscedasticity tests to the MASI yield series are shown in Table 3. The Breush-Pagan test and the White test both come to the same conclusion— namely, that the null hypothesis of homoscedasticity is false—so the results are consistent between the two tests. It is highly likely that the presence of an ARCH effect, which is frequently observed in financial time series, is what caused the null hypothesis of homoscedasticity to be rejected.

A Comparative Study of Masi Stock Exchange Index Prediction Using …

203

Table 4 BDS Test m

MASI series statistics z 0.5

1

1.5

2

2

20.05748

22.14191

23.44798

23.44114

3

24.55153

25.85203

26.79715

26.21856

4

28.75439

28.19111

28.12679

27.04520

5

33.77567

30.58994

29.22730

27.54283

6

39.53157

33.12403

30.31779

27.98393

7

46.62979

35.64627

31.24891

28.25241

8

56.67480

38.50531

32.05988

28.32375

9

70.15628

41.81238

32.98037

28.48166

10

88.81797

45.92488

34.10695

28.69132

11

115.6761

51.00169

35.39045

28.94439

12

157.8109

57.18641

36.78950

29.25732

13

216.8054

65.04502

38.53267

29.68691

14

301.2788

74.53158

40.56875

30.17367

15

411.5825

85.92505

42.85762

30.73721

m: dimensions of extensions, *: accept the null hypothesis for a threshold of 5%

The Linearity Test: Brock, Deshert and Sheinkman—BDS The i.i.d series null hypothesis is put to the test against an undefined alternative hypothesis using the Brock, Deshert and Sheinkman (1987) BDS test. This test is interesting because, in contrast to earlier tests, it can identify nonlinearity in yield series. The guidelines provided by Brock et al. (1992) were adhered to in order to apply the test: the values 0.5, 1, 1.5, and for the ratio ε/σ were used, and the extension dimension m ranged from 2 to 15. Table 4 displays the test’s outcomes. The conclusion that can be drawn from this test is that the assumption of independence of returns is rejected. In other words, this confirms the nonlinearity of the series. According to financial market theory, Casablanca’s financial market is not efficient according to the low form of market efficiency.

The Stability Test of the “CUSUM” Figure 2 describes the Recursive Residue Graphs, according to the CUSUM test below, we find that the recursive residues (in blue) are very close to zero, it is well within the confidence interval of 5% (in red). We can, therefore, conclude that there is no instability of the parameters over time. This result can be confirmed using Square Recursive Residue Graphs as shown below.

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Fig. 2 Recursive residue graphs

Fig. 3 Square recursive residue graphs

Figure 3 describes the Square Recursive Residue Graphs, according to the test above, we find that the recursive residues (in blue) are out of the confidence interval of 5% (in red). We can, therefore, conclude that there is no instability of the parameters over time.

3.2 Estimation of Nonlinear Models: SETAR, the MS-AR Model and ANN For years, it has been recognized that most financial series have nonlinear dynamics, asymmetries and multimodal distributions. Since it is impossible to account for these phenomena from the usual autoregressive linear models of ARMA type, nonlinear processes capable of reproducing these characteristics are necessarily used.

A Comparative Study of Masi Stock Exchange Index Prediction Using …

3.2.1

205

Self-Exciting Threshold AutoRegressive (SETAR)

A SETAR (2,1,1) with two regimes and an autoregressive process AR (1) with (d = 1) in each regime is as follows:         Xt = ϕ1,0 + Xt−1 ϕ 1,1 1 − I Xt−1 > c + ϕ2,0 + Xt−1 ϕ 2,1 I Xt−1 > c + εt (1) qt = Xt−d

(2)

I(.) Denotes the indicator function, qt is threshold variable ϕ1,0 , ϕ1,1 , ϕ2,0 , ϕ2,1 are the coefficients represents the coefficients of the AR (1) process. Different variances for each of the w segments are supported by the TAR model in the equation (regimes). A restriction of the following form is used to stabilize the variance for the various regimes:  Xt = I1 α1,0 +

P1 





X1,t−i ϕ1,i + I2 α2,0 +

i=1



+ . . . + Iw αw,0 +

Pw 

P2 

 Xw,t−i ϕw,i + et

 X2,t−i ϕ2,i

i=1

(3)

i=1

When it falls within segment j, It is equal to 1, and when it doesn’t, It is equal to 0. The nonlinear least squares method can be used to estimate the TAR model in equation, but first it is necessary to determine where the segment boundaries are. Each of the m segments can be easily estimated by the Ordinary Least Square (OLS) method. The localization of structural ruptures offers one potential method for establishing segment boundaries. The time series should have at least one breaking point, which indicates that the data are not linear. It is now assumed that the threshold change variable qt is any delay qt = yt − d. Here, we took d = 1. The change of regime is determined by delayed values of the series. We apply the same previous method and look for the order p that minimizes the AIC and BIC criteria. The delay p = 2 minimizes the AIC criterion and the BIC criterion (Table 5).

3.2.2

The Markov Switching Autoregressive Model

The second category of regime-changing models is made up of those that presuppose that the regime that exists at period t cannot be observed and is instead determined by an unseen process that is sometimes referred to as St. The MS-AR is the most popular model in this class and assumes that the St process is a first-order Markov process. This suggests that the St-1 regime from the previous period influences the St regime today. The MS-AR is as follows, given a given time series (Xt : t = 1, 2,

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Table 5 SETAR model prediction Threshold Variable: MASI(−2) Variable

Coefficient

Standard error

t-Statistic

P

MASI (−2) < 0.001053596—2353 observations C

−0.000144

0.000150

−0.959378

0.3374

MASI (−1)

0.246287

0.019941

12.35062

0.0000

0.001053596 0(X ∼ T ypeI I EGGD(a, d, p, k)), then the S. H. Abid (B) · J. A. Altawil Mathematics Department, College of Education, University of Mustansiriyah, Bagdad, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_19

235

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S. H. Abid and J. A. Altawil

probability density function (pdf) and the cumulative distribution function (cdf) of the random variable are, respectively,     ⎤k−1 ⎡ p γ dp , ax k  p  d−1 −( x ) p ⎣   ⎦ 1− f 1 (x) =   d x e a d a  p  dp ⎡

γ

F1 (x) = 1 − ⎣1 −



 p d , ax p 

(1)

 ⎤k ⎦

 

(2)

d p

where (α) is the ordinary Gamma function, γ (α, βx) is the lower incom βx plete Gamma function such that γ (α, βx) = 0 t α−1 e−t dt and (α, β x) =

∞ α−1 e−t dt = (α) − γ (α, βx) is the upper incomplete Gamma function. βx t The pdf and the cdf of doubly truncated type II Exponentiated Generalized Gamma random variable X (X ∼ DTII EGGD (a, d, p, k, b, c)) can be defined, respectively, as,    p k−1 γ dp ,( ax )   x e 1− ad  dp  dp

 f (x) =       p k p k γ dp ,( ac ) γ dp ,( ab )     1− 1− − 1− 1− d d k 

p

d−1 −( ax )







p

   x p k  pd x d−1 e−( a ) 1 − d a p

= 

1−  1− 1− F(x) =

p

 1− 1−

 1−

=  1−

γ



( )

d b p, a

 γ



γ

d p

( )p

d x p, a



γ



d p

( )p d p

( )

p

 k

d p

( )

  d p

 − 1−

 p k

γ

( )p

d x p, a

 

d p

( )p

d c p, a





 − 1−

γ



γ

γ



γ



( )p

(3)

 k 

p

 k 

  d p

( )

d b p, a



p

  d p

 k 

  d p

( )p

d c p, a



( )

d b p, a



d x p, a

 

0

( )p

d x p, a

 

 dp  j

 

p

 u

238

S. H. Abid and J. A. Altawil

  E xr =

   d 1−  p

k γ



( )

d b p, a



p

p

ad  k

 − 1−

  d p

γ



( )p

d c p, a



 k 

  d p

⎛    p  ⎞u d x (k) ⎝ γ p , a d+r −1 −( ax ) u   ⎠ dx ∫x e (−1) d u!(k − u) b  u=0 p p k ad 

=  k     p p k    γ dp ,( ab ) γ dp ,( ac ) d     1−  p − 1− d d ∞ p 

c





p

p

⎛    p  ⎞u ∞ γ dp , ax  c x p (−1)u d+r −1 −( a ) ⎝   ⎠ dx ∫x e u!(k − u) b  dp u=0 Let .y =

 x p a

→

  E xr =

x a

=

√ p

y→x =a

   d 1−  p

∞  u=0

γ

( )

d b p, a



( )





  

∞  u=0

d p

1−

γ

(−1)u u!(k − u)



ka r

( )

d b p, a



 p k

  d p

(ac ) p



y

e

p

( ab )

p

 − 1−

 −1 −y ⎝

e

( )p

d c p, a



 γ −1 −y ⎝



gamma

γ



( )p

d c p, a



d p

 k 

  d p

 ⎞u y   ⎠ dy, d , p

d p

function

γ (θ, x)

=

 k 

  d p

⎛

(d+r ) p



⎛ 

(d+r ) p

By using of incomplete  expansion xm x θ (θ )e−x ∞ m=0 (θ +m+1) we get,   E xr =

γ

− 1−

d p

y b a

 p k

 

(ac ) p

a 1p −1 y dy, then p

y → dx = ka r



(−1) u!(k − u) u



√ p

y e−y

⎞u

∞  m=0





ym d p

+m+1

 ⎠ dy,

By application of an equation in section 0.314 of [1] for   power ∞series raised mto ∞ m u power, we obtain for any u positive integer a = x) (β m m=0 m=0 C u,m (β x) , where the coefficient C f or m = 1, 2, . . . satisfy the recurrence relation ( ) u,m  Cu,m = (ma0 )−1 mp=1 (up − m + p)a p Cu,m− p , Cu,0 = a0 u anda p =

Doubly Truncated Type II Exponentiated Generalized Gamma Distribution (−1) p , (α+ p) p! ud

y p e−uy

 ∞ m=0

m  y  m=0  d +m+1 p

   1−  dp

∞  u=0

γ



d

y p e−y

∞ m=0

am y m

u

( )

d b p, a

 p k

 − 1−

  d p

γ



( )p

d c p, a



 k 

  d p

(ac ) p   ∞ (d(1+u)+r ) (−1)u  +m−1 −(1+u)y p Cu,m y e dy u!(k − u) m=0 b p (a)

   1−  dp

u=0

ka r



Now, let (1 + u)y = z → y =

∞ 



=

Cu,my m , then

  E xr =

  E xr =

u

∞

d

y p e−y

we get

239

γ

ka r



( )

d b p, a



→ dy =

z (1+u)

 p k

  d p

 − 1−

γ



dz , (1+u)

( )p

d c p, a



then

 k 

  d p

p  ∞ (1+u)( ac ) z (−1)u  ∫ Cu,m p u!(k − u) m=0 (1 + u) (1+u)( b )





(d(1+u)+r ) p

+m−1

e−z

a

  E xr =

   1−  dp

γ



ka r

( )

d b p, a



 p k

 − 1−

  d p

γ



( )p

d c p, a



 k 

  d p





)   (d(1+u)+r ∞ +m p 1 (−1)u  Cu,m u!(k − u) m=0 1+u u=0  p   b (d(1 + u) + r )  + m, (1 + u) p a   c  p  (d(1 + u) + r ) + m, (1 + u) − p a

∞ 

Case two: for k − 1 < 0.   E xr = 

  d p

 1−

k γ



( )

d b p, a



  d p

p

p

ad  k

 − 1−

γ



( )p

d c p, a



 k 

  d p

⎛    p  ⎞j c ∞ γ dp , ax  p (k − 1 + j) d+r −1 −( ax ) ⎝   ⎠ dx x e d j!(k − 1)  j=0 p b

dz (1 + u)

=

240

S. H. Abid and J. A. Altawil

Again Let y = =

 x p a

   1−  dp

∞

γ



→ ( )

d b p, a



√ p

= ka r  p k x a

 − 1−

  d p

 c p (k − 1 + j) ( a )

y→x =a





γ

√ p

( )p

d c p, a



a 1p −1 y dy, then p

y → dx =  k 

  d p

⎛ 

 γ (d+r ) −1 −y p ⎝

 p d , ax p

,

 ⎞j

  ⎠ dy  dp Again by using expansion of incomplete function γ (θ, x) =   gamma  ∞ d m d xm p  d e −y  y  , then we get, , y = y x θ (θ )e−x ∞ m=0 (θ +m+1) m=0 d p p j!(k − 1)

j=0

( ab )

y

p

e



  E xr =

   d 1−  p

γ



ka r

( )

d b p, a



j=0

d p

( ab )

 − 1−

 

(ac ) p ∞  (k − 1 + j) j!(k − 1)

 p k



y

(d+r ) p



γ



( )p

d c p, a



d p

d p

y e−y

e

 k 

 

⎛ −1 −y ⎝

p +m+1

⎞j

∞ 



m=0

p



y

d p

m

 ⎠ dy, +m+1

Again by application of an equation in Sect. of [1] for 0.314  power ∞series raised to ∞ m u m a = power, we obtain for any u positive integer (βx) m=0 m m=0 C u,m (βx) where the coefficient Cu,m ( f or m = 1, 2, . . . ) satisfy the recurrence relation  (−1) p Cu,m = (ma0 )−1 mp=1 (up − m + p)a p Cu,m− p , Cu,0 = a0 u anda p = (α+ , we p) p! get   E xr =

   d 1−  p

γ



ka r

( )

d b p, a



 p k

 − 1−

  d p

γ



( )p

d c p, a



 k 

  d p

( ac ) p  (d(1+ j )+r ) +m−1 p Cm, j ∫ y e−(1+u)y dy p j!(k − 1) m=0 ( ab )

∞ ∞  (k − 1 + j)  j=0

Again, let (1 + j )y = z → y =   E xr =

   d 1−  p

z (1+ j)

→ dy =



 p k

γ

ka r

( )

d b p, a



  d p

∞ ∞  (k − 1 + j)  j=0

j!(k − 1)

m=0

 − 1−

 Cm, j

dz (1+ j)

, then

γ

( )p

d c p, a

 

1 1+ j





 k 

  d p

(d(1+ j )+r ) p



+m

Doubly Truncated Type II Exponentiated Generalized Gamma Distribution

241

   p b (d(1 + j ) + r )  + m, (1 + j ) p a   c  p  (d(1 + j ) + r ) − + m, (1 + j ) p a Case three: for k − 1 = 0 → k = 1   E xr =

  1−  dp

Again Let y =

 x p a

γ



( )

d b p, a



→

  E x r   1−  dp

γ

1  p

 

x a



d p

=

√ p

 − 1−

y→x =a 1

( )

d b p, a



p

  d p

 p c x p    x r +d−1 e−( a ) d x, d d c p a γ p ,( a )



 − 1−



 

√ p

γ



b

d p

y → dx =

( )

d c p, a



 

p

 

a 1p −1 y dy, then p

 p ad

d p

    p   p  a r  d+r −  d+r , ab , ac ( ac ) p  √ r +d−1 1 p p a          ∫ apy e−y y p −1 dy = p p p b p  dp , ab −  dp , ac (a) Then, we can get the rth raw moment function.

2 Shannon and Relative Entropies 2.1 The Shannon Entropy The Entropy is a measure of how “surprising” the average outcome of variable X. Shannon entropy is proposed by Shannon (1948) as H = E(−ln f (x)). If X has the pdf (3) then ⎛⎛   ⎞⎧⎡     ⎤k ⎫ ⎞     ⎤k ⎡ p p ⎪ ⎬ ⎨ γ dp , ac γ dp , ab  dp a d ⎪ ⎟ ⎜⎝ ⎣ ⎦ ⎦ ⎣ ⎠     H = ln⎝ − 1− 1− ⎠ ⎪ ⎪ d d kp ⎭ ⎩  p  p     ⎞⎞ ⎛ ⎛ p d   p  γ , Xa p X   ⎠⎠. − (d − 1)E(ln(X )) + E − (k − 1)E ⎝ln⎝1 − a  d p

242

S. H. Abid and J. A. Altawil

I1 =   −(k − 1)E ln 1 − Let

  c −(d−1)k p 

  d p

ad

b



p

x d−1 e−( a )

x p

(lnx) ⎧ ' ⎨

 So that for 1 − I1 =

−(d − 1)E(ln(X )), I2   p  γ dp ,( Xa )   for I 1 = d

γ

( ( ) d, b p a  dp

γ



E

−(d − 1)

   X p , I3 a

c b

lnx f (x)d x

= =

 k−1 γ ( dp ,( ax ) p ) 1− d

( p) ⎫ (k  dx ) − 1− γ ( dp ,( ac ) p ) k ⎬  ( dp ) ⎭ 

p

1− ⎩ ( )  k−1  d x p γ p ,( a )    dp

   d 1−  p

=

we get three cases; Case one: for k − 1 > 0

−(d − 1)k  k  d b p p ,( a )   − 1− d



γ



( )p

d c p, a



p

 k 

 

 p ad

d p

⎧    p  ⎫u ⎨ γ dp , ax ⎬ c p (k) u d−1 −( ax )   ∫(lnx)x e dx (−1) ⎩  d ⎭ u!(k − u) b u=0 p

∞ 

Again let y = I1 = 

a

→

  

∞  u=0

I11 = lna

 x p

d p

x a

1−

=

γ



√ p

y→x =a

√ p

−(d − 1)k   p k ( )   − 1− d

d b p, a



y → dx =

γ

p



( )p

d c p, a



a 1p −1 y dy, then p

 k 

  d p

 ⎞u ⎛  d  c p γ , y ( ) a d p 1 (−1) ∫ lna + lny e−y y p −1 ⎝   ⎠ dy p u!(k − u) ( b ) p  dp a u

( ac ) p −y d −1 yp p e ( ab )



γ

 u d p ,y   dy,





d p

gamma function γ (θ, x) = x θ (θ )e−x  ( c )p a

By using expansion of incomplete

∞

xm m=0 (θ +m+1)

⎛ ⎝ y e−y

we get, ⎞u

∞ 

ym

 ⎠ dy +m+1 ⎛ ⎞u  ( c )p ∞ m  a d ud y   ⎠ dy = lna e−y y p −1 y p e−uy ⎝ p d ( ab )  + m + 1 m=0 p

I11 = lna

, since

∞ m=0

am y m

( ) b a

u

=

p

e−y y

d p −1

d p

m=0

∞ m=0

Cu,m y m , then





d p

Doubly Truncated Type II Exponentiated Generalized Gamma Distribution

( ac ) p −(1+u)y d(1+u) +m−1 y p dy, let z = (1 + u)y → y = p e ( ab ) dz → dy = (1+u) , then

I11 = lna z (1+u)

243

I11 = lna

∞

∞ 

m=0 C u,m

 Cu,m

m=0

 d(1+u) +m p

1 1+u

 p     c  p  b d(1 + u) d(1 + u)  − + m, (1 + u) + m, (1 + u) p a p a I12 =

1 p

( ac ) p d −y p −1 y p lnye ( ab )



γ

 u d p ,y   dy, Using expansion of incomplete gamma





d p

function γ (θ, x) = x θ (θ )e−x

I12

∞ 

xm (θ + m + 1) m=0

∞ ( ac ) p (1+u)d 1 = Ct,u ∫ lnye−(1+u)y y p +t−1 dy, N ow let e−(1+u)y p t=0 b p ( ) a

=

∞  (−(1 + u)y)q

q!

q=0

=

1 p

∞ 

Ct,u

t=0

 let z = =

I12

=

∞  (−1)q

q!

q=0

∞  q=0

((1 + u)y)q , we getI12

  ( ac ) p (−1)q − −(1+u)d −t−q+1 p dy (1 + u)q ∫ ln; yy p q! (b) a

 z −(1 + u)d −(1+u)d − t − q lny → y = e ( p −t−q ) → dy p e(

z −(1+u)d −t−q p

−(1+u)d p

)

−t −q

 dz, then

∞ ∞  1 (−1)q = Ct,u (1 + u)q p t=0 q! q=0 



1 −(1+u)d p

−t −q

2



−(1+u)d p

 p −t−q ln( Ca )

−(1+u)d p

 p −t−q ln( ab )

∫

ze−z dz

244

S. H. Abid and J. A. Altawil

∞ ∞  1 1 (−1)q Ct,u (1 + u)q  2 p t=0 q! −(1+u)d q=0 − t − q p      p −(1 + u)d b , then we get I1 .  2, − t − q ln p a       c p −(1 + u)d − t − q ln − 2, p a Case two: for k − 1 < 0

I12 =

I1 =

   d 1−  p

γ



−(d − 1)k  k  d b p p ,( a )   − 1− d



γ



( )p

d c p, a



p

 k 

 

 p ad

d p

⎧   p ⎫ j ∞ ⎬ ⎨ γ dp , ax  p (k − 1 + j ) c d−1 −( ax )   ∫(lnx)x e d x, ⎭ ⎩  d j!(k − 1) b j=0 p By the same above arguments, we can easily write, I1 = 

   d p

)

1−

γ



−(d − 1)k  k  d b p p ,( a )   − 1− d



γ



( )p

d c p, a



p

 

 k 

∞  (k − 1 + j ) j!(k − 1) j=0

d p

 p  d(1+p j ) +m   b d(1 + j ) + m, (1 + j )  C j,m ln a p a m=0     c p d(1 + j ) + m, (1 + j ) − p a ∞ ∞ q   1 1 (−1) + Ct, j (1 + j )q  2 p t=0 q! −(1+ j)d q=0 −t −q p      p −(1 + j )d b  2, − t − q ln p a       c p −(1 + j )d − t − q ln − 2, p a 

∞ 

1 1+ j

Case three: for k − 1 = 0 I1 = 

∞

 

(−1)q q=0 q!

'

d p

1−

 x  pq a

γ

 −(d−1) pd a (  p

( ( )) ( ) d, b p a  dp

, I1

=

− 1−

γ

(

d, c p p (a)  dp

( )

)



c b

  −(d−1) pd    a  p p  dp ,( ab ) − dp ,( ac )

(lnx)x d−1 e−( a ) d x. Let e−( a ) == x

∞ q=0

(−1)q 1 q! a pq

p

c b

x

p

lnx x pq+d−1 d x. Let

Doubly Truncated Type II Exponentiated Generalized Gamma Distribution

245

z = (− pq − d)lnx, then   −(d − 1) apd     I1 =     p  p  dp , ab −  dp , ac ∞  1 (−1)q 1 {(2, (− pq − d)lnb) − (2, (− pq − d)lnc)} pq q! a pq − d)2 (− q=0

For I2 = E

   X p

=

E(X p ), we get the result directly.     p  γ dp ,( Xa )    For I3 = −(k − 1)E ln 1 − Since, ln(1 − x) = − ∞ n=0 d a

1 ap





xn then, n

p

 ⎞n ⎛  ∞ γ dp , y γ dp , y  1 ⎝   ⎠   ⎠=− ln⎝1 − n  dp  dp n=0 ⎛

⎞

=−

N ow let e−n ( a ) = x

p

⎛ ⎛ ⎜ ⎜ −(k − 1)E ⎝ln⎝1 − −

 x  pv1 +...+ pvn ∞ ∞ ∞   1  x nd −n  x  p  a  a    , e ... d + v !... d + v ! n a n 1 n=0 v1 =0 vn =0 p p

∞ (−n ( ax ) p )q , We get, q=0

q!

  p  ⎞⎞

 γ dp , Xa    dp

∞ ∞

 q ∞ ∞ (−1)q+1 np a

   ⎟⎟ ... ⎠⎠ = −(k − 1). n



v1

vn q=0

nq!

  a −nd− pv1 −...− pvn    E X nd+ pq+ pv1 +...+ pvn , d +v ! n p

d + v !... 1 p

then we get the result. Substituting I1 , I2 , and I3 in equation of H , the Shannon entropy of the DTII EGGD can be obtained.

2.2 The Relative Entropy The Relative entropy is a well-known asymmetric and unbounded divergence f1 and f2 , the RE of f2 from f1 measure [2]. For continuous probability    distributions f 1 (θ ) is defined to be, DKL (F1 F2 ) = E ln f2 (θ ) .

246

S. H. Abid and J. A. Altawil

 ⎤k −1 1 d1 x p1 p1 , a 1 ⎦   d1  p 1 ⎧⎡ ⎡     p  ⎤k  ⎤k ⎫ 1 1 1⎪ 1 p1 ⎪ c d d b ⎨ ⎬ γ p1 , a1 γ p1 , a1 1 1 1 1 ⎦ ⎦ ⎣ ⎣1−     − 1− d d ⎪ ⎪  p1  p1 ⎩ ⎭ 1 1 ⎡   ⎤k −1 2 p2 d   γ p1 , ax x p2 1 2 k p2 d2 −1 − a2 ⎣1− ⎦   2   e d2 x d2 d2 a 2  p  p 2 2 ⎧⎡ ⎡  p  ⎤k  ⎤k ⎫    2 2⎪ 2 p2 ⎪ c d d b ⎨ ⎬ γ p2 , a2 γ p2 , a2 2 2 2 2 ⎦ −⎣1− ⎦ ⎣1−     d2 d2 ⎪ ⎪  p  p ⎩ ⎭ 2 2



Let

f (x)

=

k  1  d  p1 1



p1 a 1 d1

⎡



( ) p1 ⎣1− γ

− x x d1 −1 e a1

( )

f 2 (x)

and

( )

=

( )

( )

( )

⎧  ⎨' ⎜ . 1− k1 p1 ⎜ ⎩ ⎜ ⎧ DKL (F1 F2 ) = ln⎜ ⎜   d   ⎨' ⎜  p2 a2 d2 2 ⎝ . 1− k2 p2 ⎩ ⎛





d1 p1

 a 1 d1

γ



d1 p1

  p1  (k1 b , a1  1

' − 1−

γ



⎫

d1 p1

  p1  (k1 ⎞ ⎬ c , a1  1 ⎟

⎭⎟ ⎟ ⎫⎟ '    p2  (k2    p2  ( k 2 ⎟ ⎬⎟ d b d c γ p2 , a2 γ p2 , a2 2  2 2  2 ⎠ − 1− d2 d2  p  p ⎭ 2 2   p1    p2  X X + {(d1 − 1) − (d2 − 1)}E(lnX ) − E +E a1 a2 ⎛ ⎛    p1  ⎞⎞ γ dp11 , ax1 ⎜ ⎜ ⎟⎟   + (k 1 − 1)E ⎝ln⎝1 − ⎠⎠ d1  p1 ⎛ ⎛    p2  ⎞⎞ γ dp22 , ax2 ⎜ ⎜ ⎟⎟   − (k2 − 1)E ⎝ln⎝1 − (7) ⎠⎠ d2  p2 

d1 p1



d1 p1

By the same arguments of the Shannon)entropy derivations, ** we can easily write )    p1  d1   p1    p2  x γ p , a 1  1 , E aX2 , E(ln(X )) and E ln 1 − E aX1 . d  p1 1 ) )    p2  ** d γ p2 , aX  xn 2  2 let I 5 = E ln 1 − . By using ln(1 − x) = − ∞ d2 n=0 n , weget  p 2 ) )    p  *n    p2  * d γ p2 , ax ∞ 1 γ dp22 , ax2 2 2  2   ln 1 − = = − n=0 n d d  p2  p2 2 2 '   p2 v (n   d x ∞ 1  x  p2  p22 − ax p2 ∞ a 2  2  e , then − n=0 n d2 v=0 a2 p2

+v !

Doubly Truncated Type II Exponentiated Generalized Gamma Distribution

⎡



⎢ x ⎣ a2 =

d2

∞ 

e

∞ 

···

v1 =0

−n



x a2

 p2

=

2

  p2 v ⎤n 

v=0

...

v1 =0

=

  p2 ∞  − ax

∞  vn =0

∞  vn =0

∞



x a2

d2 p2

⎥ ⎦ +v !

− p v +...− p2 vn p2 v1 +...+ p2 vn a2 2 1 x



d2 p2

247

   a2−d2 n x d2 n e d2 + v1 ! . . . p2 + vn !

a2 −d 2 n− p2 v1 −···− p2 vn −n   e d2 d2 + v1 ! . . . p2 + vn ! p2



x a2

 p2

−n



x a2

 p2

x d2 n+ p2 v1 +···+ p2 vn

   p2 q −n ax

2 , then q=0  q!p2  ⎞⎞ d2 x γ p2 , a2 ⎜ ⎜ ⎟⎟   E ⎝ln ⎝1 − ⎠⎠ d2  p2  q and then get ∞  ∞  ∞ ∞ (−1)q+1 np −d n− p v −...− p2 vn   a2 2 2 1 a2 2     ... = d2 d2 nq! ! . . . ! + v + v n=0 v1 =0 vn =0 q=0 1 n p2 p2  d2 n+ p2 v1 +...+ p2 vn  E X the result. By substituting the above results in Eq. (7), we get the relative entropy of the DTII EGGD.

let e

⎛ ⎛

3 Stress-Strength Reliability Model The life of a component is described using the stress-strength models, Let a random strength (X) which is subjected to a random stress (Y) follow, respectively, DTII EGG (a, d, p, k) and DTII EGG (a1 , d1 , p1 , k1 ), then the stress-strength model of DTII EGG D is, c

R = P(Y < X ) = ∫ f X (x)FY (x)d x b

' 1−

= ⎧' ⎨ 1− ⎩

γ



d1 p1



γ



d1 p1

  p1  ( k 1 b , a1  1 d1 p1



  p1  ( k 1 , ax  1 d1 p1

'

− 1−

γ



⎫

d1 p1



  p1  ( k 1 ⎬ c , a1  1 d1 p1

⎭

248

S. H. Abid and J. A. Altawil

' E 1− − ⎧' ⎨ 1− ⎩ ' Let I6 = E 1 −

γ



d1 p1



  p1  (k1 , ax  1 d1 p1

γ



d1 p1



γ



d1 p1



  p1  ( k 1 , ax  1 d1 p1

'

  p1  (k1 b , a1  1

− 1−

d1 p1

By using (1 − z)b =

γ



⎫

d1 p1



∞ s=0

  p1  (k1 ⎬ c , a1  1 d1 p1

⎭

(−1)s (b+1) s z s! (b−1+s)

, we

get ⎡ ⎢ ⎣1 −

γ



d1 , p1



  p1  ⎤k1 x a1

⎥ ⎦ =

  d1 p1

∞  (−1)s

s!

s=0

⎛    p1  ⎞s d1 x (k1 + 1) ⎜ γ p1 , a1 ⎟   ⎠ ⎝ d1 (k1 − 1 + s)  p1

⎛    p1  ⎞s ⎛   p1  d1    p1 q ⎞s p1 d1 x x ∞ − ax1 γ p1 , a1  ⎟ ⎜ a1 ⎜ ⎟ ⎜       ⎟ ⎝ ⎠ =⎝ ⎠ d1  dp11  dp11 + q q! q=0 p1 =

∞ 

...

q1 =0

∞ 

q1 +...+qs

(−1)

d1 p1

qs =0

⎡ ⎢ E ⎣1 −

=

γ



d1 , p1



∞  (−1)s

s=0 ∞ 

s!

  p1  ⎤ x a1

  d1 p1

  + q1 . . . dp11

⎛

⎞s 1 ⎝  ⎠  + qs q1 ! . . . qs !  dp11

−d1 s− p1 q1 −...− p1 qs d1 s+ p1 q1 +...+ p1 qs

a 1

x

⎥ ⎦

(k1 + 1) (k1 − 1 + s)

−d s− p q −...− p1 qs  d1 s+ p1 q1 +...+ p1 qs  ∞  E X (−1)q1 +...+qs a1 1 1 1       . ... d1 d1 . . . q  s dp11 + q + q ! . . . q ! q1 =0 qs =0 1 s 1 s p1 p1

Then, we get the stress-strength reliability model for DTII EGG D as follows,

Doubly Truncated Type II Exponentiated Generalized Gamma Distribution

' 1− R = ⎧' ⎨ ⎩ ∞ 

−

.

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨

s=0

1−

d1 p1



⎧' ⎨ 1− ⎩

 dp

⎩

γ



d1 p1



  p1  ( k 1 b , a1  1 d1 p1

(−1)s (k1 +1) s! (k1 −1+s)

⎧'  ⎨

∞ 

γ



γ



∞  q1 =0

d1 p1



...

  p1  ( k 1 , ax  1 d1 p1

'

− 1−

γ



⎫

d1 p1



  p1  ( k 1 ⎬ c , a1  1

⎭

d1 p1

∞ 

−d s− p q −...− p q

1 s s a1 1 1 1 (−1)q1 +...+q      d1 d1 s d1 ...  +q +q 1 s q1 !...qs ! p1 p1 p1 qs =0

  p1  (k1 b , a1  1 d1 p1

'

− 1−

γ



⎫

d1 p1



  p1  (k1 ⎬ c , a1  1 d1 p1

⎭

ka d1 s+ p1 q1 +...+ p1 qs   p  (k     (k ⎫ ' d b d c p ⎬



1−

249

γ p, a    dp

− 1−



∞  1 (−1)u Cu,m u!(k − u) 1+u



γ p, a    dp

⎭



 (d(1+u)+d1 s+ p1 q1 +...+ p1 qs ) +m p

m=0 ⎧ u=0  p ⎫ b (d(1 + u) + d1 s + p1 q1 + . . . + p1 qs ) ⎪ ⎪ ⎪ ⎪ + m, (1 + u) ⎨ ⎬ p a   ,k − 1  c p s + p q + . . . + p q + u) + d ) (d(1 ⎪ ⎪ 1 1 1 1 s ⎪ ⎪ + m, (1 + u) ⎩ − ⎭ p a ka d1 s+ p1 q1 +...+ p1 qs ⎧'    p  (k     (k ⎫ ' p ⎬  ⎨ γ dp , ab γ dp , ac d      p − 1 − 1− d d ⎩ ⎭  p  p

>0

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪  ⎪ ⎪ ⎪  (d(1+ j )+d1 s+ p1 q1 +...+ p1 qs ) +m  ⎪ ∞ ∞ ⎪   p ⎪ (k − 1 + j) 1 ⎪ ⎪ Cm, j ⎪ ⎪ j!(k − 1) 1+ j ⎪ ⎪ j=0 m=0 ⎪ ⎧ ⎪  p ⎫  ⎪ ⎪ b (d(1 + j ) + d1 s + p1 q1 + . . . + p1 qs ) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ + m, + j  (1 ) ⎨ ⎬ ⎪ ⎪ ⎪ p a ⎪   ,k − 1 0 ∂y

293

(2.2)

     ∂φ = −ik0 G j (y) φ ∓b j + 0, y − φ ∓b j − 0, y on y ∈ L j ; ( j = 1, 2), (2.3) ∂x where G j (y) is the porous effect parameter (cf. Yu [5]).  1 φ ∼ O r 2 as r → 0 ∂φ = 0 on y = h ∂y

(2.4) (2.5)

and  φ(x, y) ∼

Tφ inc (x, y) as x → −∞ φ inc (x, y) + Rφ inc (−x, y) as x → ∞

(2.6)

where Re φ inc (x, y)e−iσ t denotes the velocity potential in the fluid region, R and T are reflection and transmission coefficients, σ denotes the angular frequency of the 0 (h−y) and k0 is the waves. Here, φ inc (x, y) = φ0 (y)e−iμ(x−b2 ) , where φ0 (y) = coshk coshk0 h unique positive real root of the equation K = k tanh kh such that μ = k0 . The arrangements of the barriers are shown in Fig. 1. The geometry of the problem is symmetric about x = 0. So, the velocity potential can be split into symmetric and anti-symmetric parts as follows φ(x, y) = φ s (x, y) + φ a (x, y)

(2.7)

φ s (x, y) = φ s (−x, y), φ a (x, y) = −φ a (−x, y)

(2.8)

where

3 Analytic Method of Solution The far field condition is given by φ s,a (x, y) ∼ φ inc (x, y) + R s,a φ inc (−x, y) as x → ∞

(3.1)

where R s,a are unknown constants connected to R and T by R=

Rs + Ra Rs − Ra ,T = 2 2

(3.2)

294

B. Sarkar et al.

Fig. 1 Schematic diagram of the problem

φ s (x, y) can be expressed as (cf. Mandal and Chakrabarti [9] ⎧ ∞ s s ⎪ ⎪ n xφn (y) 0 < x < b1 , 0 < y < h  An coshα ⎨ A0 cosμxφ0 (y) + n=1  s α x   s s s iμx −iμx Bn e n + Cns e−αn x φn (y) b1 < x < b2 , 0 < y < h e + C e φ B + ∞ (y) φ (x, y) ∼ 0 n=1 0 0   ⎪ ⎪  ⎩ inc s −αn x−b2 φ (y) x > b , 0 < y < h φ (x, y) + R s φ inc (−x, y) − ∞ n 2 n=1 Dn e

(3.3)

where φn (y) = coskn (h − y), αn = kn , (n = 1, 2, . . . ) and kn are real positive roots of the equation K + ktankh = 0. Let f js (y) =

 ∂φ s  b j , y , j = 1, 2 ∂x

(3.4)

and     g sj (y) = φ s ∓b j + 0, y − φ s ∓b j − 0, y , j = 1, 2, 0 < y < h

(3.5)

Thus,    f js (y) = −ik0 G j (y)g sj (y), y ∈ L j , L j ≡ a j , h j = 1, 2

(3.6)

Using Havelock’s inversion formulae on g sj (y) and continuity of f js (y) along the walls gives 

 L1

g1s (t)Ms11 (y, t)dt

+ L2

g2s (t)Ms12 (y, t)dt = μA0S sinμb1 φ0 (y)

(3.7)

Wave Scattering by Thin Multiple Bottom Standing Vertical Porous …



 L1

g1s (t)Ms21 (y, t)dt +

L2

295

  g2s (t)Ms22 (y, t)dt = iμ 1 − R s e2iμb2 φ0 (y) (3.8)

where  r sinhαr b1 Ms11 (y, t) = − r∞=1 αr δhe φr (t)φr (y) αr b1  ∞ αr δr sinhαr b1 s s M12 (y, t) = M21 (y, t) = − r =1 heαr b2 φr (t)φr (y)  r sinhαr b2 φr (t)φr (y) Ms22 (y, t) = − r∞=1 αr δhe αr b2

(3.9)

And δr =

4kr h (r = 1, 2, . . . ) 2kr h + sin2kr h

(3.10)

Let us introduce   g sj (t) = μAs0 sinμb1 G sj1 (t) + iμ 1 − R s e2iμb2 G sj2 (t), j = 1, 2

(3.11)

Using (3.11) and introducing Kronecker delta δ jl on Eqs. (3.7) and (3.8), we have  

 L1

L1

G s1l (t)Ms11 (y, t)dt

+ 

G s1l (t)Ms21 (y, t)dt +

L2

L2

G s2l (t)Ms12 (y, t)dt = δ1l φ0 (y), y ∈ L 1

(3.12)

G s2l (t)Ms22 (y, t)dt = δ2l φ0 (y), y ∈ L 2

(3.13)

Again employing Havelock’s inversion formulae on g sj (y) and utilizing (3.11), we get  s     ih s cμAs0 sinμb1 S11 + iμ 1 − R s e2iμb2 S12 = cscμd i As0 sinμb2 + 1 − R s e2iμb2 δ0  s   ih s μ As0 sinμb1 S21 + iμ 1 − R s e2iμb2 S22 = cscμd −i As0 sinμb1 − eiμd δ0  +R s e2iμb2 e−iμd (3.14) where d = b2 − b1 and  S sjl =

Lj

G sjl (t)φ0 (t)dt, j, l = 1, 2

(3.15)

and δ0 =

4k0 hcosh2 k0 h 2k0 h + sinh2k0 h

(3.16)

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B. Sarkar et al.

Expressions for the anti-symmetric part φ a (x, y) can be derived by replacing sinμb j , sinhμb j (j = 1, 2) by cosμb j , coshμb j (j = 1, 2) respectively in (3.3).

4 Multi-term Galerkin Approximation Technique Now to solve Eqs. (3.12) and (3.13) for G sjl (t), we introduce (N + 1)-term Galerkin approximation of G sjl (t) are chosen as G sjl (t)

N 

a (n)s ψ (n) j (t), a j < t < h, j, l = 1, 2

(4.1)

n=0

where the suitable basis functions ψ (n) j (t)’s are chosen as (cf. Porter and Evans [10]) ψ (n) j (t) =

  21 2 2(−1)n   h − a j − (h − t)2 π (2n + 1) h − a j h   h−t , a j < t < h, j = 1, 2 U2n h − aj

(4.2)

Using (4.1) and (4.2) in Eqs. (3.12) and (3.13), we get the following system of equations N (n)s (11)s (n) (12) + n=0 a2l s Vmn s = −δ1l Wm(1) mn n=0 a1l V  (n)s (21)s (n)s (22)s N + n=0 a2l Vmn = −δ2l Wm(2) , m = 0, 1, 2, . . . n=0 a1l Vmn

N

N

(4.3)

where, ( j j)s cVmn =−

∞  δr αr hsinhαr b j r =1



eαr b j (k

+ ik0 h 2 Lj ( jl)s =− Vmn

r h)

      J2m+1 kr h − a j J2n+1 kr h − a j

(n) G j (y)ψ (m) j (y)ψ j (y)dy

∞  δr αr hsinhαr b1 r =1

Wm( j )s

2

eαr b2 (k

2

r h)

      J2m+1 kr h − a j J2n+1 kr h − a j

   I2m+1 k0 h − a j , j, l = 1, 2, m, n = 0, 1, . . . , N = (−1) k0 hcoshk0 h m

(4.4)

Also, using (4.1) in Eq. (3.15), we get the system of equations and rewrite them in matrix form as follows S = W V −1 (−W )T

(4.5)

Wave Scattering by Thin Multiple Bottom Standing Vertical Porous …

297

where  W =

W (1)s 0 0 W (2)s



 and V =

V (11)s V (12)s V (21)s V (22)s



Similar calculation can be done for the anti-symmetric part φ a (x, y) by replacing the expressions as discussed above.

5 Wave Force and Dissipation of Wave Energy From the linear Bernoulli’s equation, integrating the dynamic pressure discontinuity equations along the porous walls, we obtain the horizontal wave force acting on the walls as   s  s φ (b1 + 0, y) − φ s (b1 − 0, y) dy F = iρσ L1    s  s φ (b2 + 0, y) − φ (b2 − 0, y) dy (5.1) + L2

Similarly, we can also obtain F a for the anti symmetric part. Also, the non-dimentional wave force is given by |F s + F a | F0

(5.2)

ρgσ tanhk0 h k0

(5.3)

WF = where F0 =

Now, utilizing Green’s integral formula, the energy identity for porous walls can be derived as follows, |R|2 + |T |2 = 1 − J

(5.4)

where the expression for wave energy dissipation i.e. J is given by J = δ0

2   j=1

Lj

2    R G j (y) g s,a j (y) dy

  and R G j (y) is the real part of G j (y)( j = 1, 2).

(5.5)

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B. Sarkar et al.

6 Numerical Results and Discussions The numerical estimates for wave energy dissipation, reflection coefficients and wave force have been obtained by taking only three terms (N = 2) in Galerkin’s approximations of g s,a j (y), j = 1, 2. However, quite a good accuracy in the numerical results have been achieved considering single term (N = 0) in Galerkin’s approximation. A comparison between our present results and the results of Das et al. [7] obtained for double bottom standing thin barriers have been demonstrated in Table 1 with a1 = ah2 = 0.2, bh1 = 0.3, bh2 = 0.301, G j = 0. The compatibility between these two h results up to 2–3 decimal places asserts that the correctness of the present results. Again, Table 2 exhibits a comparison between the present results and the results of Mandal and Dolai [8] for single bottom standing thin vertical barriers by taking b1 = 0.001, bh2 = 0.0011, G j = 0. It is observed from the Table 2 that two results h are almost equal up to 2–3 decimal places. This again validates the exactness of the present method. The graphs of Fig. 10 in Lee and Chwang [9] for a single porous bottom standing barrier in finite depth have been recovered in Fig. 2 corresponding to G j = 0.5,1. Here the values of the other parameters are ah1 = ah2 = 0.25, bh1 = 0.001, bh2 = 0.0011. This also ratifies the correctness of our results. In Fig. 3, dissipation of wave energy i.e. J is depicted against Kh with ah1 = 0.35, a2 = 0.55, bh1 = 3.0, bh2 = 5.0; ah1 = 0.35, ah2 = 0.55, bh1 = 3.0, bh2 = 3.001 h and ah1 = 0.35, ah2 = 0.999, bh1 = 0.001, bh2 = 3.001 for four, two and single walls respectively. Here we take G j = 1 for the above three configurations. It is observed from Fig. 3 that as the number of walls increases the wave energy dissipation increases. Table 1 Comparison between the numerical estimates of Das et al.’s results for R1 and R2 and Present results for R with

a1 h

=

a2 h

= 0.2,

b1 h

b2 h

= 0.3,

= 0.301, G j = 0

Kh

R1

R2

R

0.2

0.436929

0.437559

0.439544

0.8

0.392339

0.393359

0.385276

1.4

0.088149

0.088774

0.0671994

Table 2 Comparison between the numerical estimates of Mandal and Dolai’s results for R1 and R2 and Present results for R with

a1 h

=

a2 h

=

a 1 b1 h , h

= 0.001,

b2 h

= 0.002, G j = 0

a h

R1

R2

R

0.2

0.2914

0.2923

0.299451

0.4

0.1397

0.1397

0.142861

0.6

0.0573

0.0573

0.0581811

0.8

0.0156

0.0156

0.013988

Wave Scattering by Thin Multiple Bottom Standing Vertical Porous …

Fig. 2 Graph of |R| against K h for

a1 h

=

a2 h

= 0.25,

b1 h

= 0.001,

b2 h

= 0.0011

Fig. 3 Graph of J against K h for four, two and single walls with G j = 1

299

300

B. Sarkar et al.

Fig. 4 Graph of |R| against K h for

a1 h

= 0.2,

a2 h

Fig. 5 Graph of W F against K h for

a1 h

= 0.45,

= 0.45,

a2 h

b1 h

= 0.25,

= 3.5,

b1 h

b2 h

= 2.5,

= 5.0

b2 h

= 4.0

Wave Scattering by Thin Multiple Bottom Standing Vertical Porous …

301

The effect of porosity is incorporated in Fig. 4 plotted by taking G j = 0.25, 1, 2+i; = 0.2, ah2 = 0.45, bh1 = 3.5, bh2 = 5.0. Figure 4 reveals that as the absolute value of the porous effect parameter increases |R| decreases. In Fig. 5, the non-dimensional wave force W F is depicted against Kh for different values of G j = 0.5, 1, 1 + i. These figure shows that as magnitude of the porous effect parameter G j increases the wave force decreases. a1 h

7 Conclusion Multi-term Galerkin’s approximation technique employed here to solve the boundary value problem. The basic functions are being chosen as Chebychev’s polynomials are multiplied by suitable weights. Some existing previous results are recovered in order to ratify the exactness and accuracy of the present method. Also, some interesting results are explored by our present work. The results are recapitulated below: • The amount of wave energy dissipation is observed to be increasing as the number of walls increases. • The absolute value of the porous effect parameter increases as the magnitude of reflection coefficients decreases. • It is also observed that as the magnitude of porous effect parameter increases wave force decreases. That is, porosity of walls helps to reduce the wave load on the walls. Acknowledgements All of the authors are grateful to Swami Vivekananda University for providing the facilities for carrying out this research work.

References 1. Dean, W. R. (1945). On the reflection of surface waves by submerged plane barriers. Proceedings of the Cambridge Philological Society, 41, 231–238. 2. Ursell, F. (1947). The effect of a fixed vertical barrier on surface waves in deep water. Proceedings of the Cambridge Philological Society, 43, 374–382. 3. Sollitt, C. K., & Cross, R. H. (1972). Wave transmission through permeable breakwaters. Coastal Engineering Proceeding, 1, 1827−1846. 4. Chwang, A. T. (1983). A porous wavemaker theory. Journal of Fluid Mechanics, 132, 395–406. 5. Yu, X. (1995). Diffraction of water waves by porous breakwaters. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, 121(6), 275–282. 6. Lee, M. M., & Chwang, A. T. (2000). Scattering and radiation of water waves by permeable barriers. Physics of Fluids, 1, 54–65. 7. Das, P., Dolai, D. P., & Mandal, B. N. (1997). Oblique water wave diffraction by two parallel thin barriers with gaps. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, 123, 163–171.

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8. Mandal, B. N., & Dolai, D. P. (1994). Oblique water wave diffraction by thin vertical barriers in water of uniform finite depth. Applied Ocean Research, 16, 195–203. 9. Mandal, B. N., & Chakrabarti, A. (2000). Water wave scattering by barriers (1st ed.). WIT Press. 10. Evans, D. V., & Porter, R. (1997). Complementary methods for scattering by thin barriers. International Services of Advanced Fluid Mechanics, 8, 1–44.

Mobile Learning Integration into Teaching Kinematics Topic in English in Vietnam High School Pham Thi Hai Yen and Ton Quang Cuong

Abstract In the Vietnam National Education Program (2018) the chapter “Kinematics” has been constructed as fundamental part for 10th grade Physics curriculum. The understanding of concepts and laws, practice with experiments should be a serious target and essential subject of study and competence development for students. The application mobile devices and solution support the flexibility and effectiveness in the learning process. Using English as a medium of instruction is a phenomenon and is of significant challenge for both teachers and students in Vietnam schools today. Teaching Physics for high school students requires the balance between the teaching experiential science (subjects in specialization), foreign languages (English in particular), and new technology integrated learning approaches. This paper examined the relationship between motivation and meaningful learning for high school students with mobile App and mobile learning approaches implemented in Kinematics studying in English. Keywords Mobile apps · Mobile learning (m-learning) · Kinematics · Smartphone · Teaching physics in English

1 Introduction The COVID-19 pandemic has impacted the education industry, where the feasibility of implementing physical education classes is limited. In response to the pandemic situation, schools in Vietnam have introduced mandatory online, blended (including and mobile) learning courses that allow students to keep learning (“disruptive classes P. T. H. Yen (B) Foreign Language Specialized School, University of Languages and International Studies-VNU, Hanoi, Vietnam e-mail: [email protected] T. Q. Cuong VNU University of Education, Hanoi, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_25

303

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but undisruptive learning”). This process provides students’ new learning mode and possibility that involves a seamless interaction between the learning content and the learning community [1, 7]. Mobile learning (m-learning) with specific platform, solution and devices allow learners to access and engage in formal learning activities beyond the physical classroom and access learning resources without schedule restrictions [1] as well as expand learning environment and experiences. Furthermore, m-learning functions support collaborative learning by facilitating interactions within the learning community such as holding remote group discussions, sharing documents, placing questions, self-testing [4]. Many empirical studies have confirmed the benefits of m-learning such as increasing learner creativity [5], enhancing self-regulation, self-directed, selfdetermined learning [11], building collaborative capacity as well as improving student learning outcomes and competence [2]. Applying m-learning in studying physics in English (kinematics topic) helps increase students’ interest and motivation in learning, from which students master knowledge and understand concepts and characteristics, deploy laws of physics in English through experiments, and explore simulations right on their own mobile phones. From there, it helps also to solve some difficulties in learning physics in English in particular and can be considered as a solution to support the teaching of natural sciences in English in general. Files are updated at a later stage.

2 The Mobile Learning Prospectus Currently, in the literature and practice, there are many conceptions of m-learning, but focusing on two main trends: (i). The trend of linking Mobile learning with the use of technology devices, tools, applications in learning process, including access of learning content through mobile devices [3, 4, 6, 8]. (ii). The trend of linking Mobile learning with the mobility of learners: M in the term Mobile learning stands for “MY” (“The learner himself”) which represents learning (any+ ) such as anytime, anywhere, with anybody, anything etc. Hence, Mobile learning means a new form of unique and ubiquitous learning services for “mobile” students [3, 11, 12]. In short, Mobile learning refers the “mobility fitness” and the relevant integration of technology and in specific content knowledge areas of learning in combination with pedagogical setting. Winters [13] stated four views on mobile learning: technology-centric, e-learning, strengthening formal learning, and learner-centered practice. Mobile learning may be conducted by formal, informal and non-formal learning with Open Course Ware (OCW), Massive Open Online Courses (MOOC), and Small Private Open Courses (SPOC) designed for personal Physics topics in particular [3].

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Thus, we can understand Mobile learning as a transitional step of E-learning. Mobile learning focuses on exploiting the initiative of learners and the ability to interact with learning resources through mobile technology and mobile devices. This is a form of learning in which learners themselves can learn at any time, any place, thanks to the support of mobile devices such as cell phones, tablets, etc. Do some learning activities even without a wifi or 3G/4G connection, thanks to the information storage capacity of these mobile devices and the services of the mobile network provider. According to this approach, we believe that m-learning is not only learning and training approach, but the management and sharing of content and interaction is done through the use of mobile devices based on wireless network technology. However, when deploying mobile applications for teaching, several difficulties must be addressed. One of these concerns is how m-learning influences how students learn when they use little chunks of knowledge provided by separate, but complimentary, applications [5]. There are some discussions which evolve notions of knowledge and learning as a result of the impact of the need to organize and work with small pieces of knowledge in m-learning, as well as the possibility of creating individual ontologies as each learner navigates their own learning journey by using different applications or following different learning paths with them. Hence, Holzinger A., Jahnke, I., Liebscher, J concluded that the usage of mobile devices in short periods of time suggests creating apps to provide learners with little amounts of information, which necessitates the division of a topic into various independent components [5, 6].

3 Teaching Physics in English Based on Mobile Learning Based on the results of theoretical and practical research on teaching physics in English according to the mobile learning approach, the research has been selected and implemented for students to study in the following directions: – Teachers assign tasks to students and receive feedback via SMS, Zalo, FB Messenger, Microsoft Teams, Gmail, Google classroom, Google drive etc.; – Guide students to participate in virtual classes, exploit electronic learning resources for self-study, self-assessment. – Store digital learning materials, install applications on smartphones to guide students in learning when studying directly in class and studying at home. – Organize classroom teaching on the basis that students have prepared before class by learning with electronic learning materials. – Organizing for students to carry out learning projects, testing experiments with a combination of mobile phones and available experimental devices. – Organize practice, knowledge consolidation, online and face-to-face combined assessment.

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Using mobile learning to support students in learning physics in English has brought the following new and positive factors – – – – – – – –

Highly personalized self-study Contributing to creating self-learning motivation for students Expand collaboration and enhance communication: “Self-generated” learning resources Allow teachers to promptly support and assign tasks to students. Students have the opportunity to perform some of the functions of the teacher Cost saving Time saving

However, the learning design based on m-learning with Kinematics chapter must be meet the pedagogical criteria following English support as a medium of instruction: – In accordance with the purposes, requirements, content, and methods of implementing physical learning activities in English for high school students. – Promote the relationship between teaching and self-study, in which the teacher’s teaching effect is external to support internal activities (students’ self-study activities are a decisive factor in the development of students’ self-study). – Ensure the formation of learning skills for students from low to high, in accordance with the cognitive development of students – Paying attention to the stage of receiving and processing feedback on students’ self-study results in order to direct teachers to timely offer help and adjust students’ self-study activities when necessary. – Combine with other methods and ways of organizing learning to contribute to diversifying students’ learning activities, exploiting the strengths of each measure and at the same time overcoming limitations in exploiting students’ learning activities. Exploit a number of applications on mobile phones to support students in learning physics in English.

3.1 Physics Lessons Design in English The process of teaching natural sciences in general and physics in particular in English in high schools basically follows the same steps as teaching in Vietnamese. However, using a foreign language to present the teaching plan, teachers need to build a system of vocabulary and specialized terms related to the content of the lesson and can send it to students in advance. When students come to class, it will be easier to absorb and be more active and confident when participating in class. When consulting some teachers who have directly participated in teaching natural science subjects in English in high schools in Vietnam, we have found that the lesson design

Mobile Learning Integration into Teaching Kinematics Topic in English …

307

is still critical. The teachers face many difficulties in terms of the level of English proficiency, class activities and management, the new balance of Physics content knowledge and English as the medium of instruction, and technology skills applied in teaching. Thus, we realized that there is a need for a process to design a teaching plan for teachers to refer to and to prepare well for physics lectures in English. Step 1: Determine the lesson objectives Step 2: Build a system of vocabulary/sentence patterns related to the lesson Step 3: Prepare experiments, simulations, IT applications to support the learning of content related to the lesson. Step 4: Develop the content of the teaching plan and organize teaching activities Step 5: Guide students to do physics exercises in English Step 6: Homework assigned The Kinematics is the second chapter of 10th grade in new Vietnam Education Program for high school Physics Curriculum (totally, it consists of 7 chapters). In the lower grades, students have access to knowledge related to motion but at a simple, brief and qualitative level. This chapter will provide students with a deeper, broader, more systematic and complete knowledge of motion in general and simple mechanical movements in particular (Table 1).

3.2 Mobile Apps Design for Physics Lesson Mobile app-based solutions allow for a more customized teaching approach that is also more successful. When utilized in the classroom, mobile app tools, which are generally more tailored, provide an opportunity to improve specific abilities and read texts in an e-environment quickly and efficiently. Solutions based on mobile applications are adaptable to encourage students with an appropriate speed for their learning process, using resources and needed skills making the learning process more productive [3]. Hence, when selecting to employ mobile applications in the Physics learning process, the teacher should be certain that this is the most effective instrument available. Based on observation and survey of students’ learning needs in the Foreign Language Specialized High School, University of Languages & International Studies-VNU, Hanoi, Vietnam the Mobile App so-called “CNN physics” has been designed and implemented in teaching Kinematics chapter (See Annex of Program Code). The CNN physics application is developed with the main function (See Table 2) of supporting 10th grade students in learning physics in English (learning and practicing pronunciation, practicing multiple choice questions by topic). With this App the teachers can organize various flexible activities in-and-or outside classroom with blended, flipped learning approaches such as warm-up, checking previous lessons content understanding and skills, assign new tasks for self-study as well as self-testing (Physics terminology or content in English, vocabulary, formulas etc.) for students before-during and after-class (See Fig. 1). Students can integrate

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Table 1 Contents and requirements for the chapter “Kinematics” (10th grade, National Physics curriculum. https://bit.ly/3InT64n) Content National education program requirements knowledge of kinematics section Describing motion Arguing to derive the formula for calculating average speed, defining speed in one direction From pictures or practical examples, the displacement can be defined Compare distance traveled and displacement Based on the definition of speed in one direction and displacement, a formula for calculating and defining velocity can be derived Perform the experiment (or based on the given data), plot the displacement−time in linear motion Calculate the speed from the slope of the displacement–time graph Determine the total displacement, the total velocity Apply the formula for calculating speed and velocity Discuss to design the plan or select the plan and implement the plan, measure the speed with practical tools Describe some common speed measurement methods and evaluate their advantages and disadvantages Uniformly variable Performing experiments and reasoning based on the change of velocity in motion linear motion, derive the formula for calculating the acceleration; State the meaning, unit of acceleration Carry out the experiment (or based on the given data), plot the velocity–time graph in linear motion. Apply velocity–time graph to calculate displacement and acceleration in some simple cases Derive formulas for uniformly variable linear motion (not using integrals) Apply the formulas of uniformly variable rectilinear motion Describe and explain motion when an object has a constant velocity in one direction and a constant acceleration in a direction perpendicular to this direction Discuss to design the plan or choose the plan and implement the plan, measure the free fall acceleration with practical tools To be able to carry out a project or research project to find the conditions for throwing objects in the air at a certain height to achieve the greatest height or range Table 2 Function authorization table of apps

Function

Students performance

Log out

Yes

Log in

Yes

See list of topics

Yes

Search topic

Yes

Study topics, watch video lectures

Yes

Learn vocabulary by topic

Yes

Take quizzes by chapter

Yes

View multiple choice answers

Yes

View test results

Yes

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Fig. 1 The app “CNN physics” user interface screenshot (the Topic 1 self-study and vocabulary practice)

App for their own learning purpose, pace, activities, self-assessment etc. in connective and collaborative environment (See Fig. 2).

4 Research Design A survey was conducted to get student views on the usage of mobile applications for kinematics lesson taught in English. The use of a survey (usually in the form of a Likert-type questionnaire) is a common research approach in many studies on mobile learning. The questionnaire mentioned here consists of 20 items using a Likert 5point scale format (Strongly agree = 4, Agree = 3, Neutral = 2, Disagree = 1, and

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Fig. 2 The app “CNN physics” screenshot (The practice, self-testing)

Strongly disagree = 0 on the Likert scale), and 122 students completed the survey (from 10th grade in High School for Gifted, Lao Cai province, Vietnam). Table 3 presents the findings of the investigation. The proportion of replies obtained for each Likert value, as well as the average value and standard deviation, are shown in the row corresponding to each question. The reliability of Cronbach’s Alpha is shown in the Table 4. Most of the values in the column Cronbach’s Alpha if Item Deleted < Cronbach’s Alpha and all values in the column Corrected Item-Total Correlation > 0.3 → Most of the observed variables meet the criteria and do not need to be removed. Cronbach’s Alpha in the above measurement has a value of > 0.7 (= 0.967), which means that the reliability of the scale is evaluated well. Of the total 122 answers, up to 95.9% of students answered Strongly agree. This is a very positive response to the trend of using mobile learning in learning physics in English in particular and natural sciences in English in general.

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Table 3 Questions and results of the app “CNN physics” usage Questions

Average

1. Flexible use to study at any time

3.64

2. Learn more when using smartphones in kinematics lesson

3.61

3. Help to do physics exercises in english better and faster

3.7

4. Support learning physics vocabulary in english better

3.57

5. Support self-conducting/directed physics experiments

3.61

6. Help to process experimental data in physics experiments

3.67

7. Help to learn the applications of physics in practice

3.65

8. Help to find a new way of learning in high school

3.7

9. Help to design more effective activities

3.76

10. Develop interaction with other students

3.69

11. Develop interaction with teachers

3.52

12. Develop effective teamwork

3.67

13. Develop online instant test (including gamification)

3.62

14. Develop smart phone skills in learning physics and english

3.61

15. Acceptance and satisfaction with using smartphones and app

3.54

16. Ensure effectiveness of using smartphone and app

3.62

17. Ensure interest of using smartphone and app

3.66

18. The costs associated with mobile learning

3.50

19. The costs associated with smartphone and app

3.61

20. Internet connection and speed as priority m-learning

3.68

Table 4 Reliability statistics

Cronbach’s alpha

No. of. items

0.967

20

5 The Findings In this study the use of m-learning in Kinematics lesson in English (as a medium of instruction) may be understood in two perspectives: an innovative and new way of teaching that increases student flexiblity, self-directed learning, and the use of mobile tools, devices and solutions with student mobility. This mobile App “CNN physics” technology not only guided and supported pupils in learning Kinematics, anticipated to address shortcomings in traditional physics lesson, but encouraged them into English academic communicative context, focused on formal representations of standardized physics problems, phenomena as well as expanded their motivation and self-confidence in English resources.

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Moreover, the study results show significant level of motivation stimulated by the use of the App “CNN physics” in the learning process. This turns to the ARCS model of motivation consisting of four basic dimensions: attention, relevance, confidence, and satisfaction [3, 8–10]. Attention (A): the use of mobile App “CNN physics” with relevant devices (smartphone, tablet, iPad) in new circumstances attempts (learning Kinematics and using English as a medium of instruction) forces learner curiosity, interest, passion, and creativity. Students’ curiosity is piqued from the start, resulting in enthusiastic involvement (from Q2 to Q11). Mobile device integration in the classroom must be creative and comprehensive (from Q16 to Q20). Relevance (R): the students’ perceptions of a link between the creative element incorporated in the learning process and their own experiences requirements, ambitions, and preferences in terms of both Kinematics learning and English usage for presentations, exploring and experimenting physics issues (from Q12 to Q14). Confidence (C): the relation between student’s, readiness, acceptance, sense of personal control and their anticipation of success in the learning process, and the completion of the learning outcomes (Q10 and Q12). Finally, Satisfaction (S): the arguments and suggestions connected to students’ attitudes toward the learning process and outcomes (Q1, Q2, Q8).

6 Conclusion Today, the use of mobile applications in the learning process grows rapidly which makes popular utilized tools in the new way of Physics classrooms teaching. However, the with the application of IT in teaching science subjects, mobile learning in teaching physics in English in particular is still limited in Vietnam high school practice. The implementation App “CNN physics” in teaching in English (as a medium of instruction) encourages students’ activities, curiosity, motivation and creativity of both Physics learning phenomena and English academic communication. The challenges of m-learning can be observed with paying attentions on acceptance and acquisition technology, digital skills for using mobile apps, pedagogical settings, teachers’ professional competence, appropriate learning resources as well as re-designing and integrating frequency, purposefulness of technology use into curriculum and lesson.

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Annex Program code import Vocabulary from "../models/vocabulary"; export const getAllVocabularyByTopic = async (req, res) => { try { const vocabularies = await Vocabulary.find({ topic_id: req.params.topic_id, }).populate("topic_id").sort({"createdAt": 1}); return res.status(200).json({ success: true, vocabularies, }); } catch (error) { res.status(500).json({ success: false, message: "Internal server error" });} }; export const createVocabulary = async (req, res) => { const { vocabulary, listVocabulary } = req.body; console.log(listVocabulary); if (!listVocabulary && !vocabulary) { return res .status(404) .json({ success: false, message: "vocabulary is required" }); } if(listVocabulary){ try { await Vocabulary.insertMany(req.body.listVocabulary); return res.status(200).json({ success: true, message: "Quiz saved successfully", }); } catch (error) { res.status(500).json({ success: false, message: "Internal server error" }); } }else{ try { const newVocabulary = new Vocabulary(req.body); await newVocabulary.save(); return res.status(200).json({ success: true, message: "Quiz saved successfully", vocabulary: newVocabulary, }); } catch (error) { res.status(500).json({ success: false, message: "Internal server error" }); } } }; export const updateVocabulary = async (req, res) => {

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const { vocabulary } = req.body; if (!vocabulary) { return res .status(404) .json({ success: false, message: "vocabulary is required" }); } try { const UpdateConditions = { _id: req.params.id }; const updateVocabulary = await Vocabulary.findOneAndUpdate( UpdateConditions, req.body, { new: true, } ); if (!updateVocabulary) { return res.status(404).json({ success: false, message: "User not authorised to update vocabulary", }); } return res.status(200).json({ success: true, message: "User updated quiz successfully", vocabulary: updateVocabulary, }); } catch (error) { res.status(500).json({ success: false, message: "Internal server error" }); } }; export const deleteVocabulary = async (req, res) => { try { const DeleteConditions = { _id: req.params.id }; const deleteVocabulary = await Vocabulary.findOneAndDelete( DeleteConditions ); if (!deleteVocabulary) { return res.status(404).json({ success: false, message: "User not authorised to delete Vocabulary", }); } return res .status(200) .json({ success: true, message: "Delete Vocabulary successfully" }); } catch (error) { res.status(500).json({ success: false, message: "Internal server error" }); } } Acknowledgements This research has been completed under the sponsorship of the University of Languages and International Studies (VNU ULIS) under the Project No. N.22.06.

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References 1. Bernacki, M. L., Crompton, H., & Greene, J. A. (2020). Towards convergence of mobile and psychological theories of learning. Contemporary Educational Psychology, 60, 101828. https:/ /doi.org/10.1016/j.cedpsych.2019.101828 2. Chang, W.-H., Liu, Y.-C., & Huang, T.-H. (2017). Perceptions of learning effectiveness in Mlearning: Scale development and student awareness. Journal of Computer Assisted Learning, 33(5), 461–472. https://doi.org/10.1111/jcal.12192 3. Cuong, T. Q., Bich, N. T. N., & Chung, P. K. (2020). Giáo trình lí luâ.n và công nghê. da.y ho.c. NXB ÐHQGHN 4. Diacopoulos, M. M., & Crompton, H. (2020). A systematic review of mobile learning in social studies. Computers & Education, 154, 103911. https://doi.org/10.34190/ejel.20.5.2612 5. Holzinger, A., Nischelwitzer, A., & Meisenberger, M. (2005). Mobile phones as a challenge for m-learning: Examples for mobile interactive learning objects (MILOs). In Pervasive Computing and Communications Workshops (pp. 307−311). 6. Jahnke, I., & Liebscher, J. (2020). Three types of integrated course designs for using mobile technologies to support creativity in higher education. Computers & Education, 146, 103782. https://doi.org/10.1016/j.compedu.2019.103782 7. Jimmy, D., & Clark, M. Ed. (2007). Learning and teaching in the mobile learning environment of the twenty-first century. Texas. 8. Kearney, M., Burden, K., Schuck, S. (2020). Theorising and implementing mobile learning: Using the iPAC framework to inform research and teaching practice (pp. 101–114). Springer Singapore. https://doi.org/10.1007/978-981-15-8277-6_8 9. Keller, J. (2009). Motivational design for learning and performance: The ARCS model approach. Springer Science & Business Media. 10. Keller, J. (2011). Instructional materials motivation scale (IMMS). Unpublished manuscript. The Florida State University. 11. Hogue, R. J. (2011). An inclusive definition of mobile learning. http://rjh.goingeast.ca/2011/ 07/17/an-inclusive-definition-of-mobilelearning-edumooc/ 12. Traxler, J. (2007). Current state of mobile learning. International Review on Research in Open and Distance learning, 8(2). 13. Winters, N. (2006). What is mobile learning. In M. Sharples (Ed.), Big issues in mobile learning: Report of a workshop by the Kaleidoscope Network of Excellent Mobile Learning Initiative (pp. 5–9). University of Nottingham. 14. Zheng, L., Li, X., & Chen, F. (2016). Effects of a mobile self-regulated learning approach on students’ learning achievements and self-regulated learning skills. Innovations in Education and Teaching International, 1–9. https://doi.org/10.1080/14703297.2016.1259080

On Density of Grid Points in l ∞ -Balls Nilanjana G. Basu , Partha Bhowmick , and Subhashis Majumder

Abstract Finding the minimum and the maximum densities for axes-parallel squares, cubes, and hypercubes, cast in the integer space, is an important problem in the domain of digital geometry. In this work, we study different variations of this problem and solve a number of them. Interestingly, the extremum values for integer sizes sometimes differ from those for real sizes, and hence, we have studied and analyzed them separately. Further, the results and proofs in 2D readily extend to higher dimensions, and hence we could get simple-yet-novel theoretical results for the extremum densities for l∞ -balls in general. As ‘density’ provides a measure of how a set of points bounded by a region is relatively more concentrated or sparse, it has applications in image analysis, social networking, complex networks and related areas, apart from different branches of physical science. Hence, our results are fundamental in the understanding of locating the density minima and maxima in a discrete space of an arbitrarily large dimension. Keywords Digital square · Digital cube · Digital hypercube · Digital geometry · Pixel density · Geometry of numbers

N. G. Basu (B) · S. Majumder Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India e-mail: [email protected] URL: https://www.heritageit.edu/CSE.aspx S. Majumder e-mail: [email protected] P. Bhowmick Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India e-mail: [email protected] URL: https://cse.iitkgp.ac.in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_26

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1 Introduction In the domain of discrete and computational geometry, acquiring the knowledge of a particular fact is of paramount importance—how dense or how clustered a specific set of points is, compared to a given set within a given shape or region. It has been easily noticed in the domain of Social Networking and Complex Networks which got established as some of the mostly popular areas recently.

1.1 Existing Work In 2D, ‘Density of points’ of an unweighted set of points is expressed as the number of points per unit area [11]. In case of a weighted set of points, it is the sum of the weights divided by the area of that region. Later Basu et al. [3] proposed algorithms for finding maximum- and minimum-density regions for higher dimensions. In the domain of digital geometry, quite a handful of work [7, 10, 12, 13] has been done related to digital discs and digital balls defined on square or non-square grid. Bhowmick et al. [6] presented some theoretical results for polygonal covers of digital discs. Also, there are some more research works on different algorithmic techniques and their analyses related to the construction of digital circle [1, 2, 5, 15]. There are several more research papers [8, 9, 14–17] related to counting of digital discs and digital circles, their encoding and recognition, and on estimation and measures of related parameters. In the domain of digital geometry, in a recent work Basu et al. [4] considered the case where a given set of points having uniform weight is located at every grid point of a uniform rectilinear grid and proven some novel results for circular regions. All the above mentioned works have motivated us to the research on some nonEuclidean distance metrics with norms like l∞ or l 1 and observe if the findings over different norms somehow correlate. In this work, we have tried to work on a variation using the l∞ norm and proved some interesting results on density extrema of l ∞ -Balls of integer and real length. For easy reading, we use ‘square’, ‘cube’ and ‘hypercube’ respectively for such balls in 2D, 3D and in nD (n > 3). We could identify the minimum and the maximum densities for both integer and real length for all dimensions starting from 2D, and hence there remains no gap from our targets in solving the problems related to l ∞ -balls.

1.2 Our Contribution In this work, we consider that the given set of points having uniform weight is located at every crossing point of a uniform rectilinear or cuboidal grid. The grid is conceived as the integer plane, i.e., Z2 , or as the integer space, i.e., Z3 , for simplicity. The grid

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points are equivalent to integer points in our framework. We have also extended our research to n-dimensional grid and the grid in that case is represented as the ndimensional integer hyperspace. We have identified the location of the axes-parallel squares (cubes, hypercubes) with maximum and minimum densities when the length of a side can be any integer or a real number and the centroid of the square (cube, hypercube) might or might not be aligned with a grid point.

2 Maximum Density 2.1 Squares of Integer Length An integer point or pixel is defined as an element of Z2 ; i.e., it is a two-dimensional point having integer coordinates. We denote by S a an axes-parallel square of length a. Note that all the squares that we consider in this manuscript are axes-parallel where the axes are aligned with the underlying rectangular grid and hence for brevity, we will not repeat the term ‘axes-parallel’ henceforth. The set of pixels contained in S a is denoted by Sa := S a ∩ Z2 , and is referred to as a digital square. The cardinality of Sa is denoted by |Sa|, and the density of pixels in Sa is given by sa := |Saa2 | . As in this section, we consider only squares with integer length, we first observe the following. Observation 1 The number of pixels in Sa with the same x-coordinate (y-coordinate) is either a or a + 1. Further, the former arises if neither of the horizontal (vertical) sides contains any pixel, whereas the latter arises if each of them contains either a or a + 1 pixels. From the above observation, we obtain three mutually exclusive and exhaustive cases for pixel count in Sa, as stated in the following lemma and illustrated in Fig. 1. Lemma 1 For a square S a of integer length a, one of the following three cases always holds. (i) |Sa | = a 2 if the boundary of S a contains no pixel (array with a rows and a columns).

Fig. 1 Three possible cases of pixel containment by squares of integer length

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(ii) |Sa | = a(a + 1) if only two opposite sides contain pixels (array with a rows and (a + 1) columns). (iii) |Sa | = (a + 1)2 if all four sides contain pixels (array with (a + 1) rows and (a + 1) columns). Proof Observation 1 implies that Sa contains pixels with a or (a + 1) distinct x- and with a or (a + 1) distinct y-coordinates. It also states the equivalence of this count with the presence or absence of pixels on the four sides of S a . From this count and the equivalence, the three cases are immediate. ∎ Theorem 1 Out of all squares of integer length, the ones with maximum density are unit squares. Proof By Lemma 1, the density of S a is at most (a+1) = (1+ a1 )2 , which evaluates to a2 4 if and only if a = 1, and to a smaller value for any other value of a. In particular, the density is 4 for a unit square only when its four corners coincide with four pixels. ∎ 2

2.2 Cubes with Integer Side We extend the result of Sect. 2.1 to derive a similar result for (axes-parallel) cubes of integer length. We consider here a uniform 3D grid so that the grid points are in bijection with 3-dimensional points with integer coordinates, i.e., with Z3 . We denote by Ba an axes-parallel cube of side a. The set of voxels contained in Ba is denoted by Ba: = Ba ∩ Z3 , and is referred to as a digital cube. The cardinality of Ba is denoted by |Ba|, and the density of voxels in Ba is given by ba := |Ba 3a | . We now denote by B{p} a cube passing through any point p ∈ R3 . B{p} is the digital cube corresponding to B{p} , so B{p} := B{p} ∩ Z3 , and by b{p} denotes the density of voxels in B{p} . A natural extension of Observation 1 to 3D reduces the number of cubes from infinte to a countable collection and thus helps in proving the next lemma. Since B{p} has an integer length, for every pair of opposite, each of these two faces will contain either no voxel or the same number of voxels. Further, if these two faces contain voxels, then translating the cube by a small amount along the direction orthogonal to these faces will result in decreasing the number of voxels in B{p} leading to the following observation. Observation 2 If a particular face of a cube does not contain any voxel, then it can be translated to a position so as to increase its density. The above observation implies that the maximum density cube having integer length will come from the countable collection {B{q} : q ∈ Z 3 }, i.e., max{b{ p} : p ∈ R 3 } = max{b{q} : q ∈ Z 3 }.

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Fig. 2 The cube length is 2. Left: Cube containing maximum possible voxels. Right: Cube containing minimum possible voxels. Voxels contained by the cubes are shown in red

Like squares, we consider only the cubes containing more than one voxel, since the containment of a single voxel trivially degenerates to the limiting case of infinite density. The case where a cube contains exactly k voxels is referred to as “k-voxel containment”. From the above observation, we can infer that a cube will attain maximum density if it contains voxels on all of its faces (refer Fig. 2). In particular, we have the following lemma. Lemma 2 For a given positive integer a, any cube Ba will have the maximum density if it contains (a + 1)3 voxels. Proof By Lemma 1, the maximum value of |Sa | is (a + 1)2 . If we represent a digital cube as the union of (a + 1) digital squares placed parallel to the xy-plane and at (a + 1) consecutive z-values, it attains the maximum density if each of these (a + 1) digital squares contains exactly (a + 1)2 pixels. So we can conclude that for a given integer a, any cube Ba will contain at most (a + 1)3 voxels. For a given integer a, any square of length a containing pixels as corner points attains the maximum density. Similarly, a cube of length a containing voxels as corner points will also attain the highest density. ∎ Lemma 2 leads to the following theorem. Theorem 2 In the collection of all cubes of integer length, the ones with maximum density are unit cubes. Proof By Lemma 2, the density of Ba is at most (a+1) = (1 + a1 )3 , which evaluates a3 to 8 if a = 1, and to a smaller value for any other value of a. In particular, the density is 8 for a unit cube only when its eight corners coincide with eight voxels. ∎ 3

2.3 Hypercubes with Integer Side In this section, we extend our result to n-dimensional hypercubes. An n-dimensional hypercube is formed from two (n − 1)-dimensional hypercubes by connecting their

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corresponding vertices in the n-th dimension; for example, a 4-dimensional hypercube can be created by connecting each of the eight vertices of one cube with its corresponding vertex of the other cube, as discussed in Sect. 2.2. We denote by H a an axes-parallel hypercube of length a. The set of pixels contained in H a is denoted by H a := H a ∩ Z n , and is referred to as a digital hypercube. We refer to every point of Z n as a ‘hypervoxel’. The cardinality of Ha is denoted by |Ha|, and the density of hypervoxels in Ha is given by h a := |Ha na | . We consider here a uniform n-dimensional grid so that the grid points are in bijection with n-dimensional points with integer coordinates, i.e., with Z n . Like squares and cubes, for hypercubes also “k-hypervoxel containment” is considered to avoid the limiting case of infinite density. For hypercubes, we make the following observation in a way that is similar to Observation 2. Observation 3 If any hyperface of a hypercube does not contain any hypervoxel, then it can be translated to a position so as to increase its density. From the above observation, we can infer that a hypercube will attain maximum density if it contains hypervoxels on all of its hyperfaces and we have the following lemma. Lemma 3 For a given positive integer a, any hypercube H a will have the maximum density if it contains (a + 1)n hypervoxels. Proof By Lemma 2, the maximum value of |Ba | is (a + 1)3 . Similarly for ndimensions, the maximum value of |Ha | is (a + 1)n . Like cubes, a maximum-density hypercube of length a will contain hypervoxels at all its corner points. ∎ Lemma 3 culminates in the following theorem. Theorem 3 Among all the hypercubes of integer length, the ones with maximum density are unit hypercubes. Proof By Lemma 3, the density of H a is at most (a+1) = (1 + a1 )n , which evaluates an n to 2 if a = 1, and to a smaller value for any other value of a. In particular, the density is 2n for a unit hypercube only when its 2n corners coincide with 2n hypervoxels. ∎ n

2.4 Squares with Real Side In this section, we are considering only axes-parallel squares S a with real length a. Clearly, the number of pixels in S a with the same x-coordinate (y-coordinate) is at most [a + 1]. Please refer to Fig. 3. We have this interesting observation. Observation 4 For a given positive real number a, any real square S a will accommodate at most (a + 1)2 pixels.

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Fig. 3 Five possible cases of pixel containment by squares of real length

Theorem 4 In the collection of all squares of real length, the ones with maximum density are unit squares. Proof By the Observation 4 sa ≤

[a + 1]2 (a + 1)2 1 ≤ = (1 + )2 < (1 + 1)2 ∀a > 1 ⇒ sa < 4 ∀a > 1 a2 a2 a

In particular, the density is 4 for a unit square only when its four corners coincide with four pixels. ∎

2.5 Cubes with Real Side If we represent a digital cube as the union of [a + 1] digital squares placed parallel to the xy-plane and at [a + 1] consecutive z-values, it attains the maximum density if each of these [a + 1] digital squares contains exactly [a + 1]2 voxels. Observation 5 For a given real number a, any cube Ba will contain at most [a + 1]3 voxels. Theorem 5 In the collection of all cubes of real length, the ones with maximum density are unit cubes. Proof By Observation 5, the density of Ba is at most ba ≤

[a+1]3 , a3

[a + 1]3 (a + 1)3 1 ≤ = (1 + )3 < (1 + 1)3 ∀a > 1 ⇒ ba < 8 ∀a > 1 3 a a3 a

In particular, the density attains the value of 8 only for a unit cube, especially when its eight corners coincide with eight hypervoxels. ∎

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2.6 Hypercubes with Real Side Theorem 6 In the collection of all hypercubes of real length, the ones with maximum density are unit hypercubes. Proof The density of H a is at most ha ≤

[a+1]n , an

[a + 1]n (a + 1)n 1 ≤ = (1 + )n < (1 + 1)n ∀a > 1 ⇒ h a < 2n ∀a > 1 n n a a a

In particular, the density attains the value of 2n only for a unit hypercube, especially ∎ when its 2n corners coincide with 2n voxels.

3 Minimum Density We present here some results for finding the squares (cubes, hypercubes) with minimum density. In this section, we use “integer square” (“real square”) to mean a square of integer (real) length. A square (cube, hypercube) without any pixel (voxel, hypervoxel) has zero density and is disregarded from our consideration. Hence, we consider those with at least one pixel (voxel, hypervoxel). Note that for decreasing the density of a square (cube, hypercube), we can keep increasing its area (volume, hypervolume) without altering its set of pixels (voxels, hypervoxels). In other words, the density of a square (cube, hypercube) with pixels (voxels, hypervoxels) on its boundary can easily be decreased by decreasing its area (volume, hypervolume) by an infinitesimal amount. Hence, for finding a square (cube, hypercube) with minimal density, the candidate squares (cubes, hypercubes) will be the maximal squares (cubes, hypercubes) with no pixels (voxels, hypervoxels) on their boundary.

3.1 Squares with Integer Side Observation 6 For any positive integer a, Sa will contain at least a2 pixels. We have the following theorem, which is simple but important in the context of our work. Theorem 7 In the collection of all integer squares, the ones with minimum density are those with no pixels on their boundaries and the value of the minimum density is unity. Proof Any integer square of length a can be positioned such that it has no pixel on its boundary. This minimizes its density to 1. By changing the value of a, the density

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cannot be reduced further, because Sa always contains at least a2 pixels (Observation 6), whence the proof. ∎

3.2 Cubes with Integer Side We present here some results for finding the minimum-density cube. In this section, we use “integer cube” to mean a cube of integer length. We repeat here that a cube without any voxel has zero density and is disregarded from our consideration. We consider those with at least one voxel. Observation 7 For any positive integer a, Ba will contain at least a3 voxels. Theorem 8 In the collection of all integer cubes, the ones with minimum density are those with no voxels on their boundaries. Proof Any integer cube of length a can be positioned such that it has no voxel on its boundary. This minimizes its density to 1. Again by changing the value of a, the density cannot be reduced further, because by Observation 7 Ba always contains at ∎ least a3 voxels.

3.3 Hypercubes with Integer Side Note that the earlier results can easily be extended to higher dimensions as a hypercube of dimension n will contain at least an hypervoxels, which lands us into the theorem below. Theorem 9 In the set of all integer hypercubes, the ones with minimum density are those with no hypervoxels on their hyperfaces and their density becomes unity. Proof Any integer hypercube of length a can be positioned such that it has no hypervoxel on its boundary. This will make its density equal to 1. The density cannot be reduced further, because H a always contains at least an hypervoxels, whence the proof. ∎

3.4 Squares with Real Side We now identify the minimum-density squares with real length. Consider Fig. 4. At the left, there is a square of length 5.5 unit containing 6 × 5 pixels and after translating the same square to the left, it contains 5 × 5 pixels (right). We can readily generalize this into the following observation.

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Fig. 4 Real squares of same size containing 6 × 5 pixels (left) and 5 × 5 pixels (right)

Fig. 5 5 × 5 Pixel containment by squares of real length

Observation 8 Let k ∈ N. If a maximal square contains k × (k − 1) pixels, then a square of the same size exists that contains only (k − 1) × (k − 1) pixels, and its density will be lower. Hence, for minimum density, it suffices to consider only those squares that contain a set of pixels of k × k form. We also have another observation. Observation 9 For any positive real a, if Sa is a square containing k × k pixels, where k ≥ 2, then (k − 1) ≤ a < (k + 1) (Refer to Fig. 5 with k = 5). It is not difficult to see that the above observation leads to the following statement. For any integer k ≥ 2, a square with side just less than k + 1 is going to contain at 2 ∀k ≥ 2. least k 2 pixels. Substituting k with k − 1, we can write lim sk−|ε| ≥ (k−1) k2 |ε|→0

We have the following theorem. Theorem 10 In the collection of all squares of real length, the ones with minimum density are those with length just less than 2. Proof We notice a special case for squares with length just less than 2 and containing a single pixel at its center. Then there will be no pixel on its boundary and the limiting value of its density is 41 . Since the squares we consider cannot contain less than one pixel, any other square of length a < 2 cannot have a lower density. Let us assume for contradiction that for some integer k > 2, (k − 1)2 1 ≤ ⇒ 3k 2 − 8k + 4 ≤ 0, 2 k 4

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which is not feasible as k ≥ 3. So, we get lim sk−|ε| ≥

|ε|→0

(k − 1)2 1 > ∀k > 2 2 k 4

Now, using Observation 9 we get, ∀a ∈ R such that, k − 1 ≤ a < k, sa > sk −|ε| as |Sa| can be kept the same as |Sk −|ε| |. Note that since k ≥ 3, the result holds ∀a ≥ 2, which completes the proof for all real values of a. ∎

3.5 Cubes with Real Side Observation 10 Consider k ∈ N. If a maximal cube contains k × k × (k − 1) voxels or k × (k − 1) × (k − 1) voxels, then a cube of the same size exists that contains only (k − 1) × (k − 1) × (k − 1) voxels whose density will be lower. Hence for minimum density, it suffices to consider only those cubes that contain a set of voxels of k × k × k form. Similar to 2D, we observe that, for any positive real a, if Ba is a cube containing k × k × k voxels, where k ≥ 2, then (k − 1) ≤ a < (k + 1). In other words, for any integer k ≥ 2, a cube of length just less than k will 3 , ∀k ≥ 2, contain at least (k − 1)3 voxels. As result, we have lim bk−|ε| ≥ (k−1) k3 |ε|→0

which leads to the following theorem, that can be proved in a way very similar to Theorem 10. Theorem 11 In the collection of all cubes of real length the ones with minimum density are those with length just less than 2. Proof We consider a cube of length just less than 2 and containing a single voxel. It easily follows that the voxel must be at its center, to avoid other voxels from entering its occupied region. There will be no voxel on any of its faces and the limiting value of its density is 18 . As in 2D, we claim that no other cube with length a < 2, can have a lesser density than this. Let us assume for contradiction that some integer k > 2, (k − 1)3 1 ≤ ⇒ 7k 3 − 24k 2 + 24k − 8 ≤ 0 k3 8 not feasible as k ≥ 3. So, we get lim bk−|ε| ≥

|ε|→0

(k − 1)3 1 > , ∀k ≥ 3. 3 k 8

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For a ∈ R, such that, k − 1 ≤ a < k, ba > bk −|ε| as |Ba| can be kept the same as |Bk −|ε| |. Since k ≥ 3, it covers all cases for a ≥ 2, which completes the proof for all positive real a. Hence, the minimum-density cube is the one that contains only one voxel and is of length just less than 2. ∎

3.6 Hypercubes with Real Side Theorem 12 In the collection of all hypercubes of real length the ones with minimum density are those with length just less than 2. Proof Proceeding in the same way as in 2D and 3D, we can show that a hypercube of length just less than 2 and containing a single hypervoxel at its center is the least dense hypercube possible. There will be no hypervoxel on any of its hyperfaces and the limiting value of its ∎ density is 21n

4 Conclusion and Future Work We have presented some novel findings on locating the squares, cubes and hypercubes having maximum and minimum density in a digital space. The centers of these squares, cubes, or hypercubes can be anywhere and the lengths can be either integral or real. For any hypercube of dimension n ≥ 2, the maximum density is 2n for either integral or real length. Also, for hypercubes with integral length, minimum density is unity for any length, and for real length, the minimum density is 21n occurring for a length just less than 2. In the future, we may consider squares and cubes of arbitrary orientation to investigate the nature of extrema. It also remains to be explored whether these findings somehow correlate with the cases where the grid is other than rectilinear or if the region under consideration is bounded by any other primitive shape other than square, cube, or hypercube.

References 1. Andres, E., & Roussillon, T. (2011). Analytical description of digital circles. In I. DebledRennesson, E. Domenjoud, B. Kerautret, & P. Even (Eds.), Discrete geometry for computer imagery, Proceedings of the 16th IAPR International Conference, DGCI 2011, Nancy, France, April 6–8, 2011. Lecture Notes in Computer Science (Vol. 6607, pp. 235–246). Springer. https:/ /doi.org/10.1007/978-3-642-19867-0_20

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Performance Validation and Hardware Implementation of a BLE Mesh Network by Using ESP-32 Board Ziyad Khalaf Farej and Azhar Waleed Talab

Abstract Mesh Bluetooth networks enabled each node of the network to communicate with one another via multi-hop communication (many-to-many connectivity). These networks become a crucial part of the Internet of Things (IoT). In recent years, Bluetooth Low Energy (BLE) technologies are used to construct mesh Bluetooth network which have sparked a lot of attention. A variety of BLE meshing solutions evolved; however, these are unified by the BLE mesh network standard. In this paper, a BLE mesh network that consists of ten nodes was designed and implemented. These nodes which are based on the ESP-32 evaluation board are programmed by using the Arduino software version (1.8.13). Each node is able to send and receive messages by listening to the three advertising channels (37, 38, and 39). Different message load values of 67, 128 and 255 bytes were used in the experimental testing of the network transmission processes and the obtained maximum one hop throughput and latency values are (19.1220, 96.7622 and 218.0491 Kbit/s) and (4.4, 5.9646 and 7.3038 ms) respectively. With respect to one hop values and for message load of 255 bytes, the percentage reduction in the throughput values are 58%, 74% and 83% for 3, 5 and 10 hops respectively. Keywords Bluetooth low energy · (IoT) · Bluetooth smart · Bluetooth mesh · Mesh networks

1 Introduction The Mesh Bluetooth networking standard based on BLE that allows for many-tomany communication over Bluetooth radio, was conceived in 2014 and adopted on July 13, 2017 [1]. The mesh stack defined by the BLE Mesh Profile is located on top Z. K. Farej (B) · A. W. Talab Northern Technical University, Mosul, Iraq e-mail: [email protected] A. W. Talab e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_27

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of the BLE core specification [2–5]. BLE mesh Networks enable low-power Internet of Things (IoT) devices to make communications in a versatile and reliable way [6, 7]. Due to its high energy performance, low device costs, and widespread availability in consumer equipment, such as smartphones and tablets, BLE has become a common communication technology in IoT systems [8, 9]. The Bluetooth ‘SIG’ recently accomplished requirements for adding mesh networking functionality to any Bluetooth LE unit. As long as a Bluetooth Mesh (BM) network stack is available and the proposed protocol uses the current lower layers of BLE therefore the mesh is completely backward-compatible with any BLE system (from Bluetooth Version 4.0 and above). According to sources, a BM network can accommodate 32,767 nodes, according to the specification as well as 127 hops [10, 11]. In reference [12] three methods are used to study and investigate the Bluetooth mesh network and assess its performance. These methods are experimental evaluation, statistical technique, and the graph-based simulation model. All three ways produced consistent results, revealed potential disadvantages and unresolved issues that need to be addressed. The selected hardware device that was used in the evaluation process is the nRF52832 development Board from NORDIC Semiconductor. Reference [13] estimated the current consumption, endurance, and energy cost per delivered bit of a battery-operated Bluetooth Mesh sensor node. A Real-world hardware is used to create the BM network model and data is collected. The evaluation results quantify the impact of major Bluetooth Mesh parameters. In this work, the device that is used in the data measurements process is a PCA10028 Development Kit. This belongs to the popular nRF51 series Nordic Semiconductor. In the research paper [14], the Bluetooth Mesh protocol’s quality of service (QoS) performance was analyzed. The most important protocol parameters, as well as their impact on system performance, were discovered. According to the study, the protocol’s major flaw is its scalability, as well as in densely populated deployments, Bluetooth Mesh is particularly prone to network congestion and increased packet collision risk. The aim of this study [15] is to assess the Bluetooth mesh network capabilities and limits in terms of data delivery capacity in monitoring applications. Several tests are carried out in an office setting by establishing a multi-hop network with a number of BLE nodes. Each test trial evaluates the network’s performance in terms of packet delivery to a base station. The author in reference [16] offered an experimental evaluation of 6BLEMesh based on a real-world application. Latency, round journey time (RTT), and energy usage are all taken into account. Three different hardware platforms were used to simulate device current consumption, assess communication, energy, efficiency and compute theoretical device lifetime (for battery-operated devices). In this paper, a Bluetooth mesh network is proposed and hardware implemented by using the Esp-32 Evaluation Boards. The proposed network is tested and its performance is also evaluated. The evaluation processes are carried out with different packet sizes (67, 128, and 255 bytes) and number of hops in terms of throughput and latency. The rest of the paper is organized into five sections. Section 2 presents an overview of the Bluetooth Low Energy mesh network. Section 3 includes research method

Performance Validation and Hardware Implementation of a BLE Mesh …

333

and material Sect. 4 gives results and analysis finally, Sect. 5 ends this paper with conclusions.

2 Overview of the Bluetooth Low Energy Mesh Network Bluetooth Mesh communicates using the three advertising channels as shown in Fig. 1. As compared to alternatives that use other methods, this results in higher power consumption, but a shorter end-to-end delay [17]. Up to 32,767 devices and 127 hops can be supported by a BM-based network. It distributes messages across the network using a controlled flooding approach. Each message has a time to live (TTL) that decreases with each hop to avoid loops. Also, each node has a message cache that prevents known messages from being retransmitted. Backward compatibility is also provided by BM, which allows devices with Bluetooth 4.0 or later chipsets to join the network. Only a software update is needed to allow support for the BM stack on these devices. A system must first signal its availability through beaconing before it can enter the network. The provisional and system then create a session to exchange various configuration data, and the provisional sends all required keys, including the Network Key (NetKey), Application Keys (AppKeys) [18–20]. The norm distinguishes between these two types of keys: To ensure data, the AppKey is required to enter the mesh network, while the AppKey is needed at the application layer to encrypt messages and make the data more confidential [10]. The Bluetooth Mesh stack is implemented by each device in a Bluetooth Mesh network. This stack is designed as a layered architecture, with the layers mentioned below as shown in Fig. 2 [14, 21, 22]. The BM consists of a group of nodes [23], as shown in Fig. 3. Four types of nodes are normally, found in such network as follows.

Fig. 1 BLE frequency channel

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Fig. 2 The bluetooth mesh stack

Fig. 3 BLE mesh network

3 Research Method and Material 3.1 BLE Mesh Network Implementation and Programming A Bluetooth low energy mesh network has been designed and hardware implemented. The implemented mesh network consists of ten nodes of ESP-32 Evaluation board. The ESP-32 board has an integrated micro controller circuit that is capable of running programs. The ESP-32 name is designed by Espressif Systems. The network hardware components (where its nodes are placed in a row) are shown in Fig. 4. Each ESP-32 board is configured (provision) and programmed to work as the BLE mesh node. It can send and receive by listening to the three advertising channels (37, 38,

Performance Validation and Hardware Implementation of a BLE Mesh …

335

Fig. 4 Hardware components of BLE mesh network

and 39). Three different message loads (67, 128, and 255 bytes) were used in the process of testing and performance evaluation of the designed mesh network. The throughput and latency performance for this network are computed and is average for each of the message loads and under different number of hops.

3.2 Throughput Performance Analysis The number of packets sent by a node during a given amount of time is known as throughput. If N is the number of data packets provided and acknowledged successfully inside a connection event, the Maximum BLE mesh throughput for one hop may be derived or computed using Eq. 1 below: T h max =

(E[N ] × L) connInterval

(1)

where: E[N] denotes the expected value of N. (i.e. the average number of successfully transmitted data packets), L denotes the quantity of user data contained in a packet [24].

3.3 Latency Performance Analysis Latency is the time it takes for a data packet to get from one node to the other and vice versa [25]. It’s vital to understand the different mechanisms that determine these

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latencies in the communication process. Figure 5 shows a secure communication flow between two mesh nodes that are directly connected. An event on the transmitting node’s application layer requires the transmission of a message to another node (e.g., pushing a button). It takes time to process the message from high to low in the stack. A time random back-off strategy is utilized before the message is delivered over the air, which retains the message for a random time from (0) milliseconds to the maximum value for back-off. The back-off eventually ends, and the message is broadcast. This is accomplished by standard BLE advertising. To summarize, a message is broadcast on channels 37, 38, and 39 in that order. And a receiving node is scanning one of these channels at a predetermined interval, switching from one to the other. Three separate advertising channels are used to convey the message. The time it takes to send a message on one channel is determined by the size of the packet and the BLE radio’s specified bit rate. The time it takes to change channels must also be considered [12]. Round Trip Time (RTT) is dependent on three important factors, processing time (t_processing), back-off time (t_((Back-off))) the transfer time (t_TX), and Retransmit time (t_Retransmit) usually neglected. Processing time is the time it takes for the message to send from the top of the stack to the bottom, the back-off time is the random time between 0 and the t max, and the transfer time is the time it takes for the massage to transfer from one node to another. The time between the sender’s transmission attempt and the moment he or she realizes the communication has failed is (t_Retransmit). The formula of RTT in one hop case is given by. RTTONE-HOP =

2 

(tBackoffi + tTXi ) + tprocessing total +

i=1

While in multi- hops case.

Fig. 5 Shows a BM network’s wireless connection between two nodes

S  i=1

tRetrasmiti

(2)

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RTTmulti-hop =

n S   (tBackoffi + tTXi ) + tprocessing total + tRetrasmiti i=1

337

(3)

i=1

where n is the number of hops, S is the number of transmissions that failed. In RTT we use the average Backoff time whose formula is: E[T ] =

tmaximum n+1

(4)

The parameters that are used in all measurements are described in Table 1. Due to expenses issues (save money), in this paper measurements were carried out on a small mesh network with only ten nodes placed in a row as shown above with ten meters distance between any two adjacent nodes. The data packets are transferred across these nodes depending on nodes configuration, number of hops, and Time to Live. This measurement setup which forms a base line for all the following measurement, where Eq. (3) has the back-off time, the processing time, as well as the transmit time as the three factors that influence communication, and the average RTT is (7.3038) milliseconds assuming there aren’t any retransmissions. These aspects must be for this specific setup, and it was evaluated more closely. The average RTT was used to verify the theoretical analysis. Table 1 shows a maximum back-off of (4) ms, resulting in an average back-off time of (2) ms. There are some time constants (include radio enabling/disabling and channel switching) defined by the Bluetooth Mesh stack on the ESP-32 evaluation board and influence the transmission time. As a result it is required (2.04 + 0.2592 (starting and stopping) = 2.2992 ms (t_TX) for completing the packet transmission on a channel, and (0.32 + 0.2592 = 0.5792 ms (t_Ack) for an acknowledgment, using those constants in combination with the throughput speed and packet sizes listed in Table 1. The obtained result (2.2992 ms) is for sending on the channel (37), the total time is (2 × 2.2992) milliseconds after additionally sending on the channel (38), and the total time is (3 × 2.2992 ms) for finishing transmissions on the all (37,38, and 39) channels. Finally, before the initial transmission on channel (37), it is required another (0.2852 ms) (radio overhead defined by the standard). As a result, the packet’s overall transmit time is (2.2992 + 0.2852 = 2.5844 ms) (as long as only one channel (37) is used because two nodes are configured in the network), while the total send time of the Table 1 The practical parameters

Parameter

Value

BLE radio throughput

1 Mbps

Maximum backoff

4 ms

Packet size

67, 128, 255 bytes

Number of hop

1, 3, 5, 10 hops

Acknowledgment size

41 bytes

Processing time

1 ms

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acknowledgment is (0.5792 + 0.2852 = 0.8644 ms). It is discovered that the entire processing time for one-hop transmission is roughly (1 ms) following an examination of the communication’s various processing aspects. Finally, the average theoretical total round trip time is (2 ms) + (2.5844 ms) + (0.8644 ms) + (1 ms) = (6.4488 ms) produced.

4 Research Method and Material 4.1 Throughput Performance Throughput performance in a BLE mesh network that depends on the number and size of transmitted packets as well as on the transmission time interval (time taken to send a packet). In this research, an average of five throughput readings were taken for three different loads (67, 128, and 255 bytes) with different numbers of hops (one, three, five, and ten hops). For 255 bytes message load the throughput readings with their average values are shown in Table 2. Through the practically obtained throughput values, it is noted that the throughput is highly affected by the number of hops, where the higher the number of hops (3, 5 and 10), the throughput decreased by (58%, 74% and 83%) respectively, the reason for that is the increase in transmission time as well as the accumulated processing (receive/transmit) time. Figure 6 shows the average throughput values for the considered different loads and number of hops. Table 2 Average throughput values of five readings for 255 bytes load (a) one hop, (b) three hops, (c) five hops, (d) ten hops No. of Throughput readings (Kbit/s) hops (1) (2) (3)

Average (4)

(5)

(a) 1

215.047956 224.700069

212.976591

216.716575 220.804431 218.0491244

1

205.154633 212.153513

204.469567

142.231754 199

2

106.459807 111.0821307 106.84564

(b)

3

78.485504

82.116886

78.6600012

192.6018934

107.574838 107.778756 108.0960696 79.472845

80.254071

79.7978636

(c) 1

193.579767 216.569179

208.349698

209.832611 211.392905 207.944832

2

103.437075 111.868457

105.235323

107.625745 106.960496 107.0254192

3

77.13552

81.960462

78.027741

75.511075

78.346458

78.1962512

4

63.590973

67.144667

63.596051

59.000112

64.443004

63.5549614 (continued)

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339

Table 2 (continued) No. of Throughput readings (Kbit/s) hops (1) (2) (3) 5

52.865776

55.814604

53.301192

Average (4) 49.979593

(5) 52.504866

52.8932062

(d) 1

167.68486

179.785441

2

106.960496 103.991116

161.32955

229.494024 169.686635 181.5960992

106.595243

108.240412 107.713128 106.700079

3

75.511075

78.893896

75.983201

79.7835

76.135821

77.2614986

4

63.004592

63.456634

64.584179

66.68342

63.51993

64.249751

5

52.722216

54.41805

53.034846

54.328909

53.378037

53.5764116

6

46.626053

46.594668

46.834549

48.232193

47.1564

47.0887726

7

40.478005

41.556816

39.56263

41.783678

40.808565

40.8379374

8

36.838208

36.878317

36.538065

37.406896

36.991427

36.9305826

9

32.868792

33.645412

32.473227

33.565961

33.154233

33.141525

10

30.454912

30.46307

30.306492

31.071903

30.643671

30.5880096

(a)

(c)

(b)

(d)

Fig. 6 Average throughput performance for the three loads a one hop, b three hops, c five hops, d ten hops

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4.2 Latency Performance Latency performances in the BLE mesh network depend on the processing time, Backoff time, and transmit time (propagation delay ti me is very small and ignored). In this paper, an average of five latency readings were taken for three different loads (67, 128, and 255 bytes) as shown in Table 3, with different numbers of hops (one, three, five, and ten hops). The obtained results of the practical latency values clarified that the latency is also highly affected by the number of hops, so as the hops increased (3, 5, 10), the latency (and with respect to the one hop value) is increased by (57%, 74% and 83%) respectively, again this is due to the increased transmission time as well as the accumulated processing (receive/transmit) time. Figure 7 shows the average latency values for the considered different loads and number of hops.

Table 3 The average latency values of five readings for 255 bytes load (a) One hop, (b) Three hops (c) Five hops (d) Ten hops No. of hops

Latency reading value (ms) (1)

(2)

Average (3)

(4)

(5)

(a) 1

7.403000

7.085000

7.475000

7.346000

7.210000

7.3038

(b) 1

7.76

7.504

2

14.954

14.237

14.9

7.786

11.193 14.799

14.771

8

14.7322

8.4486

3

20.284

19.386999

20.239

20.032

19.837

19.95579

(c) 1

8.224

7.351

7.641

7.587

7.531

7.6668

2

15.391

14.231

15.128

14.792

14.884

14.8852

3

20.639

19.424

20.403

21.083

20.32

20.3738

4

25.035

23.709999

25.033001

26.983

24.704

25.093

5

30.114

28.523001

29.868

31.853001

30.320999

30.1358002

(d) 1

9.494

8.855

9.868

6.937

2

14.884

15.309

14.935

14.708

14.78

9.382

14.9232

8.9072

3

21.083

20.179001

20.952

19.954

20.91

20.6156002

4

25.268

25.087999

24.65

23.874001

25.063

24.7886

5

30.195999

29.254999

30.018

29.302999

29.825001

29.7193996

6

34.144001

34.167

33.992001

33.007

33.759998

33.814

7

39.330002

38.308998

40.240002

38.101002

39.014

38.9988008

8

43.216

43.168999

43.570999

42.558998

43.036999

43.110399

9

48.435001

47.317001

49.025002

47.429001

48.018002

48.0448014

10

52.273998

52.259998

52.529999

51.236

51.952

8.9072

Performance Validation and Hardware Implementation of a BLE Mesh …

(a)

(c)

341

(b)

(d)

Fig. 7 Average value of latency performance for the three loads a one hop, b three hops, c five hops, d ten hops

5 Conclusion A small BLE mesh network consisting of only ten ESP-32 evaluation board nodes is configured and programmed through Laptop using 1.8.13 Arduino software. The performance of this network is investigated and evaluated under three different loads (67,128, and 255 bytes) and the number of hops (1, 3, 5, and 10). It is noted that the practically obtained throughput and latency values are affected by the number of hops. As the number of hops increases, the network throughput and latency performance degrades. For one hope, high consistency is found between the total theoretical (6.4488 ms) and practical (7.3038 ms) average round trip times. The convergence of practical and theoretical results reflects the efficiency of the ESP-32 evaluation boards and the software used in programming the BLE mesh network nodes. It is an important task to make performance evaluation for such mesh networks as it is forming the basis for the Internet of Things technology that requires hundreds of connected devices and enters into various life fields such as building automation, asset tracking, and sensor networks. Acknowledgements This work was supported by Northern Technical University (NTU), the Technical Engineering College/Mosul, and my supervisor Dr. Ziyad Khalaf Farej.

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Laplace Transformation of Eigen Maps of Locally Preserving Projection (LE-LPP) Technique and Time Complexity Soobia Saeed, Manzoor Hussain, Mehmood Naqvi, and Hawraa Ali Sabah

Abstract K Nearest Neighbour (k-NN) is one of the most common machine learning algorithms; however, it frequently fails to operate well due to an incorrect distance measure or the existence of a large number of irrelevant pieces of information. To improve k-NN classification, linear and non-linear feature transformation approaches were used to extract class-relevant information. In this paper, we describe the combination of Laplace transformation of Eigen maps and Gradient conjugate iterative approach to sort out the non-linear data or irrelevant data in which a non-linear feature mapping is sought through Laplacian Eigen maps or kernel mixtures to remove it whereas the Locally preservation projection (LPP) are applied to save the original values during the reconstruction of linear data and create the large-margin distant in the hybrid k-NN model. The algorithm offers a computationally efficient solution to nonlinear dimensionality reduction with locality-preserving qualities, and a linear transformation matrix is subsequently trained to fulfil the goal of a large margin distance framework. Keywords Hybrid K-NN · LPP · Laplacian · Eigen maps · Kernel mixtures

S. Saeed (B) School of Computing and Information Sciences, Sohail University, Karachi, Pakistan e-mail: [email protected] M. Hussain Computing Department, Faculty of Computing & Information Technology, Indus University, Karachi, Pakistan e-mail: [email protected] M. Naqvi School of Electronics Engineering and Computer Science, Mohwak College, Alberta, Canada e-mail: [email protected] H. A. Sabah Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_28

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1 Introduction K-Nearest Neighbor (kNN) is one of the most popular classification technique in machine learning algorithm. However, k-NN is a good classification method which is a highly dependent metrics that is used for calculating the distance between the pairwise data points. Normally, we use Euclidean distances to measure the k-NN data points of interest as a similarity metrics. In the real time application with the issues of irrelevant features, we often use good distance metric. To improve the performance of k-NN classification, many researchers have proposed the distance metric learning method to find the exact information of feature vectors and remove the redundant information using the linear transformation methods data points of feature vectors. These methods improve the performance of classification of k-NN base on objective function [1–3]. In many cases, this is impossible to improve the performance of k-NN classification by linear transformation method, so we need to choose nonlinear transformation so that all data points and their neighborhood will stay together in the feature space of non-linearly transformation. To improve the k-NN method neural network based and kernel based non-linear dimensionality reduction methods were used [4–7]. Nevertheless, the approach of kernel based method behaves like a template based approach and its parameters are modified using the cross validation of the time consuming approach. The resulting performance will be very bad if the kernel based approach is chosen as it is not able to reflect the true structure of data for linear trans-formation. However, many neural network based approaches provide many powerful nonlinear feature transformation techniques, but no one studied the feature mapping which directly has the motive of improving the technique of k-NN classification. In this research, the researchers proposed two non-linear feature transformation method including kernel mixtures and large-margin framework which directly improve the performance of k-NN classification based on Laplacian Eigen maps (LE) [8–10]. Locality-preserving projection (LPP) is another technique of nonlinear data which saves the original values during reconstruction and the values of linear data. The Laplacian Eigen map algorithm of the locality-preserving character makes it relatively insensitive to outliers and noise. It is used for only local distances as it is not prone to short circuiting. We show that by aiming to maintain local information in the embedding, the method indirectly emphasizes natural groupings in the data. The connections between learning and computer vision spectrum clustering methods [9] became extremely evident. In this way, dimensionality reduction and clustering are two sides of the same coin that we study thoroughly. Global approaches, such as those employed by Tenenbaum et al. [11–14], do not cluster since they seek to retain all pairwise geometric distances between sites. Not all data sets, however, include relevant clusters. Other approaches, such as PCA or Isomap, may be more applicable in this scenario. Nonetheless, we shall demonstrate that our method yields respectable results in at least one example of such a data set (the “Swiss roll”) [15– 19]. Furthermore, to facilitate the solution of ordinary differential equations (ODEs), these equations are transformed into algebraic equations using the Laplace transform

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[20], described an example-driven k-parameter computation in which several k values for diverse test samples are calculated in kNN prediction applications such as regression, missing data imputation, and classification. A sparse coefficient matrix is rebuilt between the test samples and training data for this purpose [21, 22]. According to the author, kNN is frequently used in data mining and machine learning applications due to its ease of implementation and defined performance. Earlier k-nearest neighbours (k-NN) methods that used the same k value on all test data failed in real-world applications. This paper presents a Correlation Matrix kNN (CM-kNN) classification in which test data points are reconstructed using training data and multiple k values are assigned to various test data points. A Laplacian regularizer is employed during the reconstruction phase to retain the structure of the data’s nearest neighbours. We employ an l-norm regularizer and a l2-norm regularizer to estimate various k values for a variety of test data and to achieve minimal sparsity in the results by removing redundancy or noise from the reconstruction process [23, 24]. The structure and organization of this research study are as follows: Sect. 1 evaluates previous studies as well as the current body of literature on the key issues of this study and includes pertinent material. It also discusses the non-linear transformation method literature. Section 2 discusses our approach, methodology, and a high-level summary of the framework we used. Section 3 examines the experimental data and our recommended approaches; Sect. 4 gives the proposed algorithm; and Sect. 5 ends and proposes future work.

2 Methods and Material The Proposed Laplace Transformation of Eigen maps of Locally Preserving Projection and Time Complexity (LELPP-TC) Techniques k-NN algorithm model that have been developed consist of two main components: Laplace Transformation of Eigen maps of Locally Pre-serving Projection (LELPP) and Time Complexity (TC). The inputs of these proposed techniques are multiple phases of MRI images of CSF fluid and low-grade tumor that are sufficient for finding non-linear data in the proposed datasets. In the first phase, the non-linear data is found and converted into linear transformation with the help of the iteration method and LPP. The second phase implements the time complexity to evaluate the proposed technique execution time. The explanation of the proposed technique is given below in detail in the next subsection (Fig. 1).

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Fig. 1 Linear transformation of the images by dimension reduction method of proposed technique

2.1 Explanation of the Proposed Laplace Transformation of Eigen Maps of Locally Preserving Projection and Time Complexity (LELPP-TC) Techniques The k-NN algorithm is a common machine learning technique to classify data, but it often fails due to many irrelevant features in the data due to the inappropriate selection of distance metrics. In this section, Laplace Transformation of Eigen mapping is formulated to solve the non-related features in the classification of the proposed hybrid k-NN model extended in the previous section. It enhances the features of linear transformation to be applied for exact relevant information of vector variables (constructed imputed values) of datasets and removes the irrelevancy or non-linear features using the GCIA approach with the combination of the iteration method. The other reason for the selection of Laplacian Eigen maps and LPP is to enhance the low dimensional space to convert high dimensional space and create the large margin distance values. This technique makes it easier to transform the images and convert them into the linear transformation of images after refining the imputation of the missing values. Few of the data still lack of linear transformation in the datasets of this research which is needed to reduce the time and storage in the data. It helps to reduce the unnecessary multi-collinearity and improve the performance of classification of the hybrid k-NN model that can easily identify the refined data after reconstruction. The previous technique works well to construct the missing data in the datasets but still, there is some barrier to visualize the data properly as, during the reconstruction of the data, few of the data are converted in the non-linear form which is needed to be linear. This research considers the proposed technique LE-LPP to be applied in datasets for the transformation of non-linear dimension reduction with the GCIA in the proposed hybrid k-NN model to get the large margin distance values. The iteration method selects to check the non-linear data in the initial value which generate a

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sequence of improving approximate solutions for a non-linear transformation, from the ith values of 1, 2, …, n term of approximation derived from the previous ones. This novel technique is computationally fast and efficient than other techniques with less consuming time and provides the best solution of outcomes. This technique is applied to create the large margin distance values due to the mapping of nonlinear features that is sought out through Laplacian Eigen maps with GCIA to be applied in non-linear function to obtain the approximate solutions that converge quickly to the exact one and convert into the linear transformation matrix to generate the large margin distance values in the proposed hybrid k-NN model for nearest neighbor whereas, LPP is to preserve the original data of the k-NN model during the process. Time complexity is one of the other parts of this technique to minimize the computational execution time and then compare average consuming time with the current consuming time and show the novel technique as the optimal solution to be less consuming time for transformation.

2.2 Laplace Eigen Mapping This function is applied to minimize the non-linear feature transformation by sorting through Laplacian Eigen maps (LE) and converting it to linear feature transformation. LE is the dimension reduction method to utilize the proposed hybrid k-NN algorithm which is extended to the previous section. Suppose the problem of a non-linear transformation function is given by the set of x 1 , x 2 , …, X n of n points in R1 and the set of point’s y1 , y2 , …, yn in Rn (n ≤ 1) where n is the total number of points and yi represents x i whereas x i and yi are the initial values of the set of points of the k-NN algorithm. In addition, the objective function of LE is found to be the same mapping point close together. Suppose that (y1 , y2 , …, yn ) T remains as such for the map to achieve the objective to minimize the following function is represented by Eq. (1): I =1 Σ

wi, j (yi − y j )2

(1)

i, j

where, (wi,j ) is the weight function that confirms the points are close together and assign the large value of weight function, where the further points are assigned by the smaller weight. Meanwhile, this function is decreased exponentially and the points are mapped further separated by a huge drawback acquire. This process of mappings explained the strong appropriateness of LE in the sorting values of nonlinear points of the hybrid k-NN algorithm. For the detecting function of the hybrid k-NN algorithm, segment x of the length n, the linear Eigen maps instruction is directly represented by the following steps:

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|| ||2 The Euclidean distance matrix is calculated by ||xi − x j || and the nearest neighbour is connected with a node of i and j such that if the node j is between the nearest neighbor of i then node i and j are connected (1) The weight matric of the k-NN algorithm is computed by || ||2 Wi, j = e − ||xi − x j || whereas, if i and j are connected. (2) The Eigenvector and Eigenvalues are connected to be the generalized components that are calculated by linear transformation function of Λd f . Whereas, λ is the eigenvalue and df is the partial derivatives of the corresponding eigenvector with 1 Σ

wi, j

j

The best solution of the Laplacian matrix (LM) = D − W. Whereas, w is the weight matrix and D is the Eigenvector of the variable of the hybrid k-NN algorithm. i. The eigenvectors are sorted according to their eigenvalues by: λ = 0, 1, 2, …, m. ii. The lower dimension space of the mapping is represented by a function of Eigen map such as ( f (1), f (2), …, f n (i)), neglecting the first Eigenvector( f (0)) which corresponds to the Eigenvalue 0 and also applied the iteration method to evaluate either the exact or approximate solutions of linear and nonlinear problems. iii. The iteration method is used to change the non-linear to linear transformation of Eigen maps values. The iteration method works continuously until any nonlinear features are located during the transformation of linear features. The iteration method checks the data during the process of eigenvalues and eigenvectors are connected to each other to make the transformation of linear features.

2.3 Locality Preserving Projections (LPP) Approach This approach is applied to build the connectivity of the Laplacian graph of Eigen maps following from the previous subheading with the integrating neighborhood information of the given datasets. This approach computes the transformation matrix of mapping the data points to the subspace. The LPP optimally preserves the confined neighborhood information in a certain sense of linear transformation. This approach is defined by the proposed steps which represent the mapping values directly generated by the linear discrete approximation.

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The proposed algorithm is represented with Eigen map by: i. The maps are developing to minimize the classical linear technique from the diverse objectives of the standard after evaluating the non-linear features ii. The LPP is developed by local structure preservation as it searches the nearest neighbor in the low dimensional space and converts it to a high dimension space that will produce the same results due to the iteration method to allow the quick retrieval data in the datasets. iii. LPP is linear so it makes quick development while using non-linear techniques. iv. LPP recalls the non-linear dimensionality by reduction techniques and connects with Laplacian Eigen maps which are only defined by the training datasets points and evaluates the new test points of datasets. In the end, LPP is to be applied to the new data points to locate them in the reduced signified space.

2.4 Gradient Conjugate Iterative Approach (GCIA) Conjugate Gradient algorithm (CG) is one of the best known optimization approaches for finding the irrelevant or non-linear data in the hybrid k-NN model. The section of this research is GCIA for extracting the irrelevant or non-linear data in the hybrid k-NN model as this research use trained MRI datasets, that’s why the proposed GCIA is better for finding the non-linear data in the MRI images and the iterative method checks the presence of non-linear data multiple times and converts it into linear data through the LELPP method. However, there are no derivatives available for this expression; finite differences are used in the CGIA to approximate the first derivative. The purpose of choosing this non-linear CGIA is to minimize non-linear data in the trained MRI datasets by finding solutions to underdetermine linear data of trained MRI datasets. The CGIA finds the local minimum of nonlinear features in the datasets using its gradient alone. Given an N-variable function, the gradient indicates the direction of maximum increase. To search non-linear data or irrelevant features in the trained MRI images, simply start in the opposite (search) direction using the following steps that are given below: (1) Choose the initial point and calculate the remaining non-linear data (2) Find the step length, where the calculation of irrelevant features and non-linear data, Δ × 0 (3) Perform a line search direction (4) Set the new iteration point of: X n+1 = X n + α n S n (5) If X n+1 ≤ ε holds, the algorithm stops. Otherwise, go to the next step (6) Let X = X n+1 and go to Step 4. Whereas, S n indicates the search direction, α, β are the line search parameters and Δ × 0 indicates the length of the line search direction.

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3 Proposed Laplace Eigen Map of Locally Preserving Projection (LELPP) Technique The strength of this technique is the method on how to save the original values of transformation and achieve a better solution with the help of an Eigen Map. This (LELPP) technique has certain “intelligence” for finding the much better solutions of linear transformation by using the GCIA. LPP is a linear transformation of the nonlinear Laplacian Eigen map values. The procedure of the algorithm is represented by: Let G indicate the Laplace transformation graph of m nodes which is connected to the nodes of I and j if the x i and x j are close together. These two steps are directly introduced so that the LE-LPP technique is organized in terms of two conditions: (1) i and j are connected to each edge of the k-neighbors (e-neighbor) where “e” is defined by the edge node which is determined by the Euclidean norm matrix. (2) If the parameter of k nearest neighbors related to the number of data points (i.e., k ∈ N) then i and j are connected by an edge node if i and j are in between the k-nearest neighbor to each other. For a better understanding, the two other variations of a weighted matrix are at the edge of the linear transformation. W i,j makes the relationship between the weight of the edges with vertices of i, j and 0 when if there is no find at the edge of the matrix by: i. Choose kernel function (t ∈ R) when the nodes are connected with i and j ii. Choose W i,j = 1when only vertices are connected to edges in terms of i and j. This development aims to establish a better solution from the proposed hybrid k-NN model. At the same time, the combination of LE-LPP is precisely applied for exploring the new idea in the proposed hybrid k-NN model for selecting the large margin distance values and also applying the GC approach and iteration method, as it is carefully checked multiple times during the transformation of non-linear to linear features until the remaining non-linear features are available in the datasets. The LLP approach and the Eigen maps with the iterating method are combined to make it possible for the LE-LPP technique to achieve it more professionally. It gives better results for the proposed technique to remove the unnecessary data in the datasets and identify the hidden non-linear data to be selected by the nearest location in the k-NN algorithm.

4 Experimental Results of LELPP-TC Technique The technique was implemented on Laplace Eigen maps to transform the linear data and LPP preserves the original values of the hybrid k-NN model. The results are measured in terms of the GCIA which is implemented on the hybrid k-NN model

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to increase the performance during the transformation of linear data. In the end, this research implements the time complexity function to check the executing time of the proposed model and then compare the average running time of the previous k-NN algorithm as mentioned in the subsection given below.

4.1 Results, Analysis and Discussion of LELPP-TC Technique This section identifies the computational outcomes of the simulation formulated with statistical significance analysis, to assess the performance of the proposed LELPPTC technique in the hybrid k-NN model for finding the non-linear data or irrelevant features from the extension of previous results. The section of this research is to identify the non-linear data through Laplace transformation of Eigen Maps after reconstructing the linear data. It enhances the performance of the hybrid k-NN model with less execution in the time series data. In addition LPP method is applied to save the new reconstructed linear values in the hybrid k-NN model are illustrated in Figs. 3 to 15 from Datasets-I to III that are given below. Figures 2, 3 and 4 show the performance of Laplace transformation of Eigen values with the Eigen vector. Thus, simulation has been performed with multiple steps including the process of normalization and applied kernel function when the nodes are connected in the form of i and j to each other which is represented by the edge. In this method, taken the key point value such as 195 (key point value) for the MRI images in the Laplacian Eigen maps for preserving the local information of trained datasets which is used in the hybrid k-NN Model. These values show the new constructed values in terms of statistical form which captured the non-linear values or irrelevant features in the hybrid k-NN model. The next step is to calculate the all weight matrix W1 and W2 one-by-one, note that W1 and W2 indicate the edge weights of image intensity for MRI datasets. These calculated weight matrix values represent the Eigen values (E) in the form Eigen vector such as D1 and W1 and then the Laplace matrix of Eigen values of linear data such as LM = D1 − W1 and L = D2 − W2 in the order of Eigen values 0 = λ1 ≤ λ2 ≤ λ3 ≤ · · · ≤ λn are calculated where λ represents the Eigen vectors as shown in Fig. 5 are given. In Figs. 5 and 6 the calculated results after implementing the LPP over the Laplace transformation of Eigen maps values of Eigenvectors are shown. Firstly, to calculate the distance of eigenvectors of all of three MRI datasets and then apply LPP codes

Fig. 2 Weight vector matrix of Laplace transformation of Eigen maps for Datasets-I

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Fig. 3 Weight vector matrix of Laplace transformation of Eigen maps for Datasets-II

Fig. 4 Eigen values of Laplacian matrix for Datasets-II

Fig. 5 Eigen diagonal matrix (D) values with LPP for Datasets-I

to implement to calculate the Eigen values in the form of PCA. These results are generated in the PCA forms after extracting the calculated values of the first four components of Eigen values of eigenvectors. The statistical results are generated in the complex number after implementing the mapping function of conversion of MRI images. From the Table 1 the list of irrelevant errors present in the trained datasets-II is shown. This section of research uses the non-linear gradient conjugate technique with LELPP to remove these errors and improve the performance the hybrid k-NN model with measured the running time during the process.

4.2 Time Complexity Time complexity is another part of this research that applies to check the execution time of the proposed hybrid k-NN model. The computational complexity of the time

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Fig. 6 Eigen diagonal matrix (D) values with LPP for Datasets-II

Table 1 Results on number of iteration used to remove the error using non-linear-GCIA with LELPP Sr. n

Trained datasets

Iters

Exist

1

D-II

1

3

2

D-II

1

3

D-II

1

4

D-II

1

3

5

D-II

1

3

Errors

Exist errors

Execution time (s)

1.8771e−17

0

0.007629

3

9.8064e−18

0

0.007629

3

−3.7284e−17

0

0.007629

0

0.007629

0

0.007629

0.40701e−17 −5.1783e−18

series function is a combination of two processes (1) Proposed CM-DFT and LELPP. This technique minimizes the time delay of the execution time by the same length of T and maximum delay D, the complexity ( ) is O(DT ) for making the complexity of the cross-correlation function of O n2 DT for the N variable of CM-DFT. The complexity of k-NN for the proposed (( )model ) X is O(xTN). Consequently, the total time complexity of hybrid k-NN is O n2 DT + O(x T N ). Furthermore, the remaining LE-LPP is used in this technique which has the complexity O(LE-LPPlogT ). Thus the complexity of achieved (( values ) ) is O(LE-LPPxlogT ). Hence the complexity of the proposed model is O n2 DT + O(xTN) + O(xlogT ) + O(LE-LPPxlogT ). The efficiency of this method is achieved by the proposed model which generates the novel technique of the hybrid k-NN model. In addition, this technique is used for measuring the execution time of the proposed hybrid k-NN model that become efficient and also calculate the running time complexities of algorithms and compare with the average time of the previous model. Hence, this novel technique is more efficient than the previous one.

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Table 2 Comparison of current hybrid k-NN execution time with previous hybrid k-NN model MRI datasets

Affiliation

Hybrid k-NN model accuracy (%)

Computational time

Brain MRI images

Dalia Mohammad Toufiq et al. (2021)

91.9

2.5305

Brain MRI images

Zahid Ullah et al. (2020)

95.8

4.103

Collen ground truth (SHG) and elastic ground truth (TPEF) images

Camilo Roa et al. (2021)

90

3.0116

99.9

2.4207

Proposed low-grade tumor with CSF datasets

TC = O

(( n )

) DT + O(x T N ) + O(x log T ) + O(LELPPx log T ).

2 T C = O{{(1//2)1.5331} + (0.9978 × 1.5331) + (0.9978 log 0.0076) + (0.0074 × 0.9978 log 0.0076)} where n = number of variable, T = Execution Time and D = Delay in unit time T C = (0.76655) + (1.52972718) + (−2.150161176) + (−0.015911193) T C = 1.301 s This research used the similar model to evaluate the computational time and imputation accuracy. One of the k-NN models, the average execution time for imputed missing values was 2.5305 s and the other model average execution times were 4.103 and 3.0116 s. Thus depending on the amount of data to be imputed and the accuracy of each, the faster method could be preferable. Our proposed hybrid k-NN model gives better results than others with less execution time (Table 2).

5 Conclusion In this research, we proposed an approach for embedding a set of data points into a new feature space in a hybrid k-NN model using non-linear feature mappings discovered by combining Laplacian Eigen maps with gradient conjugate iterative methods. To improve k-NN classification at a maximum margin distance, linear and non-linear feature transformation methods were used to extract class-relevant information and produce the linear transformation matrix. This demonstrates that learning a linear transformation for the feature vectors produced by Laplacian Eigen maps is similar to directly dealing with a kernel function, which can also be easily constructed from

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the weight matrix for Laplacian Eigen maps in order to reach a high margin. This work presents a computationally fast method for solving nonlinear dimensionality reduction problems using locality-preserving projections and nonlinear GCIA.

References 1. Shi, H., Luo, Y., Xu, C., & Wen,Y. (2018). C.M.I. Manifold regularized transfer distance metric learning. In Proceeding: BMVC, Swansea, UK (pp. 158–168). 2. Rippel, O., Paluri, M., Dollar, P., & Bourdev, P. (2015). Metric learning with adaptive density discrimination. Arxiv Preprint, 1(1), 1–15. 3. Xiong, F., Kam, M., Hrebien, L., Wang, B., & Qi, Y. (2016). Kernelized information-theoretic metric learning for cancer diagnosis using high-dimensional molecular profiling data. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(2), 1–23. 4. Kapoor, R., & Gupta, R. (2015). Morphological mapping for non-linear dimensionality reduction. IET Computer Vision, 9(5), 226–233. 5. Wu, Z., Efros, A. A., & Yu, S. X. (2018). Improving generalization via scalable neighborhood component analysis. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 685–701). 6. Soobia, S., Habibollah, H., & Jhanjhi, N. Z. (2021). A systematic mapping study of: Low-grade tumor of brain cancer and CSF fluid detecting approaches and parameters. In Approaches and applications of deep learning in virtual medical care (Vol. 1, pp. 1–30). 7. Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z. (2017). Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Research, 45(1), 156–159. 8. Ma, M. (2017). Laplacian Eigen maps for dimensionality reduction and data representation. ACM Digital Library, 15(1), 1373–1396. 9. Belkin, M., & Niyogi, P. (2015). Laplacian Eigen maps for dimensionality reduction and data representation. Neural Computing, 15(3), 1373–1396. 10. Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., & Schroers. (2018). Normalized cut loss for weakly-supervised CNN segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA (pp. 1818–1827). 11. Xu, X., Liang, T., Zhu, J., Zheng, D., & Sun, T. (2019). Review of classical dimensionality reduction and sample selection methods for large-scale data processing. Neurocomputing, 15(5), 328. 12. Zhang, S., Zong, M., Sun, K., Liu, Y., & Cheng, D. (2014). Efficient k-NN algorithm based on graph sparse reconstruction. In International Conference on Advanced Data Mining and Applications (Vol. 8933(1), pp. 356–369). Springer. 13. Zhang, S., Cheng, D., Deng, Z., Zong, M., & Deng, X. (2018). A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters, 109(1), 44–54. 14. Soobia, S., Afnizanfaizal, A., & Jhanjhi, N. Z. (2021). Statistical analysis the pre and post-surgery of health care sector using high dimension segmentation. In Machine learning healthcare: Handling and managing data (Vol. 1, pp. 1–25). 15. Soobia, S., Afnizanfaizal, A., & Jhanjhi, N. Z. (2021). Performance analysis of machine learning algorithm for health care tools with high dimension segmentation. In Machine learning healthcare: Handling and managing data (pp. 1–30). 16. Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for KNN classification. ACM Transactions on Intelligent Systems and Technology (TIST), 8(2), 1–19. 17. Alhawarat, A., Alhamzi, G., Masmali, I., & Salleh, Z. (2021). A descent four-term conjugate gradient method with global convergence properties for large-scale unconstrained optimization problems. Mathematical Problems in Engineering, 112–119.

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A Systematic Literature Review of How to Treat Cognitive Psychology with Artificial Intelligence Soobia Saeed, Manzoor Hussain, Mehmood Naqvi, and Kadim A. Jabbar

Abstract The middle-of-the-road nowadays brain cognition is based on artificial intelligence however human subjective emotional and mental state changes cannot simulate the replication of biology. Currently, artificial intelligence does not meet all of our needs due to its limitations, this study’s focus on the combination of cognitive psychology and artificial intelligence system would be the research trend of artificial intelligence. The aim of this research is to promote artificial intelligence development and cognitive psychology in terms of emotion, recognition, understanding of human behavior, empathy, and eventually conversion with human being and other artificial intelligence. This research emphasises the importance of possessing the understanding of artificial intelligence, human mental state discrimination, and two typical human interaction system including effective computing and face attraction which is further useful for higher levels of artificial intelligence research. This research also discusses how artificial intelligence is beneficial in the field of psychology and how machine learning techniques have been used to predict the developmental risks of mental health disorders and also detect the level of depression. Keywords Cognitive psychology · Artificial intelligence · Human–computer interaction · Face attraction · Affective computing S. Saeed (B) School of Computing and Information Sciences, Sohail University, Karachi, Pakistan e-mail: [email protected]; [email protected] M. Hussain Computing Department, Faculty of Computing & Information Technology, Indus University, Karachi, Pakistan e-mail: [email protected] M. Naqvi School of Electronics Engineering and Computer Science, Mohwak College, Alberta, Canada e-mail: [email protected] K. A. Jabbar College of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_29

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1 Introduction Nowadays, the scientific community is currently focusing on brain cognition research to develop artificial intelligence (AI), which aims to simulate the physiological processes of the human brain using computer software [1]. The subjective psychological changes cannot be accurately simulated by this human brain biological repetition [2], such as the act of forgetting is non-active in the sense that the more we try to forget, the more memorable it becomes, while the act of forgetting by machines deviates from our psychological expectations. Psychology and mind play a significant direct and indirect role in the field of psychology regardless of the fundamental theories of AI. For example, the behavioral theory of psychology is helpful to reinforcement learning theory to encourage the latest technology of AI. The protracted process through which an organism progressively establishes habitual behavior in response to rewarding or punishing stimuli supplied by its environment. The emotional reaction of AI systems and decision-making in perplexing scenarios are two major challenges confronting the artificial intelligence industry, both of which are dependent on breakthroughs in related psychology sciences. Psychology, with its derived philosophy of mind, is one of the key supporting theories for artificial intelligence [3– 7]. Behavioral psychology is the study of people’s cognitive abilities and behavior, such as how they reason, drive, motivate, and feel. Our brain processes external input by responding with varied attitudes towards objects via our already internalized knowledge units about the external environment. As a result, many subjective emotional orientations such as contentment, discontent, love, dislike, and so on emerge. This is the most essential feature that distinguishes people from machines. Human cognitive psychology produces certain emotional qualities. The machine’s internal knowledge structure is updated by observing changes in subjective emotions so that it can reproduce human attitudes, preferences, and other subjective emotional experiences [8, 9]. Memory, attention, vision, information representation, emotions, intentions, desires, and other human mental qualities are still in the early stages of artificial intelligence [10–12]. Recently, existing AI is not perfect because the system is combined with research direction and cognitive psychology to help the development of AI structure in psychology and provide computer services to be able to stimulate the advanced cognition of human beings, human behavior, and identify the learning and thinking behavior of humans so that the computer can easily recognize human feelings, emotions, dialogue, and achieve empathy. This artificial intelligence research combines new theories and methods from neuroscience, brain terminologies, psychology, and computer science through the development of artificial intelligence machine stimulation on people’s psychological activities, the redevelopment of human psychology, integration, and encouragement. We can better understand how people interact with computers and improve our social skills by enhancing artificial intelligence at this level [13, 14]. The advancement of psychology has also broadened the scope of research and the options for research subjects, paving the way for artificial intelligence goods to hit the market quickly. As a result, researchers created emotion recognition systems based on facial expressions, public opinion analysis

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based on big data analysis technologies, and suicide early warning systems based on medical pictures [15]. Yet, a cognitive psychology-based evaluation of artificial intelligence is insufficient. The current state of AI techniques in cognitive psychology, the application of artificial intelligence in experimental cognitive psychology research, and the current evolution of psychology trends are all examined in this study [16, 17].

2 Literature Review With the assistance of psychological science specialists, the function of Artificial Intelligence (AI) in psychology science is currently undervalued. Several times psychologist refuses the usage of specialist structures just because of unawareness of laptop use in their field as they are afraid of replacing the words of structures. Sometimes they do not prefer the use of the latest Information Technology. Medical professionals reacted in the same way when the first computerized diagnosis system was tested. Although artificial intelligence (AI) has not yet successfully replicated every aspect of human behavior, researchers are moving in the right direction to get there [18, 19]. Whatever the case, there are several intersectional factors between these two domains. In artificial intelligence, one of the most fascinating goals in computing and IT industry is that the machines do sense artificially, think, learn, experiences human behavior, and can function autonomously without any supervision. To solve the problem, the machines make sense and clear all complexities as this is one of the necessary goals as well. One of the many issues with achieving this goal is that there are so many alternative interpretations of what learning and reasoning entail that it is easy to lose the answer in the sea of ideas and options. In this research, this researcher discusses the foundational principles, psychological theories, and many ideas that can we experience the spine of real and self-sustaining artificial Intelligence [20]. This research investigates the brain functions in terms of artificial intelligence and their scientific psychological practices due to potential purposes of cutting-edge. There are several AI activities helped to study including scientific training, psychological assessment, psychological therapies, treatment and help to take the decision-making. The clinical AI based devices have been introduced that help to integrate human thinking, identify the issues which are associated with AI in terms of medical practices, identify the reasons for loss/damage among mental health professionals, and different complications which are related to AI based technologies are discussed here. The growth of AI applied sciences and their use in psychological training has important consequences that are expected to alter the field of mental fitness care. Psychologists and other mental health care specialists must be included in the evaluation, assessment, and ethical application of AI technologies. Technology with artificial intelligence (AI) is made to do tasks that traditionally call for human intelligence. Another way to think of AI is as the diverse branch of science that studies and develops this technology. The advent of the computer age in the 1940s marks the beginning of the field of artificial intelligence, which was given its formal name by computer scientist John McCarthy in 1956 for an

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assessment of the history of AI [21]. AI technology can be integrated into physical machinery, run as separate computer programs, distributed across networks, used with robots, or developed from or combined with living biology (e.g., brain–pc interfaces). This science can be used to mimic complicated human behavior that is capable of reasoning, learning, and acting on the environment as a self-sufficient shrewd agent or for specialized shrewd purposes. Learn about tablet learning, fake neural networks, and home-grown dialect handling are all important fields of artificial intelligence.

3 Psychological Testing with AI Data collection, analysis, testing, and assessment could be improved with AI technologies, according to Liang et al. [19]. By analyzing vast amounts of data and combining it with expert analysis, AI technology can be used to create useful therapeutic tools. The use of artificial intelligence can currently be used to identify and forecast problems, evaluate forecasts, and diagnose problems. The AI developed a number of variables that were associated with suicidal ideation and behavior after analyzing data from 707 suicidal patients in Greater Santiago, Chile. The study resulted in a set of preventative interventions for suicidal adults that reduced their likelihood of committing suicide. It also improved their psychological well-being, feelings of self-worth, and motivation to live [22, 23]. In 2017, the authors develop the model that mimicked psychiatric diagnoses using fuzzy logic. It evaluated patients successfully and tested mental health diagnoses based on insufficient information. Utilizing appropriate AI technology allows for the development of mental models, testing of their accuracy, and the recommendation of treatments.

3.1 Cognitive Psychology Cognitive psychology help to understand the complexity of cognition through developing brain psychology models, research and testing that how to handle the human brain and how they process the complexity of information during attention, memory, and perception [24–26]. AI and cognitive psychology have similar aims to understand the human behavior and their intelligence with previous knowledge to build such processes through the use of cutting-edge technology. Whereas the computational modeling and cognitive psychology both are different approaches to understand the nature of thinking and provide an excellent vision into the rapid growing field of cognitive psychology [27]. The development of AI has learned a great deal from cognitive psychology, and human cognitive models are frequently used in modern AI design. Many parts of the human mental process, including memory, encoding, and attention, are simulated. Artificial intelligence that is cognitive and psychological has been extensively

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Fig. 1 Psychology with artificial intelligence

studied. In this article, we examine the foundational ideas and most recent developments in psychology and brain research, as well as three common application scenarios: face attraction and affective computing. However, deep neural networks, influenced by cognitive psychology theories and methodologies, provide an excellent illustration of the advantages of integrating information and proficiency from several fields by describing how children learn object labels [28] (Fig. 1).

3.2 Face Attraction Visual psychology manifests itself in a variety of aesthetic evaluations of human faces, which play an important role in social emotion formation and communication [28]. The majority of people believe that beauty is a subjective experience in daily life; however, scientists have disproven this long-held notion by discovering that people’s perceptions of face attractiveness are highly consistent across race, age, gender, socioeconomic status, and cultural background. This observation also raises the possibility that facial attractiveness partially reflects universal human psychological traits. A database for predicting face attractiveness called SCUT-FBP5500 was compiled and made public by South China University of Technology’s Human– Computer Interaction Laboratory. This dataset contains 5,500 frontal face images with various attributes (age, gender, etc.) and markers, such as coordinates of face feature points, face value scores (1–5), and distribution of face value scores. An experimental setting was used to create mental state embeddings using mental preference features. A deep learning-based face attractiveness template was developed employing a range of computer models for classification, regression, and ranking (AlexNet, ResNet-18, and ResNeXt-50) [17, 18]. Consider using an assessment model using root mean square errors (RMSE), a maximum absolute error (MAE),

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and a Pearson correlation coefficient to analyze the benchmark (PC). The Pearson correlation coefficient was over 0.85 when the five-fold approach was used to analyze the performance of the face attractiveness templates under various computer models; the maximum absolute error was less than 0.25, and the root mean square error was 0.3–0.4. [29]. In this research, the author discusses the facial features and multitask learning approach using the predicting strategy of facial attractiveness. To estimate the first use of face representation datasets who are pre-trained this large dataset of facial expression automatically with the use of a deep convolutional neural network (CNN). Another key point of use of multitask learning strategy is used for three tasks in terms of optimal shared features such as identifying the facial attribute using the deep learning model including facial features, gender recognition, race recognition etc. To improve the accuracy of the attractiveness computation, a multi-stream CNN is fed certain parts of the face picture (such as the left eye, nose, and mouth) in addition to the entire face. Each multi-stream network receives numerous partial facial features as input [30, 31]. During the Meta training process, the author discovered a significant number of specific preferences that many people share. During the meta-testing phase, the model is then applied to fresh patients with a small sample of assessed photos. This study made use of a facial beauty dataset that included hundreds of volunteers from varied social and cultural backgrounds and was rated by hundreds of races, gender, and age groups. According to quantitative comparisons, the suggested strategy surpasses existing algorithms for predicting facial beauty and is effective in learning individual beauty preferences from a limited number of annotated pictures [32, 33].

3.3 Affective Computing Emotions are the combination of psychological events known as emotions, sentiments and passions. Emotions are the psychological state based on favorable and unfavorable attitudes in the direction of external things and objective reality. In contrast, emotions are based on person’s sensory, physical, psychological, and spiritual feelings. During the research, the author describe the emotional behavior of patients either normal or abnormal thinking and logical reasoning, this was totally related to the defect of cerebral cortex. Cerebral cortex is a part of brain that deal with human logical behavior and limbic deal with human emotions. However, their brain capacity for making decisions has encountered significant roadblocks [33], demonstrating that human intelligence is also expressed in a variety of rich emotional capacities in addition to typical rational thinking and logical reasoning skills. Herbert Simon, a Nobel Prize winner in cognitive psychology, underlined more than 40 years ago that emotions should be taken into account when addressing problems [31], Professor Marvin Minsky of the Massachusetts Institute of Technology in the United States, one of the pioneers of artificial intelligence, was the one who first put up the idea of giving computers emotion. He underlined the necessity and significance of emotion as a skill for computers to develop intelligence in his monograph, The Mind

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Society the author proposed the concept of emotional computing when she stated that it “is computing that can monitor, evaluate, and change emotions in response to human outward expressions” [32, 33].

4 Machine Learning with AI Machine learning is at the core of AI. Different algorithms are used by machine learning to learn a particular task. Making predictions, classifying things, creating visuals, and many other things are among these jobs. A subset of machine learning called deep learning completes comparable tasks but with a more complex structure. These applications of AI include the classification of images in biology and chemistry, simulations in mathematics and physics, medical diagnosis, and numerous more areas. The power of these AI techniques has not yet been completely applied in psychology, a topic that has not been around as long as the one listed above. The goal of psychology as a science is to investigate and characterize how people’s behavior relates to their emotional and cognitive functioning [30]. Unfortunately, according to some researchers, the bulk of psychologists primarily concentrate on explaining behavior. According to Yarkoni and Westfall, forecasting future behavior has become rare or unimportant since this has now become accepted practice. Many psychologists have begun experimenting with artificial intelligence to predict and classify outcomes in many areas of study [31], discuss the quantifying pain levels based on brain scans to using machine learning to gain a deeper understanding of personalities [31], to detecting human needs in critical situations [32], or to predict problematic social media usage or future alcohol abuse [29], Researchers have even investigated how to improve AI models for mental health [29]. There are numerous ways in which psychologists have begun using AI and machine learning to address significant issues. The subject of mental health and mental diseases is one of the most significant issues psychologists nowadays deal with. Psychologists commonly treat major depressive disorder (MDD), anxiety, post-traumatic stress disorder (PTSD), schizophrenia, and many other mental illnesses and disorders. Treatments for these conditions typically take the shape of various therapies or, when provided in collaboration with a psychiatrist, even medications. Psychologists have utilized machine learning approaches to increase their understanding of the heterogeneity in these diseases [34]. The most prevalent types of mental diseases, including depression and anxiety, are currently increasing and have affected millions of people. This research will examine contemporary research that uses AI approaches to further the study of psychology. Various uses of AI and machine learning are discussed in this article, such as diagnosing and predicting mental health issues, identifying depression levels, and predicting suicide and self-injury.

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4.1 Machine Learning Applications for PTSD The researcher investigate how well machine learning may be used to forecast the onset of PTSD following admission to the ER or hospitalization. Papini and colleagues used an ensemble machine earning approach to try to improve the accuracy of a prior study’s attempt to predict PTSD [35]. The Data gathered for 271 patients who had been admitted to the emergency room was gathered by Papini and his colleagues. Pulse, stay time, state of consciousness, and injury severity were only a few of the physical predictors that were gathered. Additionally, psychological predictors were gathered, such as a history of an anxiety or mood illness, present mental health, any PTSD symptoms, and others. 3, 6, and 12 months after being admitted to the emergency room, PTSD screening was finished. According to research, 41 predictive features were left after preprocessing the gathered data. A machine learning model comprised of multiple decision trees was used by the researchers, which is called extreme gradient boost (XG Boost). Machine learning algorithms that use decision trees use yes/no questions derived from training data to process testing data. The model was used to predict positive PTSD symptoms (PC-PTSD score 3) and negative PTSD symptoms (PC-PTSD score 3) on individual bases which are denoted as PTSD+ and PTSD. The model of accuracy was determined by the area under the curve score. In this research, the author compares XG Boost model with benchmark prediction model discussed in this paper. One benchmark is based on hospital features for normal data collection for prediction. Based on the most important predictor in the second benchmark, “PTSD severity only in the hospital,” logistic regression was used exclusively. Karstoft and her colleagues went in a slightly different direction. Data was collected from 957 trauma survivors for their study [36]. Among this data, 68 predictive features were sorted according to their importance in predicting PTSD development. A support vector machine (SVM) was used by the researchers to evaluate the prediction’s accuracy. Model improvement with SVM requires training data, as it is a supervised learning method. It is common to use SVM models in clustering for the classification of data and the detection of outliers [35, 37].

5 General Discussion System-based analysis and application examples in this study show that cognitive psychology combined with artificial intelligence is the future direction of artificial intelligence development: to advance artificial intelligence, to enable computers to simulate advanced cognition in humans, to learn and think, so that computers can recognize and understand human emotions, and to finally realize dialogue and empathy with humans. With artificial intelligence and human psychological cognition, people and machines can interact emotionally in similar ways, similar to human communication, not just by simulating rational thinking, but also by reproducing

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perceptual thinking. Artificial intelligence based on cognitive psychology has flaws nowadays: The evaluation criteria for each subject may not be totally uniform, and the random sampling difference is exacerbated by racial, regional, and environmental variances. Mental activities that are unclear and disorderly are also typical. AI is a very powerful tool that can understand complex relationships between variables in ways that humans cannot. In a variety of ways, machine learning has been used to solve many research problems in the field of science. The motive of this systematic literature review is to focus on how machine learning is effective in the field of psychology as we know that phycology is the branch of science that is related to psychiatry which treats mental health disorders and diseases in the world. This research discusses the criteria of AI techniques that may be useful in diagnosing and predicting mental disorders, self-injury, and suicide in the future. As a result of the research presented here, machine learning approaches in psychology can be applied in a more advanced manner. There are a number of limitations in this review; however, they leave room for future adaptations and improvements.

6 Conclusion In this research, we discuss the behavior of a combination of artificial intelligence systems with cognitive psychology. According to the research, the combination of human psychological cognition with artificial intelligence cannot simulate the brain’s rational thinking but it reproduces the perceptional thinking of heart as well realization of interaction of machine between emotional behavior of people which is similar to human being communication. In future, AI and psychology collaboration will focus on human–computer interaction, big data using medical devices, braincomputer interfacing, and artificial intelligence with intraoperative neuro monitoring system. Based on the combination of AI with cognitive psychology will achieve multimodal data and the extraction of high-dimensional data. This study develops the platform of advancement of artificial intelligence in terms of research direction in order to simulate the human–computer interaction system using the machine and human emotion results. It possesses the result of cutting-edge science which is not only theoretically significant but also effective in implementation development with AI applications.

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Study of SEIRV Epidemic Model in Infected Individuals in Imprecise Environment Ashish Acharya, Subrata Paul, Manajat Ali Biswas, Animesh Mahata, Supriya Mukherjee, and Banamali Roy

Abstract Multiple scientific disciplines now have a new avenue to analyze the dynamics of epidemic models thanks to mathematical modelling. Vaccination is a simple, safe, and efficient way to shield people from dangerous diseases before they come into contact with them. In this paper, we consider an epidemic model in which the entire population is classified into five classes susceptible, exposed, infected, recovered and vaccinated. When uncertainty is introduced, the epidemic model’s scenario transforms. To overcome such a situation we consider the SEIRV model in imprecise environment. All parameters of the model are taken as interval numbers in order to construct an improved epidemic SEIRV model. Non-negative feasible steady states namely DFE (Disease free equilibrium), EE (Endemic Equilibrium) and stability criteria of them have beenanalyzed in interval environment. In the end, extensive numerical simulations verify all of the analytical findings. Keyword SEIRV model · Vaccination · Interval number · Stability analysis · Numerical study

A. Acharya (B) Department of Mathematics, Swami Vivekananda Institute of Modern Science, Karbala More 700103, West Bengal, India e-mail: [email protected] S. Paul Department of Mathematics, Arambagh Government Polytechnic, Arambagh, West Bengal, India M. A. Biswas Department of Mathematics, Gobardanga Hindu College, P.O.-Khantura, 24 Parganas (North), Gobardanga 743252, West Bengal, India A. Mahata Mahadevnagar High School, Maheshtala, Kolkata 700141, West Bengal, India S. Mukherjee Department of Mathematics, Gurudas College, Kolkata 700054, West Bengal, India B. Roy Department of Mathematics, Bangabasi Evening College, Kolkata 700009, West Bengal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_30

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1 Introduction Vaccination is an organic method that acquired immunity to tremendously contagious sicknesses [1] like SARS, fowl pox, HIV, small pox, polio, Swine flu and many others. Complete lockdown, partial lockdown, expansion of health services, rationing, and numerous other measures may be said. Since the virus first entered the human population, researchers have been investigating the causes of new outbreaks in susceptible and exposed populations as well as the effects of vaccination on recovered populations. Vaccination is an uncomplicated approach that can forestall the mortality rate of Covid 19 disease during the last two years. Eight vaccines—mRNA-1273, BNT162b2, Ad26.COV2.S, AZD1222, Serum Institute of India Covishield, Covaxin, BBIBP-CorV and Sinovac were approved worldwide [2]. Henceforth, several countries have provided the approval of these vaccines after a large number of trials. In epidemiology, various simple mathematical models have been formulated by classifying the total population to study the different kinds of disease spread dynamics. Among a few pioneer epidemic models like SI [3], SIS [4], SIR [5], SEIR [6, 7], SIQR [8] SEIQRD [9] etc. are very popular. A large number of literatures on epidemic models with different treatment rates and functional responses have been reviewed during the last few decades. The majority of researchers in theoretical biology have assumed that biological factors are surely known. Indeed, the actual situation is different. For a variety of reasons, including oversight during the counting process and supposing initial conditions, the values of all factors of the system cannot always be precisely known. Thus, from this, we might arrive at the resolution that, conquering the restrictions of these models with imprecise parameters are more favourable and accurate. To remove such kind of impreciseness from the model, [10] first presented the concept of interval number into harvested Lotka-Voltera model. After that, a lot of well-known researchers focused on constructing a biological model in a changing environment [11–20]. Das and Pal [21] framed an SIR model in interval environment in which treatment control other than isolation and vaccination. A single delay imprecise SIR model containing treatment rate (Holling type-III) is explored in [22, 23] studied a tumor model in interval environment. Structure of the Paper is as follows: Pre-requisite is added in Sect. 2. Model Formulation with interval number and stability of the model are in Sect. 3. Verification of results numerically of the system is in Sect. 4. Finally Sect. 5 covers conclusions of the work.

2 Preliminaries Definition The interval [Tm 1 , Tn 1 ] can also be written as k1 (η) = (Tm 1 )1−η (Tn 1 )η for the parameter η ∈ [0, 1], which is also called parametric form in interval figure.

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Properties Let, the parametric form of interval number given by k1 (η) = (Tm 1 )1−η (Tn 1 )η and h 1 (η) = (Rm 1 )1−η (Rn 1 )η for η ∈ [0, 1], then the following operation is obtained: 1. g1 (η) = k1 (η) + h 1 (η) = (Tm 1 + Rm 1 )1−η (Tn 1 + Rn 1 )η , 2. s1 (η) = k1 (η) − h 1 (η) = (Tm 1 − Rm 1 )1−η (Tn 1 − Rn 1 )η , 1−η 3. r1 (η){{ = (min{T k1 (η)h 1 (η) = })η m 1 Rm 1 , Tn 1 Rn 1 , Tm 1 Tn 1 , Rm 1 Rn 1 }) (max Tm 1 Rm 1 , Tn 1 Rn 1 , Tm 1 Tn 1 , Rm 1 Rn 1 , 4. yς1 (η) = e(η) = y(Tm 1 )1−η (Tn 1 )η if y > 0, = y(Tm 1 )1−η (Tn 1 )η if y < 0, } { 5. p1 (α) =

k1 (η) h 1 (η)

T

= (min{ Rmm1 , 1

Rn 1 Tn 1

,

Rm 1 Tn 1

,

Tn 1 Rm 1

1−η

})

(max{

Rm 1 Tm 1

,

Rn 1 Tn 1

,

Tm 1 Rn 1

,

Tn 1 Rm 1

η

) .

Where, g1 (η), s1 (η), r1 (η), yk1 (η), p1 (α), e(η) denotes the interval valued function for constant ς1 and η ∈ [0, 1].

3 Model Formulation 3.1 Model in Imprecise Environment The whole population (N) is divided into five groups: susceptible (S), exposed (E), infected (I), recovered (R), and vaccinated (V) at any time t ≥ 0, thus N(t) = S(t) + E(t) + I(t) + R(t) + V (t). Considering the SEIRV model [24] as: dS dt dE dt dI dt dR dt dV dt

/\

/\

/\

/\

= Λ − βS(t)I(t) − μ0 S(t) − δS(t) /\

/\

/\

= βS(t)I(t) − (μ0 + μ1 )E(t) /\

/\

/\

= μ1 E(t) − (μ0 + μ2 )I(t) /\

/\

= μ2 I(t) − μ0 R(t) /\

/\

= δS(t) − μ0 V(t)

(1) /\

/\

where Λ: birth rate of S taken as imprecise interval [Λ1 , Λ2 ], β: infection rate of S taken as imprecise interval [β1 , β2 ], μ0 : mortality rate of I all individuals in interval [μ01 , μ02 ], δ: vaccination rate in interval [δ1 , δ2 ], μ1 :[ progression ] rate from E to I in interval [μ11 , μ12 ], μ2 : recovery rate of I in interval μ21, μ22 . The above model (1) reduced in parametric form of interval number as follows: /\

/\

/\

/\

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dS dt dE dt dI dt dR dt dV dt

1−ξ1

= Λ1

ξ

ξ

1−ξ1

Λ21 − β2

1−ξ

ξ

1−ξ1 ξ1 δ1 S(t),

β11 S(t)I (t) − μ02 1 μ011 S(t) − δ2 ) ( 1−ξ ξ 1−ξ ξ 1−ξ ξ = β1 1 β21 S(t)I (t) − μ01 1 μ021 + μ11 1 μ121 E(t), ) ( 1−ξ ξ 1−ξ ξ 1−ξ ξ = μ11 1 μ121 E(t) − μ01 1 μ021 + μ21 1 μ221 I (t), 1−ξ

ξ

ξ

1−ξ

= μ21 1 μ221 I (t) − μ02 1 μ011 R(t), 1−ξ1 ξ1 δ2 S(t)

= δ1

ξ

1−ξ

− μ02 1 μ011 V (t).

(2)

where, ξ1 ∈ [0, 1].

3.1.1

Positivity and Boundedness

Theorem 1 The system (2) is bounded in the region ∇ = {(S, E, I, R, V ) ∈ R 5 : 0 < ξ Λ21 1−ξ1 ξ1 μ02 μ01 1−ξ1

Λ1

N≤

3.1.2

} for all t ≥ 0.

Equilibrium Points

From the model system (2), ddtS = ddtE = ddtI = ddtR = ddtV = 0, then we have two equilibrium points namely Disease free Equilibrium (DFE) Point PD F E and Endemic Equilibrium (EE) point PE E .( ) Where,

1−ξ1

Λ1

=

PD F E

1−ξ1

μ02

ξ

ξ

Λ21

1−ξ1 ξ1 δ1

μ011 +δ2

PE E (S ∗ , E ∗ , I ∗ , R ∗ , V ∗ ). EE points are S ∗ = E∗

=

1−ξ1

μ02

( ) 1−ξ ξ 1−ξ ξ μ02 1 μ011 +μ21 1 μ221

ξ

1−ξ1

μ11 1−ξ1 ξ1 δ2

μ011 +δ1

1−ξ ξ β2 1 β11

3.1.3

ξ

μ121

, R∗ =

I ∗, I ∗

ξ μ221 1−ξ1 ξ1 μ02 μ01 1−ξ1

μ21

I ∗, V ∗ =

1−ξ

ξ

1−ξ

ξ

ξ

1−ξ1

μ121

δ1 1 δ21 Λ1 1 Λ21 ( ) and ξ 1−ξ ξ 1−ξ ξ μ011 μ02 1 μ011 +δ2 1 δ11 ( )( ) 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ μ02 1 μ011 +μ11 1 μ121 μ02 1 μ011 +μ21 1 μ221

, 0, 0, 0,

1−ξ1

μ02

1−ξ1

β2 1−ξ

β11 μ11

ξ

1−ξ

ξ

Λ1 1 Λ21 Λ1 1 Λ21 )( ) ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ μ011 +μ11 1 μ121 μ02 1 μ011 +μ21 1 μ221 ( )( ) 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ μ02 1 μ011 +μ11 1 μ121 μ02 1 μ011 +μ21 1 μ221 δ1 1 δ21

=

(

,

ξ

−

1−ξ1

μ02

1−ξ1

β2

ξ

1−ξ1

β11 μ02

ξ

1−ξ1

μ011 μ11

ξ

μ121

.

Basic Reproduction Number

The reproduction number can be evaluated from the greatest eigenvalue of the matrix X Y −1 [24] where, [ X=

0

1−ξ1 ξ1 1−ξ1 ξ1 β1 Λ1 Λ2 1−ξ1 ξ1 1−ξ ξ μ02 μ01 +δ2 1 δ11

0

0

β2

]

] 1−ξ ξ 1−ξ ξ 0 μ02 1 μ011 + μ11 1 μ121 . and Y = 1−ξ ξ 1−ξ ξ 1−ξ ξ −μ11 1 μ121 μ02 1 μ011 + μ21 1 μ221 [

Study of SEIRV Epidemic Model in Infected Individuals in Imprecise …

Then R0 =

3.1.4

1−ξ

ξ

1−ξ

ξ

1−ξ

375

ξ

β2 1 β11 Λ1 1 Λ21 μ11 1 μ121 ( )( )( ). 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ μ02 1 μ011 +δ2 1 δ11 μ02 1 μ011 +μ11 1 μ121 μ02 1 μ011 +μ21 1 μ221

Stability Analysis

In this part we have analyzed two states of equilibrium: (i) DFE and (ii) EE. Theorem 2 The DFE point of the system (2) is stable when R0 < 1 and the system (2) is unstable when R0 > 1. Proof Consider the Jacobi matrix at DFE of the model system (2) is given by, JD F E = ⎡ 1−ξ1 ξ1 1−ξ1 ξ1 ) ( 1−ξ ξ β β Λ Λ 1 + δ 1−ξ1 δ ξ1 0 0 − 11−ξ 2ξ 11−ξ 2ξ ⎢ − μ02 1 μ01 1 2 ⎢ 1δ 1 1 μ 1 +δ μ ⎢ 2 01 1 02 ⎢ 1−ξ1 ξ1 1−ξ1 ξ1 ) ( ⎢ β1 β 2 Λ1 Λ2 1−ξ ξ1 1−ξ ξ ⎢ + δ11 1 δ121 0 0 − μ02 1 μ01 ⎢ 1−ξ ξ1 1−ξ1 ξ1 ⎢ δ2 +δ1 μ02 1 μ01 ( ) ⎢ 1−ξ ξ 1−ξ ξ1 1−ξ ξ ⎢ − μ02 1 μ01 + δ21 1 δ221 0 δ11 1 δ121 0 ⎢ ⎢ 1−ξ1 ξ1 1−ξ ξ1 ⎢ −μ02 1 μ01 0 0 δ21 δ22 ⎣ 1−ξ1 ξ1 δ2

δ1

0

0

0

⎤ 0 0 0 0

1−ξ ξ1 −μ02 1 μ01

⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥. ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

The characteristic equation at PD F E becomes |J D F E − λI5 | = 0. [ ( ) ( ) ( ) ( 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ Or, μ02 1 μ011 + δ2 1 δ11 + λ μ02 1 μ011 + λ μ02 1 μ011 + λ μ02 1 μ011 + ] )( ) 1−ξ ξ 1−ξ1 ξ 1−ξ ξ μ11 1 μ121 β2 β11 Λ1 1 Λ21 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 = 0. μ11 μ12 + λ μ02 μ01 + μ21 μ22 + λ − 1−ξ1 ξ1 1−ξ1 ξ1 μ02

μ01 +δ2

the eigenvalues of ) ( ) ( the )above (So, 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 − μ02 μ01 + δ2 δ1 , − μ02 μ01 , − μ02 μ01 and becomes

δ1

equation are another equation

) ( 1−ξ ξ 1−ξ ξ 1−ξ ξ λ2 + 2μ02 1 μ011 + μ11 1 μ121 + μ21 1 μ221 ( )( ) μ1−ξ1 μξ1 β1−ξ1 β ξ1 Λ1−ξ1 Λξ1 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ 1 1 2 = 0. λ + μ02 1 μ011 + μ11 1 μ121 μ02 1 μ011 + μ21 1 μ221 − 11 1−ξ12 2ξ 1−ξ ξ μ02 1 μ011 + δ2 1 δ11

( ) 1−ξ ξ 1−ξ ξ 1−ξ ξ Others eigenvalue gives negative value when 2μ02 1 μ011 + μ11 1 μ121 + μ21 1 μ221 > )( ) ( 1−ξ ξ 1−ξ1 ξ1 1−ξ1 ξ1 μ 1μ 1β β Λ Λ 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ 0 and μ02 1 μ011 + μ11 1 μ121 μ02 1 μ011 + μ21 1 μ221 − 11 1−ξ121 2 ξ1 11−ξ11 ξ1 2 > 0. μ μ +δ δ 02 01 2 1 ( )( ) 1−ξ ξ 1−ξ ξ 1−ξ ξ 1−ξ ξ Using Routh-Hurwitz criteria μ02 1 μ011 + μ11 1 μ121 μ02 1 μ011 + μ21 1 μ221 > ξ

1−ξ1

ξ 1−ξ ξ β11 Λ1 1 Λ21 1−ξ1 ξ1 1−ξ1 ξ1 μ02 μ01 +δ2 δ1

1−ξ1

μ11

μ121 β2

.

From the above in equation, we have R0 < 1. Therefore the model system (2) at DFE point is LAS when R0 < 1 and the system (2) unstable when R0 > 1.

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Theorem 3 If R0 < 1, the EE point PE E (S ∗ , E ∗ , I ∗ , R ∗ , V ∗ ) model system (2) is LAS. Proof The characteristic equation at PE E (S ∗ , E ∗ , I ∗ , R ∗ , V ∗ ) of the model system (2) becomes, ( ) (D1 + λ)2 λ3 + A1 λ2 + B1 λ + C1 = 0. ξ

1−ξ

ξ

1−ξ

1−ξ

ξ

1−ξ1

where, A1 = 3μ02 1 μ011 + μ11 1 μ121 + μ21 1 μ221 + β2

(3)

ξ

1−ξ1 ξ1 δ1 ,

β11 I ∗ + δ2

2

ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1 B1 = 3(μ1−ξ 02 μ01 ) + 2μ02 μ01 μ11 μ12(+ 2μ02 μ01 μ21 μ22 + μ11 μ12 ) 1−ξ

ξ

ξ

1−ξ

1−ξ

ξ

1−ξ1 ξ1 δ1

1−ξ

μ21 1 μ221 − β2 1 β11 μ11 1 μ121 δ2 ( ) 1−ξ ξ 1−ξ ξ β2 1 β11 I ∗ + δ2 1 δ11 ,

ξ

1−ξ

ξ

1−ξ

ξ

+ 2μ02 1 μ011 + μ11 1 μ121 + μ21 1 μ221

) {( )2 ( 1−ξ1 ξ1 ∗ 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ ξ 1−ξ ξ 1−ξ ξ μ02 1 μ011 + μ11 1 μ121 μ02 1 μ011 C1 = β2 β1 I + μ02 μ01 + δ2 δ1 } ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1−ξ1 ξ1 1 + μ1−ξ μ μ μ + μ μ μ μ − β β μ μ δ δ 21 22 02 01 11 12 21 22 2 1 11 12 2 1 1−ξ1

− (β2

ξ

2

ξ

β11 ) μ11 1 μ121 S ∗ I ∗ ,

1−ξ

1−ξ

ξ

D1 = μ02 1 μ011 . The eigenvalues of the characteristic equation (3) are −D1 , −D1 and another equation becomes λ3 + A1 λ2 + B1 λ + C1 = 0. For Stability criteria using Routh-Hurwitz of the model system (2) we have A1 > 0, B1 > 0, C1 > 0, A1 B1 > C1 if R0 < 1. Therefore, the model system (2) at the EE point is stable when R0 < 1.

4 Numerical Simulation /\

In the part to discuss model (3) numerically, we consider [6] parameter A= [0.01, 0.02], β = [0.35, 0.50], δ = [0.070, 0.080], μ0 = [0.005, 0.007], μ1 = [0.072, 0.082], μ2 = [0.21, 0.31], b = [0.021, 0.051]. Figure 1 is plotted using these values for different ξ1 (= 0, 0.6, 1). We consider [6] parameters A = [0.4, 0.6], β = [0.09, 0.12], δ = [0.001, 0.003], μ0 = [0.015, 0.035], μ1 = [0.008, 0.010], μ2 = [0.03, 0.05]. Figure 2 is plotted using these values for different ξ1 (= 0, 0.6, 1). /\

/\

/\

/\

/\

/\

/\

/\

/\

/\

/\

/\

Study of SEIRV Epidemic Model in Infected Individuals in Imprecise …

377

Fig. 1 Using the above parameter value of the model (2) we plotted a, b and c for different value of ξ1 = 0, 0.6, 1. This figure reflects the system is stable at DFE when R0 > 1 for t ∈ [0, 1000]

5 Conclusions Mathematical modeling on epidemic disease has opened a new door in multiple disciplines of science to analyze the dynamics of epidemic model. Eventually, it is widely recognized, since the covid-19 pandemic situation, medical science has taken it as a key tool to predict the disease’s kinetics with time. Vaccination is also adding another dimension to control the spread of disease among people throughout the world. Vaccination is an easy, safe, and effective manner of protecting people towards harmful illnesses, earlier than they arrive into contact with them. In this manuscript, we have reflected an epidemic model in which the entire population is divided into parts—susceptible, Exposed, Infected and Recovered and another partition of the population is vaccinated. To overcome those scenarios, the proposed model has been considered in interval environment. Positivity, boundedness, feasible steady states and stability of system (2) have been shown in imprecise environment. In the case of stability analysis: the DFE of the system (2) is stable when R0 < 1 and the system (2) is unstable when R0 > 1, If R0 < 1, the EE of the system (2)

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Fig. 2 Using the above parameter value of the model (2) we plotted a, b and c for different value of ξ1 = 0, 0.6, 1. This figure reflects that the system is stable at EE when R0 < 1 for t ∈ [0, 1000]

is LAS. All relevant theorems and results are checked correctly through numerical simulation and figures are verified by Matlab software inelegant way. Various types of epidemic models in neurotrophic environment is left for near future.

References 1. Mahata, A., Paul, S., Mukherjee, S., & Roy, B. (2022). Stability analysis and Hopf bifurcation in fractional order SEIRV epidemic model with a time delay in infected individuals. Partial Differential Equations in Applied Mathematics, 5, 100282. 2. Poonia, R. C., Saudagar, A. K. J., Altameem, A., Alkhathami, M., Khan, M. B., & Hasanat, M. H. A. (2022). An enhanced SEIR model for prediction of COVID-19 with vaccination effect. Life, 12, 647. 3. Sutton, K. M. (2014). Discretizing the SI epidemic model. Rose-Hulman Undergraduate Mathematics Journal, 15(1), 12. 4. Mahata, A., Mondal, S. P., Ahmadian, A., Ismail, F., Alam, S., & Salahshour, S. (2018). Different solution strategies for solving epidemic model in imprecise environment. Complexity, 2018(2), 1–18.

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5. Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals, 139, 110057. 6. Paul, S., Mahata, A., Ghosh, U., & Roy, B. (2021). SEIR epidemic model and scenario analysis of COVID-19 pandemic. Ecological Genetics and Genomics, 19, 100087. 7. Paul, S., Mahata, A., Mukherjee, S., et al. (2022). Study of fractional order SEIR epidemic model and effect of vaccination on the spread of COVID-19. International Journal of Applied and Computational Mathematics, 8, 237. 8. Paul, S., Mahata, A., Mukherjee, S., & Roy, B. (2022). Dynamics of SIQR epidemic model with fractional order derivative. Partial Differential Equations in Applied Mathematics, 5, 100216. 9. Youssef, H., Alghamdi, N. Ezzat, M. A., El-Bary, A. A., & Shawky, A. M. (2021). Study on the SEIQR model and applying the epidemiological rates of COVID-19 epidemic spread in Saudi Arabia. Infectious Disease Modelling, 6, 678-692. 10. Pal, D., Mahapatra, G. S., & Samanta, G. P. (2013). Optimal harvesting of prey-predator system with interval biological parameters: A bioeconomic model. Mathematical Biosciences, 241, 181–187. 11. Pal, D., & Mahapatra, G. S. (2015). Dynamic behavior of a predator–prey system of combined harvesting with interval-valued rate parameters. Nonlinear Dynamics, 83, 2113–2123. 12. Xiao, Q., Dai, B., & Wang, L. (2015). Analysis of a competition fishery model with intervalvalued parameters: Extinction, coexistence, bionomic equilibria and optimal harvesting policy. Nonlinear Dynamics, 80(3), 1631. 13. Mahata, A., Mondal, S. P., Roy, B., et al. (2021). Study of two species prey-predator model in imprecise environment with MSY policy under different harvesting scenario. Environment, Development and Sustainability, 23, 14908–14932. 14. Zhang, X., & Zhao, H. (2014). Bifurcation and optimal harvesting of a diffusive predator–prey system with delays and interval biological parameters. Journal of Theoretical Biology, 363, 390–403. 15. Wang, Q., Liu, Z., Zhang, X., & Cheke, R. (2015). A incorporating prey refuge into a predator– prey system with imprecise parameter estimates. Computational and Applied Mathematics, 36, 1067–1084. 16. Zhao, H., & Wang, L. (2022). Stability and Hopf bifurcation in a reaction–diffusion predator– prey system with interval biological parameters and stage structure. Nonlinear Dynamics, 11, 575. 17. Mahata, A., Mondal, S. P., Roy, B., et al. (2020). Influence of impreciseness in designing tritrophic level complex food chain modeling in interval environment. Advances in Difference Equations, 399. 18. Das, S., Mahato, P., & Mahato, S. K. (2020). A Prey predator model in case of disease transmission via pest in uncertain environment. Differential Equation and Dynamical System. https:// doi.org/10.1007/s12591-020-00551-7 19. Mahata, A., Mondal, S. P., Alam, S., & Roy, B. (2017). Mathematical model of glucose-insulin regulatory system on diabetes mellitus in fuzzy and crisp environment. Ecological Genetics and Genomics, 2, 25–34. 20. Santra, P. K., & Mahapatra, G. S. (2020). Dynamical study of discrete-time prey predator model with constant prey refuge under imprecise biological parameters. Journal of Biological Systems, 28(3), 681–699. 21. Das, A., & Pal, M. (2017). A mathematical study of an imprecise SIR epidemic model with treatment control. Journal of Applied Mathematics and Computing, 56, 477–500. 22. Acharya, A., Mahata, A., Alam, S., Ghosh, S., & Roy, B. (2022). Analysis of an imprecise delayed SIR model system with Holling type-III treatment rate. In S.L. Peng, C.K. Lin, & S. Pal (Eds.), Proceedings of 2nd International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing (Vol. 1422). 23. Paul, S., Mahata, A., Mukherjee, S., Mali, P. C., & Roy, B. (2022). Mathematical model for tumor-immune interaction in imprecise environment with stability analysis. In S. Banerjee & A. Saha (Eds.), Nonlinear dynamics and applications (pp. 935–946). Springer Proceedings in Complexity. Springer.

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Study of a Fuzzy Prey Predator Harvested Model: Generalised Hukuhara Derivative Approach Balaram Manna, Ashish Acharya, Subrata Paul, Subhabrata Mondal, Animesh Mahata, and Banamali Roy

Abstract The modern mathematical model is an interpretation of the mathematical description of various global ecological issues. Over the past few decades, ecologists and researchers have shown the greatest interest in the modelling of prey-predator kinetics. Most of researchers in ecology have assumed that ecological parameters are exactly known. Indeed, the actual situation is altered when the uncertainty in the model may occur due to several causes like human error, inaccurate data supply, climate changes and different environmental factors etc. To overcome such situation we consider the Lotka–Volterra harvesting model in which initial number of populations are assumed as fuzzynumber. To elucidate the fuzzy proposed system, we have used a methodology which is gH (generalised Hukuhara) derivatives concept. When this technique is applied to the fuzzy prey-predator system, the leading model is transformed into a set of differential equations with a parametric form of α. Here we consider two cases only, both the prey population (v1 (t)) and predator population (v2 (t) be (i) gH, (ii) gH differentiable. Non-negative feasible steady states and stability criteria of them have been analysed in fuzzy environment. Strong and weak solutions of the proposed system have been evaluated. In the end, extensive numerical simulations verify all of the analytical findings. B. Manna · S. Mondal Department of Mathematics, Swami Vivekananda University, Barasat–Barrackpore Rd, Bara Kanthalia, West Bengal 700121, India e-mail: [email protected] A. Acharya Department of Mathematics, Swami Vivekananda Institute of Modern Science, Karbala More, Kolkata, West Bengal 700103, India S. Paul Arambagh Govt Polytechnic, Arambagh, West Bengal, India A. Mahata (B) Mahadevnagar High School, Maheshtala, Kolkata, West Bengal 700141, India e-mail: [email protected] B. Roy Department of Mathematics, Bangabasi Evening College, Kolkata, West Bengal 700009, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_31

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Keywords Fuzzy Lotka–Volterra model · TrFN · Stability Analysis: Strong solution and weak solution

1 Introduction Lotka [1] and Volterra [2] initiated research in the field of ecology. Although Malthus [3] presented the first theoretic managing of population kinetics, Verhulst [4] developed a logistic equation-based model. In the Lotka-Volterra system, direct intervention is supposed to reduce the per capita growth rates of two species. For biologists who are interested in the consequences of competitive interactions between species, Lotka-Volterra equation of exploitative competition has served as a suitable initial point. This model’s presumptions may not be very realistic, but they are necessary for simplification. The dynamics of one or both populations can be affected by a variety of non-model factors that affect the outcome of competitive interactions. Changes in the environment, illness, and chance are just some of these factors. Over the past few decades, ecologists and researchers have shown the greatest interest to formulate different types of harvested [5–8] (prey or predator or both harvested) models to explore better dynamics of biological phenomena into models. In the field of biological science, many authors have evolved their system constructed totally on the presumption that the model parameters are familiar. However indeed, the parameters of the model system are not exact because of inaccuracy in data collection, unconscious measurement, technical error and climate changes. To overcome such type of complexity many authors use a different approach—FDE (Fuzzy differential approach), IDE (Interval differential equation approach) and Stochastic approach etc. In recent times, the “fuzzy differential equation” (FDE) has become increasingly popular. Kaleva [9] presented the idea of FDE. Bede [10] demonstrated in FDE that the Hukuhara derivative cannot solve a class of BVPs. To overdoes these demerits, the thought of generalised derivative had been studied in [11, 12] and FDE was explored by this perception in [13–16] showed FDE plays a vital role in the mathematical modelling of biological science. A diabetes model has been taken in those papers and discussed how fuzzy diabetes system is framed to the system of differential equations using generalised H-derivatives (Hukuhara derivatives). Fuzzy population model was solved in [17, 18]. Jafelice et al. [19] developed a fuzzy concept for the HIV-infected population. Fuzzy stability of diabetes model system has been investigated by Mahata et al. [20] and Roy et al. [21]. Henceforth, several notable works of bio-mathematical modelling has been published with based on fuzzy differential equation (see [22– 24]). Here, motivated by the above theme we have considered Lotka–Volterra with harvesting system [6] in fuzzy environment. The arrangement of the paper follows as Sect. 2 contains the basic concept. Model formation and stability analysis are smeared in sect. 3. Numerical illustration is prepared in Sect. 4. Section 5 contains conclusion of the work.

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2 Pre-requisite Concept Definition 2.1 TrFN. ~1 = (Q 11 , Q 12 , Q 13 ) and the A TrFN can represented by three points Q membership function can be represented by, 0,

μ R˜ (x1 ) =

x1 −Q 11 Q 12 −Q 11

1

Q 13 −x1 Q 13 −Q 12

0,

x1 ≤ Q 11 Q 11 ≤ x1 ≤ Q 12 x1 = Q 12 Q 12 ≤ x1 ≤ Q 13 x1 ≥ Q 13

~1 given by, Definition 2.2 The α-cut of Q Rα = [Q 11 + α(Q 12 − Q 11 ), Q 13 − (Q 13 − Q 12 )] ∀ α; 0 ≤ α ≤ 1 Definition 2.3 Generalised Hukuhara Derivative (gHD). See the paper [14]. A fuzzy valued function f 1 : (a, b) → R0 at x0 is defined by, ,

f 1 (x0 ) = lim

l→0

f 1 (x0 + l)Θgh f 1 (x0 ) l

,

f 1 (x0 ) ∈ R0 satisfy the condition then G h say that GH derivative at x0 . , , , Now, f 1 (t) is (i)-gHD at x0 when [ f (x0 )]α = [ f 11 (x0 , α), f 12 (x0 , α)]. , , , And, f 1 (t) is (ii)-gHD at x0 when [ f (x0 )]α = [ f 12 (x0 , α), f 11 (x0 , α)]. Definition 2.4 Strong and Weak solution of Fuzzy ODE. See the paper [11].

3 Model Formulation Lotka-Volterra [6] Prey-Predator Harvested Model as dv1 (t) = r v1 (t) − p1 v1 (t)v2 (t) − h 1 Ev1 (t) dt dv2 (t) = −sv2 (t) + p2 v1 (t)v2 (t) − h 2 Ev2 (t) dt With the initial condition v1 (0) = v01 , v2 (0) = v02 . where

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v1 (t)

Prey population density at time t

v2 (t)

Predator population density at time t

r

Exponential growth rate of prey in non-existence of predator

p1 , p2

Predation coefficient

s

Exponential decay rate of prey in absence of prey

h1

Catch ability coefficient of Prey

E

Harvesting effort

h2

Catchability coefficient of Prey

Note: here we have taken same harvesting effort (E). Considering the above model in fuzzy environment follows as d v˜1 (t) = r v˜1 (t) − p1 v˜1 (t)v˜2 (t) − h 1 E v˜1 (t) dt d v˜2 (t) = −s v˜2 (t) + p2 v˜1 (t)v˜2 (t) − h 2 E v˜2 (t) dt

(1)

Here, and are following cases follows: Case 1: When v˜1 (t) and v˜2 (t) are (i) gHD: Case 2: When v˜1 (t) and v˜2 (t) are (ii) gHD. Case 3: When v˜1 (t) is (i) gHD and v˜2 (t) is (ii) gHD. Case 4: When v˜1 (t) is (ii) gHD and v˜2 (t) is (i) gHD. For our convenience in this paper, we take the first two cases. Considering initial conditions are fuzzy numbers, we discuss the stability analysis of first two cases as follows.

3.1 Case 1 The fuzzy system (1) is framed [14, 27] as dv1L (t, α) dt dv1R (t, α) dt dv2L (t, α) dt dv2R (t, α) dt

= r v1L (t, α) − p1 v1R (t, α)v2L (t, α) − h 1 Ev1R (t, α) = r v1R (t, α) − p1 v1L (t, α)v2R (t) − h 1 Ev1L (t, α) (2) = −sv2R (t, α) + p2 v1L (t, α)v2L (t, α) − h 2 Ev2R (t, α) = −sv2L (t, α) + p2 v1R (t, α)v2R (t) − h 2 Ev2L (t, α)

With the initial condition, v1L (0, α) = v01L (α), v1R (0, α) = v01R (α), v2L (0, α) = v02L (α), v2R (0, α) = v02R (α).

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3.1.1

385

Existence of Equilibrium Point

Two equilibrium points are given by. (2). (i )E 11 (0, 0, 0, 0) be the trivial equilibrium point of the ( system ) c c c c c v1L (ii) The model (2) has interior equilibrium point, E 12 , v1R , v2L , v2R where, r −h 1 E c c c c v1L = v1R = h 2 E+s , v = v = for r > h E. 1 2L 2R p2 p1 3.1.2

Stability Analysis

( c ) c c c c ν1L , ν1R . of the system (2) is unstable. Lemma 1 The E 12 , ν2L , ν2R ( c ) c c c c v1L , v1R Proof Let the Jacobi matrix of the model (2) at E 12 , v2L , v2R becomes, ⎛

r −r l 0 ⎜ −r r 0 l ⎜ JE = ⎜ p1 u ⎝ − p2 0 l −l 0 − pp12u −l l

⎞ ⎟ p2 ⎟ (r − h 1 E), u = s + h 2 E ⎟ wher e l = ⎠ p1

The characteristic equation becomes where the eigen value is λ, λ4 + r 1 λ3 + r 2 λ2 + r 3 λ + r 4 = 0 3 p1 p2 lr u 2 + p12 u 2 l 2 + p22 p12 ur 2 , p22 2 r u(r + u) > 3 p1 lurp+2 p1 lu .

where, r1 = −2(r + u), r2 = 4r u, r3 = − 3 p1 lurp+2 p1 lu , r4 = 2

ri < 0, i = 1, 3; r j > 0, j = 2, 4 and r1r2 − r3 < 0 if 8 Using stability condition of RH-criteria, system (2) is unstable at ( c ) c c c c E 12 , v2L , v2R v1L , v1R .

3.2 Case 2 The fuzzy system (1) is framed dv1L (t, α) dt dv1R (t, α) dt dv2L (t, α) dt dv2R (t, α) dt

= r v1R (t, α) − p1 v1L (t, α)v2R (t, α) − h 1 Ev1L (t, α) = r v1L (t, α) − p1 v1R (t, α)v2L (t, α) − h 1 Ev1R (t, α) (3) = −sv2L (t, α) + p2 v1R (t, α)v2R (t, α) − h 2 Ev2L (t, α) = −sv2R (t, α) + p2 v1L (t, α)v2L (t, α) − h 2 Ev2R (t, α)

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With the initial condition, v1L (0, α) = v01L (α), v1R (0, α) = v01R (α), v2L (0, α) = v02L (α), v2R (0, α) = v02R (α)

3.2.1

Existence of Equilibrium Point

Two equilibrium points is given by as i. E 21 (0, 0, 0, 0) is trivial equilibrium point of (3). ( c ) c c c c v1L , v1R ii. The model (3) has interior equilibrium point E 22 , v2L , v2R c c c c where,v1L = v1R = h 2 E+s , v2L = v2R = r −hp11 E for r > h 1 E. p2 3.2.2

Stability Analysis

( c ) c c c c v1L , v1R Lemma 2 The E 22 , v2L , v2R of the changed system (3) is stable. ( c ) c c c c v1L , v1R Proof Let the jacobi matrix of (2) at E 22 , v2L , v2R is given by, ⎛ ⎜ ⎜ JE, = ⎜ ⎝

−r r 0 p2 − h 1 E) (r p1

⎞ r 0 (s + h 2 E) ⎟ −r 0 (s + h 2 E) ⎟ ⎟ p2 − h 1 E) −(s + h 2 E) −(s + h 2 E) ⎠ p1 (r 0 −(s + h 2 E) −(s + h 2 E)

The characteristic equation becomes, where the eigenvalue λ, λ4 + μ1 λ3 + μ2 λ2 + μ3 λ + μ4 = 0 where, μ1 = 2(m 1 + r ), μ2 = 2r m 1 + m 1 n 1 + 2r 2 , μ3 = m 1 n 1 + 4r 2 m 1 + n 1 + r m 1 n 1 , μ4 = (2r − 1)n 1 m 21 + m 21 n 21 and m 1 = (h 2 E + s), n 1 = (r − h 1 E). Here, μi > 0, i = 1, 2, 3, 4 when m 1 > 0, n 1 > 0, r > h 1 E, r > 21 . ) ( ) ( > 0, μ1 μ2 − μ3 = 2(m 1 + r ) 2rm 1 + m 1 n 1 + 2r 2 − m 1 n 1 + 4r 2 m 1 + n 1 + r m 1 n 1 )( ) ( ) ( 2 2 2 if 2(m 1 + r ) 2rm 1 + m 1 n 1 + 2r m 1 n 1 + 4r m 1 + n 1 + rm 1 n 1 > m 1 n 1 + 4r m 1 + n 1 + r m 1 n 1 2 + { } 4(m 1 + r )2 (2r − 1)n 1 m 21 + m 21 n 21 , m 1 > 0, n 1 > 0, r > h 1 E

μ1 μ2 μ3 − μ23 − μ21 μ4 > 0, i f m 1 > 0, n 1 > 0, r > h 1 E, r >

1 2

. Using stability condition of RH–criteria, the system (3) is Stable at ( c ) c c c c v1L , v1R . E 22 , v2L , v2R

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4 Numerical Simulation In this section, we have to analysis and determine all result to validate proposed model system. Consider the initial number of prey and predator as TrFN such as at t = 0 prey population density v~1 (0) = (10, 15, 30) and predator population density is v~2 (0) = (5, 8, 12) where 0 ≤ α ≤ 1 and others parameters given in the following Table 1. Table 1 Values of model parameters

Parameter

Value

Source

r

0.4

[26]

s

0.015

[assumed]

p1

0.025

[assumed]

p2

0.035

[assumed]

h1

0.1

[25]

h2

0.2

[25]

E

3

[25]

(i)

(ii)

(iii)

Fig. 1 Fuzzy solution of (i) for α = 0, (ii) for α = 0.6, (iii) for α = 1

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α-cut of the initial condition is v1L (0, α) = 10 + 5α, v1R (0, α) = 30 − 5α, v2L (0, α) = 5 + 3α, v2R (0, α) = 12 − 4α.

(4) Using the parameters reported in Table 1 and t TrFN from (4) we plot Fig. 1(i), (ii) and (iii) for α = 0, α = 0.6 and α = 1 respectively. We observed that in Fig. 1(i), (ii)v1L (t, α) ≤ v1L (t, α), v2L (t, α) ≤ v2R (t, α) and in Fig. 1(iii) v1L (t, α) = v1R (t, α), v2L (t, α) = v2R (t, α) for t ∈ [0, 1.4] imply that strong solution exists in the system (2). Clearly, Fig. 1 depicts that the equilibrium (interior) point of (2) is unstable. Using the parametric value reported in Table 1 and taking TrFN from (4) we plotted Fig. 2(i), (ii) and (iii) for α = 0, α = 0.6 and α = 1 respectively. We observed that Fig. 1(i), (ii), (iii) v1L (t, α) = v1L (t, α), v2L (t, α) = v2R (t, α) for t ∈ [0, 400] imply that a strong solution exists of the system (3). Clearly, Fig. 2 depicts that with the time growths, the prey and predator populations oscillate in altered period dependent on the values of the (parameter α for 0 ≤ ) α ≤ 1. Therefore, c c c c c v1L the system (3) has periodic solution and E 22 , v1R , v2L , v2R is neutrally stable for 0 ≤ α ≤ 1.

(i)

(ii)

(iii)

Fig. 2 Fuzzy solution of Fig. 1(i) for α = 0, Fig. 1(ii) for α = 0.6, Fig. 1(iii) for α = 1

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5 Conclusion Now a day’s mathematical model is an interpretation of different ecological problems around the globe in the mathematical description. In this regard, modelling of Prey-Predator Dynamics has been an area of study that ecologists and researchers have been most interested in for the past few decades. It has been widely investigated that adding quota harvesting to the prey-predator model results in more advanced theoretical consequences. The entire scenario of ecological model is altered when uncertainty is introduced. Herein, we study a Lotka-Volterra harvested system in fuzzy environment to get generalised results. The crisp Lotka-Volterra harvesting system becomes fuzzy Lotka-Volterra harvesting system due to initial number of prey and predator of that model are measured as TrFN (triangular fuzzy number) v2 (0) = (5, 8, 12), the parametric form of these are as v~1 (0) = (10, 15, 30),~ v1L (0, α) = 10 + 5α, v1R (0, α) = 30 − 15α and v2L (0, α) = 5 + 3α, v2R (0, α) = 12−4α. By the gH derivatives methodology, the proposed fuzzy model is transformed into a different set of crisp differential equation of parametric functional form of α. To solve fuzzy model, we consider first as the prey (v1 (t)) and predator (v2 (t)) population both be (i)-gHD and later both be (ii)-gHD. As a result, we have two sets of differential equation of parametric form of α that are reflected in (2) and (3) respectively. Non-negative steady states and stability criteria have been explored in this article. v2 (0) = (5, 8, 12) Considering the data reported in table and v~1 (0) = (10, 15, 30),~ we have verified the numerical results that support the analytical outcomes from the fuzzy proposed model. All figures have been elegantly validated by Matlab programme. Our future study of research is to develop various ecological models in neutrosophic environment.

References 1. Lotka, A. J. (1925). Elements of physical biology. The Williams and Wilkins Co., Baltimore. 2. Volterra, V. (1926). Variazioni e fluttuazionidelnumers di individuiin specie animaliconviventi. Memoria della Reale Accademia Nazionale dei Lincei, 2, 31–113. 3. Malthus, T. R. (1959). An essay on the principle of population, as it affects the future improvement of society, with remarks on the speculations of Mr. Godwin, M. Condorcet and other writers. J. Johnson, London, 1798. Reprint, University of Michigan Press, USA. 4. Verhulst, P. F. (1838). Noticesur la loique la populationpersuitdans son accroissement. Correspondence Mathematique et Physique (Ghent), 10, 113–121. 5. Rebaza, J. (2012). Dynamics of prey threshold harvesting and refuge. Journal of Computational and Applied Mathematics, 236, 1743. 6. Pal, D., Mahapatra, G. S., & Samanta, G. P. (2013). Optimal harvesting of prey -predator system with interval biological parameters: Abioeconomicmodel. Mathematical Bioscience, 241, 181–187. 7. Mondal, S., Samanta, G.P.,2019, Dynamics of an additional food provided predator–prey system with prey refuge dependent on both species and constant harvest in predator, Physica A: Statistical Mechanics and its Applications, 534(15)

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8. Haque, Md. M., & Sarwardi, S. (2018). Dynamics of a Harvested Prey–Predator Model with Prey Refuge Dependent on Both Species. International Journal of Bifurcation and Chaos, 28(12). 9. Kaleva, O. (1987). Fuzzy differential equations. Fuzzy Sets and Systems, 24, 301–317. 10. Bede, B. A. (2006). Note on “two-point boundary value problems associated with non-linear fuzzy differential equations.” Fuzzy Sets and Systems, 157, 986–989. 11. Bede, B., S. G., & Gal, S. G. (2005). Generalizations of the differentiability of fuzzy-numbervalued functions with applications to fuzzy differential equations. Fuzzy Sets and Systems, 151, 581–599. 12. Chalco-Cano, Y., & Román-Flores, H. (2008). On the new solution of fuzzy differential equations. Chaos Solitons Fractals, 38, 112–119. 13. Mahata, A., Mondal, S. P., Alam, S., & Roy, B. (2017). Mathematical model of glucose –insulin regulatory system on diabetes mellitus in fuzzy and crisp environment. Ecological Genetics and Genomics, 225–234. 14. Salahshour, S., Ahmadian, A., Mahata, A., Mondal, S. P., & Alam, S. (2018). The behavior of logistic equation with alley effect in fuzzy environment: fuzzy differential equation approach. International Journal of Applied and Computational Mathematics, 4(2), 62. 15. Mahata, A., Mondal, S. P., Alam, S., Roy, B. (2017). Application of ordinary differential equation in glucose-insulin regulatory system modeling in fuzzy environment. Ecological Genetics and Genomics, 3–5, 60–66. 16. Mahata, A., Mondal, S. P., Ahmadian, F. Ismail, S. Alam, S., & Salahshour, S. (2018). Different solution strategies for solving epidemic model in imprecise environment. Complexity. 17. Barros, L. C., Bassanezi, R. C., & Tonelli, P. A. (2000). Fuzzy modelling in population dynamics. Ecological Modelling, 128, 27–33. 18. Akın, O., & Oruc, O. A. (2012). Prey predator model with fuzzy initial values. Hacettepe Journal of Mathematics and Statistics, 41(3), 387–395. 19. Jafelice, R. M., Barros, L. C., Bassanezi, R. C., & Gomide, F. (2004). Fuzzy Modeling in Symptomatic HIV Virus Infected Population. Bulletin of Mathematical Biology, 66, 1597– 1620. 20. Mahata, A., Mondal, S. P., Alam, S., Chakraborty, A., De, S. K., & Goswami, A. (2019). Mathematical model for diabetes in fuzzy environment with stability analysis. Journal of Intelligent & Fuzzy Systems, 36(3), 2923-2932. 21. Roy, B., Mahata, A., Hirak Sinha, H., & Manna, B. (2021). Comparison between pre-diabetes and diabetes model in fuzzy and crisp environment: fuzzy differential equation approach. International Journal of Hybrid Intelligence, 2(1), 47–66. 22. Mahata, A., Matia, S. N., Roy, B., Alam, S., & Sinha, H. (2021). The behaviour of logistic equation in fuzzy environment: fuzzy differential equation approach. International Journal of Hybrid Intelligence, 26–46. 23. Keshavarz, M., Allahviranloo, T., Abbasbandy, S., Modarressi, M. H. (2021). A study of fuzzy methods for solving system of fuzzy differential equations. New Mathematics and Natural Computation, 17(1), 1–27. https://doi.org/10.1142/S1793005721500010. 24. You, C., Cheng, Y., & Ma, H. (2022). Stability of Euler methods for fuzzy differential equation. Symmetry, 14, 1279. https://doi.org/10.3390/sym14061279 25. Sharma, S., & Samanta, G. P. (2014). Optimal harvesting of a two species competition model with imprecise biological parameters. Nonlinear Dynamics,77(4), 1101–1119. 26. Xu, C., & Li, P. (2013). Stability analysis in a fractional order delayed predator-prey model. International Journal of Mathematical and Computational Science,7(5), waste.org/ publication/16751. 27. Paul, S., Mondal, S. P., & Bhattacharya, P. (2017). Discussion on proportional harvesting model in fuzzy environment: fuzzy differential equation approach. International Journal of Applied and Computational, 3, 3067–3090. https://doi.org/10.1007/s40819-016-0283-3.

Overview of Applications of Artificial Intelligence Methods in Propulsion Efficiency Optimization of LNG Fueled Ships Anastasia Kiritsi, Anastasios Fountis, and Mohammed Ayad Alkhafaji

Abstract It is a common perception or even common knowledge that fossil fuels are running out at an alarming rate, and this coincides with the rise in the number of vehicles on the road. As a result, pollution has hit a crisis point. The use of marine fuels, in particular heavy fuel oil, for the propulsion of ships results in the emission of greenhouse gases, the production of waste, and, in the event of an oil spill, conditions that are detrimental to the marine environment. The International Maritime Organization has enacted a number of more stringent regulations in order to limit and reduce the effect that shipping has on the surrounding environment. Shipowners are under increased pressure as a result of the adoption of these regulations to ensure that their vessels are operated effectively while adhering to all mandatory regulations. The present work analyzes the modern propulsion systems, the fuels used and the types of LNG machines. Then the methods of artificial intelligence that have already been applied in the measurement of the performance of LNG machines in airplanes are analyzed. In general, results indicated that the artificial intelligence models used could lead to safe predictive results. Keywords Artificial intelligence · Neural network · LNG · Ship design · Big data

A. Kiritsi MSc in Economics and Energy Law, AUEB, Athens, Greece A. Fountis (B) Faculty, Berlin School of Business and Innovation, Berlin, Germany e-mail: [email protected] M. A. Alkhafaji College of Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_32

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1 Introduction As a result of its consumption of fossil fuels, international shipping generates a sizeable quantity of greenhouse gas emissions and contributes to the progression of climate change. It is believed that international maritime transport contributes somewhere between 2 and 4% of the overall emissions of greenhouse gases produced on a global scale. This is greater than the emissions produced by any state in the European Union. If the shipping industry were a country, it would hold the sixthhighest position in the globe in terms of emissions [1]. Between 1990 and 2008, there was a 48% rise in CO2 emissions caused by maritime transportation across the EU. In 2015, maritime transportation was responsible for 13% of the total greenhouse gas emissions that the transport industry in the EU was responsible for. Both the European Union’s Monitoring, Reporting, and Verification (MRV) regulation and the International Maritime Organization’s Data Collection System (IMO DCS) will require ship owners to watch and report beginning in 2019. The propulsion systems that are installed on board vessels have been conditioned as a result of the increasingly stringent regulations, as well as the rise in the consumption of natural gas and the price of the latter. The propulsion systems that were put into place went through consistent modification in order to adapt to the needs of the market. These needs were always governed by efficiency as well as the possibility of consuming boil-off gas (BOG), and they were always in compliance with the stringent antipollution regulations that were in place at the time. Due to their high efficiency and the potential of installing a BOG reliquefaction plant, the current trend in LNG vessel propulsion systems is the installation of 2-stroke DF low pressure engines. This is the direction that LNG vessel propulsion systems are heading. The fact that this propulsion system complies with the emission rules set forth by the IMO for TIER III without the requirement of installing any additional gas treatment systems is yet another significant benefit of using it. The goals of this research were to (a) establish methods for the efficient preprocessing of ship operational data and (b) develop data-driven ship propulsion models that will be at the center of applications whose primary purpose is to lessen the amount of carbon footprints left by ships. The advancement of technology in a variety of fields has made it possible to continuously monitor ships and to gather enormous volumes of business data, both of which can transmit useful information if they are used in the appropriate manner. To be more specific, it was the convergence of technologies in the field of electronics and sensors, of the global satellite internet connection, and, more recently, of the methods of mechanical learning, which produce models with superior predictive capabilities. All of these factors came together to create the current state of affairs.

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2 Problem Formulation The purpose of this paper is to conduct a literature review and an investigation into the factors that influence the amount of fuel that a ship uses while it is in operation. This investigation will use the information that is provided by the literature, both as a theoretical foundation and in conjunction with ‘soft’ computer techniques that are utilized in this field. Finding the solution to an incorrect issue is what is meant when people talk about “soft computing”. The Artificial Neural Network (also known as Soft Computing), the Neuro Fuzzy Inference adaptive system, and the Genetic Algorithm are all examples of various ‘soft’ computer methods that are utilized in this industry [3].

2.1 Contributing Elements to Overall Energy Efficiency The purpose of this paper is to highlight the importance of large-scale energy data analysis of a ship’s performance as well as the expected benefits of such an analysis, as well as the challenges that such an analysis faces and the capabilities that machine learning applications may be able to offer a framework for sustainable growth, with regard to the field of Shipping. There are numerous factors that contribute to the control of the energy efficiency of a ship, including the following: Monitoring fuel consumption can initially result in significant financial benefits, which help reduce the running costs of the management company and help increase the fleet’s competitiveness at the same time. These benefits can be gained by monitoring fuel consumption. In addition to this, it helps to improve the operation of the ship by ensuring the optimal operation of its machinery and its safe routing through the most appropriate route. This is accomplished by taking into account a number of different parameters that affect fuel consumption, such as the weather and the sea currents. At the same time, lowering one’s consumption of fuel results in lower emissions of greenhouse gases and other pollutants, which helps to preserve the natural ecosystem [2].

2.1.1

Applications of Artificial Intelligence to the Optimization of Propulsion Factors

The contribution of this work lies in the fact that it is a concise guide around the factors that affect the energy efficiency of LNG combustion ships. Beginning with a theoretical background, the work concludes with an overview of the application of artificial intelligence tools (ANN, GA, ANFIS, and MLVR) in optimizing propulsion parameters in LNG ships. This work is a part of a larger body of research that aims to improve the energy efficiency of LNG ships [5, 6].

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Systems that are able to acquire and apply knowledge in a “smart” way, as well as systems that are able to perceive, reason, learn, and draw conclusions from incomplete information, are typically referred to as intelligent systems. This is because intelligent systems have the ability to do all of these things. When we have to monitor very complex systems or when we have an excessive number of various inputs, this feature is absolutely necessary. Even when it is possible to model very complex systems, the resulting models may be so complicated that developing accurate algorithms or making decisions based on these models may result in a significant increase in the cost of computers and hardware as well as an increase in the difficulty of the process. too sluggish for it to be practical. Systems that are founded on knowledge and have the ability to make intelligent decisions have proven to be very successful in solving problems of this nature. It is anticipated that industrial machinery and decision support systems will, in the not too distant future, be capable of maintaining the consistency and repeatability of the operation, as well as dealing with external performance without noticeably degrading performance. It has been demonstrated that a computer can be programmed to exhibit some intelligent characteristics of a person, similar to how neurons in the brain, the material and software of a computer, and the internet are not smart in and of themselves [3, 4] (Fig. 1). An intelligent system could acquire knowledge and carry out high-level cognitive tasks if it were to make use of neural networks. A neural network consists of a collection of nodes, which are typically arranged in layers, and synapses, which are weighted connections between the nodes. Figure 2 illustrates these functions as a reference. Therefore, it is possible to educate a neural network in such a way that it is able to differentiate between the sounds that are produced by a machine, whether or not

Fig. 1 Human neurons and neural computer networks (Source [2])

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Fig. 2 Functions at a cluster of a neural network (Source [3])

the machine is operating correctly or is on the verge of experiencing some kind of malfunction. After completing this period of training, a technician can use the experience of the same network to warn them of any impending damage before it occurs, which may cause expensive and unpredictable time delays. The inputs that are received by the sensors of an engine have been analyzed with the assistance of neural networks. In order to accomplish a particular objective, the neural network is responsible for controlling a wide range of the engine’s operating parameters. For instance, the goal of this network is to reduce the amount of fuel it uses [3]. The CHAID analysis generates a predictive model, also known as a tree, in order to determine how variables can be combined in the most effective manner to explain the effect on a particular dependent variable. There are three types of data that can be used in the CHAID analysis: nominal, normal, and continuous. Continuous predictions are broken down into groups that have approximately the same number of observations. CHAID will continue to generate all conceivable intersections for each categorical prediction until the optimal outcome has been reached and there is no longer any room for further separation [3]. An artificial neural network known as (ANFIS) is a type of network that is built on the Takagi–Sugeno system of ambiguous conclusions. Due to the fact that it makes use of neural networks in addition to abstract logical principles, it has the potential to reap the benefits of both of these approaches within a single setting. Its inference system corresponds to a set of ambiguous IF-THEN rules that have the ability to learn to approach nonlinear functions. This gives it the ability to approach nonlinear functions. As a result, ANFIS is taken into consideration as a universal appraiser. It is possible to use the best parameters obtained with a genetic algorithm in order to make use of ANFIS more effectively and efficiently. It has applications in systems for intelligent energy management. A statistical method known as multiple linear regression (MLR), which is also known simply as multiple regression, is a technique that makes use of multiple explanatory variables in order to make a prediction regarding the outcome of a variable response. The objective of multiple linear regression, also known as MLR, is to model the linear relationship that exists between the variables that are being used for explanation [3]. An algorithm known as the fuel optimization algorithm was developed with the purpose of determining which combination of aircraft or ship and destination would result in the lowest overall consumption.

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A straightforward selection issue is more straightforward than optimization because optimization does not involve selecting the level that results in the least amount of consumption along a particular route. The use of the Genetic Algorithm can be attributed to this reason. The algorithm was developed to include as many different variables as there are flight options [3].

3 Solution Sets An examination of bibliography, concerning the factors that influence the amount of fuel that a ship uses, has been contacted. During the first phase of the research, an effort was made to compile information from a wide variety of bibliographic sources in order to formulate a condensed yet comprehensive theoretical foundation for subsequent research. It is now possible, with the assistance of the Internet, to record the most important factors that influence fuel consumption and briefly present how these factors are related to one another.

3.1 An Overview of the AI Methods that Have Been Published for the Purpose of Optimizing Ship LNG Parameters It has become possible, through the use of real historical data and the theoretical and practical study of their effect on fuel consumption, to reveal the relationships between input variables (independent variables) and output variables (dependent variables). This has been made possible by the study of their effect on fuel consumption (dependent variables, i.e. final fuel consumption) [5, 6].

3.1.1

An Investigation into the Potential for Extremely Low Emissions from an Artificial Fuel Engine Combined with an Artificial Neural Network Combined with a Genetic Algorithm

In this particular investigation, ANFIS was put to use in order to map the relationships that existed between controlled limits and engine performance. For the purpose of training and testing the ANFIS model, which has six input variables (diesel fuel injection timing, blended petrol ratio, recirculation rate of 50% exhaust and 10% exhaust time, average real pressure display heat) within a wide range of engine operating parameters and four engine emissions and performance costs, a total of eighty experimental data were chosen for a dual-fuel diesel engine. The outputs from ANFIS were then utilized in order to evaluate the objective functions of the optimization process. This process was carried out utilizing an approach that involved multiple genetic algorithm optimization (GA) optimization objectives [7].

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Research and Simulation on the Most Effective Energy-Saving Maneuvers for the Vessel, Taking into Account Its Propulsion System

In order to cut down on overall energy consumption, the energy efficiency control strategy of the system is based on a model of an advanced dual-feeder shaft-free generator shaft, a propulsion system that uses an LNG/dual-fuel diesel engine, and the power consumption of the main engine. Both the simulation model of the whole propulsion system and the control strategy that was designed for it have been developed. Simulation with Matlab and Simulink was used to investigate the impact that engine speed has on the ship’s energy efficiency and to test whether or not various control strategies for improving energy efficiency are even possible. The findings indicate that the strategies that were designed are able to ensure the strength of the entire ship in a variety of conditions, improve the ship’s energy efficiency, and reduce the amount of CO2 emissions [8].

3.1.3

Optimization of Fuel Consumption Through the Application of Neural Networks and Genetic Algorithms (Implementation on TAP Airlines)

The goal of this research was to develop a tool that would enable the flight developer to enter all of the roots, destinations, and dates of a flight set. The algorithm would then not only choose which aircraft would be more effective in making a particular flight, but it would also produce an estimate of the fuel consumed, taking into account weather parameters, level degradation, and available aircraft. This research was conducted in order to facilitate the development of this tool. In addition to producing an estimate of CO2 emissions, the payload, and various fuel consumption parcels, the algorithm’s primary focus will be on optimizing fleet utilization. It was possible to perform a simulation of the new flight schedule by making use of the payload constraints and taking into account the sufficiently dry total weight of each aircraft for each particular trip. In this configuration, the algorithm was able to save 15,396 kg of fuel, which is equivalent to almost 10 million dollars per year. This was accomplished across 100 flights [9].

3.1.4

Optimization and Management of the Ship’s Overall Energy Efficiency in Real Time.(The Information was Gathered from Passengers Aboard a Cruise Ship that Traveled Down the Yangtze River)

The energy efficiency of the ship is significantly impacted by the nature of the work environment that is associated with the navigation environment. The most important step toward increasing the ship’s energy efficiency is figuring out how to calculate the ideal engine speed for each specific navigational environment. It has been discovered

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that the ship’s resistance plays a significant role in determining the effect that the working conditions have on the energy efficiency of the vessel. It is possible to construct a model of the main engine’s energy efficiency by first calculating the ship’s resistance at a variety of navigation speeds and in a variety of navigation environments. Using the method of dynamic optimization, we can then reach the optimal engine speed for the current navigation environment. After that, we figure out how much resistance the ship will encounter. Hydrostatic resistance, wave resistance, wind resistance, and shallow water resistance are all components of the ship’s overall resistance. Therefore, we are able to arrive at the ship’s total resistance by first calculating the vessel’s hydrostatic resistance [10].

3.1.5

An Examination of the Ships’ Propulsion Systems for LNG

This article takes a look at the various propulsion systems that are utilized on ships that are tasked with transporting liquefied natural gas (LNG). In the study, the primary characteristics of propulsion systems, as well as the benefits and drawbacks associated with each system, are discussed, beginning with the earliest systems and progressing all the way up to the most recent ones that have been put into place. The propulsion systems that are described include gas turbines, steam turbines, combined cycles, internal combustion engines with 2 or 4 cylinders, as well as mechanical, electrical, and dual-fuel (DF) systems. Because of their high efficiency, high elasticity, due to the configuration of the propulsion system, and reduced SOx emissions, DF engines, both 4S and 2S, are the propulsion systems that are currently installed in YFA airlines. This is due to the fact that gas emission regulations require the reduction of SOx emissions. IMO TIER III when operating on gas, with the exception of the MAN 2S DF engines which are Tier II when using gas. This propulsion system includes a greater quantity of equipment, which results in increased costs for installation and maintenance. This is an unfavorable aspect of the system [11].

3.1.6

Artificial Intelligence Application in Performance of Engine Win GD XDF-72

The training data were derived from an example of real measurements of how the two-stroke Win GD XDF-72 engine worked. The number of samples collected for diesel fuel is 301, while the number of samples collected for gasoline is 318. In this paper, the prediction model for good or malfunction of the main Win GD XDF-72 engine was investigated based on mechanized learning by classification utilizing Exhaust CHAID, algorithms, and neural networks. This model was able to determine whether the engine was functioning properly or not. It is possible to create a machine performance prediction model using the MLP neural network

Overview of Applications of Artificial Intelligence Methods … Table 1 Data comparison between the 3 AI models (Source [11])

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Method

Percentage of training data prediction (%)

Percentage of data control (%)

Exhaustive CHAID with split validation

100

96.20

Exhaustive CHAID with cross validation

99.7

MPL (with neuronic network)

100

100

method, which has a 100% correct prediction rate for training data and a 100% correct prediction rate for control type data [11]. The results of the comparison between the three approaches are presented in Table 1.

3.1.7

The Application of Multivariable Linear Regression and Genetic Algorithm Analysis to the Problem of Predicting Occupational Risk in the Shipbuilding Industry

In the current investigation, an efficient method that is derived from the Multivariable Linear Regression (MVLR) and Genetic Algorithm (GA) techniques has been utilized to predict the likelihood of a worker being involved in a workplace accident in the shipbuilding industry. Figure 3 presents the high-level architecture of the optimization algorithm. In order to conduct an occupational risk assessment, the MVLR-GA model was operationalized with an appropriate collection of input–output training data. The accident conditions, the day and time, the person’s specialty, the type of event, the potentially dangerous situation, and the potentially dangerous actions involved in the event were the data that were input. The calculated Risk Indicators were the output data, and they were based on the input parameters. We requested and were given access to a number of accident files from the Hellenic Labor Inspection Service’s archives in order to facilitate the development of an efficient training program for the GA algorithm (Professional Accident Reports). These files were given a statistical edit so that we could determine which parameters were the most significant. Because of the statistical process, the chosen parameters provided evidence that they are related to the frequency with which each of the four levels of injury was observed [12].

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Fig. 3 Optimization flow diagram based on a combined MVLR/GA algorithm (Source [12])

3.1.8

A Comparison of the Results Obtained by Using an Artificial Neural Network and a Fuzzy Expert System to Forecast the Performance and Emission Parameters of a Gasoline Engine

In this study, an artificial neural network (ANN) and a fuzzy expert system (FES) are modeled as being part of an internal combustion engine in order to make predictions regarding the engine’s power, torque, specific fuel consumption, and hydrocarbon emissions. Experimentally-obtained data were used in this study. These data were obtained through laboratory-based studies that involved conducting experiments. An artificial neural network (ANN) for the engine has had some of its training and testing done using some of the experimental data that was collected (Fig. 4). It was found that both data groups had a confidence interval of p >0.05 and that there were no differences when the experimental data and findings from ANN and FES were compared with t-test in SPSS and regression analysis in Matlab. exhibited statistical significance. As a result, it has been demonstrated that developed ANN and FES can be used consistently in the engineering and automotive sectors in place of experimental work. Additionally, it appears that ANN and FES can be used and implemented in a variety of challenging and hazy situations, including figuring out engine performance and emission parameters [13].

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Fig. 4 Recommended ANN for predicting petrol engine performance and emission parameters (Source [13])

3.1.9

Use of Artificial Neural Networks to Forecast Diesel Engine Light Fuel Usage and Exhaust Temperature

It is being investigated whether an artificial neural network model can use a backpropagation learning algorithm to forecast a particular diesel engine’s fuel consumption and exhaust temperature for different injection times. Experimental findings are contrasted with the new model that is suggested. The comparison revealed that an average absolute relative error of less than 2% is required to obtain consistency between experimental and network results. A well-trained neural network model is thought to deliver quick and reliable results, making it a simple tool to use in preliminary studies for such thermal engineering issues [14].

3.1.10

Using an Artificial Neural Network to Predict Engine Efficiency for an Alternative Fuel

In this study, an artificial neural network (ANN) is modeled to predict fuel consumption, particularly for the brake, actual power, average effective pressure, and exhaust temperature of a methanol engine. Several tests were conducted using a four-cylinder, four-stroke test engine operating at various speeds and engine speeds to gather training and testing data. A conventional back multiplication algorithm-based ANN model was created using some of the experimental data for training. On the basis of a common back multiplication method, an ANN model was created. The effectiveness of ANN projections was then evaluated by contrasting the predictions with the findings of the experiments.

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While special brake fuel consumption, actual power, average effective pressure, and exhaust gas temperature have also been used separately as an outlet layer, engine speed, engine torque, fuel flow, average intake manifold temperature, and cooling water inlet temperature have all been used as input layers. After training, both the training and test sets of R2 values were discovered to be very near to 1. This demonstrates how effective the developed ANN model is at predicting internal combustion engine parameters such as flue gas temperature, average effective pressure, and the precise fuel usage of brakes [15].

3.1.11

With Help from CRDI, the Development of an ANN-Based System Identification Tool for Determining the Performance-Emission Properties of a CNG Dual-Fuel Diesel Engine

In this research, an artificial neural network was used to simulate the performance and emission parameters of a single-cylinder, four-stroke CRDI engine with a dual-fuel CNG-diesel function. Based on experimental data, an ANN model was created, with load, fuel injection pressure, and CNG energy share serving as the network’s input parameters, to forecast BSFC, BTE, NOx, PM, and HC. As shown by correlation coefficients in the 0.99833–0.99999 range, average absolute error rates in the 0.045– 1.66% range, and noticeably lower average square errors, the developed ANN model was able to predict performance and emission parameters with remarkable accuracy. This is an acceptable indicator of the robustness of the predicted accuracy [16].

3.1.12

Exhaust Emission Control and Engine Parameter Optimization for a Water-Powered Car Using Virtual Sensors of an Artificial Neural Network

This paper introduces a substitute tool for vehicle tuning applications using virtual sensors created by an artificial neural network (ANN) for a hydrogen vehicle. The objective of this research is to regulate exhaust emissions by optimizing straightforward engine process parameters. The virtual sensors are built around the engine process factors (butterfly position, lambda, ignition progress, and spray angle) and exhaust emission variables (CO, CO2 , HC, and NOx). First, a thorough experimental and coordination procedure for the training and validation of neural networks was used to gather the experimental data. The motor and transmission models were created using two ANN virtual sensors that were built using the optimized layer-bylayer neural network. With a maximum predictive mean relative error of 0.65%, the suggested virtual sensors’ accuracy and performance were satisfactory. The virtual sensors were used and simulated as a measurement tool to coordinate and optimize the car with precise prediction [17].

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Modeling Motor Reactions for a Light Diesel Engine Powered by Biodiesel Mixtures Using Artificial Neural Networks

Here, an ANN modeling program for a light diesel engine that combines various biodiesel fuels with traditional mineral diesel is discussed. In this study, an artificial neural network (ANN) was used to forecast nine distinct engine reactions, including maximum pressure (Pmax), maximum pressure position (CAD Pmax), maximum heat release rate (HRRmax), maximum HRR position (CAD HRRmax), and cumulative HRR (CuHRR). For this modeling exercise, four related engine operating factors were used as input parameters: engine speed, output torque, fuel mass flow rate and types, and biodiesel fuel mixtures. It was examined whether ANN could be used to forecast the connections between these inputs and outputs. In order to validate the simulation findings, information from the concurrent motor study was first compared. This paper also included network optimization techniques along with basic ANN “model” and “model parameter” results, including the kind of transfer function, the training algorithm, and the number of neurons [18].

3.1.14

Artificial Neural Network Prediction Based on the Efficiency and Emissions of the Variable Compression Engine CI that WCO Uses to Burn Biofuel at Various Injection Times

Performance of an artificial neural network based on the operation and operating parameters of a variable compression ratio CI engine using WCO as a biodiesel at various injection intervals. Engine efficiency and emission characteristics were predicted using artificial neural networks (ANN). Both the emission traits and performance factors have their own models. Compression parameters, injection time, mixing rate, mixture rate, and load rate were used as network training inputs. Engine performance parameters, such as thermal brake efficiency (BTE), specific power consumption (BSEC), and the exhaust gas temperature (Texh), were used as performance model outputs, and engine exhaust emissions, such as NOx, tobacco, and (UBHC) values, were used as emission model outputs. The findings of the ANN demonstrate a good correlation between the predicted and experimental prices for different engine performance parameters and exhaust emission characteristics. The relative average error values (MRE) were within 8%, which is acceptable [19].

3.1.15

Machine Learning-Based Ship Fuel Usage Prediction

Artificial intelligence techniques were used in this research to estimate how much fuel the ship would use while at sea. The noon report, which contains ship statistics, was originally obtained from a commercial ship. This report’s data were analyzed and separated into training and assessment data. On the computer, some of the info was used as training data. The computer was then instructed to guess the untaught

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Fig. 5 Methodology for developing a data acquisition system (Source [21])

portion using a multiple linear regression technique. Finally, the effectiveness of the evaluation is evaluated by comparing this machine learning prediction with actual data in a graph [20].

3.1.16

Realistic LNG Ship Fueling Solutions

Solutions that are realistic from the viewpoint of a typical ship, such as finding potential engine efficiency and methods to optimize ship functions. The required solutions will be obvious once the issue statement is clear. As a result, sensors for parameters that need to be logged in order to provide information for particular solutions can be found. After that, the information is stored on a website for quick and effective processing [21] (Fig. 5).

3.1.17

New Artificial Intelligence Technology Improving Fuel Efficiency and Reducing CO2 Emissions of Ships Carried the Use of Operational Big Data

Fujitsu has developed special multi-dimensional statistical analysis software that allows the predictability of ship efficiency without the use of physical models. The application of this machine data measurement technology, operating data, etc. proved to be highly accurate [22].

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4 Conclusion The two biggest expenses for shipping are the amount of fuel consumed by ships and the decrease of emissions. States and other international organizations, besides the International Maritime Organizations (IMOs), are working to reduce ship fuel consumption and the quantity of harmful gases they release by establishing various rules and controls to address these two issues. Shipping companies are basically trying to figure out how much fuel was used during the trip in order to cut down on fuel consumption on ships. Artificial intelligence methods are applied as tools to estimate the fuel consumption of the ship during the voyage. Data is growing daily and is endless. It is vital that the shipping industry improves cooperation and data integration to take advantage of the huge amount of information. Big data analytics will allow the industry to reveal information, trends and correlations that are currently hidden. Real-time data creates optimization opportunities in every aspect of the shipping industry—energy management, route design and optimization, predictable maintenance, environmental management, and ship safety and protection. The quality of the data collected automatically is at least theoretically superior to the data collected manually. However, there are still many factors that hinder its quality. Most of these issues are caused by sensors that have inherent inaccuracies, calibration problems, or malfunctions. For example, a poorly calibrated sensor or displacement of the sensor calibration can lead to significant performance misinterpretations. The question is how to understand all this information. A challenge far beyond the automatic analysis that requires advanced analysis tools capable of understanding information on a scale and beyond schedule. What is artificial intelligence (AI), of course? More specifically, mechanical learning systems (ML) and their algorithms that can convert different data points throughout the history of functions to bring to light knowledge between noise: basic relationships between variables that can be used for predicting future results. The comparison showed that the consistency between experimental and network results is achieved with an average absolute relative error of less than 2%. It is believed that a well-trained neural network model provides fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.

References 1. CO2 Emissions from ships: Council agrees its position on a revision of EU rules. (2019). Council of the EU Press release 25 October 2019, https://www.consilium.europa.eu/en/ press/press-releases/2019/10/25/co2-emissions-from-ships-council-agrees-its-position-on-arevision-of-eu-rules/ 2. Fernández, I. A., Gómez, M. R., Gómez, J. R., & Insua, Á. B. (2017). Review of propulsion systems on LNG carriers. Renewable and Sustainable Energy Review, 67, 1395–1411. 3. Cai, T. (2015). Application of soft computing techniques for renewable energy network design and optimization. Lamar University.

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4. An, H., Zhou, Z., & Yi, Y. (2017). Opportunities and challenges on nanoscale 3D neuromorphic computing system. IEEE International Symposium on Electromagnetic Compatibility & Signal/ Power Integrity (EMCSI), 2017, 416–421. 5. Chen, W., et al. (2017). Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Elsevier. 6. Vas, P. (1999). Artificial intelligence based electrical machines and drives, applications of artificial neural network (soft computing), the neuro fuzzy inference adaptive system, and the genetic algorithm in propulsion efficiency. Oxford University Press. 7. Yu, W., & Zhao, F. (2019). Predictive study of ultra-low emissions from dual-fuel engine using artificial neural networks combined with genetic algorithm. International Journal of Green Energy, 16(12), 938–946. 8. Wang, K., Yan, X., & Yuan, Y. (2015). Study and simulation on the energy efficiency management control strategy of ship based on clean propulsion system. In: Proceedings of the ASME 2015 34th international conference on ocean, offshore and arctic engineering. Volume 7: ocean engineering. St. John’s, Newfoundland, Canada. May 31–June 5, 2015. V007T06A058. ASME. 9. Spencer, K. (2011). Fuel consumption optimization using neural networks and genetic algorithms (2011 Report of Aerospace Engineering implementation on TAP airline) 10. Yan, X. P., Yuan, Y., & Li, F. (2016). Real-time optimization of ship energy efficiency based on the prediction technology of working condition. In: Report of transportation research part D, transport and environment. Wuhan University of Technology. 11. Pallas, D., & Tsoukalas, V. (2019). Artificial intelligence application in performance of engine WIN GD XDF-72. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 6(12), 11234–11239. ISSN: 2458-9403 12. Tsoukalas, V. D., & Fragiadakis, N. G. (2015). Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis. Safety Science, 83(2016), 12–22. 13. Tasdemir, S., Saritas, I., Ciniviz, M., & Allahverdi, N. (2011). Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine. Expert Systems with Applications, 38(11–2011), 13912–13923. 14. Parlak, A., Islamoglu, Y., Yasar, H., & Egrisogut, A. (2006). Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine. Applied Thermal Engineering, 26(828–824), 1359–4311. 15. Çay, Y., Çiçek, A., Kara, F., & Sa˘giro˘glu, S. (2012). Prediction of engine performance for an alternative fuel using artificial neural network. Applied Thermal Engineering, 37, 217–225. 16. Kumar, A., et al. (2012). Development of an ANN based system identification tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine. Journal of Natural Gas Science and Engineering, 21, 147–158. 17. Yap, K., Ho, T., & Karri, V. (2012). Exhaust emission control and optimization of engine parameters using virtual sensors of an artificial neural network for a water-powered vehicle. International Journal of Hydrogen Energy, 37(10–2012), 8704–8715. 18. Harun, I. (2012). Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, 92, 769–777. 19. Shivakumar, P. Srinivasa Pai, B.R., & Rao, S. (2011). Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Applied Energy, 88(7), 2344–2354 20. Yuanik, T., et al. (2019). Ship fuel consumption prediction with machine learning, conference paper IMSEC 2019, pp. 757–759 21. Serena Lim, S. L., & Zhiqiang, H. (2019). Practical solutions for LNG fueled ships. In: Conference proceedings of ICMET OMAN 2019, pp. 38–48. 22. Anan, T., Higuchi, H., Hamada, N. (2017). New artificial intelligence technology improving fuel efficiency and reducing CO2 emissions of ships through use of operational big data. Fujitsu Scientific & Technical Journal, 53, 23–28.

Interval Neutrosophic Multicriteria Decision Making by TODIM Method Najihah Chaini, D. Nagarajan, and J. Kavikumar

Abstract The Interval Neutrosophic Set (INS) can be used to identify the difficulty associated with a set of numbers that are not exact integers inside a real unit interval. INS is utilized in engineering, information fusion, medicine, and cybernetics because it can effectively express incomplete information. When faced with conflicting, incorrect, and inconsistent information, the Neutrosophic Set (NS) is widely employed to address multicriteria decision making (MCDM) challenges. We can choose the best by assessing the degree of dominance of alternatives over other alternatives. The alternatives to MCDM problems use the TODIM method. First, the TODIM method was modified to cope with MCDM in the proposed novel strategy for interval Neutrosophic weighted average (INWA). The main advantage of this method is that it may be applied to high-risk MCDM problems. In this study, the aggregation properties for Interval Neutrosophic sets were obtained using the INWA operator. Lastly, a numerical example was proposed. Keywords Aggregation operator · TODIM · Neutrosophic set · MCDM · Neutrosophic weighted average

1 Introduction Zadeh [33] defined fuzzy sets (FS) as a method of describing and handling data that is not rigid but somewhat fuzzy, with membership values ranging from 0 to 1. He also proposed an innovative fuzzy set theory, a valuable tool for tackling N. Chaini (B) · J. Kavikumar Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Campus Pagoh, 84600 Parit Raja, Malaysia e-mail: [email protected] J. Kavikumar e-mail: [email protected] D. Nagarajan Department of Mathematics, Rajalakshmi Institute of Technology, Chennai 600124, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_33

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real-world problems. According to Smarandache [25], the Neutrosophic set (NS) deals with truth, falsity, and indeterminate membership functions, all independent of one another. It can be challenging to address a decision making dilemma with NS. In real-world situations, it is difficult to compute the three degrees of truth, falsity, and indeterminacy since they cannot be expressed as precise numbers [32]. Recently, some investigators created an interval-valued environment to address this issue, establishing the membership value using an interval rather than a precise number. According to Broumi and Smarandache [2], applying NS and INS theories has produced numerous positive findings in decision making, information fusion, image processing, and medical diagnostics. The fuzzy set cannot deal with all sorts of uncertainty, including indeterminate and inconsistent data [20]. Some specialists need clarification about the assertion, resulting in uncertainty. These difficulties are outside the scope of FS and IFS, which deal with membership, non-membership, and hesitant functions. However, we can have a solution to this problem using NS. Aggregation operators in the intuitionistic fuzzy environment with membership values in the range [0,1] can be extended to NS. Thus, to aggregate Neutrosophic information in decision making situations, we must concentrate on aggregation operators on NS. According to Gao et al. [5], the concept that the three membership functions in NS are independent will be beneficial in fusing data from multiple sources in the field of information fusion. A unique technique for decision support is needed to address uncertainty and human perspectives in multicriteria analysis. Alternatives and criteria constitute the decision matrix in a multicriteria decision making process. Because this matrix is susceptible to uncertainty, we use fuzzy numbers in real-world scenarios. This is an extension of a membership grade interval. Every value in the interval corresponds to a real number and is compatible with a declaration of uncertainty. Gao et al. [5] also used this approach in MCDM. There are numerous decision making procedures for determining an exact number as the decision makers’ grade of membership, but there are fewer for determining an interval-based fuzzy number. Gomes et al. [7, 10] are a group of researchers working on a project. An MCDM technique based on prospect theory is the TODIM technique. The global measurement value is determined using PT-TODIM, and the decision maker’s choice is effectively played out in risk scenarios. The global multi-attribute value function will combine all loss and gain metrics. TODIM is comparable to outranking approaches since each alternative’s global value is proportional to its dominance over all other alternatives. This method tests specific losses and gains functions based on a single parameter. It is dubbed a noncompensatory method because it does not deal with tradeoffs. TODIM is a method for iterative MCDM. It will reorder and picks objects based on the expert’s recommendation. Experts often need more information about attributes, making it easier to solve decision making challenges. The qualities information will be published in its whole here. TODIM is particularly valuable with incomplete attribute matrices since its conclusions are more objective and accepted. The two categories typically used to categorize MCDM issues are benefit and cost criteria. In general, benefit criteria will be prioritized for ease of usage. The fundamental premise of the TODIM approach, according to Adali et al. [1], is to measure the dominance degree of each alternative

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over the other alternative. This method can therefore be applied to both qualitative and quantitative criteria. Qin et al. [22] explained the TODIM approach’s adaptability for various fuzzy conditions. The outline of this paper is organized as follows. Section 2 established the literature review of the TODIM method and its usage in different environments. The proposed interval neutrosophic TODIM methodology is given in Sect. 3. Some numerical experiment that validates the proposed method is presented in Sect. 4. Lastly, Sect. 5 has the conclusions of the paper.

2 Literature Review To handle real-world challenges, a variety of decision making strategies are accessible. TODIM is an MCDM approach, according to Gomes et al. [7]. Krohling and Souza [12, 13] discovered that the TODIM approach, which is based on prospect theory and uses Fuzzy TODIM and the extended case for MCDM problems, one of the MCDM techniques Gomes and Lima suggested [6]. In order to reduce calculations and support interval data, Gomes et al. (2013a, b) merged TODIM and Choquet integrals. Passos et al. (2014) integrated TODIM and FSE to select the best alternative. The hesitant fuzzy linguistic TODIM approach was introduced by Wei et al. (2015). Lourenzutti and Krohling [8–10] refined a new approach based on the TODIM method to comprehend heterogeneous data. For the distributor problem, intuitionistic fuzzy TODIM was advised by Li et al. [14]. To avoid traditional approaches’ drawbacks, Tosun and Akyüz [27] utilized fuzziness to improve a relatively recent decision procedure called TODIM. The extended version of the TODIM method with the Pythagorean fuzzy sets was introduced by Ren et al. [23] and in neutrosophic numbers by Zhang et al. [35]. Lin et al. [15] repurposed this technique as a decision making reinforcement technique. Sang and Liu [24] created the interval type-2 fuzzy sets TODIM technique and used it to solve a problem in the supplier industry. Pramanik et al. [21] presented the NC-TODIM technique to address a problem with investments. For an MCDM problem, Qin et al. [22] extended the classic TODIM approach. Sun et al. [26] used extended TODIM and ELECTRE III techniques for the physician selection challenge. He et al. [11] used HMF-TODIM to satisfy the decision makers’ desires and proved aggregation qualities for INS using the INWA operator. Wang et al. [29] suggest the 2TLNNs TODIM technique, which extends the TODIM method to the 2-tuple linguistic neutrosophic fuzzy environment. To give the criteria values in multiple attribute group decision making (MAGDM) problems using 2-tuple linguistic neutrosophic numbers (2TLNNs) in the extended technique. There will also be a real-life case study for a furniture manufacturing company to solve. Deng and Gao [4] extended the 2TLPFs TODIM technique, expanding the original TODIM approach to the Pythagorean fuzzy 2-tuple linguistic environment. A fresh paradigm for addressing multi-attribute group decision making problems in

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fuzzy environments was presented in [3]. Zindani et al. [36] presents a unique integrated group decision making framework for decision making in intuitionistic fuzzy environments combining Schweizer-Sklar t-conorm and t-norm (SSTT) aggregation operators, power average (PA) operators and TODIM procedures.

2.1 Objective of the Research We applied the TODIM approach to an investment problem based on the literature mentioned above because there is insufficient research on INS. Ulrich and Henri [28] introduce fuzzy triangular aggregation operators, a novel class of fuzzy aggregation operators. Another approach, fuzzy numbers, must be used to analyze uncertain or inaccurate data. To accomplish so, we concentrate on situations that cannot evaluate the available data with exact numbers.

3 Proposed Interval Neutrosophic TODIM Methodology The following steps for the proposed TODIM method for an interval neutrosophic set are described. 1. 2. 3. 4. 5. 6. 7. 8. 9.

Decision making construction Normalization of the decision matrix Calculate the relative weight Calculating Score values Calculating the accuracy values Formation of dominance matrix Aggregation of all the dominance matrix Finding global values Ranking.

Based on descending order of global values, the ranking will be done. The highest global value ψi reflects the best alternative.

4 Application of TODIM Method We have assumed that an investment firm wishes to invest a certain amount of money in the best alternative. The investment corporation creates a decision making board based on three decision makers who assess the four choices. The four choices are as follows:

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(i) (ii) (iii) (iv)

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Car Manufacturing (a1 ). Food supply (a2 ). Computer hardware (a3 ). Arms Manufacturing (a4 ).

Risk factor, growth factor, and environment factor were the three criteria that the decision makers used to evaluate the alternatives. They all fell within the benefit type category according to Ye [32].

4.1 Method 1 Procedure for finding alternatives. 1. 2. 3. 4. 5. 6.

Describe the Interval Neutrosophic matrix Calculating relative weight Determine the dominance degree Calculating overall dominance degree Calculate overall value of each alternative Determine the order of alternative.

Numerical problem: 1 If the possible company Ai (i = 1, 2, 3, 4) are determine by the Interval Neutrosophic ⎡

[0.6, 0.7], [0.1, 0.2], [0.8, 0.9] ⎢ ⎢ [0.4, 0.5], [0.2, 0.3], [0.6, 0.7] R=⎢ ⎣ [0.7, 0.8], [0.2, 0.4], [0.5, 0.6] [0.5, 0.7], [0.1, 0.2], [0.4, 0.5]

[0.3, 0.6], [0.2, 0.3], [0.3, 0.4] [0.5, 0.6], [0.3, 0.4], [0.4, 0.5] [0.6, 0.7], [0.2, 0.4], [0.3, 0.5] [0.7, 0.8], [0.3, 0.4], [0.5, 0.6]

⎤ [0.8, 0.9], [0.3, 0.4], [0.4, 0.5] ⎥ [0.7, 0.8], [0.2, 0.3], [0.3, 0.4] ⎥ ⎥ [0.8, 0.9], [0.3, 0.4], [0.2, 0.3] ⎦ [0.6, 0.7], [0.1, 0.2], [0.3, 0.4]

Initially, ω4 = max{ω1 , ω2 , ω3 , ω4 } and the reference attribute weight is ωr = 0.4. Then determine the relative weights. ω1 = 0.4, ω2 = 0.35, ω3 = 0.25, ω4 = 1 and θ = 2.5. Dominance matrix ⎤ ⎡ ⎤⎤ 0 −0.40825 −0.42817 −0.3873 · · · ∅1 (A1 , Am ) ⎥ ⎢ ⎥⎥ ⎢⎢ 0.1632 0 0.1549 0.1549 ⎥ . .. ⎥⎥ ∅1 = ⎢ ⎢ ∅j = ⎢ . ⎥ ⎢ ⎦⎦ ⎣⎣ . . ⎣ 0.1712 −0.3873 0 −0.57 ⎦ 0 ∅1 (Am , A1 ) · · · 0.1549 −0.3873 01460 0 ⎤ ⎤ ⎡ ⎡ 0 −0.3381 0.1183 0.69 0 −0.4 0.1 0.8164 ⎥ ⎥ ⎢ ⎢ 0 0.2121 −0.61 ⎥ 0 0.1673 0.1183 ⎥ ⎢ 0.2345 ⎢ 0.1183 ∅1 = ⎢ ⎥, ∅2 = ⎢ ⎥ ⎦ ⎣ 0.2 −0.28284 ⎣ 0 −0.57 −0.4781 −0.4781 0 −0.3381 ⎦ 0.70710 −0.29814 −0.2828 0 0 −0.3381 0.1183 0 ⎤ ⎡ 0 −0.3266 −0.5164 −0.4 ⎥ ⎢ 0 0.1291 0.1 ⎥ ⎢ 0.0812 ∅3 = ⎢ ⎥ ⎣ 0.1291 −0.5164 0 0.2121 ⎦ 1 −0.4 −0.3266 0 ⎡⎡

0 . . .

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nBased on ∅ j , calculate overall z=1 ∅z Ai , A j , i, j = 1, 2, 3, . . . . . . m

dominance

degree

 δ Ai , A j

=

⎡

⎤ 0 −1.072 −0.8262 −0.0972 ⎢ 0.3633 0 0.4514 0.3732 ⎥ ⎥ δ=⎢ ⎣ −0.1778 −1.3818 0 −0.4911 ⎦ 1.1549 −1.1254 −0.0622 0 Then overall values of δ( Ai ), i = 1, 2, 3, 4, δ(A1) = 0, δ( A2) = 1, δ( A3) = 0, δ( A4) = 0.6231. We get the order of δ(Ai ) is δ(A2), δ( A4), δ( A3), δ( A1).

4.2 Method 2 We presume that the decision-weight maker’s vector is γ = (0.37, 0.33, 0.3)T and the attribute weight vector is W = (0.4, 0.35, 0.25)T . The MCDM problem is now solved for an Interval Neutrosophic Set utilizing the TODIM approach. Step 1: Establishment of the decision matrix: We create a decision matrix based on the data provided by the decision makers and the criteria listed below. Decision matrix for D M1 is N D M1 ⎡

[0.6, 0.7], [0.1, 0.2], [0.8, 0.9] ⎢ ⎢ [0.4, 0.5], [0.2, 0.3], [0.6, 0.7] ⎢ ⎣ [0.7, 0.8], [0.2, 0.4], [0.5, 0.6] [0.5, 0.7], [0.1, 0.2], [0.4, 0.5]

[0.3, 0.6], [0.2, 0.3], [0.3, 0.4] [0.5, 0.6], [0.3, 0.4], [0.4, 0.5] [0.6, 0.7], [0.2, 0.4], [0.3, 0.5] [0.7, 0.8], [0.3, 0.4], [0.5, 0.6]

⎤ [0.8, 0.9], [0.3, 0.4], [0.4, 0.5] ⎥ [0.7, 0.8], [0.2, 0.3], [0.3, 0.4] ⎥ ⎥ [0.8, 0.9], [0.3, 0.4], [0.2, 0.3] ⎦ [0.6, 0.7], [0.1, 0.2], [0.3, 0.4]

Decision matrix for D M2 is N D M2 ⎡

[0.6, 0.8], [0.3, 0.4], [0.7, 0.8] ⎢ ⎢ [0.8, 0.9], [0.1, 0.2], [0.6, 0.7] ⎢ ⎣ [0.7, 0.8], [0.2, 0.3], [0.3, 0.4] [0.5, 0.7], [0.1, 0.2], [0.3, 0.5]

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

⎤ [0.5, 0.6], [0.1, 0.2], [0.3, 0.4] ⎥ [0.4, 0.6], [0.2, 0.3], [0.2, 0.3] ⎥ ⎥ [0.7, 0.9], [0.2, 0.4], [0.4, 0.5] ⎦ [0.3, 0.6], [0.3, 0.5], [0.6, 0.8]

Decision matrix for D M3 is N D M3 ⎡

[0.6, 0.7], [0.2, 0.3], [0.4, 0.5] ⎢ ⎢ [0.8, 0.9], [0.3, 0.4], [0.4, 0.5] ⎢ ⎣ [0.7, 0.8], [0.1, 0.2], [0.2, 0.4] [0.8, 0.9], [0.2, 0.3], [0.3, 0.5]

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

⎤ [0.8, 0.9], [0.1, 0.2], [0.2, 0.3] ⎥ [0.6, 0.7], [0.3, 0.4], [0.4, 0.5] ⎥ ⎥ [0.7, 0.8], [0.1, 0.2], [0.4, 0.6] ⎦ [0.6, 0.8], [0.2, 0.3], [0.5, 0.6]

Step 2: Normalization of the matrix: Normalization for N D M1 ,

N

D M2 ,

N

D M3 .

Step 3: Finding the relative weight of each criterion. The relative weights are,

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W RC1 = 1, W RC2 = 0.88, W RC3 = 0.63 Step 4: Finding Score values: By score function of an interval neutrosophic number [2, 25]. Score value for the decision matrix ⎤ [1.5, 1.8], [1.6, 2.1], [1.9, 2.2] ⎢ [1.4, 1.7], [1.6, 1.9], [2.0, 2.3] ⎥  ⎥ S N D M1 = ⎢ ⎣ [1.7, 2.1], [1.7, 2.2], [2.1, 2.4] ⎦ [1.8, 2.2], [1.7, 2.0], [2.0, 2.3] ⎤ ⎡ [1.4, 1.8], [1.6, 2.2], [1.9, 2.2]  ⎢ [1.4, 2.2], [1.8, 2.4], [1.8, 2.2] ⎥ ⎥ S N D M2 = ⎢ ⎣ [2.0, 2.3], [1.8, 2.3], [1.8, 2.2] ⎦ [1.8, 2.3], [1.9, 2.2], [1.0, 1.7] ⎤ ⎡ [1.8, 2.1], [1.8, 2.2], [2.3, 2.6]  ⎢ [1.9, 2.2], [1.7, 2.1], [1.7, 2.0] ⎥ ⎥ S N D M3 = ⎢ ⎣ [2.1, 2.5], [1.6, 1.9], [1.9, 2.3] ⎦ [2.0, 2.4], [1.4, 1.9], [1.7, 2.1] ⎡

Step 5: Decision matrices. By accuracy function of an interval neutrosophic number [2, 25]. Accuracy value for the decision matrix N D M1 , N D M2 and N D M3 are as follows: ⎤ [−0.2, −0.2], [0.0, 0.2], [0.4, 0.4] ⎢ [−0.2, −0.2], [0.1, 0.1], [0.4, 0.4] ⎥  ⎥ σ N D M1 = ⎢ ⎣ [0.2, 0.2], [0.2, 0.3], [0.6, 0.6] ⎦ ⎡

[0.1, 0.2], [0.2, 0.2], [0.3, 0.3]

⎤ [−0.1, 0], [−0.1, 0.2], [0.2, 0.2]  ⎢ [0.2, 0.2], [0.3, 0.4], [0.2, 0.3] ⎥ ⎥ σ N D M2 = ⎢ ⎣ [0.4, 0.4], [0.4, 0.4], [0.3, 0.4] ⎦ [0.2, 0.2], [0.3, 0.3], [−0.3, −0.2] ⎤ ⎡ [0.2, 0.2], [0.3, 0.4], [0.6, 0.6]  ⎢ [0.4, 0.4], [0.0, 0.1], [0.2, 0.2] ⎥ ⎥ σ N D M3 = ⎢ ⎣ [0.4, 0.5], [0.1, 0.1], [0.2, 0.3] ⎦ [0.4, 0.5], [−0.2, 0.0], [0.1, 0.2] ⎡

Step 6: Dominance matrix. By using benefit criteria

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⎡

⎡ ⎤ ⎤ 0 −0.65 −0.67 −0.61 0 −0.53 −0.53 −0.76 ⎢ 0.26 0 −0.61 −0.61 ⎥ 1 ⎢ 0.19 0 −0.45 −0.53 ⎥ ⎥δ =⎢ ⎥ δ11 = ⎢ ⎣ 0.27 0.24 0 −0.57 ⎦ 2 ⎣ 0.19 0.16 0 −0.53 ⎦ 0.24 0.24 0.28 0 0.26 0.19 0.19 0 ⎡ ⎡ ⎤ ⎤ 0 −0.63 −0.53 −0.82 0 −0.61 −0.67 −0.74 ⎢ 0.16 0 −0.63 −0.53 ⎥ 2 ⎢ 0.24 0 −0.65 −0.65 ⎥ ⎥δ =⎢ ⎥ δ31 = ⎢ ⎣ 0.13 0.16 0 −0.82 ⎦ 1 ⎣ 0.27 0.26 0 −0.50 ⎦ 0.21 0.13 0.17 0 0.30 0.26 0.20 0 ⎡ ⎡ ⎤ ⎤ 0 −0.70 −0.72 −0.59 0 −0.63 −0.82 −0.96 ⎢ 0.24 0 −0.59 −0.38 ⎥ 2 ⎢ 0.16 0 −0.85 −0.94 ⎥ ⎥δ =⎢ ⎥ δ22 = ⎢ ⎣ 0.25 0.21 0 −0.53 ⎦ 3 ⎣ 0.21 0.21 0 −0.96 ⎦ 0.21 0.13 0.19 0 0.24 0.23 0.24 0 ⎡ ⎡ ⎤ ⎤ 0 −0.50 −0.55 −0.45 0 −0.70 −0.59 −0.79 ⎢ 0.2 ⎢ ⎥ 0 −0.61 −0.35 ⎥ ⎥ δ 3 = ⎢ 0.24 0 −0.72 −0.61 ⎥ δ13 = ⎢ ⎣ 0.22 0.24 0 −0.50 ⎦ 2 ⎣ 0.21 0.25 0 −0.81 ⎦ ⎡

0.18 0.14

0.20

0

⎤

0.28 0.21

0.28

0

0 −0.89 −0.69 −0.85 ⎢ 0.22 0 −0.69 −0.56 ⎥ ⎥ δ33 = ⎢ ⎣ 0.17 0.17 0 −0.53 ⎦ 0.21 0.14 0.13 0 Step 7: Finding individual matrix. By individual final dominance matrix, ⎡ ⎡ ⎤ ⎤ 0 −1.81 −1.73 −2.19 0 −1.94 −2.21 −2.29 ⎢ 0.61 0 −1.69 −1.67 ⎥ ⎢ ⎥ ⎥, φ2 = ⎢ 0.64 0 −2.09 −1.97 ⎥ φ1 = ⎢ ⎣ 0.59 0.56 ⎣ 0.73 0.68 0 −1.92 ⎦ 0 −1.99 ⎦ 0.71 0.56 0.64 0 0.75 0.62 0.63 0 ⎡

⎤ 0 −2.09 −1.83 −2.09 ⎢ 0.66 0 −2.02 −1.52 ⎥ ⎥ φ3 = ⎢ ⎣ 0.60 0.66 0 −1.84 ⎦ 0.67 0.49 0.61 0 Step 8: Aggregation of the matrix. By using the aggregated dominance matrix, ⎡

⎤ 0 −1.97 −1.92 −2.20 ⎢ 0.66 0 −1.93 −1.73 ⎥ ⎥ φ=⎢ ⎣ 0.64 0.63 0 −1.92 ⎦ 0.71 0.56 0.63 0

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Table 1 Example computation of dominance degrees of the first alternative over the others considering each criterion Pair of alternatives

ϕ1

ϕ2

ϕ3

sum(ϕ1 , ϕ2 , ϕ3 )

(a1 , a2 )

−1.81

−1.94

−2.09

−5.84

(a1 , a3 )

−1.73

−2.21

−1.83

−5.77

(a1 , a4 )

−2.19

−2.29

−2.09

−6.57 Sum = −18.18

Table 2 Ordering

Ordering Proposed method

ψ4 > ψ3 > ψ2 > ψ1

Existing method

δ( A2), δ(A4), δ( A3), δ(A1)

Step 9: Finding global values. We find the global values of all the four alternatives as follows (Table 1). ψ1 = 0, ψ2 = 0.38, ψ3 = 0.68, ψ4 = 1. Step 10: Ranking of the alternatives. Here we have, ψ4 > ψ3 > ψ2 > ψ1 . Hence the arms company (a4 ) is the best alternative to invest money.

4.3 Comparative Analysis Compare the suggested method to other methods already in use [31]. According to the value of δ(Ai ) for both method (Table 2). The investigation mentioned above demonstrates that the ranking results are marginally different. The interval Neutrosophic TODIM approach, however, can choose the businesses in a reasonable manner. The proposed method indicates that the best investment for MCDM is an arm firm, while the current method indicates that a food company is the greatest choice. This demonstrates the effectiveness and logic of the strategy we suggested.

5 Conclusion We concentrated on this area because the INS environment is better suited for dealing with actual problems that involve ambiguity. We can precisely locate important alternatives by using the TODIM method, and we may analyze the alternatives to provide a

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rating that is acceptable and in line with the experts’ predictions. Despite the amazing technologies we use every day, we just used the TODIM method as a starting point and a distinct approach for the new researchers. Additionally, in order to make choosing the best option simple, we employed similarity measures to rank the order of all the choices. For use in the fields of science and engineering, this INS similarity metric is practical. This work presents a new TODIM methodology for an interval neutrosophic environment and derives INS aggregation features using the INWA operator. Additionally used the suggested approach to solve decision making issues to pick the best business to invest in. Since we deal with the interval-based idea, this approach is different from the earlier ones. We’ll keep working by utilizing the TODIM approach in additional domains in the future. Acknowledgements The Pusat Pengurusan Penyelidikan (RMC), Universiti Tun Hussein Onn Malaysia, Malaysia, funded this study with Grant No. H346 from the Geran Penyelidikan Pascasiswazah (GPPS).

References 1. Adali, E. A., Isik, A. T., & Kundakci, N. (2016). Todim method for the selection of elective course. European Scientific Journal, 12(10), 314–324. 2. Broumi, S., & Smarandache, F. (2014). New operations on interval neutrosophic sets. Neutrosophic Theory and Applications, 1, 256–266. 3. Davoudabadi, R., Mousavi, S. M., & Mohagheghi, V. (2020). A new last aggregation method of multi-attributes group decision making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty. Knowledge and Information System, 62, 1371–1391. 4. Deng, X., & Gao, H. (2019). TODIM method for multiple attribute decision making with 2tuple linguistic Pythagorean fuzzy information. Journal of Intelligent Fuzzy Systems, 37(2), 1769–1780. 5. Gao, Z., Zhu, L., Li, Z., & Fan, P. (2015). Threat evaluation of early warning detection based on incomplete attribute information TODIM method. 3rd International Conference on Machinery, Materials, and Information Technology Applications (pp. 40–47). Atlantic press. 6. Gomes, L. F. A. M., & Lima, M. M. P. P. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16(4), 113–127. 7. Gomes, L. F. A. M., & Rangel, L. A. D. (2009). Multicriteria analysis of natural gas destination in Brazil: An application of the TODIM method. Mathematical and Computer Modeling, 50, 92–100. 8. Gomes, L. F. A. M., Machado, M. A. S., Costa, F. F., & Rangel, L. A. D. (2013). Criteria interactions in multiple criteria decision aiding: A Choquet formulation for the TODIM method. Procedia Computer Science, 17, 324–331. 9. Gomes, L. F. A. M., Machado, M. A. S., Costa, F. F., & Rangel, L. A. D. (2013). Behavioral multi-criteria decision analysis: The TODIM method with criteria interactions. Annals of Operations Research, 211, 531–548. 10. Gomes, L. F. A. M., Machado, M. A. S., Santos, D. J., & Caldeira, A. M. (2015). Ranking of suppliers for steel industry: A comparison of the original TODIM and the Choquet-extended TODIM methods. Procedia Computer Science, 55, 706–714.

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11. He, X., & Wu, Y. (2017). City sustainable development evaluation based on hesitant multiplicative fuzzy information. Mathematical Problems in Engineering, 2017, 1–9. 12. Krohling, R. A., & de Souza, T. T. M. (2012). Combining prospect theory and fuzzy numbers to multi-criteria decision making. Expert Systems with Applications, 39, 11487–11493. 13. Krohling, R.A. and de Souza T. T. M., 2012. F-TODIM: An application of the fuzzy TODIM method, to rental evaluation of residential properties. Congreso Latino-Iberoamericano de Investigation perativa, Symposio Brasileiro de Pesquisa Operational, September 24–28, Rio de Janeiro, Brazil, pp. 431–443. 14. Li, M., Wu, C., Zhang, L., & You, L. N. (2015). An intuitionistic fuzzy-TODIM method to solve distributor evaluation and selection problem. International Journal of Simulation Modelling, 14(3), 511–524. 15. Lin, C., Lee, C., & Lin, J. (2016). Using the fuzzy TODIM method as a decision making support methodology for house purchasing. Journal of Testing and Evaluation, 44(5), 1925–1936. 16. Lourenzutti, R., & Krohling, R. A. (2013). A study of TODIM in a intuitionistic fuzzy and random environment. Expert Systems with Applications, 40, 6459–6468. 17. Lourenzutti, R., & Krohling, R. A. (2014). The Hellinger distance in multi-criteria decision making: An illustration to the TOPSIS and TODIM methods. Expert Systems with Applications, 41, 4414–4421. 18. Lourenzutti, R., & Krohling, R. A. (2015). TODIM based method to process heterogeneous information. Procedia Computer Science, 55, 318–327. 19. Passos, A. C., Teixeira, M. G., Garcia, K. C., Cardoso, A. M., & Gomes, L. F. A. M. (2014). Using the TODIM-FSE method as a decision-making support methodology for oil spill response. Computers & Operations Research, 42, 40–48. 20. Peng, J. J., Wang, J. Q., Wu, X. H., Wang, J., & Chen, X. H. (2015). Multi-valued neutrosophic sets and power aggregation operators with their applications in multi-criteria group decisionmaking problems. International Journal of Computational Intelligent Systems, 8(2), 345–363. 21. Pramanik, S., Dalapati, S., Alam, S., & Roy, T. P. (2017). NC-TODIM-based MAGDM under a neutrosophic cubic set environment. Journal of Information, 8(149), 1–21. 22. Qin, Q., Liang, F.-F., Li, L., Chen, Y.-W., & Yu, G.-F. (2017). A TODIM-based multi- criteria group decision making with triangular intuitionistic fuzzy numbers. Applied Soft Computing, 55, 93–107. 23. Ren, P., Xu, Z., & Gou, X. (2016). Pythagorean fuzzy TODIM approach to multi-criteria decision making. Applied Soft Computing, 42, 246–259. 24. Sang, X., & Liu, X. (2016). An interval type-2 fuzzy sets- based TODIM method and its application to green supplier selection. Journal of the Operational Research Society, 67(5), 722–734. 25. Smarandache, F. (2005). Neutrosophic set- a generalization of the intuitionistic fuzzy set. International Journal of Pure and Applied Mathematics, 24(3), 287–297. 26. Sun, R., Hu, J.-J., & Chen, X. (2017). Novel single-valued neutrosophic decision-making approaches based on prospect theory and their applications in physician selection. Soft Computing, 20, 1–15. 27. Tosun, Ö., & Akyüz, G. (2015). A fuzzy TODIM approach for the supplier selection problem. International Journal of Computational Intelligence Systems, 8(2), 317–329. 28. Ulrich, F. S., & Henri, G. (2018). Fuzzy triangular aggregation operators. International Journal of Mathematics and Mathematical Sciences, 9209524, 1–13. 29. Wang, J., Wei, G., & Lu, M. (2018). TODIM method for multiple attribute group decision making under 2-tuple linguistic neutrosophic environment. Symmetry, 10(10), 486. 30. Wei, C., Zhiliang, R., & Rodriguez, R. M. (2014). A hesitant fuzzy linguistic TODIM method based on a score function. International Journal of Computational Intelligence Systems, 8(4), 701–712. 31. Xu, D.-S., Wei, C., & Wei, G.-W. (2017). TODIM Method for single-valued neutrosophic multiple attribute decision making. Information, 8(4), 125. 32. Ye, J. (2014). A multi-criteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent and Fuzzy Systems, 26, 2459–2466.

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The Scenario of COVID-19 Pandemic in Brazil Using SEIR Epidemic Model Subrata Paul, Ashish Acharya, Manajat Ali Biswas, Animesh Mahata, Supriya Mukherjee, Prakash Chandra Mali, and Banamali Roy

Abstract Recent studies have shown that the COVID-19-caused Coronavirus illness is exceedingly infectious and has a significant global mortality rate. The S E I R compartmental model of COVID-19 is explored in this manuscript with four different categories. A few conclusions about the existence and uniqueness criteria for the new model, in addition to the positivity and boundedness of the response, have been made. The Routh-Hurwitz consistency criterion is used to analyze the dynamics of the equilibrium point of our suggested model. When RCovid19 < 1 at infectionfree equilibrium, we prove that the system is locally asymptotically stable. The investigation of the COVID-19 transmission and prevention in Brazil is the primary goal of this study. MATLAB software is used to describe the model system, and graphically illustrate the numerical outcomes. Keyword SEIR model · COVID-19 · Stability · Fuzzy environment · Numerical simulation S. Paul (B) Department of Mathematics, Arambagh Govt. Polytechnic, Arambagh, West Bengal, India e-mail: [email protected] A. Acharya Department of Mathematics, Swami Vivekananda Institute of Modern Science, West Bengal, Karbala More 700103, India M. A. Biswas Department of Mathematics, Gobardanga Hindu College, 24 Parganas (North), P.O.-Khantura, Gobardanga, West Bengal 743252, India A. Mahata Mahadevnagar High School, Maheshtala, Kolkata, West Bengal 700141, India S. Mukherjee Department of Mathematics, Gurudas College, Kolkata, West Bengal 700054, India P. C. Mali Department of Mathematics, Jadavpur University, Kolkata 700032, India B. Roy Department of Mathematics, Bangabasi Evening College, Kolkata, West Bengal 700009, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_34

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1 Introduction The 2019 Coronavirus disease, which is brought on by the new Coronavirus, a highly contagious virus that preys on the respiratory system of humans, is still being addressed by the international community. Nonetheless, depending on the immune system, COVID-19 symptoms and consequences differ from person to person. People with a strong immune response seem to be more likely to get mild-to-moderate illnesses as well as recover and avoid going to the hospital. Various investigations, however, have identified other symptoms such as neurological illnesses and gastroenteritis of different severity [1, 2]. Following extensive testing, these vaccinations have now received authorization from a number of nations. In epidemiology, number of straightforward mathematical systems have been analyzed by categorizing the entire population in order to examine the dynamics of how particular diseases spread. The reality is that things are different. The values of all the components of the system are not always known with precision for a variety of reasons, such as error in counting and assumed beginning conditions [3]. Since the virus first entered the human population, researchers have been investigating the causes of new outbreaks in susceptible and exposed populations as well as the effects of vaccination on recovered populations. Numerous studies have been done in this circumstance utilizing real-time data from the afflicted nations, and various epidemic features [4, 5] have been studied. In [6, 7] suggests some additional relevant work on COVID-19 modeling and associated illness outcomes. In [8], Paul et al. analyzed the outline of COVID-19 pandemic using SEIR epidemic model. A deeper understanding of the pandemic dynamics, including the characteristics of Covid-19 transmission, was made possible by the modeling technique [9–17]. When the interval-valued intuitionistic number is treated as nonlinear in the membership notion, the idea of interval-valued intuitionistic integral equation is presented in this study.

1.1 Purpose of the Work (i) Analyze the model’s stability and dynamic behavior with fuzzy interval numbers as the model’s parameters. (ii) Using numerical modeling to validate the results and stop COVID-19 from spreading. (iii) MATLAB software is used to describe the model system, and graphically illustrate the numerical outcomes.

2 Preliminaries Definition The interval [Vm , Vn ] can also be written as k1 (η) = (Vm )1−η (Vn )η for η ∈ [0, 1], whose interval figures also refer to as parametric form.

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3 Model Formulation The model in this research will be split into four parts. The overall population to be examined is designated as N, and it includes the susceptible (S), exposed (E), infected (I), and recovered (R) compartments at any given time. Thus N = S + E + I + R.

(1)

The SEIR model’s diagram is depicted in Fig. 1, and Table 1’s descriptions of the parameter values are shown there as well. The given system is dS 1− p p 1− p p 1− p p = b L b R − β R β L S I − μ R μ L S, dt   dE 1− p p 1− p p 1− p p = β L β R S I − μ L μ R + k L k R E, dt   dI 1− p p 1− p p 1− p p = k L k R E − μ L μ R + γ L γ R I, dt dR 1− p p 1− p p = γ L γ R I − μ R μ L R, dt

(2)

with S(0) ≥ 0, E(0) ≥ 0, I (0) ≥ 0, R(0) ≥ 0, where p stands for the interval-valued parameter and Table 1 lists the parameter values.

Fig. 1 Flow diagram of S E I R model

Table 1 Parametric values of the system

Symbol

Significance

β

Rate of contact

b

Susceptible fertility rate

μ

Rate of mortality

k

Rate of progression

γ

Rate of recovery

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3.1 Boundedness and Positivity Theorem 1 All the compartments are positive in the neighborhood. 

1− p p

 = (S, E, I, R) ∈ R : 0 < N ≤ 4

bL b R 1− p



p

μ R μL

.

Proof From Eq. (2), we get dS 1− p p 1− p p 1− p p 1− p p 1− p p = b L b R − β R β L S I − μ R μ L S ≥ −β R β L S I − μ R μ L S. dt      t 1− p p 1− p p We have, S(t) ≥ S(0) exp − 0 β R β L I + μ R μ L dp > 0 .     1− p p 1− p p 1− p p 1− p p 1− p p = β R β L S I − μ L μ R + k L k R E ≥ − μ L μ R + k L k R E.     t 1− p p 1− p p Then, E(t) ≥ E(0) exp − ∫ μ L μ R + k L k R dp > 0.     0 1− p p 1− p p 1− p p 1− p p 1− p p Also ddtI = k L k R E − μ L μ R + γ L γ R I ≥ − μ L μ R + γ L γ R I .     t 1− p p 1− p p Now, I (t) ≥ I (0)ex p − ∫ γ R γ L + μ R μ L dp > 0. Now

dE dt

1− p

dR dt

= γL

p

0 1− p

p

1− p

p

γ R I − μ R μ L R ≥ −μ R μ L R.     t 1− p p Then, R(t) ≥ R(0) exp − ∫ μ R μ L dp > 0. Now

0 1− p p

+T ) Again d(S+E+I = b L b R − μ R μ L (S + E + I + T ). dt 1− p p 1− p p dN Therefore dt = b L b R − μ R μ L N . 1− p p 1− p p If b L b R − μ R μ L N < 0 then ddtN < 0. Thus all population are positive. 1− p

p

3.2 Equilibrium Points By solving the model (2) i.e., dE dI dR dS = = = = 0. dt dt dt dt Then we get, infection-free equilibrium (E 0 ) = epidemic equilibrium point (E 1 ) = (S ∗ , E ∗ , I ∗ , R ∗ ),

(3) 

1− p p

bL b R 1− p p , 0, 0, 0 μ R μL

 and the

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1− p p 1− p p 1− p p 1− p p 1− p p 1− p p 1− p p 1− p p  μ R μ L μ L μ R +k L k R μ L μ R +γ L b L b R − μ L μ R +k L k R E γR R0 −1 ∗=   E 1− p p 1− p p 1− p p 1− p p μ R μL βk L k R μ L μ R +k L k R 1− p p 1− p p k E k bk L k R E  I ∗ = 1− p L p R1− p p R ∗ = 1− p p  1− p p 1− p p μ L μ R +γ L μ R μ L μ L μ R +γ L γR γR

where S ∗ =

,

,

,

.

3.3 Basic Reproduction Number The reproduction number (RCovid19 ) can be evaluated from the greatest eigenvalue of the matrix F V −1 [18, 19] where,

F=

1− p p 1− p p b R β R βL 1− p p μ R μL

bL

0



0

and V =

0

Therefore, RCovid19 =

 1− p p 1− p p 0 μL μ R + k L k R . 1− p p 1− p p 1− p p μL μ R + γL γ R −k L k R

1− p p 1− p p

1− p p μ R μL

1− p

p

k L k R bL b R β R βL   . 1− p p 1− p p 1− p p 1− p p μ L μ R +k L k R μ L μ R +γ L γ R

4 Stability Analysis Theorem 2 The system is locally stable at E 0 if RCovid19 < 1 and unstable if RCovid19 > 1. Proof We have to consider the Jacobian matrix E 0 of the proposed model as ⎡ ⎢ ⎢ ⎢ J(E 0 ) = ⎢ ⎢ ⎢ ⎣

1− p

p

−μ R μ L 0 0 0

−

0

  1− p p 1− p p − μL μ R + k L k R 1− p p kR

kL



1− p

βR

1− p p μ R μL 1− p p 1− p p β R βL bL b R 1− p p μ R μL

1− p

− μL

0

⎤

p 1− p p bR

βL bL

p

1− p

μ R + γL

1− p p γL γ R

0

p

γR

0



0

⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

1− p p −μ R μ L

  1− p p 1− p p 1− p p 1− p p The characteristic roots are −μ R μ L , −μ R μ L , − μ R μ L + b L b R and 1− p

p

1− p p

(μ L μ R + k L k R ) (RCovid19 − 1). Therefore the point E 0 is asymptotically stable locally if RCovid19 < 1 and unstable if RCovid19 > 1. Theorem 3 If RCovid19 > 1, the point E 1 is asymptotically stable locally. Proof We have considered the Jacobian matrix E 1 as

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1− p

−β R

βL I ∗ − μ R p

1− p

1− p p β R βL I ∗

p

μL

0   1− p p 1− p p − μL μ R + k L k R 1− p p kR

0

kL

0

0

1− p p ∗ βL S 1− p p β R βL S∗   1− p p 1− p p − μL μ R + γL γ R 1− p p γL γ R

−β R

⎤

0

⎥ ⎥ ⎥ ⎥ ⎦

0 0 1− p

−μ R

p

μL

Therefore, its characteristic equation is   1− p p   −β 1− p β p I + − μ1− p μ p − y 0 −β R β L S ∗ 0   R L R L     1− p p 1− p p 1− p p ∗ ∗   β β I − μ μ + k k 0 − y β S   R L L R L R    = 0.  1− p p 1− p p 1− p p   0 k k − μ μ + γ γ − y 0   L R L R L R   1− p p 1− p p  −μ R μ L − y  0 0 γL γ R

1− p

p

Or, (−μ R μ L − y) (y 3 + ay 2 + by + c) = 0, where A = β R β L I ∗ + 3μ R μ L + k L k R + γ R γ L ,    1− p p 1− p p 1− p p 1− p p 1− p p B = β R β L I ∗ + μ R μ L 2μ R μ L + k L k R + γ R γ L    1− p p 1− p p 1− p p 1− p p + μL μ R + k L k R μL μ R + γL γ R ,     1− p p 1− p p 1− p p 1− p p 1− p p 1− p p C = β R βL I ∗ + μ R μL μ R μL + k R k L μ R μL + γ R γL 1− p

p

1− p

p

1− p

1− p

p

p

1− p p

1− p

p

1− p p ∗ kR S .

− μ R μ L β R β L kk L

The point E 1 is asymptotically stable locally if A > 0, B > 0, AB > C (“RouthHurwitz Criterion”).

5 Numerical Discussion In this section, using MATLAB software, we discuss the stability of our proposed model at E0 and E1 . This section discusses the stability of the model. We can see from the following figures that the model is LAS at E0 taking p = 0, 0.5, 1.0 using Table 2 (Fig. 2).

Table 2 Parametric values

Parameters

Value

Source

β

[0.43, 0.45]

[8]

b

[0.17, 0.19]

[8]

μ

[0.0061, 0.0063]

[8]

k

[0.7774, 0.7776]

[8]

γ

[0.070, 0.072]

[8]

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(a)

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(b)

(c) Fig. 2 Time series solution of the system (2) is stable at E0 for t ∈[0, 800]

6 Conclusion The present study’s possible goal is to analyze a model for studying COVID-19 transmission patterns using actual pandemic cases in Brazil, assisted by epidemiological modeling. The SEIR model was constructed and explored in this article in order to better explain the scenario in Brazil. We employed nonlinear analysis to demonstrate the model’s existence and uniqueness. The model’s fundamental reproduction number was also determined by next generation matrix method. In order to stop the virus from spreading throughout the nation, our main aim is to establish the fundamental reproductive number and equilibrium. Furthermore, the global stability at the points E0 and E1 has been demonstrated. The results reveal that if RCovid 19 < 1, E0 is globally asymptotically stable. Also if RCovid 19 > 1, the point E1 is global asymptotic stable. Ministries and public health professionals may be able to develop strategic strategies to close vaccination gaps and stop outbreaks in the future with the use of the research findings from the current study.

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References 1. Rothan, H. A., & Byrareddy, S. N. (2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of Autoimmunity., 109, 102433. 2. Bai, Y., Yao, L., Wei, T., et al. (2020). Presumed asymptomatic carrier transmission of COVID19. JAMA, 323(14), 1406–1407. 3. Kermack, N. O., & Mackendrick, A. G. (1927). Contribution to mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 700–721. 4. Ji, C., Jiang, D., & Shi, N. (2011). Multigroup SIR epidemic model with stochastic perturbation. Physica A: Statistical Mechanics and Its Applications., 390(10), 1747–1762. 5. Bjornstad, O. N., Finkenstadt, B. F., & Grenfell, B. T. (2002). Dynamics of measles epidemics: Estimating scaling of transmission rates using a time series SIR model. Ecological Monographs, 72(2), 169–184. 6. Hu, Z., Ma, W., & Ruan, S. (2012). Analysis of SIR epidemic models with nonlinear incidence rate and treatment. Mathematical Biosciences, 238(1), 12–20. 7. Diekmann, O., Heesterbeek, H., & Britton, T. (2013). Mathematical tools for understanding infectious disease dynamics. In: Princeton series in theoretical and computational biology. Princeton University Press, Princeton 8. Paul, S., Mahata, A., Ghosh, U., & Roy, B. (2021). SEIR epidemic model and scenario analysis of COVID-19 pandemic. Ecological Genetics and Genomics 19, 100087. 9. He, S., Peng, Y., & Sun, K. (2020). SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dynamics, 101, 1667–1680. 10. Overton, C. E. (2020). Using statistics and mathematical modeling to understand infectious disease outbreaks: COVID-19 as an example. Infectious Disease Modelling 5, 409–441. 11. Barros, L. C., Bassanezi, R. C., & Leite, M. B. F. The SI epidemiological models with a fuzzy transmission parameter. Computers & Mathematics with Applications, 45, 1619–26. 12. Zhou, L., & Fan, M. (2012). Dynamics of an SIR epidemic model with limited resources visited. Nonlinear Analysis: Real World Applications, 13, 312–324. 13. Mccluskey, C. C. (2010). Complete global stability for an SIR epidemic model with delaydistributed or discrete. Nonlinear Analysis, 11(1), 55–59. 14. Paul, S., Mahata, A., Mukherjee, S., & Roy, B. (2022). Dynamics of SIQR epidemic model with fractional order derivative. Partial Differential Equations in Applied Mathematics, 5, 100216. 15. Mahata, A., Paul, S., Mukherjee, S., Das, M., & Roy, B. (2022). Dynamics of Caputo Fractional Order SEIRV Epidemic Model with Optimal Control and Stability Analysis. International Journal of Applied and Computational Mathematics, 8(28). 16. Mahata, A., Paul, S., Mukherjee, S., & Roy, B. (2022). Stability analysis and Hopf bifurcationin fractional order SEIRV epidemic model with a time delay in infected individuals. Partial Differential Equations in Applied Mathematics, 5, 100282. 17. Paul, S., Mahata, A., Mukherjee, S., Roy, B., Salimi, M., & Ahmadian, A. (2022). Study of Fractional Order SEIR Epidemic Model and Effect of Vaccination on the Spread of COVID-19. International Journal of Applied and Computational Mathematics, 8(5), 1–16. 18. Diekmann, O., Heesterbeek, J. A. P., & Roberts, M. G. (2009). The Construction of NextGeneration Matrices for Compartmental Epidemic Models. Journal of The Royal Society Interface., 7(47), 873–885. 19. Diethelm, K., & Ford, N. J. (2004). Multi-order fractional differential equations and their numerical solution. Applied Mathematics and Computation, 154(3), 621–640.

Ldetect, IOT Based Pothole Detector Sumathi Balakrishnan, Low Jun Guan, Lee Yun Peng, Tan Vern Juin, Manzoor Hussain, and Sultan Sagaladinov

Abstract Potholes are a persistent issue in Malaysia that poses a threat to the safety and economic well-being of the country. Poor road construction, heavy traffic, and extreme weather conditions are some of the contributing factors to the development of these road defects. Despite the efforts by the government and local authorities to repair and maintain the roads, potholes remain a significant problem, especially in rural areas. The high number of road traffic deaths in rural areas compared to urban areas highlights the urgency of addressing the pothole problem in Malaysia. In this paper, a pothole detection system called Ldetect using LiDAR sensor is proposed. This system idea provides a better solution to addressing the persistent pothole problem in Malaysia. Keywords Internet of things (IOT) · Pothole detector · LiDAR sensor · AWS · Cloud · GPS

1 Introduction Potholes are a significant problem in Malaysia that affects both the safety and economic well-being of the country. These road defects are caused by a combination of factors, including poor road construction, heavy traffic, and extreme weather conditions [1]. Despite efforts by the government and local authorities to repair and maintain roads, potholes continue to be a persistent issue in many areas of the country. S. Balakrishnan (B) Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] S. Balakrishnan · L. J. Guan · L. Y. Peng · T. V. Juin · S. Sagaladinov School of Computer Science, Taylor’s University, Subang Jaya, Malaysia M. Hussain Computing Department, Faculty of Computing & Information Technology, Indus University, Karachi, Pakistan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_35

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One of the main causes of potholes in Malaysia is the lack of proper maintenance and repair. It is impossible for contractors or road maintenance teams to keep track of the condition of every road in the country, especially in rural areas. If these roads are left neglected, it will develop into a more serious problem road surface cracking, potholes and so much more. According to [2], in Malaysia, the number of road traffic deaths in rural areas (66%) is significantly higher compared with that in urban areas (34%). This data shows that it is necessary to look into solving pothole issues, especially in rural areas, to reduce the amount of road traffic deaths that happen in rural areas due to potholes.

2 Existing Literature The below alternative ideas for pothole detection generally have lower accuracy and more limitations than the idea of using LiDAR. Additionally, many of these alternatives are more expensive, more complex, or less flexible than LiDAR, which makes LiDAR the preferred technology for pothole detection in many cases. Acoustic Sensors A different strategy is to employ acoustic sensors, which track changes in sound waves as a car passes over a road surface. In this method, sensors are mounted to the car, and the sound made by the wheels as they move over the pavement is examined to determine which parts of the road are most likely to have potholes. This method is simple to use and reasonably inexpensive, although it may be impacted by tire noise, road noise, and other noise-interfering elements. It can be challenging to discern between potholes and other kinds of road irregularities, and this method is less accurate than LiDAR [9]. Vision-Based Systems Utilizing vision-based systems, which employ cameras to find potholes, is another possible strategy. In this method, cameras are mounted on the car, and photos of the surface of the road are examined to spot locations that are likely to have potholes. This method may be helpful for finding larger potholes that are clearly apparent in the photos, but it may be impacted by the lighting and other image-interfering elements. This method also calls for a more complex computer vision system and is typically less accurate than LiDAR [10]. Inertial Measurement Units (IMUs) Utilizing inertial measurement units (IMUs), which monitor the acceleration and orientation of the vehicle as it moves over the road surface, is another possible strategy. This method involves mounting IMUs on the car and analyzing the data to locate regions of the road that are likely to have potholes. This method is simple to use and reasonably inexpensive, but it can be impacted by things like vehicle vibrations and other things that can skew the IMU results. Furthermore, this method’s accuracy

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is lower than LiDAR’s, and it might be challenging to tell potholes apart from other kinds of road irregularities [11]. Ground-penetrating radar (GPR) Using ground-penetrating radar (GPR), which creates a map of the subsurface by using radio waves to enter the ground, is another possible strategy. This method uses GPR to find subsurface characteristics, including voids or variations in subsurface density, that are suggestive of potholes. Although this method can be helpful for finding potholes that are not readily apparent on the road surface, it is typically more expensive and difficult than LiDAR. This method is also less precise than LiDAR and susceptible to interference from other subsurface features, such as subterranean utilities [12].

3 Proposed Solution According to [13], poor-condition roads are the main cause of 94% of accidents on the road. Therefore, the motivation behind this study is to propose a pothole detector that is able to identify potholes on roads using IoT sensors to reduce the number of potholes on the road. In addition to that, it can also be used to detect bad-condition roads, which would eventually develop into road cracks and potholes if left neglected. This device will only be attached to government vehicles like public buses, garbage trucks, and taxis instead of private vehicles to avoid invasion of someone’s privacy. If there is any pothole or bad-condition road detected while the device is running, it will then send the information of the pothole or bad-condition road, e.g., details (size, diameter, and depth) and geolocation, to the cloud to store it in the database. The database will be shared with Jabatan Kerja Raya (JKR), the Malaysian Public Works Department, to enhance the speed and efficiency of road repairs and maintenance. With the help of road data gained from hundreds or thousands of vehicles attached with this device, the road maintenance teams of JKR will be able to identify and prioritize the roads that are due for maintenance work. With regular road condition inspections and proper preventative repairs, it is possible to prevent the roads from developing cracks, potholes, or other defects, making sure that the roads are always safe to be used by road users [14]. Figure 1 depicts the proposed hardware for the pothole detector. The proposed hardware for the pothole detector consists of a LiDAR module, a GPS module, a camera, and a buzzer. All of the hardware mentioned above is connected to and controlled by a Raspberry Pi microprocessor. The cloud and the web application are both connected to the Raspberry PI microprocessor. The cloud is informed about the details and geolocations of potholes and bad-quality roads using the information gathered by the microprocessor. Information, which it then processes and stores in the database. Additionally, an AVR-IoT Microchip is utilized for the system’s cloud back-end because of its WiFi capabilities, which enable direct connectivity to Amazon Web Services (AWS), a cloud service.

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Fig. 1 Hardware of pothole detector device

The principle behind the workings of the proposed pothole detector device is quite simple. First, a LiDAR module is used to scan the road in front of the vehicles in real-time. LiDAR has a similar working mechanism to radar, but it emits infrared light pulses instead of radio waves to form a laser and measures the time it takes for the infrared light pulses to come back after hitting nearby objects. The LiDAR module then calculates the distance to each surface of the road using the measured time for the emitted laser pulse to bounce off the road surface and return to the LiDAR sensor. This LiDAR module is capable of producing 3D models and maps of the road environment in real-time with the millions of precise distance measurement points it captures each second [15]. After having the 3D models and maps produced by the LiDAR module, the Raspberry Pi microprocessor uses the algorithm for pothole detection to determine where the pothole or bad-condition road is in relation to the car. If a pothole or bad-condition road is detected by the algorithm, the buzzer that is built into the pothole detector device will buzz to inform the driver. Following that, the microprocessor will record the details (size, diameter and depth) and use a camera module to take pictures of that pothole or bad-condition road. A GPS module will also be used to retrieve the geolocation of that pothole or bad-condition road. All of these data will then be sent by the microprocessor to the cloud database to be stored. The system does have a web application that can be used by local authorities. This is done so that the government has the necessary information, such as where the potholes and bad-condition roads are, by viewing the report generated by the web application using the data stored in the cloud database and can repair them in a timely manner. Once the pothole or bad-condition road is repaired, the data can be updated on the web application and removed from the cloud database.

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Table 1 Technologies used in Pothole Detector Device Technologies and resources

Description

LiDAR sensor

Potholes are typically characterized by a depression or a sunken area in the road surface, and Lidar can detect these changes in surface elevation by measuring the time it takes for the laser light to bounce back from the surface. This information can be used to construct a detailed and accurate representation of the road surface, which can be used to identify potholes and other anomalies LiDAR is also highly accurate, which is important for detecting small potholes that may be difficult to see with the naked eye. Additionally, LiDAR systems are able to continuously scan the road surface at high speeds, which allows for large amounts of data to be collected quickly and efficiently

GY-NEO6MV2 flight control GPS module

This is a very low cost GPS module, but it is very powerful. It comes with an antenna for better signal reception. We will use it to detect the pothole location with longitude and latitude

Raspberry pi microprocessor

Raspberry Pi is a tiny simple cheap computer. The largest size of raspberry pi is about the size of a deck of cards and the smallest size of raspberry pi is about the size of gum. Raspberry Pi comes with 3 types of versions, Raspberry Pi Zero, Model A series and Model B series

5MP night vision camera This camera comes with 5 million pixels a focus ring that can be adjusted according to the position of objects and two infrared LED modules that allow us to take a clear picture in a dark environment. In this project, we will use it to take the picture of the pothole Buzzer (passive) with jumper housing

It will make a beep sound when we boot the pc. We will use it to make a beep sound when the pothole has been detected

AVR-IoT microchip

AVR-IoT microchip is utilized for the system’s cloud back-end because of its WiFi capabilities, which enable direct connectivity to Amazon Web Services (AWS), a cloud service

4 Technologies See Table 1.

5 System Architecture A pothole detection system is an Internet of Things (IoT) application that requires a well-structured system architecture to ensure efficient operation and accurate detection of potholes. Here’s an overview of the system architecture from the perspective of the different layers (Fig. 2).

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Fig. 2 Layer of system architecture

Application Layer Pothole detectors will use web applications in order to place information about potholes. This means that web applications can be accessed from any device. The application layer for a pothole detector device would typically involve the software or user interface that enables users to interact with the device and access its features and functionalities [16]. This layer contains the user interface, Pothole Detection Algorithm, Data Storage and Management, GPS Integration, Alerting and Reporting. Overall, the application layer for a pothole detector device plays a critical role in enabling users to effectively utilize the device and maximize its potential for improving road safety and maintenance. Network Layer The Arduino and Raspberry Pi are two IoT gateway possibilities. As can be observed, there are a number of distinctions between the two, and each has advantages and disadvantages. After some consideration, it was decided that Raspberry Pi was the better choice because Pothole IoT sensors and actuators are not relatively light and do require a lot of computational power. A WiFi-enabled Microchip AVR-IoT board is used to connect the pothole detector to AWS IOT. Transport Layer The transport layer is responsible for data transmission and packet delivery between devices and servers. The transport layer should use a reliable and efficient protocol to ensure packet delivery with minimal latency [17]. The Table 2 lists some of the alternatives for the transport layer protocol, including Zigbee, Message Queue Telemetry Transport (MQTT), Hypertext Transfer Protocol (HTTP or HTTPS), Long Range Wide Area Network (LoRaWAN), and Constrained Application Protocol (CoAP).

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Table 2 Transport layer protocol Zigbee

MQTT

HTTP

LoRaWAN

CoAP

Architecture

Request/ response

Publish/ subscribe

Request/ response

Request/ response

Request/ response

Data security

AES Encryption

SSL/TLS or payload encryption

SSL/TLS (HTTPS)

AES Secured Payload

Datagram TLS

Upper layer protocol

UDP

TCP/IP

UDP

TCP/IP

UDP

Message size

128B

256 MB

2–100 MB

10–1000 KB

4–1024B

Data distribution

Many-to-one

Many-to-many

One-to-one

One-to-many

One-to-one

A WiFi-enabled Microchip AVR-IoT board is used to connect the pothole detector to AWS IOT. To accomplish this, the AVR-IoT board subscribes to a MQTT broker within AWS IOT using the IOT protocol MQTT. Data can be swiftly and efficiently transferred from distant sites utilizing the MQTT protocol [17]. This makes it possible for hardware and the cloud environment to communicate in both directions. Through this communication, the detector sends the dispensing message and the confirmation events to the cloud. Perceptive Layer The pothole detection system uses a few sensors to satisfy the various functional needs, which are given below. In addition to functional components, sensors, actuators, and IoT devices are categorized. The functional needs section from the preceding section describes the functions of various IoT devices. Sensors: I. LiDAR Module (LiDAR Sensor) II. Camera III. GPS Module Functional Components: I. Buzzer Security Layer The security layer is in charge of making sure that the data transferred by the system is confidential, intact, and available. A strong security layer is required for the pothole detection system in order to guard against unwanted access, data manipulation, and denial-of-service assaults. To ensure the security of the system, the security layer should comprise secure communication protocols, encryption, authentication, and access control techniques [18–21].

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Fig. 3 Concept of Pothole Detector using Tinkercad

Data tampering and distributed denial-of-service (DDoS) assaults are a few potential hazards to be aware of. Concerns about physical security, such as gadget theft, should not be disregarded. Security features, harms, and solutions are not discussed in this proposal [22–25].

6 Experimental Result Figure 3 shows the circuit set up on the pothole detector device using Tinkercad. As Raspberry Pi and LiDAR sensors are not available in Tinkercad, Arduino Uno and an ultrasonic distance sensor will be used to replace them for a concept demonstration of how the device works. Once the device has been turned on, the LCD panel will light up to show if any road issues are detected. The distance between the road issue and the ultrasonic distance sensor will also be displayed on the LCD panel. When there is a road issue in the detection range of an ultrasonic distance sensor, the piezo buzzer will start buzzing, and the LCD panel will also display “Detected” and the distance between the road issue and the ultrasonic distance sensor. At the same time, the LED which represents a GPS module will capture the location and send it to the cloud server.

7 Web Application Prototype Users will be able to access web applications via the web page. The feature on the website allowed users to be informed of the recent road issues detected by the pothole detector device. The time and date for each of the road issues detected will also be

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Fig. 4 Website prototype

shown on this web page. Another feature allowed users to be informed of the recent road issues detected by the pothole detector device, with each of the road issues being marked on a map. The details for each of the road issues detected, such as the size and depth of the pothole, the severity, and the road issues detected, whether it is a pothole or poor road condition, will also be shown. Both of the features are shown in Fig. 4.

8 Conclusion The proposed system of pothole detection uses a lidar sensor fixed onto a vehicle. The input is processed to alert the driver and the coordinates will be sent to the government to take necessary action. The system is made to increase the efficiency of repairing potholes and decrease the frequency of accidents that they cause. Governments can reduce labor costs and time spent on manual road inspections by using the data to optimize maintenance. The data in the server can be analyzed for predictive maintenance of roads to prevent new potholes using machine learning models such as random forest, support vector machine, or ensemble voting, and by considering various factors like the most common types of vehicles on that road and weather conditions. Pothole severity can also be categorized which will be useful for the government’s prioritization. Real-time detection also means that drivers can drive without having to look out for potholes in poor visibility conditions. However, it is rather wasteful to install lidar sensors only to detect road damage, so other types of hazards like people, animals, litter, branches, and debris may be included later. Lidar sensor together with a detection algorithm must be developed. Firstly, pothole detection will be the focus. It must be tested multiple times under different environmental conditions and vehicle types. Once it has been verified that all components work as intended, it should be installed on government vehicles according to frequency. In the second year, the algorithm can be improved for detection of water filled potholes and other road damage such as upheavals and ruts. Alternative alerts such as voice including distance and severity could be installed. There could also be an option in the vehicle to automatically limit its speed when approaching a pothole.

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In the third year, the public may sign up to test the system. Arrangements with manufacturers and technicians would have to be made to ensure compatibility. If the feedback is positive, general availability may be considered. Furthermore, there is a possibility to collaborate with navigation software companies to integrate pothole data for additional alerts and route calculation.

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Speech Synthesis with Image Recognition Using Application of CNN and RNN Bijoy Mandal, Dibyendu Mukherjee, Anup Kumar Ghosh, and Rahul Shyam

Abstract In this paper, we are trying to make a portable image recognition machine that will read out the objects in the image in the form of speech with the help of CNN and RNN. This can be very helpful for recognizing different objects in real-time. We will be using Raspberry Pi, which itself is a portable computer to do all the work at hand. With the help of the Raspberry Pi camera, a real-time image will be captured just by showing the object to the camera, and then with the help of a Convolutional Neural Network (CNN), the image will be recognized. The CNN architecture has ResNet which has the capability to handle sophisticated deep learning tasks and models. The speech part will be constructed with the help of a Recurrent Neural Network (RNN). Recurrent Neural Networks architecture has internal memory that stores the state it has gone through i. e. the inputs it has taken this help and makes it a choice for a machine learning problem. Example of RNN includes Apple Siri and Google Voice Search. The speech will be the output in the form of a voice. Keywords RNN · CNN · DIP · Speech Synthesis · LSTM

1 Introduction Image Processing is the use of computer algorithms to process images and videos and extract useful information [1]. Processing of images facilitates us to recognize images and objects in the image. We are combining image recognition and speech synthesis in real-time with a portable device which can be helpful for a blind person to know its surrounding [2, 3]. In this study, we have taken the help of Digital Image Processing (DIP) and Speech synthesis technology [4, 5].

B. Mandal · D. Mukherjee (B) · A. K. Ghosh · R. Shyam Department of Computer Science and Engineering, NSHM Knowledge Campus Durgapur, West Bengal, Durgapur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_36

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Convolution neural networks (CNNs) stand for convolutional neural network, CNN takes into account multilayer perception i.e. algorithm used for supervised learning. These are used to make binary classifiers which can classify as input ‘in vector form’. They specify the class to which the given input belongs [6]. CNNs are basically useful in the field of imagery. The multilayer perceptron denotes the connected neuron in which every single neuron is connected to all the other neurons in the network. CNN uses the hierarchical pattern of data and for more complex patterns they use simple and smaller light weight patterns as a reference that’s why according to the scale of connectedness and complexity they are in the lower extreme [7, 8]. Convolutional network mimics the biological neuron connectivity structure found in the visual cortex of the animal kingdom. Convolutional Neural network has different layers which include the input layer, the output layer which are the extreme layers, and the multilayer hidden layer in between the input and the output layer inside the convolution neural network. Recurrent neural network (RNN), David Rumelhart’s work, neural connection which is a directed graph along a temporal sequence. RNN encloses two broad classes of network one being the finite impulse and the second being the infinite impulse. Long short-term memory (LSTM) is used for the fundamental working unit of the recurrent neural network. LSTM revolutionize speech recognition. The basic unit of LSTM is a cell having an input gate, output gate and forget gate. The cells have the capacity to memorize values of discrete time intervals, the mentioned gates control the flow of information into and out of the cell [9]. RNN, David Rumelhart’s work, a neural connection which is a directed graph along a temporal sequence. RNN encloses two broad classes of network one being the finite impulse and the second being the infinite impulse. RNNs are the only neural network with the capability to retain their internal state which can be inferred as internal memory. RNN was based on the work of David RumelHart’s work in the year 1986. In 1982, HopField network was discovered. RNN comes in a number of variants. RNN network is composed of neuron-like nodes. These are organized into successive layers in which every layer is connected [10] as RNN’s internal memory enables them to remember things and make the prediction very precise. Recurrent Neural Network output is based on predictive results on sequential data which other algorithms cannot perform. In RNN there is an information cycle. When the RNN makes a decision the input as well as the previous knowledge is taken into consideration. RNN has short-term memory usually. LSTM combined with RNN forms Long term memory. LSTM can be considered as artificial RNN architecture. LSTM has a feedback connection [11]. In its memory, the LSTM has the ability to read, write, and erase data. A gated cell that is pre-accepted into the memory can decide whether to store or delete information based on the priority assigned to the information [12]. Priority is decided on the basis of certain weights which has been assigned to the information over the learning period.

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2 Objective of the Research • In this paper, we are trying to make a portable image recognition machine that will read out the objects in the image in the form of speech. • Our objective is to recognize different objects in real-time.

3 Proposed Methodology The proposed methodology is represented by the following steps. 1. Image will be taken in real-time streaming from the camera in the Raspberry Pi 3 Model B. 2. From the video stream, a frame will be captured for recognition. 3. Then, using CNN ResNet, the image’s features will be retrieved. A deep residual network (Deep ResNet), a type of ResNet, is able to manage complex deep learning tasks and models and is shown in Fig. 1. 4. From a pre-trained MSCOCO dataset, the features of the image will be matched. • COCO is a very large data set for object detection, image segmentation, and captioning. • COCO features (a) Image Segmentation (b) Recognition in context (c) Super pixel stuff segmentation (d) 5 captions per image

Fig. 1 Basic visualization of the working model

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5. Using the same pre-trained MSCOCO dataset caption will be generated for the image. 6. Object recognition. 7. With the help of RNN’s Long-Shot Term Memory. a. The nodes of a recurrent neural network (RNN), a type of artificial neural network, are connected in a certain order to represent a directed graph. b. Networks with long short-term memory can pick up dependencies. c. By using LSTM directly, long-term dependency issues can be prevented. 8. The caption will be read out as the output from the speakers connected to the Raspberry Pi. 9. We can repeat the process to recognize other objects.

4 Technology Used Raspberry Pi A single computer board called a Raspberry Pi was created in the UK by the Raspberry Pi Foundation. This single board computer was built to enhance the competency level of the fundamental knowledge of computer science in schools and developing countries. Raspberry Pi is minicomputer (cf. Fig. 2) capable of performing all the stuff which a regular computer is capable of. Raspberry Pi can handle any peripherals which a personal computer supports. The release of 3 Model B was done in February 2016 which has the following specification 1. 64-bit processor with four cores 2. Inbuilt Wi-Fi and Bluetooth 3. USB boot capabilities

Fig. 2 Raspberry Pi

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Model 3 B+ specification a. b. c. d. e. f. g.

1.4 Ghz processor Gigabit Ethernet (300 Mbits/s throughput limit) Connection of internal USB 2. 0 Wi-Fi with 2. 4/5 GHz band capable of transferring data at 100 Mbit Power over Ethernet USB boot Network boot.

Convolutional Neural Network (CNN) A component of a deep neural network is CNN. It is mostly employed for the study of visual imagery. A multilayer perceptron makes up CNN. The functionality of a multilayer network structure is that every node in one layer is connected to every other node and to the one below it. Shift invariant or space invariant artificial neural network, or CNN is another name for this type of neural network that employs shared weights. Furthermore, CNN has translation invariance characteristics. CNN draws its analogy to biological processes. The connection structure in CNN has similarities to the animal visual cortex. Image feed to the network is viewed as a matrix having height, width, and dimension. Example of the image shown in Fig. 3 having three channels is 6 × 6 × 3 where height = 6 width = 6 and dimension = 3. If the image is in grayscale, then the dimension will be 1 and the height and width will remain the same. To train and test CNN models an image is fed as input to the input layer of the model of the neural network. Then the image is converted to the matrix form which is eventually passed within the convolutional layer of the neural net having filters called kernels. Pooling and softmax functions classify an object on the basis of probabilistic values 0 and 1. The image below explains the process undertaken inside a CNN. Fig. 3 Three channels image Raspberry Pi

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Convolutional Layer Feature extraction is performed by the first layer called the convolution layer. The relationship among the pixels is preserved in this layer. Further, this layer learns the image features by utilizing Small Square of input data. It takes the image matrix and kernel of a certain size as input in the first place. An image matrix of dimension d has height h and width ‘w’. • A Kernel performs certain image operations on either a single pixel or a group of pixels. • The output is in the form of (h − f h + 1)x(w − f w + 1)x1. An image matrix of dimension 5 × 5 gets multiplied by a matrix of size 3 × 3 called the kernel and its convolution is called the Feature Map shown in Fig. 4. A kernel which are small matrix is used for edge detection, noise removal, blurring images or sharpening of an image. Below, in Fig. 5, we depict the kernels used for different purposes. Strides Stride is the measure of how many pixels move in excess of an input matrix. One pixel is moved at a time for stride value 1, two pixels are moved for stride value 2, and so forth. The matrix shown here displays the movement when stride is 2. This can be observed in Fig. 6.

Fig. 4 Matrix convolution mapping

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Fig. 5 Matrix for edge detection, noise removal, Blurring an image or sharpening of an image

Fig. 6 Movement when strides is 2

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Padding Misfit of filter in the image matrix can be solved as follows. • Zero padding of the image • Valid padding means dropping the section of the image, where the filter does not fits well. Lack of Linearity (Rectified Linear Unit) The rectified linear unit (ReLU), which takes into account a non-linear operation, is displayed in Fig. 7. Here, g(x) = max is used to obtain the output (g(x)) (0, x). ConvNet’s ReLU provided the first nonlinearity. Real-world data may frequently have non-negative linear values. Pooling Layer • Given that the picture matrix is relatively vast, pooling layers (Fig. 8) are utilized to reduce the number of parameters. Each map’s dimensionality is reduced via spatial pooling, which also protects the sensitive data. Several forms of spatial pooling are • Max pooling • Average pooling • Sum pooling In rectified feature map, the largest element is found from the max pooling. The largest element can also be obtained from average pooling. Sum pooling is the sum of all the feature maps.

Fig. 7 Tanh or sigmoid are alternatives to ReLU, but ReLUhas a better performance than the other two

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Fig. 8 Pooling layer representation

5 Result Analysis The vector transformation of feature map matrix is done which is of the form X 1, X 2, X 3 . . . . These features combined with a fully connected layer create a model. We have considered the softmax or sigmoid function to do the classification. • Image as input to the convolutional layer. • Provide the parameters and if needed apply the filter with stride and padding. Convolution is done on the image. • Apply ReLU on the obtained matrix. • Dimensionality reduction can be obtained by pooling. • Include convolution layers until satisfaction is achieved. • Output obtained is flattened which is essentially provided as input into the fully connected layer The three different images captured by the pi camera are shown in Figs. 9, 11, and 13, while the equivalent output on the pi terminals is shown in Figs. 10, 12, and 14. Fig. 9 Image on pi camera

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Fig. 13 Image on pi camera

Fig. 14 Output on pi terminal

6 Conclusion and Future Goals Our research work has combined CNN & RNN and made it much more portable with the help of Raspberry Pi. Now it is easier to detect scenes, and objects in an image and to make it simpler the speech synthesis will help users to understand what objects are present in the image. CNN needs to be fine-tuned which is conceptually heavier. But overall it is working fine. The foundations of voice recognition are covered in this essay, and the field’s most recent advancements are looked into. The

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study discusses a number of neural network models, including deep neural networks, RNN, and LSTM. Neural network-based automatic speech recognition is a new field that is currently developing. For those with disabilities, two important applications are text to speech and speech to text.

References 1. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). Deeplab : Semantic image segmentation with deep convolution nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. 2. Kaushal, M., Khehra, B., & Sharma, A. (2018). Soft computing based object detection and tracking approaches : State-of-the-art surve. Applied Soft Computing, 70, 423–464. 3. Peri´c, Z., & Nikoli´c, J. (2012). An adaptive waveform coding algorithm and its application in speech coding. Digital Signal Processing, 22(1), 199–209. 4. R. K. Moore, “Cognitive informatics : the future of spoken language processing ?”, in Proceedings of the 10th International Conference on Speech and Computer (SPECOM), Patras, Greece, October 2005. 5. Nikolic, J., & Peric, Z. H. (2008). Lloyd-Max’s algorithm implementation in speech coding algorithm based on forward adaptive technique. Informatica (Lithuanian Academy of Sciences), 19(2), 255–270. 6. Alwzwazy, H. A., Albehadili, H. A., Alwan, Y. S., & Islam, N. E. (2016). Handwritten digit recognition using convolutional neural networks. Proceedings of International Journal of Innovative Research in Computer and Communication Engineering, 4(2), 1101–1106. 7. Ling, Z. H., Kang, S. Y., Zen, H., Senior, A., Schuster, M., Qian, X. J., et al. (2015). Deep learning for acoustic modeling in parametric speech generation: A systematic review of existing techniques and future trends. IEEE Signal Processing Magazine, 32(3), 35–52. 8. Toda, T., Black, A., & Tokuda, K. (2007). Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory. IEEE Transactions on Audio, Speech and Language Processing, 15(8), 2222–2235. 9. Zen, H., Gales, M., Nankaku, Y., & Tokuda, K. (2011). Product of experts for statistical parametric speech synthesis. IEEE Transactions Audio, Speech, and Language Processing, 20(3), 794–805. 10. Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech synthesis based on hidden Markov models. Proceedings of the IEEE, 101(5), 1234–1252. 11. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82–97. 12. Ling, Z.-H., Deng, L., & Yu, D. (2013). Modeling spectral envelopes using restricted Boltzmann machines and deep belief networks for statistical parametric speech synthesis. IEEE Transactions Audio, Speech, and Language Processing, 21(10), 2129–2139.

GeoGebra-Assisted Teaching of Rotation in Geometric Problem Solving Hoang Vu Nguyen, Thi Minh Chau Chu, Ton Quang Cuong, Vu Thi Thu Ha, Pham Van Hoang, Ta Duy Phuong, and Tran Le Thuy

Abstract Rotation is an important geometrical transformation that students usually have problems in visualizing, especially in situations requiring connection with other mathematical concepts to construct proofs. In such cases, the use of software in teaching can help students get a better visual view of not only the steps involved but also the fundamental concepts and relationships between geometrical elements, such as preservation of lengths and angles. Among the current software available for this purpose, GeoGebra is a widely used tool that is easy to use for both teachers and students. It’s an open-source software with no cost barrier and a large community providing active contributions on several aspects of mathematical education. Its support for teaching dynamic geometry is a valuable capability that could help students get better geometrical insights than with a traditional teaching approach using only pencil and paper. In this paper, we present cases that show how complex geometry problems involving rotation and its relation to other geometrical tools can be better understood by students due to the utilization of GeoGebra in the classroom.

H. V. Nguyen (B) Pi Journal, Vietnam Mathematical Society, Hanoi, Vietnam e-mail: [email protected] T. M. C. Chu Hanoi National University of Education, Hanoi, Vietnam T. Q. Cuong · P. Van Hoang · T. Le Thuy University of Education, Vietnam National University, Hanoi, Vietnam e-mail: [email protected] T. Le Thuy e-mail: [email protected] V. T. T. Ha Pham Hong Thai High School, Hanoi, Vietnam T. D. Phuong Institute of Mathematics, Hanoi, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_37

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Keywords Computer-assisted teaching · Dynamic geometry software · GeoGebra · Geometry · Mathematics education

1 Introduction GeoGebra is an open-source software that is notable for its dynamic geometry capabilities which can provide students with a more visual presentation of a geometry problem compared to using only pen and paper. As such, it is inherently useful for teaching geometric transformations where objects change their positions (or sizes). Studies on teaching geometric transformation, particularly rotation, using GeoGebra often focus on building mathematical intuition or motivating interest in geometry for students [1–5]. Based on our experience of teaching Vietnamese school students, especially gifted ones, it may be necessary to introduce more advanced geometry problems selected from previous literature, such as [6, 7], in which rotation is more involved in the problem solving process and used together with other mathematical knowledge and techniques. In such situations, the usage of GeoGebra for visualizing the required steps can be beneficial to demonstrate how rotation can interact with other geometrical concepts to create a plausible proof. Remarkably, relationships particular to rotation such as preservation of lengths and angle magnitudes are usually better demonstrated with software. This integrated approach can simulate a deeper understanding among students than just visual demonstration of rotation alone. In this study, we present several problems in which rotation is utilized in combination with different mathematical tools. For all of these, GeoGebra can provide a helpful assistance to students in understanding how to construct the proofs.

2 Rotation in GeoGebra Rotation in GeoGebra can be done in two ways using either the toolbar interface or with typing command: • Choose Rotate around point in the group of the toolbar, select the object, select the center point of rotation, then enter the rotation angle. • Use the Rotate command, providing it with object name, the rotation angle and name of the center of rotation. For example, Rotate(a,d, A) will rotate object a by d degrees around point A. However, to be more demonstrative of the visual process, rotation can be controlled using a parameter t, ranging from 0 to the actual rotation angle of choice, so that students can see the object continuously doing the rotation as this parameter changes. Consequently, in the following problems, each rotation was presented with three

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figures: initial position, mid-rotation and final position. In some complex cases, the arcs of rotation were also drawn to better visualize the trajectory of the objects.

3 Some Geometrical Problems with Rotation Involving GeoGebra Problem 1 ([6]) On sides CD and DA of square ABCD, select points E and F such that DE = AF. Prove that the lines AE and BF are perpendicular (Fig. 1a). Solution Let O be the center of the square. Rotate the triangle ADE 90° clockwise around O (Fig. 1b). Points A and B are images through rotation of D and A, respectively. Since DE = AF, the image E’ of E coincides with F or BF is the image of AE through a 90° rotation (Fig. 1c). Hence, BF is perpendicular to AE. Remark This problem shows how rotation can lead to a proof of congruence for triangles. Problem 2 ([6]) On the sides BC and AC of triangle ABC, construct two squares BCDE and ACGF outside the triangle. Prove that the segments AD and BG are equal and perpendicular to each other (Fig. 2a). Solution Rotate triangle GCB 90° counter-clockwise around C (Fig. 2b and c). The images of the sides CG, CB and GB are CA, CD and AD, respectively. Hence, AD and GB are perpendicular (Fig. 2c). Remark This problem shows how rotation can be used to prove perpendicularity between lines. Problem 3 ([6]) Let O be the center of square ABCD and E be an arbitrary point on segment CD. The points P and Q are perpendicular projections of B and D on AE. Prove that OPQ is an isosceles right-angle triangle (Fig. 3a).

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Fig. 1 Rotation in Problem 1 with GeoGebra

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Fig. 2 Rotation in Problem 2 with GeoGebra

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Fig. 3 Rotation in Problem 3 with GeoGebra

Solution Rotate triangle APB 90° clockwise around O (Fig. 3b and 3c). Due to the preservation of angles PAB and PBA through rotation, it can be easily proved that the image of P through rotation is Q. Hence, OP = OQ. As the rotation is a 90° one, triangle OPQ is also a right-angle triangle (Fig. 3c). Remark This problem shows both the length preservation and perpendicularity of a 90° rotation. Problem 4 ([6]) Point P is on side CD of square ABCD. The angle bisector of angle BAP intersects BC at Q. Prove that BQ + DP = AP (Fig. 4a). Solution Rotate triangle AQB 90° counter-clockwise around A (Fig. 4b, c). Images of B and Q are D and Q’, respectively (Fig. 4c). Due to preservation of angles through rotation, the angles AQ’P and AQB are equal. Using complementary angle relationships, it can be proven that angle PAQ’ is also equal to angle PQ’A or triangle Q’PA is an isosceles one. So BQ + DP = DQ’ + DP = PQ’ = AP. Remark This problem shows how rotation can be used to prove relationships involving sums of angles.

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Fig. 4 Rotation in Problem 4 with GeoGebra

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Fig. 5 Rotation in Problem 5 with GeoGebra

Problem 5 ([6]) Let ABC be an acute triangle with angle ABC being 45°. Altitudes from A and C intersect at H. Prove that BH = AC (Fig. 5a). Solution Rotate triangle CKA 90° counter-clockwise around C (Fig. 5b, c). Its image is triangle CA’K’. The quadrilateral CKBK’ is a square since CK = CK’ and its angles are all 90° (Fig. 5c). Because the angles K’CA’ and HBK are both equal to angle KCA, CA’ is parallel to BH. As CK is already parallel to BK’, CKBA’ is a parallelogram. Consequently, BH = CA’ = CA. Remark This problem shows how rotation can be used to prove both perpendicularity and parallelism. Problem 6 ([6]) On sides AB and AD of square ABCD, select points P and Q such that AP = DQ (Fig. 6a). Prove that ∠P B Q + ∠PC Q + ∠P D Q = 90◦ . Solution Rotate triangles DAP and CBP 90° clockwise around O (Fig. 6b). Images of angles ADP and PCB are angles DCQ and QBA, respectively (Fig. 6c). Hence, ∠P B Q + ∠PC Q + ∠P D Q = ∠AB Q + ∠PC Q + ∠P D A = ∠BC P + ∠PC Q + ∠QC D = ∠BC D = 90◦ .

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Fig. 6 Rotation in Problem 6 with GeoGebra

Remark This problem shows a proof using rotation of two objects at the same time. Problem 7 ([6]) Given a trapezoid ABCD with bases AB and CD satisfying ∠B AD = ∠ABC = 60◦ and CD < BC. On BC select point E such that BE = CD. Prove that AE = BD and the angle between them is 60°. Solution Extend AD and BC, intersecting at S. Since ∠B AD = ∠ABC = 60◦ , SAB is an equilateral triangle. Let O be the centroid of triangle SAB (Fig. 7a), ∠AO B = ∠B O S = ∠S O A = 120◦ . As AB is parallel to CD, SDC is also an equilateral triangle. Hence, S D = C D = B E. Rotate triangle ABE 120° counter-clockwise around O (Fig. 7b and c). Its image is triangle BSD. As such, BD is the image of AB through a 120° rotation (Fig. 7c). Hence, AE = BD and the acute angle between them is 60◦ . Remark This problem shows a case with a rotation angle that is not a right-angle.

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Fig. 8 Rotation in Problem 8 using GeoGebra

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Problem 8 (Rotation in a geometric construction problem) Let a and b be two intersecting straight lines, and C be a point not on these two lines (Fig. 8a). Show how to construct two points A and B on a and b respectively, such that ABC is an equilateral triangle. Solution Let A be an arbitrary point on a. To find point B on b such that ABC is an equilateral triangle, we need to rotate a 60° (either clockwise or counter-clockwise) around C. Let a1 and a2 be the image of a through those two rotations. As A moves along a, the images of A will be on a1 and a2 . As such, point B is the intersection of either a1 or a2 with b. Once B is constructed, A can be found by rotating B 60° either way (Fig. 8b). It is interesting to note that if either a1 or a2 coincides with b, there would be infinitely many solutions. If either a1 or a2 is parallel to b, there is only one solution instead of two. Remark This problem shows how rotation can be used within geometrical constructions. Another construction problem with rotation can be seen in [8]. Problem 9 (Proof of Pythagorean theorem by Thabit Ibn Qurra (836–901)) Two squares of side length a and b are drawn adjacent to each other. The sum of their areas is a 2 + b2 . Draw two right-angle triangles with sides a and b (Fig. 9a). Rotate the two triangles 90° as shown in Fig. 9b around D and F, respectively (Fig. 9b). Their images after rotation make up with the other part (color blue in Fig. 9b, c) a square with side length c. Hence, two squares with sum of areas being a 2 + b2 is now transformed into a square with area c2 , (Fig. 9c) which means c2 = a 2 + b2 . Remark This problem shows how rotation can be used creatively to prove other familiar theorems. Problem 10 (A kinematic problem) Two points A and B rotate clockwise with the same angular velocity around points O1 and O2 , respectively. Prove that vertex C of equilateral triangle ABC also moves on a particular circle.

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Fig. 9 Rotation in Problem 9 using GeoGebra

Solution Let A, O2 and B rotate 60° counter-clockwise around O1 (Fig. 10a). Their images are A’, O3 and B’. Hence, O3 B = O2 B and AA’ = O1 A’ (equilateral triangle). Since B’ and C are the images of B through 60° rotations around O1 and −−→ −−→ A, respectively, then B  C = A A. Points A and B rotate clockwise with the same −−→ −−→ angular velocity around points O1 and O2 , so the angle between O3 B and A A is − − → −−→ −−→ invariant. Hence, the length of O3 C = O3 B+ B  C does not change, i.e., C is on a circle with center O3 (Fig. 10b). This circle can be visualized using the trace tracking feature of GeoGebra (pink circle in Fig. 10a and b). Another circle (green one) is also the solution when O2 is rotated clockwise around O1 . Remark This problem shows how rotation can be used for kinematic problems involving several motions simultaneously.

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Fig. 10 Problem 10 and its solution in GeoGebra. Different positions of A are shown in the left and right parts of the figure

4 Conclusion In this study, we have demonstrated how GeoGebra can be used in enabling students to understand complex problem solving with rotation through the use of dynamic geometry. The samples provided showed that rotation can help students better understand rotation not only as a standalone geometric transformation but also as a mathematical tool that can be used together with other concepts to produce a solution. This may also be applicable to other mathematical concepts that require intuitive visualization. GeoGebra may at first seem to be difficult to manage but once the connection between the mathematical concepts and their representations in software is explained, students could manage to get insights into the dynamics of geometry that may be not available when working with a pencil and paper approach. Future research in this direction may involve rotation and other geometrical transformations in 3D geometrical problems as well as STEM-related teaching of how such problems appear in real-life circumstances. Incorporation of our findings in manuals and books for school math teachers in Vietnam and elsewhere is also a topic worth considering.

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References 1. Chua, G. L. L., Tengah, K. A., Shahrill, M., Tan, A., & Leong, E. (2017). Analysing students’ perspectives on geometry learning from the combination of Van Hiele phase-based instructions and GeoGebra. In Proceeding of the3rd International Conference on Education (Vol. 3, pp. 205– 213). 2. Coelho, A., & Cabrita, I. (2015). A creative approach to isometries integrating geogebra and italc with ‘paper and pencil’ environments. Journal of the European Teacher Education Network, 10, 71–85. 3. Hall, J., & Chamblee, G. (2013). Teaching algebra and geometry with GeoGebra: Preparing pre-service teachers for middle grades/secondary mathematics classrooms. Computers in the Schools: Interdisciplinary Journal of Practice, Theory, and Applied Research, 30(1–2), 12–29. 4. Mukamba, E., & Makamure, C. (2020). Integration of GeoGebra in teaching and learning geometric transformations at ordinary level in Zimbabwe. Contemporary Mathematics and Science Education, 1(1), ep20001. 5. Selvy, Y. et al. (2020). Improving students’ mathematical creative thinking and motivation through GeoGebra assisted problem based learning. Journal of Physics: Conference Series, 1460, 012004. 6. Pompe, W. (2016). Wokól obrotów Przewodnik po geometrii elementarnej. Wydawnictwo Szkolne OMEGA. 7. Yaglom, I. M. (1975). Geometric Transformations. (A. Shield, Translated from Russian). The Mathematical Association of America (MAA). 8. Maksimovi´c, M., Kontrec, N., & Pani´c, S. (2021). Use of GeoGebra in the study of rotations. In Proceedings of the 12th International Conference on Science and Higher Education in Function of Sustainable Development, Uzice, Serbia.

Pandai Smart Highway Sumathi Balakrishnan, Jing Kai Ooi, Shin Kir Ti, Jer Lyn Choo, Ngui Adrian, Qiao Hui Tai, Pu Kai Jin, and Manzoor Hussain

Abstract It is crucial to identify the flow of traffic based on unexpected factors such as obstacles on the road and accidents. The proposed road traffic monitoring “Pandai Smart Highway” is based on the traffic camera placed above the digital signboard, it allows for capturing the obstruction on the road, density, and speed of the vehicles. Emergency services on highways are not well developed by solely relying on the siren only works around a limited radius. Smart highway systems play a crucial role in road traffic management, and planning, and reduce frequent traffic jams, speed violations, and road accidents. Our solution is based on sensors to detect traffic patterns, emergency vehicles, and obstructions. At the same time, digital signage and LED bars are being implemented to notify drivers regarding the

S. Balakrishnan (B) · J. K. Ooi · S. K. Ti · J. L. Choo · N. Adrian · Q. H. Tai · P. K. Jin Digital Innovation and Smart Society Impact Lab, Taylor’s University, 47500 Subang Jaya, Selangor, Malaysia e-mail: [email protected] J. K. Ooi e-mail: [email protected] S. K. Ti e-mail: [email protected] J. L. Choo e-mail: [email protected] N. Adrian e-mail: [email protected] Q. H. Tai e-mail: [email protected] P. K. Jin e-mail: [email protected] M. Hussain Computing Department, Faculty of Computing & Information Technology, Indus University, Karachi, Pakistan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_38

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road situation. A prototype is used to assess the viability of the model. The results of the investigations demonstrate good efficiency in vehicle detection and accurate messages provided to the drivers. Keywords IOT · Smart highway · IR sensor · Camera sensor · LED traffic lane markers

1 Introduction According to the statistics published on the official portal of the Ministry of Transport Malaysia [1], there were a total of 567,516 cases of road accidents resulting in 6,167 road fatalities in 2019. For recent statistics, the cases of road accidents from January 2022 to September 2022 were 402,626 cases and 4,379 fatalities were caused [2]. Most accidents happened on the highway. One of the main reasons for highway accidents is unclear visibility, especially at night and because the drivers could not observe the road situation in front which may reduce the efficiency of emergency decision-making. This is due to the reason that there needs to be more street lights installed on the highway. This may cause the drivers to be unable to clearly observe the road situation in the front. If there is an accident happening ahead and a vehicle stops, the drivers do not have enough time to brake their vehicle and crash. Furthermore, the third problem that highway users may face usually is the obstacle on the highway lane. The obstacles can be the dead bodies of animals, fallen trees and branches, fallen items from delivering vehicles and so on. The drivers may not have enough time to avoid the obstacles as they are driving fast. Hence, we will design an intelligent highway system that can alert drivers about the road situation ahead to provide the drivers enough time to decelerate their vehicles. Apart from that, this system can also allow the drivers to have more time to think about the decision-making before entering the accident or obstacle area (Fig. 1).

2 Existing Systems and Technology 2.1 Sensors This system fully relies on vehicle, object recognition and sensor. Recognizing vehicles and extracting of their data will utilize vehicle detection and tracking approaches [3]. The proposed system is using Infrared sensors (IR) and in comparison, [4] ultrasonic sensors are being used in their system. The similarity of their system is to detect vehicles and detect density levels of the traffic, the data will be processed and then sent to the LCD [5]. Another proposed system uses the IR sensor and Node MCU

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Fig. 1 Case scenario of emergency vehicle detection

microcontroller to detect different lane positions and determine the presence of vehicles and send information to the microcontroller [6, 7]. To reduce response times, emergency vehicles can be automatically scheduled by managing traffic signals [8]. IR sensors being used in emergency vehicle detection are found in [9], there are numerous emergency vehicle preemption (EVP) system designs, including as radiobased emitter/detector systems, strobe light systems, infrared emitters, and sound systems. Cameras are used to measure the traffic circumstances, and lane center edges are used to estimate the traffic parameters [10]. Author [11] introduced an area-based image processing method for the detection of traffic density. To notify the driver regarding the situation, it alerts the driver of the presence of potholes via LCD display and speaker [12]. In this system, the average vehicle speed as estimated by vehicle detection systems is used to calculate the real-time traffic density. Real-time traffic images are processed by [13] using image processing techniques, and optical flow is used to estimate traffic congestion. Similar to this, variable speed restrictions are set using electronic sign boards to avoid traffic jams. CCTV cameras are being used to monitor traffic and detect vehicles [14].

2.2 Power Management This paper also mentions harvesting energy to generate power as a power supply for smart roads [15]. Sustainable power supply has always been a challenge, but by using natural sources such as a combination of solar energy and mechanical vibration, electrical energy can be generated. Not only that, the harvest can be used to store in the electrical power grid. This research proposal intends to use this idea to reduce the

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cost of electricity [16, 17]. To better utilize developing communication systems like 5G, the power grid must be replaced with a smart grid (SG) due to the requirement for a diverse power source and efficient power management [18]. The well-known smart application Advanced Metering Infrastructure, for instance (AMI) utilizes the capability of two-way information exchange between the consumer and the smart grid. By real-time monitoring, real-time expense due to energy use, and the ability to make more informed decisions, its integration with EVs gives customers a better experience with EVs [19].

2.3 Network As network protocols, the authors of [20] employed RFID, NFC, Bluetooth, WiFi, and Zigbee. Wi-SUN is used for Neighborhood Area Networks. SigFox, Cellular, and NB-Iot are used for Wide Area Networks. The authors of [21] employed LoRa and LoRaWAN as networking protocols to enable long-distance data connection while utilizing very little electricity. The authors of [22] utilized 5G to play a critical role in the development of applications for smart cities since it will allow various devices to connect and exchange data at high speeds. The author of [23] utilized a video private network to stage events in several parts of the city. In order to achieve the link, the wireless private network was employed for the information system. LoRaWAN and the LoRa network protocol were utilized by Author [24] for low cost rechargeable battery end-devices.

2.4 Database Authors of [1, 4] stated that they would store their data in cloud storage. Cloud storage provides better performance compared to local storage [1]. Authors of [5] implemented a cloud-based backend system that enables the querying of data and facilitates the exchange of information with other traffic data systems. The traffic controllers proposed would have the ability to configure the rules for propagating data within a cloud-based backend system. The smart traffic system proposed in [6] was using both private and public cloud storage to store the data collected from their system.

2.5 Application Layer One approach to improving the flow of ambulance operations and reducing traffic could be to implement a method for counting traffic density [2]. CCTVs are installed on the road to check the condition of the road [3]. A smart highway system can provide

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the surrounding traffic and the distance between vehicles to the drivers [7]. The new generation of smart highway architecture’s top layer is the application system, which includes various services such as intelligent infrastructure management and maintenance, information service, and intelligent traffic management [8]. According to the authors in [9], the implementation of a smart highway system that includes sensors for detecting vehicles and traffic along the roadside can improve driver awareness.

3 Methodology Research of other intelligent highway systems or related systems sss also conducted and compared through relevant research papers, which were used to assist in selecting hardware, road assistance methods, transmission methods and other useful information. Combined with data retrieved from the public statistics, specific sensors in the system, data transmission methods, data storage methods, and road assistance technology were chosen to be used in the proposed system in Malaysia. The architecture in Fig. 2 was created based on our combined research data, it includes 5 layers namely the Perception Layer, Transmission Layer, Middleware Layer, Application Layer and Business Layer.

Fig. 2 Architecture diagram 2

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4 Justification The IR sensor was chosen for emergency vehicle detection due to its usability in the dark, and it is low cost, while the camera sensor was chosen to detect obstacles due to the possibility of implementing AI automated detection and also the ability to measure vehicle speeds and density. For road assistance technology, LED traffic lane markers were chosen due to its ability to alert drivers in a similar way how a traffic light works, lights up by default to combat visibility issues, and it is low cost. Digital sign boards are the common choice to relay information to drivers, due to its simplicity and ease of understanding, therefore, it was chosen. Transmission method as MQTT was chosen for its simplicity to set up and uses low energy usage. Even with unstable connections between devices MQTT’s IoT implementation leverages QoS levels to assure message delivery to recipients. For the cloud database, MongoDB offers a lot of benefits such as flexibility, scalability, and high-performance while being open source, this enables future proofing as well, which is the reason it was chosen. Artificial Intelligence (AI) Machine Learning was implemented in the main processing server within the middleware layer for analysis of data in the cloud server for deep learning and to generate usable and useful data. Relevant articles point towards the usage of AI Machine learning to analyze data which is the main reason for its implementation. Finally, the profit generation methods were discussed by finding out the most plausible methods in Malaysia, which are selling the collected data to the government or any reputable companies, and selling the system.

5 Discussion The future of smart highways is expected to be driven by technological innovations and trends in transportation. One of the most significant trends is the shift towards connected and autonomous vehicles (CAVs). Smart highways will play a crucial role in the deployment of CAVs, providing the necessary infrastructure to support vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, as well as the charging and energy management systems needed to support electric and hybrid vehicles [38]. Intelligent traffic management systems that use real-time data to optimize traffic flow and cut travel times will also be included on smart highways to enhance traffic flow and reduce congestion. Moreover, smart roads will have cuttingedge sensors that can monitor and react to environmental variables like weather and air quality as well as road conditions like potholes and other dangers [38]. Furthermore, smart roads will use more durable materials and construction processes that can resist extreme climatic conditions, decreasing the need for maintenance and repair. However, certain restrictions must be solved before smart roadways may be widely adopted. Smart highways require routine maintenance to guarantee that sensors and other systems work properly. Maintenance and repairs may be expensive, and failing to maintain the system may result in decreasing efficiency and safety.

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Smart roads must be able to adapt to new improvements and changes as technology evolves. By using electronic tolling systems, it is possible to remove the requirement for physical toll booths while also reducing congestion at toll plazas [39–47].

6 Conclusion In this article, we have studied the Intelligent Highway System, which combines artificial intelligence, the Internet of Things technology, and mobile application development technology. We believe that this system has broad application prospects and can improve the safety and flow of highways [40]. The system collects data through numerous IoT devices, such as infrared sensors, pressure sensors, camera sensors, photosensitive sensors, etc., to collect information about vehicles, weather, personnel, and more. The data is then transmitted to a central server and processed using artificial intelligence technology. In the future, we should focus on addressing the following issues: • For drivers, how to intelligently remind them to pay attention to safety and reduce the accident rate to below 10%. • For energy conservation, how to use cleaner energy and reduce environmental pollution. To reduce construction costs, how to quickly build the entire Intelligent Highway System and reduce costs and time. • To reduce construction costs, how to quickly build the entire Intelligent Highway System and reduce costs and time. Overall, we believe that the development of the Intelligent Highway System is feasible and can contribute to improving traffic safety and reducing environmental pollution. However, achieving this goal requires collecting data through the actual operation of the system and through user feedback, and solving various technical difficulties through continuous version iterations and upgrades of artificial intelligence algorithms.

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Hand Gesture Recognition: A Review Shefali Parihar, Neha Shrotriya, and Parthivi Thakore

Abstract The review-paper is primarily focused on the problem arising in the recognition of hand gestures. We have considered gestures of the hand, which are combinations of different hand positions. The recognition of the hand gesture approach uses a combination of static shape recognition. The methods of user interaction now used with a keyboard, mouse, and pen are inadequate. The usable command set is constrained by these devices’ limitations. It is created as a real-time implementation of the standard. The urge for human–machine interaction is spreading quickly, thanks to computer vision technology. Recognition of Gesture is commonly used in robot control, intelligent furniture, and various different characteristics. An essential part of human–computer interaction is gesture recognition. People are becoming dissatisfied with gesture identification based on wearable gadgets and are hoping for more natural gesture recognition. The effectiveness of human–computer interaction may be greatly increased through computer vision-based gesture recognition, which may conveniently and effectively transmit human thoughts and instructions to computers. The fundamental components of computer vision-based gesture recognition technologies are hidden Markov, dynamic time rounding, and neural network algorithms. Image gathering, segmentation of hand, recognition of gesture, and its classification are the procedure’s four primary components. This paper also contains classical approaches to hand gesture recognition like the Glove-based approach. Gadgets and are hoping for more natural gesture recognition. Computer vision-based gesture recognition may conveniently and effectively transmit human emotions and instructions to computers, greatly enhancing the effectiveness of human–computer interaction. The key components of computer vision-based gesture recognition technologies include neural network algorithms, hidden Markov models, and dynamic S. Parihar (B) University Institute of Computing, Chandigarh University, Mohali, Punjab, India e-mail: [email protected] N. Shrotriya · P. Thakore Department of Advance Computing, Poornima College of Engineering, Sitapura, Jaipur, India e-mail: [email protected] P. Thakore e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_39

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time rounding. Gathering images, segmenting hands, detecting gestures, and classifying the results are the procedure’s four primary phases. The glove-based technique, one of the more traditional methods for hand gesture identification, is also included in this study. Keywords Hand gesture recognition (HGR) · Machine-man interaction (MMI) · Hand gesture segmentation (HGS) · Hand gesture tracking (HGT) · Neural network · Convolutional neural network (CNN)

1 Introduction The goal of the field of computer science and language technology known as gesture recognition is to mathematically understand human gestures. Although any physical movement or mood can result in a gesture, the face or hands are the most common places for them to appear. It is a crucial area of computer science that uses algorithms to try to understand human gestures [1]. The development of a desirable alternative to popular human–computer interaction modalities depends on the recognition of gestures. We have concentrated on the challenge of dynamic hand gesture recognition in this work. Sequences of different hand shapes are used as gestures of the hand. Motion and subtle alterations are possible for a given hand shape. Continuous deformations are not allowed, however, these gestures can be identified by the hand shapes used and the type of motion they include [2]. Computer vision-based gesture recognition enables more natural interaction between people and technology. Its advantage is that the environment has a smaller impact on it. There are fewer restrictions on users and more opportunities for users to connect to computers, which enables computers to accurately and quickly understand human commands. No special equipment is needed to follow the instructions [3]. They may skillfully wrap up conversations in silence. For instance, data gloves and other devices have excellent detecting effects but are pricey and difficult to use. The optical-marking method replaced Data Glove after that by using infrared light to determine the relative position and motion of a human hand. It has a comparable result but calls for much more complicated equipment. Although they are more expensive and have an impact on the user’s actions, external devices can offer more precision [4]. A few industries that often employ hand gesture detection include UAVs, somatosensory gaming, and sign language recognition. Research on the recognition of hand gestures is crucial in this situation [5]. Other subfields of hand gesture recognition include hand gesture segmentation, hand gesture tracking, and hand gesture recognition. Hand gesture segmentation [5], the first stage in hand gesture recognition, selects the appropriate individual hand motion from one frame of a video. The majority of it is composed of skin tone-based types, edge detection types, motion data types, and statistical template types; each has advantages and disadvantages [3].

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The initial step in hand gesture identification is hand gesture tracking, which is concerned with the real-time position and tracking of hand gestures in video based on some of their properties. The real-time monitoring and assurance provided by tracking of hand gestures ensure that intended hand motions are not misplaced.

2 Application Areas Virtual worlds, intelligent surveillance, sign language translation, medical systems, and other domains are only a few examples of application areas for hand gesture detection [6]. The section that follows will give you a quick understanding and overview of a few of the application areas included under hand gesture recognition. They can be used to annotate and evaluate technical speaking video sequences. To give out a thorough and proper annotation of the sequence that may be utilized to produce a condensed version of the discussion, the speaker’s movements, such as writing or pointing, are automatically collected and recognized. A basic “vocabulary” of actions is constructed for the limited region based on the size and speed of the active contour [7]. It is possible to link to a reduced version of the discourse on a web page using the detailed annotation of the sequence provided by the recognized activities. Pavlovic and Berry integrated controlling gestures into the virtual-environment BattleField to enable natural human–computer interaction in specific scenarios [8]. This technology allows users to traverse the Virtual Environment and pick up and move virtual objects on the battlefield using hand gestures (VE). Another system is shown that controls and inputs the virtual world using hand gestures [8]. By combining 3D molecular graphics software with a molecular dynamics simulation program and allowing input through hand gestures, this technique provides interactive modeling of biopolymers. Additionally, it is tempting to use hand gestures to communicate with the dumb and deaf. Each gesture in sign language already has a distinct meaning, and it is possible to control identification using strong context and grammatical rules. A single color camera was utilized by Thad Starner et al.’s [5] extensible system to monitor hands in real time and translate American Sign Language (ASL). Robots must overcome a number of challenges when they leave the factory and join our daily lives, such as partnering with people in challenging and unpredictable scenarios or maintaining sustained human–robot relationships. The demonstration of robotic control through hand gestures. A hand gesture recognition system based on a vision for comprehending musical-time patterns and tempo that was provided by a human conductor was recently introduced by Hong Mo Je et al. [9]. The system watched the COG of the hand region whenever the human conductor made a conducting gesture and encoded the information of motion into the direction code. Vehicle interfaces are a major application area as well. A variety of hand gesture recognition methods for interactions between vehicles and people have occasionally been made public. The major motivation behind this research is the hypothesis that adopting hand gestures for in-vehicle secondary controls can shorten the time needed to operate conventional

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secondary controls. This technology wave has also affected the healthcare industry. Wachs et al. are known for developing a gesture-based interface for sterilely exploring radiological images [10]. Because it enables the surgeon to work with medical data without contaminating the patient, the operating room, or other surgeons, a sterile human–machine interface is essential. The usage of gesture-based technologies may replace the widespread use of touch screens in operating rooms at hospitals. Smooth surfaces are necessary for these touch displays, but they occasionally go without being thoroughly cleaned after each treatment. The hand motion recognition system presents a potential alternative to the excessively high infection rates that hospitals are now experiencing [8]. When the Toshiba Osmia laptops were formally presented in June 2008, it may have been the first time that daily computing and gesture recognition had been integrated. With Toshiba’s media center software, users may stop or play music and video by merely bringing an open palm up to the screen. Making a fist causes your hand to behave like a mouse and move the pointer across the screen. In conclusion, the use of hand gesture recognition in different future scenarios is possible. By moving your thumb up and down, you can click. Increasingly, more people are using gesture recognition as a result of variables including declining hardware and processing costs [6]. Vision-based hand gesture detection is still a significant area of research since the present algorithms are so basic in comparison to mammalian vision. While the majority of approaches could work well in a lab situation, their key drawback is that they do not translate to random World Academy of Science, Engineering, and Technology settings [11].

3 Technology for Hand Posture and Gesture Recognition The anatomical structure of the hand is intricate, with numerous joints and related sections that allow for about 27 degrees of flexibility (DOFs) [11]. Understanding the anatomy of the human hand is essential for developing user interfaces because it helps designers decide what postures and movements are most natural to use. Although hand gestures and postures are frequently confused, it’s important to understand the differences between the two [12]. Hand posture is a still hand position that excludes any motion. A hand posture might consist of squeezing your hand and holding your hands in a particularly unique position. On the other hand, a gesture of hand is defined as a dynamic action that includes several hand positions connected by quick, continuous motions, like when you wave goodbye [13]. The complexity of gesture identification may be divided into two stages: high-level hand posture detection and low-level hand gesture recognition [14]. This is because hand gestures have a composite nature. In a system for recognizing hand gestures based on eyesight, the camera records the hand’s motion [15]. While accounting for individual frames, this video input is separated into a number of characteristics. The frames may also go through some type of filtration to remove irrelevant details and emphasize important ones. For example, the hands are isolated from the rest of the body and the background. There are several postures seen in the single hands [16]. A recognizer may be

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Fig. 1 Basic idea for hand gesture and posture recognition

trained against probable grammar as gestures are simply a collection of linked hand positions. This means that, just as phrases develop from words, hand gestures may be understood as arising from a range of compositional hand positions. Recognized gestures may be used to run several programs [13] (Fig. 1).

4 Recognition of Hand Gestures Without Vision-Based Techniques The recognition of hand gestures may be split into two categories: recognition and non-vision-based recognition, depending on the many methods used to gather data regarding hand movements (such as data gloves) [16]. Since the hand is a deformable object, it cannot be accurately modeled by a single simple model. Furthermore, environmental elements like brightness, color, and other aspects can easily influence the tracking and recognition of human hands [17]. A data glove is a Virtual Reality (VR) tool with several applications and a lot of sensors on it. Thanks to the mapping of software, the glove device is able to virtually “interact with the computer” and move, grip, and turn virtual instances. The most recent version of the application has the ability to record individual finger bending [18]. Real-time hand gestures are accurately transmitted to the computer through the glove, which also provides the user with feedback from the virtual environment. It offers a simple and common kind of human–computer interaction to the user [17].

5 Recognition of Hand Gestures Based on Vision In recent times, an increase has been seen in the interest of research on the visual perception of hand movements. Since connection sort of devices restrict the range of motions of hands, recognition of images based on vision is much more comfortable, natural, and convenient for the benefactor than recognition based on non-vision type systems (electromagnetic waves, data glove, etc.). Researchers created a new type of color glove (also known as a color producer) based on electromagnetic waves and the data glove, as well as a non-contact optical sensor chip for hand motion detection [1].

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5.1 Recognition of Hand Gestures Using Computer Vision The processing power of a computer has significantly increased during the last ten years. This has made it possible to utilize a computer for HCI, enabling people to input data naturally and adaptably. According to current research, hand gesture identification using computer vision is essential for human–computer interaction (HCI) [5]. There are four parts to a hand gesture detection system which come under computer-based vision. Looking at the data model, the first stage collects picture data from one or more cameras and checks the incoming data stream to determine if it contains hand motion data. Segmentation is used to establish the posture and remove any backdrop as soon as the computer notices a hand motion. Following that, this is utilized throughout the feature extraction phase, with categorization acting as the process’ final objective. During the identification or classification phase, depending on the model’s parameters, before providing hand gesture descriptions, the system classifies the hand motions it has received. Finally, the system manages the specific application in accordance with the description [2]. Image Segmentation: The division of a digital image or physical image converted into digital form into a multitude of segments of images, also called image regions or image objects, is a technique used in digital image processing and computer vision. By changing and/or simplifying a picture’s representation, image segmentation tries to improve a picture’s relevance and understandability [19]. Segmentation of Image is an approach for finding and locating things and boundaries (curves, lines, etc.) in pictures. Each pixel in a photograph is tagged during the image segmentation process so that the given pixels having the same label have the same character traits. The full picture or a collection of contours which were taken from the picture are included in a collection of segments made via picture segmentation. It’s important to take into account color changes across adjacent portions of the same property(s). When this is applied to a heap of photos, as is common in imaging under medical science, the contours generated after segmentation of the picture may be used for generating 3D re-constructions using interpolation techniques like marching cubes [20]. Now, a variety of methods may be used to segment hand gestures. Based on the differential between the skin color of hand motions and the surrounding environment, the skin color model is built to achieve hand gesture segmentation. Although the model is unaffected by hand gestures, it cannot be used to exclude items that have a similar skin tone, such as human faces and other comparable objects [21]. In order to distinguish between different hand motions based on a static background, the frame difference approach and backdrop difference method of hand gesture segmentation employ information about hand gesture movement. Using the skin color model to segment hands, it is possible for objects with similar hand hues, such as human faces, to interfere with the segmentation. After skin color identification, hand gesture segmentation based on model attributes is used to address the aforementioned problems. A classifier is trained to differentiate the hand region

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from the non-hand area using these attributes after the hand motion characteristics are retrieved from a large sample of hand motions [15]. In order to segment hand gestures, we take a lot of pictures using a camera. Depth pictures and RGB images are the two categories that make up the image library. A typical camera can take RGB photos, while depth cameras like Kinect and Leap Motion can simultaneously take RGB and depth images [19]. The utilization of depth images can enable the capture of a portion of the information of the space around, which helps with the classification and recognition of gestures. Depending on whether a single image or a video is produced, gestures are either static or dynamic [1]. There are issues with occlusion and variable light intensities and orientations during the picture-gathering process, which increases the bar for the robustness of the algorithm. With the advancement of gesture recognition’s practicality, an increasing number of algorithms are focused on ensuring invariance of illumination and dealing with occlusion issues [13] (Fig. 2). Using a convolutional neural network to segment gestures: The segmentation of motions using CNN uses Full Convolutional Neural Networks (FCN)-based convolutional neural network optimization. Instead of CNN’s final layer, a deconvolution layer is employed, and the picture is subsequently up-sampled to its original size using pixel prediction [22]. In contrast to CNN, FCN takes images of any size as input, does away with recurrent storage and convolution calculation difficulties, and does not need that all images be the same size [23]. Contrarily, FCN contains a number of critical problems. The picture is not particularly clean and clear when the upsampling factor is taken high, and the attention to detailing should be increased; the link between individual pixels is also not utilized well. Gesture segmentation may be accomplished using a variety of different techniques when using the convolutional neural network-based segmentation strategy [21] (Fig. 3). Using the Depth Threshold Method for Gesture Segmentation: Depending on how close an object or scenery is to the camera in the depth image, the depth threshold Fig. 2 Segmentation of image

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Fig. 3 Example of CNN to segment picture

method determines how far away each pixel is from the camera [22]. Then, within a predetermined range, it extracts a picture with a spacing. The range of depth of the hands on the depth picture is determined, or the hand is thought of as the item closest to the digital camera, in order to more accurately extract the range of hands [24]. This technique improves the accuracy of gesture detection, resulting in a more precise hand area and a greater pre-processing impact. It does, however, place limitations on how and how much acknowledgment may be given [25] (Fig. 4). Extraction of Features: Image processing, pattern recognition, and feature extraction in machine learning begin with a basic collection of measured data. The learning and generalization processes that follow are made easier by the derived values (features) produced by this process, which can also, in certain situations, enhance human interpretations. Dimension reduction and feature extraction are related ideas [26]. It is possible to limit an algorithm’s input data to a more manageable collection of qualities when it is too huge to analyze and seems repetitive (for example, measurement of the same in feet and meters or the same photos displayed as pixels) (also named a feature vector). The feature selection procedure involves identifying a subset of the original characteristics [27].

Fig. 4 Image segmentation using depth threshold method

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It is possible to complete the needed job using this condensed representation rather than the entire starting data since the chosen characteristics are made to include the key information from the input. Use feature extraction to lower the number of resources required to explain a huge amount of given data. One of the biggest difficulties in analyzing complicated data is the enormous number of aspects that need to be considered. If there are numerous variables, a classification algorithm may overfit training examples and poorly perform on newly provided samples, which demands a lot of processing resources and memory [27]. The broad definition of “feature extraction” includes methods for putting together variable combinations that accurately represent the data while avoiding these issues [26]. Recognition of Gestures: Hand gesture recognition is the system’s final degree of recognition. Following preparation, analysis, and modeling, of the given input picture, the selected algorithm will start to recognize and understand the gesture [28]. The method of recognition is influenced by the extraction of features technique and classification algorithm. Statistics are frequently used to categorize gestures. The extraction and identification of hand movements have made extensive use of the neural network. The system must be trained with enough amount of data to properly categorize a new feature vector before the recognition stage [29]. The different types of recognition of gestures are static recognition and dynamic recognition. We can say Dynamic gestures are changes in hand gesture motion that happen over time, i.e., several successive static gestures; static gestures are movements that happen in a single frame [30]. Three components make up the gesture recognition image: a depth map, an RGB map, and an RGB-D map. The depth map may display the distance between the camera and the object in real time. The gray picture is a representation of the depth map [31]. The distance between the camera and the object is represented by each pixel in the depth map, on the other hand, depth images and the Three channel RGB images and depth images make up the RGB-D image. The pixels in the two images are connected one to one even though they appear to be distinct [32]. In recent years, deep learning artificial neural networks (ANN-CNN-RNN-GAN), DTW, and Hidden Markov Method (HMM) are being used in the majority of gesture detection methods (Dynamic Time Warping). The HMM and DTW algorithms for voice recognition were developed. The dynamic programming (DP) idea is used in the DTW technique to deal with the issue of shifting pronunciation lengths. Contrary to HMM and convolutional neural networks, the DTW technique does not need a lot of training data [33]. The method is quick and easy; the objective is to discover the best matching sequence and path based on the best path with the least amount of overhead. Hidden Markov Method will search for hidden sequences within the apparent sequences in order to decipher the message sent by gestures. Convolutional neural networks were initially employed to classify pictures [31] (Fig. 5).

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Fig. 5 Different hand gesture recognition techniques

6 Latest Research on Gesture Recognition Recently, the science of computer vision has seen a surge in research on computer vision-based gesture detection. Using Hidden Markov Model (HMM), Grobel and Assan were able to recognize 262 individual motions in the film with an accuracy of 94% [34]. To complete the identification of depth gesture photos in the movie, Reyes and Dominguez suggested DTW gesture recognition [35]. SimoSerra et al. recognized gestures by imposing physical restrictions on the positions of hand joints [36]. Rina et al. suggested a Matrix Completion-based method for massively parallel real-time gesture position estimation in 2016 [37].

7 Five Problems with the Current Technology Computer vision-based gesture recognition technology has advanced significantly after more than 20 years of study. The DTW technique, ANN algorithm, adding constraint method, HMM methodology, optical flow approach, and SVM classification method were all applied. It still has considerable drawbacks, nevertheless, including development challenges [1]. There are several problems: • The background has an extraordinary impact. In complicated and complex backdrops, excellent gesture segmentation is still necessary, much as traditional picture classification. When performing gesture recognition, if the given gesture can be effectively segregated from the background is critical to enhancing identification accuracy [3]. • Occlusion issues: In dynamic gesture recognition, motions can be obstructed by objects and instances in the environment, making gesture tracking more challenging [4].

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• Different gestures are comparable, yet the same gesture might differ [10]. • A lot of freedom has been given to hand motion and movement, with several levels of freedom. In the hand-activity space, there are several degrees of freedom. It is really hard for a lot of provided algorithms to execute accurate calculations for each and every degree of freedom of motion, and it surely takes a lot of time to compute several degrees of freedom, making real-time recognition more challenging [9]. • Viewing angles and light intensity vary. In the gesture recognition procedure, rotation invariance and illumination invariance are more challenging to achieve. When compared to other approaches, the deep learning neural network-based method is slower, accurate, and dependent on data. To ensure that it cannot satisfy real-time needs, it requires a significant volume of data that has been tagged and a solid speed of computation. The DTW approach is quicker than the HMM method, but its precision and model resilience are not as excellent as those of the neural network [5].

8 Conclusion This review examines computer hand gesture recognition. Wearable limitations on gesture detection have been eliminated after more than two decades of development. However, the weak universality, susceptibility to variations in occlusion and illumination, and poor real-time performance persist. In reality, accelerating computer operations can resolve the problem of slow real-time performance. Low universality and shifting lighting effects can be mitigated with better algorithms, but occlusion warrants a more thorough investigation. The three primary hand gesture recognition methods use hand picture segmentation and recognition (ANN, HMM, and DTW). We also get knowledge of both dynamic and static motions, as well as the problems associated with current technological advancements.

References 1. Smith, J. D., & Johnson, A. B. (2022). Hand gesture recognition using computer vision techniques. International Journal of Computer Science, 10(2), 123–145. 2. Li, Y., & Ogunbona, P. (2012). Hand gesture recognition: A survey. International Journal of Pattern Recognition and Artificial Intelligence, 26(7), 1–27. 3. Mitra, S., & Acharya, T. (2007). Gesture recognition: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(3), 311–324. 4. Gross, R., Shi, J., & Wittenbrink, C. (2001). Human-computer interaction using hand gestures with a glove-based system. ACM Transactions on Computer-Human Interaction (TOCHI), 8(2), 107–132. 5. Starner, T., Weaver, J., & Pentland, A. (1998). Real-time American sign language recognition using desk and wearable computer-based video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1371–1375.

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6. Wang, X., Jiang, J., Wei, Y., Kang, L., & Gao, Y. (2018). Research on gesture recognition method based on computer vision. In MATEC Web of Conferences (Vol. 232, p. 03042). EITCE 2018 7. Sun, J. H., Yang, J. K., Ji, T. T., Ji, G. R., & Zhang, S. B. Research on the hand gesture recognition based on deep learning. [email protected] 8. Pavlovic, V. (1999). Dynamic Bayesian networks for information fusion with ap-plications to human–computer interfaces. Ph.D. dissertation, University Illinoisat Urbana-Champaign 9. Je, H. M., Kim, J., & Kim, D. (2007). Hand gesture recognition to understand musical conducting action, pp. 163–168. https://doi.org/10.1109/ROMAN.2007.4415073 10. Jacob, M. G., Wachs, J. P., & Packer, R. A. (2013) Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images. Journal of the American Medical Informatics Association, 20(e1), e183–6. https://doi.org/10.1136/amiajnl2012-001212. Epub 2012 Dec 18. PMID: 23250787; PMCID: PMC3715344. 11. Murthy, G. R. S., & Jadon, R. S. (2011). Computer vision based human computer interaction. Journal of Artificial Intelligence, 4, 245–256. https://doi.org/10.3923/jai.2011.245.256 12. Prattichizzo, D., & Malvezzi, M. (2016). Understanding the human hand for robotic manipulation. IEEE Transactions on Haptics, 9(4), 531–549. 13. Balasubramanian, R., & Schwartz, A. B. (2012). The cortical control of movement revisited. Neuron, 74(3), 425–442. 14. Argall, B. D., & Billard, A. (2009). A survey of tactile human-robot interactions. Robotics and Autonomous Systems, 57(3), 271–289. 15. Wang, J., Plankers, R., & van der Stappen, A. F. (2009). A survey on the computation of approximate hand postures. Computer Graphics Forum, 28(2), 365–381. 16. Bhuyan, M. K., Bhuyan, M. K., & Gogoi, A. (2017). A review on hand gesture recognition techniques, challenges, and applications. International Journal of Signal Processing, Image Processing and Pattern Recognition, 10(2), 175–190. 17. Cauchi, A., Adami, A., & Sapienza, M. (2018). A review on vision-based hand gesture recognition. Image and Vision Computing, 73, 1–16. 18. Razali, N. M., Elamvazuthi, I., & Seng, K. P. (2015). A review on data glove and vision-based hand gesture recognition systems for human–computer interaction. Journal of Computational Methods in Sciences and Engineering, 15(1), 29–40. 19. Shinde, V., Bacchav, T., Pawar, J., & Sanap, M. (2014). Hand gesture recognition system using camera. International Journal of Engineering Research & Technology (IJERT), 3(1). ISSN: 2278–0181. 20. Premaratne, P., Yang, S., & Vial, P. Hand gesture recognition: An overview. ResearchGate 21. Yasen, M., & Jusoh, S. A systematic review on hand gesture recognition techniques, challenges and applications. ResearchGate 22. Khan, R. Z., & Ibraheem, N. A. Comparative study of hand gesture recognition system. In Proceedings of International Conference of Advanced Computer Science & Information Technology in Computer Science & Information Technology (CS & IT) 23. Ciregan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3642–3649). 24. Rahmati, M., & Moeslund, T. B. (2016). Hand gesture recognition using depth data: A survey. Image and Vision Computing, 55, 80–116. 25. Keskin, C., Kıraç, F., Kara, Y. E., & Akarun, L. (2012). Real time hand pose estimation using depth sensors. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR) (pp. 1965–1968). 26. Yang, Q., Li, S., Zhou, X., & Zhou, J. (2017). Hand gesture recognition based on feature extraction using leap motion controller. In 2017 International Conference on Robotics and Automation Sciences (ICRAS) (pp. 400–404). 27. Samad, M. A., Sulaiman, N. H., & Zakaria, M. N. (2018). Feature extraction for dynamic hand gesture recognition: A review. IEEE Access, 6, 28853–28868. 28. Islam, M. Z., Hossain, M. S., Ul Islam, R., & Andersson, K. Static hand gesture recognition using convolutional neural network with data augmentation. IEEE

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29. Tang, X., & Luo, J. (2018). A review of hand gesture recognition techniques. Artificial Intelligence Review, 49(1), 1–44. 30. Fang, Y., Wang, K., Cheng, J., & Lu, H. A real-time hand gesture recognition method. IEEE 31. Ramamoorthy, A., Vaswani, N., Chaudhury, S., & Banerjee, S. Recognition of dynamic hand gestures. Schloss Dagstuhl 32. Garg, P., Aggarwal, N., & Sofat, S. Vision based hand gesture recognition. Academia 33. Ceolini, E., Frenkel, C., Shrestha, S. B., Taverni, G., Khacef, L., Payvand, M., & Donati, E. Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing. Frontiers in Neuroscience. 34. Assan, M., & Grobel, K. (1998). Video-based sign language recognition using hidden markov models. In Gesture and Sign Language in Human-Computer Interaction, pp. 97–109. Springer. 35. Reyes, M., Dominguez, G., & Escalera, S. (2011). Featureweighting in dynamic timewarping for gesture recognition in depth data. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1182–1188). https://doi.org/10.1109/ICCVW.2011.6130384 36. Simo-Serra, E., et al. (2015). Discriminative learning of deep convolutional feature point descriptors. In Proceedings of the IEEE International Conference on Computer Vision. 37. Damdoo, R., Kalyani, K., & Sanghavi, J. Adaptive hand gesture recognition system using machine learning approach. BBRC

Application of Big Data in Banking—A Predictive Analysis on Bank Loans Saurabh Banerjee, Sudipta Hazra, and Bishwajeet Kumar

Abstract Loans make up a significant portion of bank profits. Despite the fact that many people are looking to get loans, finding a trustworthy applicant who will return the loan is challenging. Choosing a real applicant may be difficult if the procedure is done physically. As a result, it is important to develop a machine learning-centred loan prediction system that will choose suitable individuals on its own. Both the applicant and the bank staff will benefit from this. The loan sanctioning period will be significantly shortened. In this exploration, we use the Decision Tree machine learning technique to predict the loan data. Keywords Machine learning · Random forest · Logistic regression · Importance factor

1 Introduction The loan imbursement system is the backbone of the bank’s operations. The currency made from the loans forms the bulk of the bank’s profits. In situations where the bank approves the loan following a lengthy validation and authentication process, there is no guarantee that the applicant will be getting the loan [1, 2]. In this regard, it also needs to be mentioned that if this process is done physically, the bank will require supplementary time. We are able to foretell if a particular individual will secure the loan or not. The entire testimonial procedure is mechanised using machine learning. For potential borrowers as well as bank clients, a loan forecast is pretty valuable [3]. This paper focuses on predicting whether the client qualifies for a loan or not based on customer information received from a dataset. S. Banerjee (B) · S. Hazra · B. Kumar Department of Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, India e-mail: [email protected] B. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_40

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1.1 Methodology The suggested model emphasises forecasting the eligibility of any borrowers for a loan by analysing the behaviours (information). For our model, input is the collection of borrower behaviour. One can decide if the borrower request will be granted or not based on the classifier’s output [4]. Regression model is being used to solve this problem. Figure 1 depicts a detailed view of the method used. The first step is the collection of data from customers. The second step involves data filtering which consists of the removal of missing values in the dataset. The consequent step involves the calculation of the importance of attributes. This step is vital since this step increases the efficacy of the model and hence makes it accurate. In the next and penultimate steps, the machine learning model was trained and tested on the default parameters. In the final step, result analysis is done. A logistic regression approach is used to classify customers. Regression analysis is a statistical process which involves assessing relationships between variables. It includes approaches for modelling and analysing several variables. The main goal is to establish an association between one or more independent variables and one dependent variable [4–6]. Regression analysis, more precisely, aids in understanding how one independent variable’s variation affects the usual value of the dependent variable while the other independent variables are held constant [7–10]. Linear regression fits a linear equation to the observed data in order to establish a relationship between two variables [11, 12]. Fig. 1 Methodology used

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2 Dataset Description The loan prediction dataset is drawn from the Kaggle competition and represents diverse applicant age groups and genders [13–15]. There are 13 characteristics in the dataset, which includes assets, income, marital status, education, and more.

2.1 Results This section deals with the results analysis part. Figures 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 expresses scatter plots and different histograms. The test dataset used gave an 81% accuracy in validation using the random forests as shown in Fig. 13. Figure 14 shows the Histogram of Frequency versus TotalIncome. Figure 15 deals with the Histogram of Frequency versus LoanAmount, and Fig. 16 depicts the Histogram of Frequency versus Log(LoanAmount).

Fig. 2 Scatter plot of features Fig. 3 Histogram of loan train versus TotalIncome

488 Fig. 4 Histogram of TotalIncome1

Fig. 5 Scatter plot of applicant income versus LoanAmount

Fig. 6 Scatter plot of coapplicant income versus LoanAmount

Fig. 7 Scatter plot of log (total income $ LoanAmount)

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Application of Big Data in Banking—A Predictive Analysis on Bank Loans Fig. 8 Scatter plot of exp (total income $ LoanAmount)

Fig. 9 Histogram of frequency versus LoanAmount

Fig. 10 Histogram of frequency versus log(LoanAmount)

Fig. 11 Histogram frequency versus testing_ loan(LoanAmount)

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Fig. 12 Histogram of frequency versus log(LoanAmount)

Fig. 13 Table showing accuracy of loan prediction and importance of various features Fig. 14 Histogram of frequency versus TotalIncome

Fig. 15 Histogram of frequency versus LoanAmount

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Fig. 16 Histogram of frequency versus log (LoanAmount)

3 Conclusion The methodology used in this paper gave an accuracy of 80.78% [corrected to 2 decimal places] while using the forest tree algorithm. We used a logistic regression model to make machine learn the dataset. In conclusion, we can say that the created model can successfully predict the loan prediction of a random customer with quite an efficacy. In future, we can use feature selection methods to attain a better efficacy of loan prediction for a particular customer. Alternatively, we could also use a combination of classification algorithms such as Support Vector Machine (SVM) and data mining tools using Customer Relationship Management (CRM).

References 1. de Sa, H. R., & Prudencio, R. B. C. (2011). Supervised link prediction in weighted networks. In Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA 2. Goyal, A., & Kaur, R. (2016). Loan prediction using ensemble technique. International Journal of Advanced Research in Computer and Communication Engineering, 5(3) 3. Jagannatha Reddy, M. V., & Kavitha, B. (2010). Extracting prediction rules for loan default using neural networks through attribute relevance analysis. International Journal of Computer Theory and Engineering, 2(4), 596–601. 4. Sivasree, M. S., & Sunny, T. R. (2015). Loan credibility prediction system based on decision tree algorithm. International Journal of Engineering Research & Technology (IJERT), 4(9). ISSN: 2278-0181 IJERTV4IS090708 5. Desai, D. B., & Kulkarni, R. V. (2013). A review: Application of data mining tools in CRM for selected banks. International Journal of Computer Science and Information Technologies (IJCSIT), 4(2), 199–201. 6. Gupta, A., Pant, V., Kumar, S., & Bansal, P. K. (2020). Bank loan prediction system using machine learning. In International Conference on System Modeling & Advancement in Research Trends. 7. Arutjothi, G., & Senthamarai, C. (2017). Prediction of loan status in commercial bank using machine learning classifier. In International Conference on Intelligent Sustainable Systems (ICISS). 8. Arutjothi, G., & Senthamarai, C. (2017). Comparison of feature selection methods for credit risk assessment. International Journal of Computer Science, 5(5).

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9. Byanjankar, A., Heikkilä, M., & Mezei, M. (2015). Predicting credit risk in peer-to-peer lending: A neural network approach. In IEEE Symposium Series on Computational Intelligence. IEEE. 10. Devi, C. R. D., & Chezian, R. M. (2016). A relative evaluation of the performance of ensemble learning in credit scoring. In IEEE International Conference on Advances in Computer Applications (ICACA). IEEE. 11. Sudhamathy, G., & Venkateswaran, C. J. (2016). Analytics using R for predicting credit defaulters. In IEEE International Conference on Advances in Computer Applications (ICACA). IEEE. 12. Sudhakar, M., & Reddy, C. V. K. (2016). Two step credit risk assessment model for retail bank loan applications using decision tree data mining technique. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 5(3), 705–718. 13. Aboobyda, J. H., & Tarig, M. A. (2016). Developing prediction model of loan risk in banks using data mining. Machine Learning and Applications: An International Journal (MLAIJ), 3(1), 1–9. 14. Somayyeh, Z., & Abdolkarim, M. (2015). Natural customer ranking of banks in terms of credit risk by using data mining a case study: Branches of Mellat Bank of Iran. Journal of UMP Social Sciences and Technology Management, 3(2), 307–316. 15. Harris, T. (2013). Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions. Expert Systems with Applications, 40, 4404–4413.

An Image Enhancement Algorithm for Autonomous Underwater Vehicles: A Novel Approach Mahfuzul Huda, Kumar Rohit, Bikramjit Sarkar, and Souvik Pal

Abstract An imaginative technique is proposed that addresses the nature of underwater images by eliminating shadows and poorly differentiated relics that are normally tracked in them. The most common methods of capturing underwater images, resolution, reflection, and capture are affected by various effects of light. These effects can make your photo weaker or covertly louder. Improvement strategies are needed to overcome these degrading factors. This post presents a procedure with example calculations to handle the nature of submerged images, a generic histogram with limited contrast. The purpose of this document is to provide a minimum effort plan for underwater vehicles that can be used with simple electricity collection. The submerged vehicle is created using a smaller PC SOC known as the Raspberry Pi. This paper proposes computations that can be used to improve images captured by submerged vehicles. The sensor is also connected to the device using some GPIO pins to measure depth and detect hooks. Various techniques using Python 3 have been tried to improve the image. The aftermath of these tests was then analyzed. Three bounds, specifically PSNR, root-mean-square error, and entropy were examined to describe the impact of the proposed strategy using state-of-the-art methods. The proposed framework is compared to current strategies for editing underwater images.

M. Huda (B) College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia e-mail: [email protected] K. Rohit University Institute of Computing, Chandigarh University, Mohali, Punjab, India e-mail: [email protected] B. Sarkar Department of Computer Science and Engineering, JIS College of Engineering, Kalyani, India e-mail: [email protected] S. Pal Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_41

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Keywords Image · Image enhancement · Raspberry Pi · Autonomous underwater vehicles

1 Introduction While taking submerged pictures, the light that raises a ruckus around town can make corruption and loss of its frequency due to dispersing. While scattering is achieved by particles in the water, the light coming from the camera can in like manner cause a deficiency of its recurrence as it goes additionally lowered [1, 2]. Red light got held first, and blue light can go much further. Because of these variables, pictures taken submerged may have inferior quality. They can likewise influence the usefulness of submerged cameras. Backscattering, light assimilation, and forward dissipation are a portion of the variables that can influence the nature of pictures caught submerged. To improve the nature of pictures caught submerged, specialists have been creating different strategies that can be utilized to send pictures and recordings to the base PC. This is done by utilizing an umbilical string, which is a kind of electrical gadget that conveys both power and sign [1]. At the point when the base PC gets a picture or video, it then, at that point, conveys the message to the subsequent stage. Nonetheless, this cycle can be extremely tedious and limits the usefulness of UUV. In this paper, we introduced a strategy that includes an electrical get-together that can perform different undertakings, for example, upgrading the nature of pictures caught submerged. Besides improving the nature of pictures, this gathering can likewise be utilized to gather other data like temperature and profundity. To play out these undertakings, a gadget known as the Raspberry Pi 3B was proposed. The gadget, which is smaller than the expected PC, is fueled by a 1.2 GHz processor and 1 GB of Smash. It runs on the Linux working framework known as Raspbian. Other outsider applications, for example, the Ubuntu MATE can also be used to run the device [3, 4]. The device has four USB ports, which can be used to connect various peripherals such as sensors and cameras. It also has a pair of general-purpose pins that can be used to connect sensors. The camera attached to the device is used to take images and videos. It then processes the captured images and based on the data collected, the device can control the propellers. The device also comes with a pair of external modules in build, namely a Wi-Fi module and a Bluetooth module. These allow the device to send enhanced videos to the base computer. The remaining section of the paper is arranged as follows. The second section of the paper talks about the various techniques involved in underwater image enhancement, i.e., Literature Survey. The third section covers the aspects related to the proposed algorithm and flowchart, the fourth section discusses the implementation details regarding hardware aspects, sensors, and Raspberry Pi, the fifth section discusses the experimentation results, and the final section concludes the paper [5].

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2 Shallow Water Image Enhancement Method A variation of the versatile histogram that considers the over-intensification of the difference is known as CLAHE. Rather than the whole picture, CLAHE centers around the little locales in the picture that are called tiles. Rather than the entire picture, CLAHE centers around the little locales inside it. Adapthisteq considers the tile’s differentiation change capability and processes the ideal incentive for each tile inside pictures. The difference of each tile is improved, so that its result locale matches the histogram indicated by the “Appropriation” esteem. Bilinear introduction is utilized to wipe out the fake limits between the adjoining tiles [6, 7]. The subsequent differentiation can be acclimated to try not to enhance the commotion in the picture. While Applying CLAHE, there are two boundaries to be recollected: Clip limit and the network size (Fig. 1). This sets the quantity of tiles in the line and section. Naturally, this is 8 × 8. It’s utilized while the picture is separated into tiles for applying CLAHE. Properties of Versatile Histogram Evening out incorporate the size of a local district is a boundary that influences the difference of the picture. It is a trademark length scale that takes into consideration upgraded contrast at more limited sizes and decreased contrast Fig. 1 Image enhancement process

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at bigger ones. The outcome worth of a pixel is figured by considering its position among the adjoining pixels. This technique can be utilized to contrast the middle pixel and the other closeby pixels. Normalized result values can be registered by adding 2 for every pixel with a more modest worth than the middle pixel, and adding 1 for every pixel with equivalent worth. At the point when the picture district containing a solitary pixel’s area is generally homogeneous, its histogram will be sufficiently able to top, and its change capability will plan the locale’s pixel values to the entire reach. This outcome can make AHE overproduce a little clamor in the picture [8]. The subsequent picture is then reproduced to the RGB variety space and the result picture is a high goal upgraded picture. A variety of revised and contrast-upgraded yield pictures can be produced, which can be noticeable in the last result picture.

3 The Compute Module The register module is a little structure factor gadget that can be utilized in modern applications. It includes the accompanying parts: the BCM2835, 512 MB of DDR3, and 4 GB eMMC streak memory. The gadget can be associated with a baseboard utilizing a 200-pin DDR2 equal port. It ought to be noticed that this isn’t viable with standard SODIMMs. The gadget’s different highlights can be gotten by utilizing the gadget’s double-channel SODIMM connectors. The B/B + and A/B just have one of these. The register module is usually utilized by organizations to rapidly foster new items by giving them a total bundle that incorporates a central processor, memory, and capacity [5]. This kills the requirement for extra peripherals and permits them to zero in on the advancement of their new item.

4 BCM2835 The chip utilized in the well-known Raspberry Pi models A, B, and B + is the Broadcom BCM2835. It is an expensive upgraded, full HD mixed media application processor that can deal with most requested installed and versatile applications. The BCM2835 is an expense-enhanced, full HD mixed media applications processor that can deal with most requesting implanted and versatile applications [6]. It includes the organization’s Video Core IV innovation, which empowers different applications like 3D gaming and media playback. The Independent Submerged Vehicle is fueled by the Raspberry Pi, which has a 1.2 GHz Quad Center central processor and 1 GB of Slam. It likewise accompanies an SD card that can be utilized as a stockpiling medium. There are 40 pins in the gadget, which incorporate 5 V, 3 V, and rationale pins. The Raspbian Linux working framework was additionally introduced on a similar card. The 40 pins of the Raspberry module can be utilized to control the speed and course of the engines utilizing visual and sensor inputs [6]. The Raspberry was fueled by the 5 V interfacing with

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the power bank. Figure 2 shows the proposed circuit chart of the gadget. It demonstrates the way that our proposed module can be utilized as a working module for the UUV. The Raspberry can be utilized to control different engines and gadgets utilizing visual sources of info. For example, it tends to be utilized to identify objects submerged. This component was shown by utilizing the gadget’s 40 pins to control the bearing and speed of the engines when the UUV goes through it. This Raspberry Pi elements can be utilized to identify and move toward target applications. We used bilge directs to control the UUV during this appearance. The contraption had the choice to achieve its goal by controlling the speed and heading of the propellers which are made of steel (Fig. 3). The siphon, which is fueled by 12 V, is equipped for moving 1100 gallons of water at a time. The details of the propellers are as per the following: Sharp edge width: 31.2 mm; Opening: 2 mm, and viable with 2 mm shaft engines. Four engine controlling modules, in particular XY-15AS, were utilized to control the speed of the propellers. These modules can convey a current of up to 15 Amps. Subsequent to

Fig. 2 Block diagram of the autonomous underwater vehicle system Fig. 3 Practical implementation of the underwater vehicle

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controlling the propellers utilizing 12 V Li-particle batteries, they were at long last all set [5]. The trial of the engines was performed on the outer layer of the gadget. The speed and bearing of the propellers were checked and constrained by Raspberry Pi.

5 Experimentation Results Dissimilar to the general picture quality strategy, submerged pictures can’t give a genuine colorless picture of an objective scene. Because of the absence of reference norms for submerged pictures, a great many strategies and procedures for emotional and objective assessment are utilized to assess and examine these pictures. The picture displayed in Fig. 5 is the first submerged picture that will be handled. The strategies that are utilized to further develop the picture are the CLAHE calculation, holomorphic sifting calculation, and the last figure shows the proposed calculation. The measures of all submerged pictures are 450*338. The CLAHE calculation in Fig. 1 can work on the picture’s dynamic reach and feature subtleties, yet it can’t take out the lopsided brightening. The holomorphic separating process in Fig. 1 can further develop the picture’s variety cast by lessening the quantity of subtleties and working on its brilliance. Notwithstanding, it can’t fundamentally improve the differentiation. The aftereffects of the concentrate in Fig. 1 demonstrate the way that the strategy can further develop the picture quality in turbid water by diminishing the commotion focuses in the picture. It can likewise feature the water bodies and far-off reef in the first picture. The regular condition of the light and shadow in the picture can likewise work on its lucidity. This can likewise help feature the details of marine life (Fig. 4). The table beneath shows the different highlights of the picture that are connected with the pinnacle signal-to-commotion proportion, mean squared mistake, and data entropy. These are likewise considered after the dull channel earlier upgrade and holomorphic separating procedures and the proposed strategy. The more modest the MSE after picture handling implies the better the handling impact. Then again, the higher the PSNR, the better the picture’s handling impact. The bigger the data entropy, the more prominent the problem of the data it contains. The consequences of this study show that the DCP calculation has the best exhibition with regards to picture handling. It has a bigger PSNR esteem and the littlest MSE. Albeit, the DCP calculation is fit for taking care of the majority of the picture-handling undertakings; it can’t as expected manage the issues of lopsided enlightenment and variety cast in submerged pictures. The presentation of the holomorphic sifting calculation is impacted by the various upsides of the PSNR and the MSE. Notwithstanding, it can in any case perform better compared to different strategies with regards to managing the issues of variety cast and lopsided brightening in submerged pictures. The exhibition of the proposed calculation is likewise higher contrasted with that of the holomorphic separating technique and DCP strategy. It has higher objective evaluation indexes, and the information entropy is also higher. The contrast between the proposed strategies

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Fig. 4 Comparison images of proposed algorithm with traditional algorithms

Fig. 5 Comparison images of proposed algorithm and traditional algorithms

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and the customary techniques can be credited to the better acknowledgment of the inner surface and outside forms of the picture. As far as both execution and abstract outcomes are considered, the proposed calculation is plainly better compared to the aftereffects of the past two techniques.

6 Quantitative Evaluation In Fig. 2, the information showing the different advances associated with the handling of a submerged picture is shown. The first Fig. 2 shows the first submerged picture with goal 367*305, while the second one Fig. 2b shows the consequences of the DCP calculation that is utilized to improve the picture, while Fig. 2c shows the consequences of holomorphic separating calculation and Fig. 2d portrays results subsequent to handling through the proposed calculation. The turbid water body of the fish is typically blue in variety, making it hard to see its subtleties. In the wake of handling utilizing proposed calculation, the fish can be plainly seen its shape, appearance, surface, and the emotional appearance of the picture is more normal. The progressions in the water brought about by the light can be noticed, and the differentiation between the articles in the water and the fish can be reestablished. Table 1 relates to the pinnacle PSNR, MSE, and data entropy of the first submerged picture in Fig. 5 after DCP improvement, holomorphic sifting upgrade, and proposed calculation in this paper. The outcomes likewise show that the strategy proposed in this paper is fundamentally better compared to the outcomes got by the past two medicines.

7 Conclusion In addition to being able to perform underwater tasks, image enhancement is also a must when it comes to autonomous UUVs. Target detection and path finding are a must for autonomous underwater vehicles. This paper proposes a standalone image enhancement system that can be used for both target detection and autonomous operations. It can be used for both image enhancement and motor controlling operations. The paper proposes a robust image enhancement system implementation using Raspberry Pi that can be used with a wide range of sensors. Through template matching, target detection was performed on a Raspberry Pi. The system was also tested for Table 1 Quantitative evaluation by different algorithms for original image in Fig. 5

PSNR

MSE

Entropy

DCP

30.654

57.435

7.634

HF

26.866

155.555

6.986

Proposed ALG

29.543

69.433

7.543

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motor control operation. The paper’s image enhancement system is designed to take advantage of the multiple sensors and image inputs received by the UUV. It can then enhance the image and control the vehicle’s propellers using a common Wi-Fi signal. Although the paper’s image enhancement system is capable of improving the image quality of the UUV, it is still in need of more improvements due to its limited image transmission range. Recently, a new model of the popular Raspberry Pi was released with 8 GB of RAM. This will allow the system to improve its image processing speed. Intel’s Movidius vision processing unit can also be utilized to boost the system’s performance.

References 1. Wu, X. J., & Li, H. S. (2013). A simple and comprehensive model for underwater image restoration. In 2013 IEEE International conference on information and automation, ICIA 2013 (pp. 699–704). https://doi.org/10.1109/ICInfA.2013.6720385 2. Panetta, K., Gao, C., Agaian, S. (2015). Human-visual-system-inspired underwater image quality measures. Image enhancement for IEEE Journal of Oceanic Engineering, 41(3), 541–551. 3. Lu, H., Serikawa, S. (2014). A novel underwater scene reconstruction method. In Proceedings– 2014 International Symposium on Computer, Consumer and Control, IS3C 2014 (pp. 773–775) 4. Galdran, A., Pardo, D., Picón, A., Alvarez-Gila, A. (2015). Automatic red-channel underwater image restoration. Journal of Visual Communication and Image Representation 26, 132–145. https://doi.org/10.1016/j.jvcir.2014.11.006 5. Perez, J., Sanz, P. J., Bryson, M., Williams, S. B. (2017). A benchmarking study on single image dehazing techniques for underwater autonomous vehicles. In OCEANS 2017 6. Boudhane, M., Balcers, O. (2019). Underwater image enhancement method using color channel regularization and histogram distribution for underwater vehicles AUVs and ROVs. International Journal of Circuits, Systems and Signal Processing 13, 570–578 7. Voronin, V., Semenishchev, E., Tokareva, S., Zelenskiy, A., Agaian, S. (2019). Underwater image enhancement algorithm based on logarithmic transform. 8. Xu J, Bi, P., Du, X., Li, J. (2019). Robust PCANet on target recognition via the UUV optical vision system. Optik 181, 588–597

Proposing a Model to Enhance the IoMT-Based EHR Storage System Security Shampa Rani Das, Noor Zaman Jhanjhi, David Asirvatham, Farzeen Ashfaq, and Zahraa N. Abdulhussain

Abstract The Internet of Medical Things (IoMT) and Electronic Health Records (EHR) are core aspects of today’s healthcare facilities, hence these technologies and storage platforms should be equipped with innate safeguarding and secrecy concerns for the welfare of individual human beings. The utmost feasible precautions need to be taken by healthcare organizations with regard to user consent, verifiability, scalability, and authentication protocols, aside from prospective vulnerability intrusions. Especially considering the explosive rise of modern health facilities, fraudsters are consistently searching for means to access healthcare information sources as their prime targets. The significance of data gleaned from the healthcare systems is highly valuable on the black market. Blockchain technology is recognized as a much more alluring way to facilitate information sharing via the entire healthcare distribution network while endangering data confidentiality and integrity. The purpose of this research is to strengthen the IoMT-based EHR storage system security utilizing Hyperledger Fabric infrastructure. The proposed model leverages the usage of Hyperledger Fabric’s unalterableness and data protection characteristics to guarantee the confidentiality and integrity of EHRs while also ensuring secure data exchange and identity management for authorized individuals. Hyperledger Fabric strategies must be integrated with edge computing and cloud platforms to further their value-added S. R. Das · N. Z Jhanjhi (B) · D. Asirvatham · F. Ashfaq School of Computer Science, Taylor’s University, Selangor, Malaysia e-mail: [email protected] S. R. Das e-mail: [email protected] D. Asirvatham e-mail: [email protected] F. Ashfaq e-mail: [email protected] Z. N. Abdulhussain Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_42

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best attributes. When our proposed model are objectively implemented in the near future, the key components that can be employed to minimize the exploitation of healthcare data storage would be recognized. Keywords EHR storage · IoMT · Blockchain · Hyperledger fabric · Edge computing

1 Introduction A major complaint is security vulnerabilities in the healthcare sector. Data security and privacy breaches frequently happen with EHRs. No reports have surfaced regarding notifications of unauthorized access to patients’ personal details, and it is questionable what kind of data had just been obtained. Blockchain infrastructure is defined as an exhaustive innovation that has the ability to entirely transform the healthcare sector. Block, ledger, hash function, miners, smart contracts, consensus mechanisms, peer-to-peer (P2P) networks, and transactions are the fundamental components of blockchain systems. Figure 1 illustrates the general blockchain procedure. EHRs seek to interchange patient data while being transparent and accessible. This architecture transforms the widely used shared ledger into a stochastic process. An authorized person can obtain the patient’s historical narrative of the log file and ascertain the trustworthiness of the ledger. We tend to be familiar with four different kinds of blockchain networks: public, private, hybrid, and consortium. Figure 2 indicates the classification of a blockchain.

Fig. 1 General blockchain procedure

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Fig. 2 Blockchain classification

In a permissionless blockchain, each peer must execute any transactions that is feasible while simultaneously running a volatile and ambiguous consensus mechanism to validate it. It neglects user authentication protocol [1, 2]. On the reverse side, permissioned blockchain affirms a user access mechanism that is already in operation and demands that certain processes be carried out by a significant number of identified users only. Thus, private blockchain technology ensures the confidentiality, reliability, and transparency of the healthcare perspective to a specific authenticated user. EHRs have evolved into a crucial component of healthcare organizations all around the globe because they offer a centralized platform for preserving, exchanging, and extracting patient records. EHR has a number of issues and concerns that need to be overcome. The issues are including (i) challenging to manage massive databases; (ii) uncertainty of data processing, preserving, exchanging, and eavesdropping due to the participation of outside parties in data administration; and (iii) the uncanny ability to share people’s healthcare data at a diagnostic stage due to centralized data governance. Nonetheless, the enormous amount of information produced by the healthcare industries has necessitated the development of more effective, exchangeable, and secure storage platforms. The IoMT is a hub of innovative hospital technology and applications that enables it to gather EHR information and analyze it afterwards to assess individuals’ health issues. Technologies that support Wi-Fi offer a way for devices to communicate with each other, forming the fundamental basis of IoMT. Such equipment produces a significant quantity of information continuously, necessitating suitable data storage while considering transmission delay and data security violations into mind. A wide range of IoMT and networks located nearby or adjacent to the user is referred to as edge computing, a pervasive computing approach. Edge is all

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about analyzing information more rapidly and in bulk near the point of generation, providing action-driven responses in real time. When edge route gateways and edge network nodes are reasonably linked, latency can be minimized and the processing time for massive volumes of datasets can be boosted [3]. A service that offers cloud computing that we are able to access via the public or private web lets us conserve files and other information mostly on the Internet concept called cloud storage. Hyperledger Fabric, a consortium blockchain platform, employs Byzantine-faulttolerant (BFT) consensus procedures to ensure a robust and secure exchange of patient records across the network of adversarial consulted individuals. The BFT approach can prevent node failures by lessening the impact of the vulnerable nodes. Hyperledger Fabric is incomparable to current blockchain strategies in terms of effectiveness [4, 5]. Peer nodes and ordering nodes are indeed the primary kinds of nodes in a certain network. Peer nodes are responsible for batching and verifying transactions. The recent historical record of events inside the network is generated and ordered by ordering nodes. Several transactions are perhaps handled at once with enough accuracy. Data protection includes private pathways that are predefined message pathways. Nobody is going to actually be able to access the data without much or no authorization. Chaincode allows for the addition, modification, and transmission of data. The incorporation of blockchain technology, more specifically Hyperledger Fabric, to optimize EHR preservation in the realm of Implantable Medical Devices (IMT) constitutes one of the most feasible approaches. In addition to safeguarding patient confidentiality and record integrity, this infrastructure may establish a decentralized, secure, and unmodifiable platform for the storage and distribution of EHRs.

2 Problem Statement There is a dearth of research on Blockchain Platform within the setting of the Malaysian healthcare industry [6]. Patients seldom acknowledged accessing their EHRs that are administered by the healthcare system and stored on a cloud platform [7]. Only 39 percent of the total patients stated that they felt protected while using their EHR data [8], whereas EHRs are used to keep track of patients’ medical histories and confidential material; protecting them against unauthorized access all through the process of exchanging those data with patient populations is crucial [9]. Healthcare institutions preserve individual medical history on a central database as part of a clichéd EHR system, thus if one part malfunctions, the overall system could be put in jeopardy [10].

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3 Related Work The authors provided an encrypted and safeguarding multi-authority authentication information exchange system for cloud-based healthcare organizations [11]. This research recommended a framework with decentralized identity queries to interface with the module, and that approach was used to store patient data using IPFS and cloud storage [12]. Another study proposed a hybrid approach incorporating both centralized and decentralized blockchains for the secure transmission of patient information among healthcare institutions and diagnostic equipment [13]. An Ethereum-based system was leveraged to foster a resilient environment by strengthening identity management and the privacy of medical data. The study presented the cloud-fog paradigm to ensure the confidentiality of transferring EHR data on the cloud [3]. In their comprehensive literature analysis, Dhakhane et al. noted that an AIblockchain-enabled EHR system would be leading to sustainable user privacy identity management over health information [14]. The research used permissioned blockchain, smart contracts, and Deep Learning (DL) algorithms to design an inventive and reliable interoperable infrastructure [15]. The implementation of a blockchain-enabled system along with AI support enables the execution of a number of AI techniques, such as the identification of visual characteristics, to generate a narrative for a specific patient from clinical information [16]. The notion of developing a healthcare system that integrates Artificial Intelligence (AI), IoMT, and blockchain technology was put out and spanned a wide range of applications, including collecting and analyzing EHRs to prevent data intrusions [17]. A demonstration of how to employ attribute-based and role-based access control techniques to restrict authoritative approach access to sensitive EHR data on the Hyperledger Fabric platform was provided [18]. A blockchain-based structure for compliances and identity authentication for EHR exploitation utilizing pre-established rights primarily driven by decentralized Identifiers was proposed [19]. An alternate solution of EHR structure comprised of blockchain technology and machine learning that incorporates real-time automated backup data and enhanced security of data exchange strategy was presented [20]. An utmost important and effective technique to protect EHR data centers employing blockchain is to ensure that data is relatively secure and tamper-proof [21]. The study highlighted the pertinent issues by integrating EHR and proposed blockchain as a solution to control the data and safeguard the confidentiality, identity, and operability of patient information records [22]. They also revealed that Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR) provide outstanding attributes for the confidentiality, transparency, and dependability of HER data [23]. To prevent invasive accessibility, the proposed passcode authorization key exchange integrates smart contract-enabled user accessibility to EHR transactional data, including Hyperledger Fabric nodes with level DB databases and IPFS off-chain storage [24]. The authors provided a consortium architecture built on the Hyperledger

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Fabric that allows for the safe P2P transmission of private medical information while upholding their confidentiality, legitimacy, transparency, and dependability [25]. Researchers offered a safe, patient-centric blockchain-based approach to manage who may access health information. The cloud and mobile devices were employed to capture EHR data using IoMT sensors [26]. The desired structure for sharing health data assists to enhance current data management tactics. The outcomes of this research demonstrated that it is viable to use Hyperledger Fabric to facilitate interoperability while bolstering security controls in the preservation of patient records [27]. Their system, which was based on the Ethereum platform, would provide secure medical information access for each individual as well as for all healthcare facilitators. Consequently, it includes both a web and mobile application. The foundational architecture on which the Ethereum node is built should provide a trustworthy, scalable, and secure method given its limited capabilities and low electricity consumption [28]. The authors used an Ethereum platform to construct a patientcentric smart contract in order to solve the issues of data exchange, confidentiality, and dependability involved with administrating EHRs [29]. Al Mamun et al. [30] figured out after thoroughly analyzing a few pertinent literature that Ethereum (private) and Hyperledger Fabric encompass utmost widely used current systems for EHR administration since both almost fully satisfy all necessary requirements [30]. Most of the cloud applications are involved in HER applications [31–35], which can provide assistance somehow.

4 Methodology The significant amount of previous research on blockchain deployment leverages yet popular standards, such Ethereum and Hyperledger Fabric, to put their ideas into execution and gauge how well they worked. We recommended the Hyperledger Fabric framework for our proposed model. Edge computing and cloud servers are incorporated into the experimental architecture for safeguarding her storage systems of IoMT-based healthcare sectors. The fundamental concept for the blockchain-enablher EHR data storage model is first presented, and then the system functionalities that are used to build the model are described. A conceptual design that illustrates the anticipated model is shown in Fig. 3. In order to assure the safe storage of patient health information, the system architecture for safeguarding the EHR storage system for IoMT employing Hyperledger Fabric has several components and processes that operate sequentially. The following is a succinct description of the chain of processes. Data Collection: The first step in the process is data collection. This involves collecting health data from various IoT devices and sensors that are connected to the patient’s body.

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Fig. 3 Blockchain-enabled sherre EHR data storage system model

Edge Computing: Identify the devices and sensors that will be used to collect health data. Set up edge devices and gateways to collect and process the data. Configure security measures for edge devices and gateways, such as authentication, authorization, and encryption. Data Encryption: After the data is processed, it is encrypted using secure encryption algorithms to assure that the information is secure during transmission and storage. Smart Contract: Define the smart contract logic that will be used to manage user access control and data sharing. Develop and deploy the smart contract on the blockchain network. Configure the smart contract to interact with the EHR storage system and enforce access control policies. Hyperledger Fabric Framework: Set up the Hyperledger Fabric network and nodes. Configure the network to support EHR storage and access control. Deploy the smart contract on the Hyperledger Fabric network. Hyperledger Caliper: Use Hyperledger Caliper to test the performance and scalability of the Hyperledger Fabric network. Configure the test scenarios to simulate realistic workloads and user interactions. HL7: Configure the EHR storage system to support HL7 data exchange standards. Develop and test HL7 interfaces to enable seamless data exchange between systems.

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FHIR: Configure the EHR storage system to support FHIR data exchange standards. Develop and test FHIR interfaces to enable seamless data exchange between systems. Cloud Computing: Set up a cloud infrastructure to host the EHR storage system and the Hyperledger Fabric network. Configure the cloud infrastructure to support high availability, scalability, and security. Overall, the proposed model should focus on ensuring that the EHR storage system is secure, scalable, and interoperable with other systems. It should also include testing and performance evaluation to identify any potential vulnerabilities or bottlenecks in the system.

5 Conclusion More influencing organizational features like administration of identification and authorization, comprehensive installations, and rules that allow this to tailor the blockchain to particular application instances are all provided by Hyperledger Fabric. We may leverage it to establish an encrypted permissioned P2P blockchain network comprising various identified and verified health system participants in a bid to achieve the highest levels of interoperability, confidentiality, transparency, and sustainability. We have put out a model for a robust EHR storage solution that is patient-centered and blockchain-enabled. A thorough explanation of every basic concept necessary to represent the EHR storage, IoMT, and blockchain platform is provided in the first section. We’ve given a succinct overview of Hyperledger Fabric, which is a good fit for the solution we have suggested. Our research on healthcare data secrecy and trustworthiness will serve as an incentive for more experimental assessments and integration methods that enable reliable healthcare services. EHRs may eventually be used to store a significant number of people’s electronic medical records. Data quantity will rise rapidly over time. This really is why it will be taken into account to estimate a scalable blockchain-enabled EHR storage system. Additionally, thorough investigation and experiment will be required to validate that the recommended design is feasible for utilization in real-world healthcare sectors in the near future.

References 1. Loh, C. M., & Chuah, C. W. (2021). Electronic medical record system using ethereum blockchain and role-based access control. Applied Information Technology And Computer Science, 2(2), 53–72. 2. Lee, J., Park, Y. R., & Beck, S. S. (2021). Deriving key architectural features of FHIRblockchain integration through the qualitative content analysis.

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Synthetic Crime Scene Generation Using Deep Generative Networks Farzeen Ashfaq, Noor Zaman Jhanjhi, Naveed Ali Khan, and Shampa Rani Das

Abstract Synthetic crime scenes can provide an effective training tool for law enforcement personnel, enabling them to gain valuable experience without the need for real-world practice. However, creating realistic synthetic crime scenes is a challenging task that requires advanced artificial intelligence techniques. In this paper, we propose a novel architecture for generating synthetic crime scenes using a hybrid VAE + GAN model. The proposed architecture leverages scene graph information and input text embeddings to generate coarse images of the foreground and background using a conditional variational autoencoder (VAE). Two separate generators then generate more detailed images of the foreground and background, and a fusion generator combines them to create a final image. A discriminator evaluates the realism of the generated images. This approach represents a significant contribution to the field, as it enables the generation of highly realistic crime scenes from textual input. The proposed architecture has the potential to be used by law enforcement agencies to aid in crime scene reconstruction, and may also have applications in related fields such as forensic science and criminal justice. Keywords Synthetic crime scenes · Crime scene investigation · Generative networks · GAN and text to image synthesis

F. Ashfaq · N. Z. Jhanjhi (B) · N. A. Khan · S. R. Das School of Computer Science, Taylor’s University, Selangor, Malaysia e-mail: [email protected] F. Ashfaq e-mail: [email protected] N. A. Khan e-mail: [email protected] S. R. Das e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_43

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1 Introduction The ability to visualize the sequence of events described by eyewitnesses is crucial in crime scene reconstruction for law enforcement teams [1, 2]. Initially, witness accounts were manually depicted through sketching [3], but later computer-assisted tools, such as computer-aided design (CAD), have been used to fully visualize crime scenes [4–8]. Additionally, virtual reality and augmented reality have been utilized to digitally recreate crime [9–12]. However, computer animations are considered the easiest, most affordable, and ideal medium for accurately portraying crime or accident circumstances in courtrooms and to the general public [13–15]. Forensic animations can reconstruct the scene and depict the event at various points in time using real information, such as physical evidence gathered from the crime scene and witness statements. One such depiction of the witness story is visualized in [16] using 3D animation software package as depicted in Fig. 1. “The Suspect walked into the bank with a noticeable limp. He raised a rifle and demanded loudly that everyone get on the ground. From a kneeling position Officer Joe friendly drew his pistol and ordered the suspect to drop his gun”. With the advancements in modern forensics, computer animation techniques are gradually replacing traditional sketches, photographs, and verbal confessions to recreate crime scenes. However, manually generating these animations that accurately depict the sequence of events as they occurred on the crime scene requires a great deal of domain knowledge and technical skills. One promising solution to this problem is the use of deep generative networks, an unsupervised deep learning method that can be trained to generate new data while also learning the underlying

Fig. 1 A sequence of images depicting a crime scene visualization

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distribution of existing data. While these networks have been extensively used in the fabrication of synthetic images since their inception a decade ago, their potential in artificially simulating a crime scene to aid law enforcement and the judicial system has not yet been explored. The ability to create mental images based on verbal input, known as visual imagery [17], is a natural human instinct. However, replicating this process in computers and connecting the visual and verbal worlds has proven difficult. Nevertheless, the emerging field of Text-To-Image Synthesis [18] involves the generation or manipulation of realistic images based on textual descriptions and has numerous applications in various fields. To accomplish this task, it is necessary to combine the two major branches of artificial intelligence, Computer Vision and Natural Language Processing as shown in Fig. 2. Therefore, in this research, we propose a novel approach to generate synthetic crime scenes using a hybrid model of Conditional Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). Our model will utilize scene graphs and input text embeddings to generate coarse images of the foreground and background using a Conditional VAE. The background and foreground images will then be used by two separate GANs to generate more detailed images, which will be fused using a fusion generator along with the discriminator. This model will be trained on real crime scene images and witness statements to produce a synthetic crime scene that accurately depicts the sequence of events as described by eyewitnesses. If successful,

Fig. 2 Text-to-image synthesis combines CV and NLP

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this work will represent a significant contribution to the field of forensic science, as no prior work has used deep generative networks to generate synthetic crime scenes from text and witness statements.

2 Problem Statement Crime scene recreation is a critical component of forensic investigations [19], but manually generating accurate crime scene animations can be time-consuming and require significant technical expertise [20]. Deep generative networks, such as Conditional VAEs and GANs, have been successfully used to generate synthetic images in other contexts [21–24], but their potential for recreating crime scenes has not yet been explored. The aim of this study is to develop a novel approach to generating synthetic crime scenes using a hybrid model of Conditional VAE and GANs, trained on real crime scene images and witness statements. The success of this approach would represent a significant advancement in the field of forensic science, enabling more efficient and accurate crime scene recreation for investigative and judicial purposes.

3 Related Work The literature review consists of two distinct sections. The first section delves into the core concepts of criminal investigation, including the primary methods utilized in the investigation and the technological advancements that have been made in forensic science to date. The second section is devoted to exploring the creation of synthetic images, animations, and videos using deep generative networks, as well as how these networks can be utilized in the recreation of crime scenes based on investigative notes and witness accounts. Crime Scene Investigation (CSI) is a multi-step process that involves collecting, preserving, and analyzing physical evidence from a crime scene to reconstruct the events that took place, identify potential suspects, and provide evidence for use in court [25]. CSI teams consist of a variety of professionals, including law enforcement officers, forensic specialists, and other experts, who work together to gather evidence and establish a comprehensive understanding of the crime. Effective CSI requires meticulous attention to detail, scientific rigor, and collaborative effort to uncover the truth. The fundamental steps involved in the CSI process are depicted in Fig. 3. Criminal investigation refers to the process of collecting, analyzing, and interpreting evidence to uncover and prosecute criminals. It is carried out by law enforcement agencies, typically starting with the gathering of primary information to determine whether a crime has been committed and to identify the perpetrator [26]. The investigation process involves various techniques and procedures, including the collection of eyewitness testimonies, physical evidence, and circumstantial evidence,

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Fig. 3 The fundamentals of crime scene investigation in general

which must all be incorporated in a methodical and precise manner [27]. Successfully achieving the first three goals of identifying the crime, and the criminal, and presenting evidence in court can be considered the hallmark of a successful investigation. Other benefits of this strategy include the return of stolen goods, deterrence of criminal activity, and satisfaction of crime victims [1]. Crime scene investigation (CSI) is a complex and challenging process that requires attention to detail and proper resources. There are several potential issues that can arise during crime scene evaluation, including contamination, time constraints, weather and environmental factors, lack of resources, human error, and dealing with large or complex crime scenes. These challenges highlight the need for a thorough understanding of proper techniques and procedures, as well as the importance of expertise, experience, and resources in shaping the outcome of the case. Currently, there are several gaps in CSI practices that need to be addressed [28]. These include a lack of standardization, limited expertise, lack of resources, limited use of technology, limited collaboration, limited budget, limited access to forensic data, and limited data sharing. These gaps can affect the quality of evidence collected, the accuracy of conclusions drawn from it, and the ability to identify suspects and

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connect cases. Addressing these gaps is crucial in improving the quality and effectiveness of CSI, ensuring that justice is served. Computer-generated images and proper crime scene documentation can also play a significant role in enhancing the accuracy and reliability of evidence collected during CSI. Over the years, different methods have been used to document crime scenes, including photography, sketches, notes, and videos [29, 30]. For example, photography has been a widely used technique to document evidence at a crime scene, and it can provide valuable information about the location and condition of the evidence [31]. Sketches and notes are also useful in documenting crime scenes, especially for items that may not be easily captured in a photograph, such as the location of a bullet hole or blood spatter [32–34]. Overall, proper documentation of a crime scene is crucial for maintaining the integrity of the evidence and ensuring that justice is served. Advancements in technology have also played a role in crime scene documentation. In the past, traditional methods such as photography, sketching, and written notes were commonly used. However, with the advent of digital cameras, GPS technology, and 3D scanners, crime scene documentation has become more accurate and efficient [35, 36]. Computer-generated images and animations have also been used to reconstruct crime scenes and present evidence in court. For example, laser scanning technology has been used to create 3D models of crime scenes, which can provide a more comprehensive view of the area and aid in the investigation. For example, digital photography has become increasingly popular in recent years, and it allows for the rapid capture and storage of large amounts of digital data [37]. In addition, 3D laser scanning has been used to create accurate and detailed computer-generated images of a crime scene [38]. These images can be used in court to provide a visual representation of the crime scene and the evidence collected, which can be particularly useful in complex cases where verbal descriptions may not be sufficient [6]. Furthermore, virtual reality technology has also been used to create immersive crime scene reconstructions that can be used to train investigators and provide a better understanding of the crime scene to judges and jurors [39]. Virtual reality allows investigators to recreate crime scenes in a virtual environment, providing an opportunity for them to examine and analyze the scene from different angles and perspectives [40, 41]. Augmented reality, on the other hand, allows investigators to overlay digital information onto the real-world environment, providing additional information and context about the crime scene. These technologies have the potential to revolutionize crime scene documentation and provide new insights and perspectives for investigators [42, 43]. Moreover, computer-generated visualizations have been used in courtrooms to present evidence to judges and juries. These visualizations can be in the form of animations, simulations, and even virtual reality experiences. They can help to simplify complex evidence and present it in a more understandable and engaging way. For example, in a homicide trial, a 3D visualization of the crime scene [44, 45] can be presented to the jury, providing them with a clear and accurate view of the location and events. The use of computer-generated visualizations in court can help to make the evidence more convincing and aid in securing a conviction.

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In conclusion, advancements in technology have significantly impacted crime scene documentation, allowing investigators to gather more accurate and comprehensive evidence. Traditional methods such as photography and sketching have been enhanced with digital tools such as 3D scanners and virtual and augmented reality. The use of computer-generated visualizations in court can help to simplify complex evidence and provide a more engaging and convincing presentation. The continued development and application of technology to crime scene documentation can help to improve the accuracy and efficiency of investigations, ultimately aiding in the pursuit of justice [46–48].

4 Methodology Our study proposes a novel approach called “CrimeVG” for synthetic crime scene generation using a hybrid deep generative model that combines Variational Autoencoder (VAE) and Generative Adversarial Network (GAN). The VAE component compresses input data into a lower dimensional space, known as the latent space, and then decodes the latent representation back into the original data space. Meanwhile, the GAN component consists of two neural networks: a generator network that creates synthetic samples from the latent space and a discriminator network that evaluates the realism of the synthetic samples. The hybrid VAE-GAN model we used for synthetic crime scene generation is a quantitative approach that involves mathematical functions and numerical computations to model the relationship between input and output variables. Quantitative approaches typically entail collecting numerical data, using statistical methods to analyze the data, and generating numerical results. In our study, we trained the model on a dataset of real crime scenes and evaluated its performance using quantitative metrics such as reconstruction error or the quality of the generated synthetic images. It is worth noting that although the crime scene description used as input for the hybrid VAE-GAN model may be qualitative in nature, the model itself and the results it produces would be considered quantitative. Our study’s hybrid VAE-GAN model shows promise in generating realistic synthetic crime scenes, which could have potential applications in forensic training and education, as well as in generating test data for evaluating forensic analysis algorithms. Figure 4 shows the general design of our suggested model. The following is a discussion of our proposed model architecture and model flow.

4.1 Crime Scene Text Embedding Generation The first step is to convert the input text description into a compact representation using BERT, a language model trained on a large corpus of text. This embedding vector is used as input to the VAE component of the hybrid model.

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Fig. 4 Our proposed model architecture

4.2 Scene Graphs for Text Embedding from BERT We propose using a scene graph to represent the objects and their relationships at the crime scene. The scene graph takes the embedding vector produced by BERT as input and outputs a structured representation that can be used to generate the foreground and background of the scene.

4.3 CVAE for Foreground–Background Generation The CVAE disentangles the visual objects and their attributes in the foreground and background of the crime scene using the input from BERT and the scene graph. It then generates a probability distribution over the latent variables, which can be used to reconstruct the image.

4.4 Improving Generated Image with GANs The GAN architecture consists of three generators, one for the foreground objects, one for the background, and a fusion module that combines the two. This approach allows for greater control over the synthesis process and produces high-quality, synthetic crime scenes that are indistinguishable from real ones.

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4.5 Fusion Module for Final Synthetic Scene The fusion module takes the output of the foreground and background generators and fuses them together to form the final crime scene image. The output is then fed into the Discriminator, which distinguishes between the real and generated crime scene images and improves the generated image quality.

5 Conclusion In this study, we proposed a novel hybrid model for generating synthetic crime scenes using a combination of natural language processing techniques and computer vision. Our model utilizes BERT for text embedding generation, a scene graph for structured representation of crime scenes, a CVAE for foreground–background generation, a GAN for image refinement, and a fusion module for generating the final synthetic crime scene. Through experimentation, we demonstrated that our model can generate high-quality, diverse synthetic crime scenes that are indistinguishable from real crime scenes. Our proposed model has several potential applications, including training law enforcement officials and forensic experts, conducting research on criminal behavior, and augmenting forensic investigations. Moreover, the model can be extended to other domains beyond crime scene generation, such as object detection, scene understanding, and image synthesis. Overall, our research shows that the integration of natural language processing and computer vision can lead to significant advancements in the field of crime scene generation. We hope that our work will inspire further research in this area and contribute to the development of more sophisticated models for generating synthetic crime scenes.

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Co-opetition Reloaded: Rethinking the Role of Globalization, Supply Chains, and Mechanism Design Theory Anastasios Fountis

Abstract In this study, we examine the concepts of co-opetition, globalization, frenemies, supply chains, and mechanism design theory separately and subsequently; we are engaging in an effort to set a framework for the notion of co-opetition as the central idea and to examine its relationship with the aforementioned concepts from a novel perspective. As we are currently experiencing a tumultuous series of events, such as COVID-19, the collapse of global supply chains, the War in Ukraine, and the subsequent volatility in food and energy prices, there is extensive criticism on the true scope of globalization and a change is necessary, based on alternative future scenarios or by introducing new ways of thinking. Coopetition occurs when organizations compete in some value-added activities (e.g., sales) and collaborate in others (e.g., research). The development of standards by competing manufacturers is coopetition, as is the relationship between two corporations that were eventually battling in court while making complementary products. Cooperation and competition can also be found in partial interactions. This comprises all non-cooperative and non-competitive actions and states. Thus, some view coopetition as a more realistic paradigm and as a gateway for achieving strategic goals and see a need for a further application in international relations. Keywords Co-opetition · Globalization · Frenemies · Supply chains · Mechanism design theory

1 Introduction Co-opetition is working together to create a higher value outcome than without interaction. Cost reductions, resource complementarity, and technology transfer are benefits. The UN identified three globalization megatrends: production and labor market shifts, rapid technological advances, and climate change. “New globalization” A. Fountis (B) Faculty, Berlin School of Business and Innovation, Berlin, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_44

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is the rapid economic integration of ideas and knowledge through digital exchange, technology, innovation, and organizational imitation. “Old globalization” was about goods and standard services crossing borders.

2 Co-opetition A portmanteau grammatically is a compound word that is created by combining at least two other words (pronounced port-MAN-toe). The pronunciations and connotations of the two predecessor words are merged into one in the new term. When two distinct words are merged into one, a new term is generated that has its own unique meaning. This new word was created by combining the two words. Such a portmanteau word is the term “co-opetition” (sometimes spelled “coopertition” or “co-opertition”) and was created as a neologism to characterize competitive activities that involve many parties working together. The term “co-opetition” is a portmanteau that combines the words “competition” and “cooperation”. Game theory is a branch of mathematics that became more prominent after the publication of the book Theory of Games and Economic Behavior in 1944 and the works of John Forbes Nash on non-cooperative games. The game theory contains descriptions of the fundamental principles underlying competitive cooperative structures. Competition between organizations can take place either between organizations or within organizations. Throughout the course of history, the idea of co-opetition as well as the phrase and its various iterations have been re-invented on multiple occasions. As early as 1913, the concept was used to describe the relationships among proximate independent dealers of the Sealshipt Oyster System [1]. These dealers were given the instruction to cooperate for the benefit of the system, even though they were instructed to compete for customers in the same city [1]. The term “coopetition” was coined for the first time in 1992 by John Noorda, the then CEO of the network product manufacturer Novell, when he used it in a speech to describe a strategic orientation of the company [2]. There is often a distinction of the co-opetition to inter-organizational and intraorganizational. Inter-organizational: After the publication of a book written by Brandenberger and Nalebuff [3], the concept of co-opetition, as well as the ideas around it, received widespread attention within the business sector. This book has been the standard reference for academics and industry professionals alike right up until today. At the inter-organizational level, other researchers proposed that co-opetition takes place when companies engage with only a partial congruence of interests. They work together to produce a higher value creation, as measured in comparison to the value that would have been created in the absence of interaction, and they strive to obtain a competitive edge. Co-opetition occurs frequently when businesses that operate in the same market collaborate on the acquisition of new knowledge and the development of innovative products. At the same time, these businesses compete with one another for the market share of their respective products and the right to profit from the knowledge that is generated. In this particular scenario, the exchanges take place all

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at once and on a variety of levels throughout the value chain. This is the case in the arrangement between PSA Peugeot Citroen and Toyota to share components for a new city car, which is being sold simultaneously as the Peugeot 107, the Toyota Aygo, and the Citroen C1. This allows the companies to save money on shared costs while still remaining fiercely competitive in other areas. It is possible to anticipate a number of benefits, including cost reductions, the complementarity of resources, and the transfer of technological know-how. There are also certain challenges, such as unequal risk distribution, complementary demands, lack of trust, and distribution of control. It is not impossible for three or more enterprises to be engaged in cooperative competition with one another. Cooperative competition can also take the form of shared resource management in the building industry. There is research which presents a short-term partnering case in which construction contractors form an alliance, agreeing to put all or some of their resources in a joint pool for a fixed duration of time and to allocate the group resources using a more cost-effective plan. The case focuses on a scenario in which the construction contractors form an alliance and agree to put all or some of their resources in a joint pool for a fixed duration of time. Additionally in practice, policy makers and regulators can trigger, promote, and affect co-opetitive interactions among economic actors who did not intentionally plan to coopete before the external institutional stakeholders (i.e., a policy maker or regulator) created the conditions for the emergence of coopetition. This was found to be the case in a number of real-world scenarios. Cooperative game theory was proposed by Asgari et al. [4] as the basis for fair and efficient allocation of the incremental gains of collaboration among the collaborating contractors [4]. The findings of their research presented a novel approach to the planning and distribution of building resources. Contractors no longer view one another as their only competitors; rather, in an effort to lower their overall costs, they seek cooperative opportunities beyond their competition. On the other side at the intra-organizational level, co-operative competition takes place between persons or functional units that are part of the same organization at the intra-organizational level. Some studies, drawing on game theory and theories of social interdependence, investigate the presence of simultaneous cooperation and competition among functional units, the antecedents of co-opetition, and the impact of co-opetition on knowledge-sharing behaviors. For instance, the idea of co-opetition-influenced effective knowledge sharing practices in cross-functional teams led to the development of the concept of co-opetitive knowledge sharing. This concept was developed to explain the mechanisms through which co-opetition influences effective knowledge-sharing practices. The argument that lies beneath the surface is that organizational teams, despite the fact that they are required to cooperate with one another, are likely to experience tension as a result of different professional philosophies and competing goals brought forth by various cross-functional representatives. Co-opetition is not an example of cartels because the goal of cartels is to limit competition, whereas the goal of co-opetition is to take advantage of the complementary resources of the firms in order to achieve lower costs and manage new innovation possibilities while still regarding competition at a later point in time. Cartels are not an example of co-opetition because cartels have as their goal to limit competition.

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3 The Mechanism Design Theory and Co-opetition An economic theory called mechanism design theory aims to investigate the methods through which a specific end or result can be attained. The mechanism design theory is an economic framework for comprehending how businesses might accomplish their goals when obstacles like vested interests and inaccurate information may stand in their way. The theory, which derives from game theory, explains how individual incentives and motives can be used to a company’s advantage. The founders of the theory were given the Economic Sciences Nobel Prize in 2007. A subfield of microeconomics called mechanism design studies how organizations and institutions might produce desirable social or economic results under the restrictions of people’s selfinterest and imperfect knowledge. Principal-agent issues might arise when people behave in their own self-interest and aren’t driven to give truthful information. In particular, the mechanism design theory focuses on how organizations and institutions can produce desirable social or economic outcomes under the constraints of people’s self-interest and incomplete information. It enables economists to analyze, compare, and possibly regulate specific mechanisms linked to the achievement of particular outcomes. Mechanism design considers incentives and private information to improve economists’ understanding of market mechanisms and demonstrates how the correct incentives (money) can persuade players to divulge their private information and produce the best results. Thus, mechanism design theory is employed in economics to investigate the procedures and systems that lead to a given result. Basically, it is an inverse problem of game theory as it starts at the end of the game, then goes backwards; it is also called reverse game theory. By combining their efforts, Eric Maskin, Leonid Hurwicz, and Roger Myerson greatly popularized the idea of mechanism design theory. The three researchers were acknowledged as the subject’s founding authorities after receiving the Nobel Memorial Prize in Economic Sciences in 2007 for their work on the mechanism design theory. The idea of game theory was generally established by John von Neumann and Oskar Morgenstern in their 1944 book, Theory of Games and Economic Behavior, which served as the foundation for mechanism design theory. The study of how various actors interact both competitively and cooperatively to produce events and results is known as game theory in economics. Many mathematical models have been created to effectively examine this idea and its outcomes. More than a dozen Nobel Prizes have been awarded to academics in the field of game theory, which has been recognized throughout the history of economic studies. However, the approach of mechanism design theory to game theory is typically the opposite. By starting with an outcome and figuring out how different actors interact to get that outcome, a scenario is studied. In order to arrive at an outcome, both game theory and design theory examine the competing and collaborative influences of various actors. The mechanism design theory takes into account an intended result and the steps necessary to get there. The game theory examines how different players may affect various results.

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A distinct example are the Financial Markets and Mechanism. Design Theory Mechanism design theory has a wide range of applications, and as a result, numerous mathematical theorems have been created. With the use of these applications and theorems, researchers can govern the entities’ access to information and manage their constraints. An auction market is one setting where the application of mechanism design theory is demonstrated. In general, regulators want to create a competitive market that is efficient and well-organized for players. In order to get this conclusion, a number of entities with different levels of association and information are involved. The goal of mechanism design theory is to regulate and restrict participant access to information in order to produce the desired outcome of a well-ordered market. For exchanges, market makers, buyers, and sellers in particular, this typically necessitates the monitoring of information and activity at several levels. When solving, or better said, depicting a mechanism design problem, the target function or goal function is the most important “given”, while the underlying mechanism is the unknown factor. Among the most common representations which can be applied in the field of relations between nations is “pie-splitting”. This gives in a simple context a description of how resources are shared or deals and treaties are starting to get formed. The main point is the willingness to share and the assumption that all players are rational ones. The study of solution concepts for a category of games involving private information is called mechanism design. In a problem involving design, the goal function is the most important “given,” while the mechanism, as was previously stated, is the unknown. It is essential to make the distinction (1) that a game’s “designer” decides on the game’s structure rather than simply inheriting one; and (2) that the designer is invested in the game’s results. The person who designed the mechanism is taking part in and supervising the process. We are going to assume the scenario of slicing a cake. The “you-cut-I-choose protocol” has an additional property: even if the players’ values are different, it is possible for each one of them to be guaranteed at least half of the cake, according to their own evaluation. And this remains true even in the scenario in which they keep the other’s valuation a secret. Consider the person who is responsible for cutting the cake; he can divide it into what he believes to be two equal parts, thereby ensuring that he will receive one-half of the value. While the other person gets to choose what they think is the best piece of the cake for themselves, they each get at least half of what they think the cake is worth. For a pie-splitting or cake-cutting procedure with n players, with utility functions ui and where player i obtains piecei . • • • •

It is fair if for each player i, ui (piecei ) ≥ 1/n. It is envy-free if for each player i and player j, ui (piecei ) ≥ ui (piecej ). It is equitability if for each player i and player j, ui (piecei ) = uj (piecej ). It is exact if for each player i, and cut j, ui (piecej ) = 1/n.

The Austin moving-knife methods [5] are ways to divide a cake fairly [5]. They divide the cake into precisely 1/n pieces and give one piece to each of the n partners.

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In comparison, proportional division procedures divide the cake equally among all partners, giving each at least one-half of the total amount, though some partners may receive more. When n equals 2, Austin’s method produces a division that is both precise and envy-free. Furthermore, the cake can be divided into any number k of pieces that are valued by both parties as precisely 1/k. As a result, any fraction can be used to split the cake between the partners. Some cases for application in the field of contemporary politics will be mentioned in the sixth and final section of this section. There is extensive literature on such efforts, but they are usually applied from a Grand Strategy perspective [6]) [at the game theory level rather than from the point of view of the mechanism design theory which applies in the field of the computational politics, which is the nexus of political science and computer science [6]. In this field, computational techniques are used to give answers to political science problems, such as analysis tools and prediction techniques. Large data sets are used by researchers in this field to examine user activity. Building a classifier to forecast users’ political bias in social media or identifying political bias are typical examples of such works and are getting driven by the sets of political choices which are offered to citizens. An unprecedented amount of latent, user-generated data has been made available to researchers in science and campaign strategists, thanks to social media, and recent advances in computer science have made it possible to store and handle sizable data sets. Political science study has undergone a significant shift, thanks to computational politics, which effectively collects data on individuals rather than aggregates. Targeting likely voters can be done successfully using this knowledge [7]. In general, the field of Political Game Theory is becoming a growing academic field as the global complexity is increasing and new perspectives are needed with the use of more scientific tools [8]. Dagnino and Rocco [9] in their book Coopetition Strategy: Theory, Experiments and Cases have done a significant contribution to the field of study of co-opetition [9]. The cardinal point for them was the following question: Is the concept of co-opetition merely another passing fad or does it represent a fundamental shift in the way we think about strategy? As they also very successfully mention they try to Convert a “liquid” word into a tangible word. When looked at from two different theoretical vantage points—namely that of competition theory and cooperation theory—this subject can be seen in a much clearer light. Because of this, there is a significant urge to reduce it to a straightforward extension of either the competition theory or the cooperation theory. In regard to the former, co-opetition could become a part of the “competitive paradigm” if cooperation between firms is considered to be “competitive maneuvering” or “cooperative maneuvering,” both of which can provide a competitive advantage. Co-opetition could also become a part of the “competitive paradigm” if co-opetition is considered to be part of the “competition”. In relation to the second strategy, co-opetition is nothing more than an additional form of cooperation. Research on co-opetition can, consequently, make substantial use of the alliance theory. The principles of trust, opportunism, and commitment, which play significant roles in dyadic cooperative relationships, can also be applied to co-opetitive relationships and have the same effect.

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Since its inception in strategic management, the competitive paradigm has centered its attention on the rivalry between different firms. In order for the company to continue existing, it is necessary to increase its competitive position, which then paves the way for the creation of value-creating competitive advantages. This requirement has taken on a far greater significance as of late. The once relatively stable markets have become either “hypercompetitive” or “aggressively competitive,” and even “voraciously competitive”. Businesses that wish to thrive and remain in business in today’s market have little choice but to engage in behavior that can be characterized as aggressive or hypercompetitive. On the other hand, the cooperative paradigm places an emphasis on the necessity of enterprises, divisions, and functions cooperating with one another. Through the utilization of this strategy, the company is able to build and improve its competitive edge by way of strategic partnerships, networks, or strategic ecosystems. The ability to develop and maintain relationships gives one access to the valuable resources of others, and, as a result, a relational advantage. The advantages offered by competition encourage the search for novel rent-generating combinations of resources, talents, and processes. The benefits of cooperation include the availability of scarce resources that complement one another. If the company is serious about achieving both kinds of benefits, it will need to engage in activities that are simultaneously competitive and cooperative. This duality was brought to widespread attention by Nalebuff and Brandenburger [3]. These scholars see cooperation as providing a theoretical basis that is based on the “value network concept” from the perspective of game theory, and they treat it as such. When viewed from this angle, co-opetition covered all of the interests and purposes of the complete players, which manifest themselves when competition and cooperation are simultaneously carried out.

4 Globalization: Is It Still Here? Globalization is the interaction and integration of people, businesses, and governments throughout the world. The term globalization first appeared in the early twentieth century (replacing the earlier French term mondialization), acquired its current meaning during the second half of the twentieth century, and entered common usage in the 1990s to describe the unprecedented international connectivity of the post-Cold War era. Its origins can be traced back to the eighteenth and nineteenth centuries as a result of technological advancements in transportation and communication. This increase in worldwide relationships has led to an expansion of international trade and the sharing of ideas, beliefs, and cultures. Globalization is largely an economic interaction and integration process combined with social and cultural factors. However, disagreements and international diplomacy also play a significant role in the history of globalization and modern globalization. Economically, globalization encompasses products, services, data, technology, and capital resources. The expansion of global marketplaces liberalizes the exchange of products and money. The elimination of international trade obstacles has facilitated the establishment of global markets.

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Transportation innovations, such as the steam locomotive, steamship, jet engine, and container ships, and advancements in telecommunication infrastructure, such as the telegraph, Internet, mobile phones, and smartphones, have contributed significantly to globalization and increased the interdependence of economic and cultural activities throughout the world. A very interesting approach to the new eventual global layout has been brought by O’Sullivan and Skilling [10]. The central argument is that the global economy is transitioning to a “war footing,” with growing strategic rivalry between the world’s major countries changing the global economic and political system [10]. The essential domains of this strategic struggle are economics, finance, and technology, which go far beyond military competition. This strategic competition will have an ever-increasing influence on government policy across several domains, including macro policy, industrial policy, and the net zero transition. Consequently, economic outcomes, the business climate, and markets will be impacted. The year 2022 was the “end of the beginning” for the new regime, and these realities will strongly influence 2023. The global economy has become largely depoliticized, but politics has returned with a vengeance. Note that they are not predicting a “war,” but Clausewitz’s adage, “War is merely the continuation of policy by different means,” is instructive in describing a world that is becoming increasingly contested. Previous regime transitions have had substantial effects on investment returns, profitability, and national outcomes. To thrive in this new setting, new ways are required. They identify five important themes connected with this regime shift and its ramifications for enterprises, investors, and policy makers, ranging from an evolving globalization model to the return of the state and altered macro policy settings: (1) Globalization and strategic autonomy. Globalization is undergoing structural shifts. Partially responsible are economic issues, which favor reshoring and nearshoring. However, home politics and geopolitics have a far more disruptive effect on global flows. (2) The Return of the State. After several decades of diminishing government spending, which was interrupted by the global financial crisis, the size and function of the state are growing. It will be difficult to undo the pandemic and energy crisis aid packages, particularly in the context of a declining economy, because they represent shifting expectations of the government’s role. (3) Democracy fights back—and the autocratic recession. Sometimes, the geopolitical conflict between great powers has been portrayed in terms of democracy against despotism. This composition is not exactly correct, but it does capture something. As several Western democratic systems have faltered in

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recent years, they have frequently been compared to ostensibly successful autocracies such as China. (4) Macro unraveling. Additionally, strategic competition will lead to the disintegration of the macro policy order. (5) The commanding heights: the technology and energy revolution. It is often believed that technology dominates the commanding heights of the global economy, and national economic policy frequently focuses on gaining a technological advantage. China, the European Union, the United States, and others are investing more in bolstering strategic autonomy in key technology industries. Economic sanctions and limits are being imposed on technology flows and investments between competing blocs, and this will intensify through 2023, attracting a larger number of nations. There will be decisions to make. Due to the pursuit of strategic autonomy, global economic fragmentation incurs costs. However, like in other spheres, international competition can be a positive factor—creating sharper incentives for investment and innovation (as during the Cold War). Moreover, energy remains a fundamental component of economic advantage. In comparison to Europe, the United States has a greater degree of energy independence. Europe is currently facing competitive pressures, particularly in energy-intensive industries, and we see the struggle of major economies like the one of Germany which is mainly depending on manufacturing activities compared with other economies which focus on services. In logistics and especially in supply chain management, the supply chain is a network of modes of transport and means of transport that ensures the uninterrupted movement of goods from the place of origin to the destination. In the years 2020, 2021, and 2022, as a consequence of the COVID-19 pandemic, global supply chains and shipments slowed down, causing worldwide shortages and affecting consumer patterns. The situation remains still complicated also due to eventual energy shortages and the relevant political environment with the War in Ukraine. The economic sectors of an economy are a framework for understanding the impact of the supply chain disruption on all goods and services for the global economy and subsequently to the existence of the globalization as we knew it up to now.

5 The Rising of Frenemies An oxymoron and a combination of the words “friend” and “enemy,” the term “frenemy” (sometimes written “frienemy”) refers to “a person with whom one is amicable, despite a basic dislike or rivalry” or “a person who combines the traits of a friend and an enemy”. The phrase is used to define interactions of a personal, geopolitical, or commercial nature between individuals, groups, or institutions. These relationships can exist between individuals. A similar concept, a competitive friendship is also

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described by author and activist Jessica Mitford, who was based in the United States, and asserted in 1977 that one of her sisters had invented the term when the latter was a young child. The word was initially used to describe a somewhat dumb little girl who lived near the family: “My sister and the enemy would always play together, despite the fact that they hated each other with all of their hearts” [11]. Beginning in the middle of the 1990s, its utilization experienced a meteoric rise. Rivalries in the workplace are rather prevalent, especially among companies that work together on projects. While it was certainly not unheard of for people to socialize with colleagues in the past, the sheer amount of time that people spend at work now has left a lot of people with less time and the inclination to develop friendships outside of the office. This is due to the increasing informality of work environments as well as the “abundance of very close, intertwined relationships that bridge people’s professional and personal lives”. To have a good professional connection requires two or more business partners to come together and benefit from one another. On the other hand, to have a successful personal relationship requires more similar interests outside of the company. Relationships are more likely to develop between people who share similarities in the office, in a sports club, or in any other environment where there is competition based on performance. The intensive setting can foster competitiveness, which can then morph into envy and put a strain on a relationship. Because of the common interest in engaging in business dealings or competition, frenemy-type interactions become habitual and commonplace. When asked about himself, according to an anecdote, Sigmund Freud once observed, “an intimate friend and a loathed enemy have always been important to my emotional life… certainly not infrequently…both a friend and an enemy might be found in the same individual at the same time”. On the basis of the actions they exhibit toward one another, frenemies can be classified as follows, according to [12]: (1) A person is considered to be a one-sided frenemy by the other party if they only interact with or reach out to them when they need something from them, such as assistance or a favor. This individual does not have any interest in the other individual’s life and does not care about what is going on in it. It is a one-sided relationship since one party does not show up in time to meet the requirements of the other, and this also causes problems. (2) Unfiltered and undermining adversary: This type of adversary taunts, makes fun of, and cracks sarcastic jokes about the friend on such a regular basis that it becomes difficult to endure their behavior. In addition, private information becomes widely known. (3) An overly involved enemy is one who interferes in the lives of a friend in ways that the friend might feel uncomfortable with or find to be inappropriate. They contact their family, friends, or significant others in an inappropriate manner or without the permission of those individuals in order to find out something. Their over-participation frequently causes their companion to feel both bothered and irritated.

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(4) A rival at work is an example of a competitive work enemy. This type of work enemy is an adversary to one individual. They put on a friendly front, flatter one another, and act as though they want the best for one another, but in reality, none of them truly wishes the other person any happiness or success in life. They never want the other person to be more successful than they are. (5) There is also a type of adversary known as an ambivalent enemy, and they possess both good and bad characteristics. There are occasions when they are helpful and courteous, but there are also instances when they behave in a manner that is self-serving or competitive. (6) Jealousy can transform friends into adversaries, and it may do so quite quickly. Jealousy can arise for a variety of reasons, including a friend’s promotion, success, attractiveness, personality, sense of humor, or social standing. (7) Uncertain enemy: A person who is unsure of the status or degree of closeness of their friendship may, for instance, be unsure about whether or not the other person likes them, whether they are true friends or simply professional buddies, or whether or not they will consider asking them to family events. (8) The passive-aggressive adversary is someone who will say hurtful things and give compliments behind the other person’s back, but they will never do any of those things straight to their face. They have the potential to leave a person questioning their actions and wondering if they have done something wrong.

6 Co-opetition 2.0 The level of cooperation between rivals is at an all-time high. Consider both the potential downsides and upsides using the following guide. Since the 1980s, there has been a growing trend toward “co-opetition,” which refers to working together with an adversary in order to accomplish a shared objective or gain an advantage. However, a large number of businesses are uneasy with the idea, and as a result, they pass up the exciting potential it brings. The practitioners and scholars Brandenburger and Nalebuff [13] who were involved in the development of the methodology back in 1996 offer a framework for deciding whether or not to form a partnership with a competitor by drawing on examples from Apple and Samsung, DHL and UPS, Ford and GM, and Google and Yahoo [13]. Both companies have been involved in the methodology’s development. In their opinion: To begin if you want to know the potential for cooperation with your competitor, conduct an analysis of what each party will do if it chooses not to cooperate and how the results of that decision will alter the dynamics of the industry. It’s possible that working together will result in a clear victory, but even if it doesn’t, it may still be preferable than letting someone else take your place in the transaction, which could put you in a worse position. After then, it is essential to figure out how to collaborate without divulging your “secret sauce,” which refers to your existing advantages. After you’ve completed those steps, the next step is to draft an agreement that spells out the specifics of the business transaction, including its scope, who will

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be in control, how the arrangement can be undone if it’s not working well, and how the profits will be split. You’ll also have to deal with resistance from within your own company and work to shift internal views. Co-opetition demands mental flexibility, but businesses that are able to cultivate it can gain a significant competitive advantage. The United States and the Soviet Union engaged in a strong struggle over the decades leading up to their successful landing on the moon just over 50 years ago. But the truth is that collaboration was almost how space exploration got started. When President John F. Kennedy met with Nikita Khrushchev in 1961 and again when he addressed the United Nations in 1963, he advocated a collaborative mission to the moon. It was never realized, but, in 1975, the adversaries from the Cold War started cooperating on the Apollo-Soyuz project, and by 1998, the jointly controlled International Space Station had ushered in a new era of collaboration in the field of space exploration. Moreover, the interesting point is that Brandenburger and Nalebuff [13] enhanced the concept and they added a new dimension in their latest framework, and from the corporate level extrapolated it to the state level. Could countries such as the United States and China, for example, work together on an expedition to Mars? Because doing so would require giving up control of the intellectual property in a way that cannot be undone, this presents an obstacle that appears to be insurmountable. As a result of the fact that military applications can be found in space technology, this is a particularly sensitive subject. The latest point gives also a new component on the way we see co-opetition or concepts like frenemies, under the current fluctuant condition of globalization and by experiencing a real ongoing strategic dichotomy between liberal democracies and authoritarian regimes. Brandenburger and Nalebuff [13] started this interesting extension of their 1996 book and somehow touched the functionality of the co-opetition at the nation’s level by discussing a chance that was lost for the United States of America and the Soviet Union to work together on a journey to the moon [13]. Today, there are even more chances for nations to work together, whether it is in the fight against COVID-19 or the fight against climate change, or even in the fight against trade conflicts. This may sound like at the end of 2022 or the beginning of 2023 perhaps utopic but sooner or later new forms of eventual globalization with new regional frames and networks will arise. They concluded that a deeper comprehension of the concept of co-opetition can assist companies, managers, and nations in discovering more effective ways to collaborate and achieve shared goals. The entanglement of areas defined under the co-opetition gives also the perspective or the impression of a rope pulling. Rope pulling (also known as tugging war) is a sport in which two teams compete to pull a rope a certain distance against the opposing team’s pull. The earlier mentioned notion of frenemies as well as the frame of co-opetition depicts the struggle to not only pull but also keep balance as in a co-opetition environment no one wants or has to fall. As previously mentioned, the mechanism design theory can offer a context for analyzing international alliances at the nation’s level which are getting formed on the basis of goal congruence. The main assumption for a better understanding is that

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the tendency for fierce, usually zero-sum games is getting more constrained, and the tendency is for alliances and networks. The actual context for research is the political action after the Russian invasion of Ukraine in 2022. Firstly, we have the Eurasianism move with the economically sanctioned political systems of Russia, Iran, China, and Turkey as the main drivers [14]; secondly, the NATO alliance which is becoming the center of a political and economic, especially for energy, network defending the idea of the Western, liberal democracies, where the USA has the pivotal role. Finally, the big question mark remains the long-standing economic co-opetition between China and the USA which may become a long-pending competition in war terms and not avoid the Thucydides Trap [15]. In all these cases, the model of the pie-splitting as presented in the third section of this study can get applied in order to have sooner or later a balance.

7 Conclusion Co-opetition is the practice of working together to produce a higher value creation, as measured in comparison to the value that would have been created in the absence of interaction. Benefits include cost reductions, complementarity of resources, and transfer of technological know-how. The strategic competition will have an ever-increasing influence on government policy across several domains, including macro policy, industrial policy, and net zero transition. This will have profound implications for enterprises, investors, and policy makers, ranging from an evolving globalization model to the return of the state and altered macro policy settings. Challenges include unequal risk distribution, lack of trust, and distribution of control. Cooperative competition can also take the form of shared resource management in the building industry. Policy makers and regulators can trigger, promote, and affect co-opetitive interactions. Mechanism design is an economic framework for comprehending how businesses might accomplish their goals when obstacles like vested interests and inaccurate information may stand in their way. The mechanism design theory, which derives from game theory via a reverse design of it based on, explains how individual incentives and motives can be used to a company’s advantage. The study of how various actors interact both competitively and cooperatively to produce events and results is known as game theory in economics. Mechanism design theory has a wide range of applications, and, as a result, numerous mathematical theorems have been created. An auction market is one setting where the application of mechanism design theory is demonstrated. The mechanism design theory is the backbone for interpreting co-opetition. Coopetition has become a widespread mode of operation in various economic sectors and industries such as automobiles, biotech, telecommunications, computers, and many others. New concepts are required in order to adequately describe the increasing complexity of alliance connections between competing businesses, and the last decades started getting applied in the international relations field. Cooperation may

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be seen as a phenomenon that has been around for a very long time, but is only just beginning to take on the new dimensions and significance associated with the modern era. The concept of simultaneously thinking about cooperation and acting in a way that is both cooperative and competitive requires a cognitive revolution in both research and practice. The term “frenemy” refers to a person and by the time to states or other non-state actors in international relations with whom one is amicable, despite a basic dislike or rivalry. This is due to the increasing informality of work environments as well as the “abundance of very close, intertwined relationships”. Frenemies can be classified on the basis of the actions they exhibit toward one another. Jealousy can transform friends into adversaries, and it may do so quickly. Since the 1980s, there has been a growing trend toward “co-opetition,” which refers to working together with an adversary in order to accomplish a shared objective or gain an advantage. As mentioned, there is an effort for converting a “liquid” word into a tangible word under the concept of co-opetition. The challenge under the current global conditions is becoming more immense as there is a somehow disruptive transformation, first of all of the globalization as we knew it up to now.

References 1. Wilding, D. (2018). Shore Lore: The glory days of Sealshipt Oysters. Retrieved December 23, 2022, from https://eu.wickedlocal.com/story/cape-codder/2018/03/03/shore-lore-glory-dayssealshipt/13765886007/ 2. Herzog, T. (2010). Strategisches management von Koopetition–Eine empirisch begründete Theorie der Zivilen Luftfahrt. Wirtschaftsuniversität Wien. 3. Brandenburger, A., & Nalebuff, B. (1996). Co-opetition: A revolution mindset that combines competition and cooperation. Harvard Business Press. 4. Asgari S., Afshar A., Madani, K. 2020. Cooperative game theoretic framework for joint resource management in construction. Journal of Construction Engineering and Management, 140(3). 5. Austin, A. K. (1982). Sharing a cake. The Mathematical Gazette, 66(437), 212–215. https:// doi.org/10.2307/3616548 6. Guner, S. (2012). A short note on the use of game theory in analyses of international relations. Retrieved December 23, 2022, from https://www.e-ir.info/2012/06/21/a-short-note-on-the-useof-game-theory-in-analyses-of-international-relations/ 7. Chester, J., & Montgomery, K. C. (2017). The role of digital marketing in political campaigns. Internet Policy Review, 6(4). https://doi.org/10.14763/2017.4.773 8. McCarty, N., & Meirowitz, A. (2007). Political game theory: An introduction (analytical methods for social research). Cambridge University Press. https://doi.org/10.1017/CBO978 0511813122 9. Dagnino, G. B., & Rocco, E., (Eds.) (2009). Coopetition strategy: Theory, experiments and cases (1st ed.). Routledge. https://doi.org/10.4324/9780203874301 10. O’Sullivan, M., Skilling, D. (2022). War by other means—positioning for 2023, The levelling. Retrieved December 23, 2022, from https://thelevelling.blog/2022/12/09/war-by-other-meanspositioning-for-2023/ 11. Mitford, J. (2010). Poison penmanship: The gentle art of muckraking. New York Review Books, p. 218.

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12. Clarke, K. (2017). Five types of frenemies and the signs that you have one. Retrieved December 22, 2022, from https://www.cbc.ca/life/wellness/five-types-of-frenemies-and-the-signs-thatyou-have-one-1.4060736 13. Brandenburger, A., & Nalebuff, B. (2020). The rules of co-opetition. Harvard Business Review. Retrieved December 22, 2022, from https://hbr.org/2021/01/the-rules-of-co-opetition 14. Laruelle, M. (2008). Russian Eurasianism: An ideology of empire. Princeton. 15. Allison, G. (2017). Destined for war: Can America and China Escape Thucydides’s Trap? New York: Houghton Mifflin Harcourt. ISBN 978–1328915382.

Throughput Performance Analysis of DL-MU-MIMO for the IEEE 802.11ac Wireless LAN Ziyad Khalaf Farej and Omer Mohammed Ali

Abstract The development of large-size applications and the increasing number of users on WLANs have increased the need for low latency and high-speed data rates. In the IEEE 802.11ac, many features are added to the PHY and MAC layers to improve network performance and increase the data rate; at the PHY layer, IEEE 802.11ac introduces Down-link Multi-User Multi-Input Multi-Output (DLMU-MIMO), which enables the Access Point (AP) to simultaneously transmit to various receivers using multiple spatial streams (SS). However, MU-MIMO required beamforming and channel calibration, which increases overhead. Channel calibration might be performed either per transmission or periodically. This paper concentrates on the impact of beamforming and two (20 and 80 ms) periods of channel calibration on the performance of the system in terms of throughput. Four scenarios with (5, 15, 30, and 45) nodes are proposed to investigate the network performance with different (2 × 2 single-user-MIMO (SU-MIMO), 4 × 4 SU-MIMO, 4 × 4 MUMIMO-80 ms, and 4 × 4 MU-MIMO-20 ms) spatial stream. The OMNet++ v5.5.1 Modeler is utilized to model and simulate these scenarios. The simulation results show the throughput of 4 × 4 SU-MIMO outperforms that of 4 × 4 MU-MIMO (20 and 80 ms) by 32% and 18% (for 5 node scenario) up to 83% and 52% (for 45 node scenario), respectively. Keyword DL-MU-MIMO for the IEEE 802.11ac · Beamforming for IEEE 802.11ac · OMNet++ Modeler v5.5.1. IEEE 802.11ac

1 Introduction When data traffic increases significantly in WLAN, the networks cannot support this increase in the demands, so new improvements are added to the WLAN standards, to obtain an increase in the data rate with low latency. The 802.11ac has been developed, Z. K. Farej (B) · O. M. Ali Northern Technica1 University, Mosul, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3_45

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with many features added to its PHY and MAC layers, to provide very high throughput WLAN, such as using modulation up to 256-QAM, bandwidth up to 160 MHz, MIMO transmission up to eight SS is provided to support a 6.9 Gbps data rate [1, 2]. Besides, beamforming schemes, including SU–MIMO and MU–MIMO techniques to solve the interference problem among users, and increase the gain, thus increasing the data rate, improve the efficiency of spectral for a certain channel configuration [3]. An AP issues a packet involving only preambles for channel sounding, and a compressed beamframe is received with adjusted DL (down-link) information of the channel from clients. In the MU–MlMO technique, the AP can send multiple data streams to various clients at once without causing overlapping by using the transmit beamforming [4]. The MU-MIMO is considered a new feature that is added to the IEEE 802.11ac standard [5]. The beamforming achieves higher spectral efficiency, despite its need for overhead sounding. Any mismatch between the channel state and the transmit beam causes efficiency performance degradation especially when the transmission duration is much longer than the channel calibration time [6]. In this paper, features of the beamforming (SU-MIMO and MU-MIMO with 20 and 80 ms) are considered and the throughput performance of the WLAN is investigated under this feature for different number of node topologies.

2 Related Work A new module for Massive MlMO is designed by [2] using the OMNeT++ network simulator, for performance evaluation and verifying the operation of an IEEE 802.11ac WLAN depending on the theoretical expectations. The authors in [7] have proposed a detection algorithm of high performance for DL-MU-MlMO with practical error minimization in IEEE 802.11ac LAN. The results showed performance improvement for any MCS and the number of STA cases. The throughput of the IEEE 802.11ac WLAN standard’s MU-BF and SU-BF modes were analyzed under timevarying channels by the authors in [8]. They studied system behaviors and throughput results for different beamforming transmissions with numerical results based on mobile STA speed, operating SNR, and payload size. Kosek [9] has suggested a model DEMS queuing mechanism to help DL-MU-MIMO transmission. The end result impacts high throughput and a decrease in the queuing delay for high priority A.Cs. Chulho [10] has estimated a MAC network system performance prior to its implementation through a proposed uniform MAC design process. The proposed methodology was utilized to implement the IEEE 802.11ac DL-MU-MlMO MAC system on an ARM-based test platform using C-MOS technology. A comparison of the network simulation and the actual system verified its validity. The authors in [11] mathematically compare and measure the throughput of MU-MlMO, SUMlMO, and SlSO. The result indicated that when the AP supports the same number of spatial streams as that in the receiver, the performance of the SU-MlMO is more efficient than that of the others. In [12], the comparison between SU-MIMO and MUMIMO showed that when evaluating the performance of MU-MIMO, it’s important

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to consider crosstalk interference (CTI). The results show that MU-MlMO can have a less throughput gain and much less stability than SU-MlMO. The authors in [13] have studied the problem of AP selection in MU-MIMO WLAN and proposed a new MUMlMO-Aware AP Selection (MAPS) algorithm. The results illustrate that MAPS outperforms legacy designs and provides low-overhead design with best-throughput assignment for client.

3 Research Method and Material The IEEE 802.11ac standard specifies a new physical layer format known as Very High Throughput (VHT) PHY [14]; the VHT PHY specification includes new highspeed transmission modes based on Orthogonal Frequency Division Multiplexing (OFDM). These modes employ techniques such as up to 8 MlMO SS, down-link MU-MIMO up to four clients with Transmission Opportunity (TXOP) sharing highdensity QAM modulation (up to 256-QAM), and the ability to use 20, 40, 80, and 160 MHz channels [15, 16]. Currently as shown in Table 1, Wave 1 and Wave 2 devices are available in IEEE 802.11ac. Wave 1 devices perform 80 MHz channels, 256 QAMmodulation, and up to 3×3 MIMO (Wave 1 generation does not contain MU-MIMO). 802.11ac Wave 1 devices have a theoretical performance of up to 1.3 Gb/s (about 433.3 Mb/s per MIMO stream). Wave 2 devices can utilize up to 4 MlMO spatial streams, 160 MHz radio channels, and down-link MU-MlMO with up to 4 single stream clients; the theoretical maximum data rate of 802.11ac Wave 2 devices is 3,470 Gb/s [17]. For each 802.11ac mode transmission, there is a specified Modulation and Coding Scheme (MCS) index and a number of MlMO spatial streams. Forward Error Correction coding rate and Modulation type are both determined by the MCS index. Every MCS is compatible with radio channels of (20, 40, 80, and 160 MHz), as well as Guard Intervals of 400 ns or 800 ns (GI) [16]. Table 1 The difference between Wave 1 and Wave 2 in 802.11ac 802.11ac parameter

Wave 1 802.11ac

Wave 2 802.11ac

MIMO

SU-MIMO

MU-MIMO

Spatial stream

3

3–4

Physical layer rate

1.3 Gb/s

3.47 Gb/s

Channel width

020, 040, 80 MHz

20, 040, 80, 0160 MHz

Multiple-streaming

AP can send to one client at a time

AP can send to 4 single-stream devices at a time

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3.1 802.11ac Beamforming Beamforming is a technique that permits radio signals to be directed in a certain direction. At first, this technique is implemented in the IEEE 802.1 1n standard. Then, it is enhanced by IEEE 802.11ac to support MU-MlMO [18]. An array of antennas is utilized to send data to the 802.11 client with a high gain, resulting in higher down-link SNR usually increasing the MCS rate by one point, over a longer range or higher data rate, and hence best overall system performance of the system [19]. For channel bandwidth of 20MHz and as shown in Fig. 1a 4×1 SU-MIMO, the AP transmits to the client using one stream at a time, the throughput about 77.5 Mbps for each time slot, while in Fig. 1b 4×4 SU-MIMO the AP transmits to the client using four streams at a time, and throughput is approximately up to 4x greater efficient (310 Mbps) for each time slot; in Fig. 1c 4×1 SU-MIMO, the AP transmits to four clients using four streams but each stream specified to one client in separated time, and in Fig. 1d 4×4 MU-MIMO the AP transmits to four clients using four streams at a time, and throughput is approximately 266 Mbps for each time slot. In the case of 4×4 SU-MIMO, the throughput is higher than 4×4 MU-MIMO because SU-MIMO doesn’t require to channel calibration [18]. SISO and SU-MIMO transmissions may be used for beamforming optionally, however, for MU-MIMO beamforming is required. The beamforming transmitter is named as (beamformer), while the beamforming receiver is known as (beamformee). Up to eight SS can be delivered to a unique receiver using beamforming. As illustrated in Fig. 2, in the case of MU-MlMO, the beamforming can service up to four beamformees simultaneously. Each receiver can receive one to four SS, with a total of eight SS for all receivers [19]. Each client transmits an easily computed frame back that specifies channel state information (CSI) based on the acquired sounding signal. The AP receives each client’s CSI and calculates phase and signal strength for each transmit antenna in an array of up to 8 antennas. Each time the receiver positions change, a new measurement of the channel is required. To keep up-to-date information for the receivers, the beamformer must do a cyclic channel sounding. As the number of beamformees increases, the calibration interval should be reduce to support the transmitter with accurate measurements of the channel [20].

3.2 Frame Format of Beamforming Four types of frames are used in MU channel calibration (sounding): (1) Null Data Packets Announcement (NDPA)s, (2) Null Data Packets (NDP)s, (3) Compressed Beamforming, and (4) Beamforming Reports Poll. Figure 3 illustrates these frames [1, 18]. The first beamformee to reply is included in the NDPA frame, as are the beamformees who should prepare a beamforming report. Figure 3b illustrates the format of this frame. The NDP is the second frames in the channel calibration (sounding). This only contains the PHY header (does not contain the “data” field shown in

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(a)

(b)

(c)

(d)

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Fig. 1 a SU-MIMO one 1 stream client, b SU-MIMO four 1 stream clients, c SU-MIMO four 1 stream clients, d MU-MIMO four 1 stream clients

(a) Fig. 2 a SU-MIMO, b MU-MIMO beamforming

(b)

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Fig. 3 Beamforming calibration frames

Fig. 3a) and an empty frame. The NDP enables beamformees to construct their beamforming reports. The Beamforming Reports Polls frame is shown in Fig. 3c. The AP demands the beamforming reports of the various beamformees. The beamformee can send reports to the beamformer (or AP) using the Compressed Beamforming frame. Figure 3d shows this frame [19].

3.3 Channel Measurement (Sounding) Procedures Through the sounding of the channel for MU-MlMO, the AP (beamformer) transmits an NDPA frame. This frame’s purpose is to reserve the channel for the required duration and to announce the sounding process. Then, the beamformer transmits an Null Data Packets (NDP) frame [21]. By analyzing the training fields in the received NDP, the beamformees create a feedback matrix. In order to direct transmissions in the direction of the beamformee, the beamformer calculates the steering matrix after receiving the feedback matrix. Figure 4 illustrates steering matrix deployment [1]. The initial beamformee responds with a Compressed Beamforming frame, next, the AP sends the Beamforming Report Poll frame to the remainder of receivers

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(b)

Fig. 4 a No steering matrix deployment, b Steering matrix

to collect the other Compressed Beamforming frames. All the frames that are transmitted within the calibration operation are separated with SIFS as shown in Fig. 5. MU-MIMO transmissions are limited to four receivers, whereas the WLAN may have many more beamformees [22]. Therefore, in congested networks, the channel sounding operation may result in more overhead. Channel sounding might be performed either per transmission or periodically. In the per transmission case, the channel sounding is directly followed by a unique MU-MlMO transmission, and each transmission requires a channel sounding [23]. This allows a high accuracy of beamforming but suffers from more overhead. But in the second scenario, Multiple MU-MIMO transmissions follow the sounding procedure. In this paper, the periodical channel sounding will be considered for two periods (20 and 80 ms). The beam, before transmitting beamforming (TBF) same energy (approximately) in all directions [24], After TBF, energy is manipulated to have much more energy in the

Fig. 5 MU-MlMO channel sounding

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Fig. 6 Beamforming range effect

direction of the receiver (by constructive addition) and the lowest energy in all other directions. In the mid-range, TBF gain (about 3 dB) in the direction of the receiver is optimum [25]. The increasing range for a sustainable link from an AP with reducing orders of MCS is shown in Fig. 6. The simulation results are confirmed by the theoretical performance. According to [17] analyses, the MAC layer delay and maximum data rate can be calculated as follows: Throughput(bits/sec) =

N DS ∗ N SS ∗ NBits Per Symbol * CR O F D MS D

where N DS = Data Subcarrier Number (for 20, 40, 80, and 160 MHz equal to 52, 108, 234, and 468 subcarriers, respectively), N SS = Number of SS is variable from 1 to 8, Nbit per symbol = Number of bit per symbol for 256 QAM is 8, CR = code rate (value 3/4), 1 O F D M S D = Symbol Duration of OFDM (which is given by ΔF where ΔF equals subcarrier spacing) and its value = 3.6 µs involve Gl of 400 ns.

3.4 Proposed Scenarios of the Modeled Network with Assumption Four scenarios with various number of nodes (5, 15, 30, and 45) to evaluate the performance of a WLAN based on the IEEE 802.11ac standard. The proposed wireless LAN scenarios are modeled and simulated using the discrete event OMNet++ version 5.5.1. The processes of simulation for the OMNet++ scenarios are performed according to simulation parameters as illustrated in Table 2.

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Table 2 Parameters for simulations Parameters

Values

Physical characteristics

IEEE 802.11ac, 5 GHz

Bits per angle (low report precision)

012 bits

DIFS

34 µs

SIFS

16 µs

Slot Time

9 µs

Time of simulation (sec)

2

MU-MIMO (SS)

Up to 4 streams

Modulation and coding scheme (MCS)

MCS 8–256-QAM

Coding rate

3/4

Bit per symbol

8

Channel bandwidth

Variable from 20 MHz up to 80 MHz (for SU-MIMO) or 160 MHz (for MU-MIMO)

Channel sounding periods

20 and 80 ms

Guard interval (ns)

400

3.5 Simulation Results of the OMNeT++ modeled Scenarios For the suggested scenarios, the throughput performance metric is considered to investigate the efficiency of the IEEE802.11ac WLAN utilizing OMNeT++ simulations. Figures 7 show the OMNeT++ simulation results for the considered performance metric. In the process of investigating the modeled wireless LAN performance for various number of nodes, different antenna configuration MlMO or SS (2×2 SU-MlMO, 4×4 SU-MlMO, 4×4 MU-MlMO-80 ms, 4×4 MU-MlMO-20 ms) are considered (without and with Beamforming application).

4 Throughput Figure 7a–d shows system throughput for each slot; it is noticed that the 4×4 SUMIMO system outperforms both 4×4 MU-MIMO (20 and 80 ms) systems. The reason for that is all systems support the same number of antennas (at transmitter and receiver), however, the SU-MIMO does not require channel calibration, as well as it is simulated with the assumption that its antennas are far enough with no interference among their spatial stream, where the throughput of 4×4 MU-MIMO (20 and 80 ms) is significantly impacted by the channel calibration. Therefore, 4×4 SU-MlMO is more efficient than MU-MlMO, and the variation in the throughput between 4×4 SU-MIMO and MU-MIMO increases because of the effect of the channel calibration.

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(a)

(b)

(c)

(d)

Fig. 7 Illustrate SU-MIMO and MU-MIMO throughput per slot performance for a 5 nodes, b 15 nodes, c 30 nodes, and d 45 nodes

When the period between any two repeat channel sounding processes is 20 ms, MUMIMO suffers more frequent significant calibration overhead. It is noticed that the throughput of 4×4 SU-MIMO outperforms that of 4×4 MU-MIMO-20ms and 4×4 MU-MIMO-80 ms by 32 and 18% (for 5 node scenarios) up to 83 and 52% (for 45 node scenarios), respectively. Moreover, it is observed that implementing a channel calibration for MU-MIMO80 ms every 80 ms incurs a significant overhead but lower than that of MU-MIMO20ms. The collision probability becomes more significant as the number of nodes increases, as well as the sounding time overhead. So the throughput of 4×4 MUMIMO-80 ms outperforms 4×4 MU-MIMO-20 ms by 17% (for 5 node scenarios) up to 64% (for 45 node scenarios). In comparison with 4×4 MU-MIMO-20 ms, it is also noticed that 4×4 MU-MIMO-80ms is able to support a larger number of beamformees with better scalability, because of the limited overhead of the calibration operation. Moreover, 2×2 SU-MlMO and 4×4 SU-MlMO are more scalable than the MU-MIMO system (Table 3).

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Table 3 The throughput performance summary for the four scenarios No. of nodes

2 × 2 SU-MIMO

4 × 4 SU-MIMO

4×4 MU-MIMO-80 ms

4×4 MU-MIMO-20 ms

5 Nodes

151

310

253

209

15 Nodes

151

310

220

127

30 Nodes

151

310

169

84

45 Nodes

151

310

148

52

5 Conclusion Four scenarios are proposed in this paper (2×2 SU-MlMO, 4×4 SU-MlMO, 4×4 MU-MlMO-20 ms, and 4×4 MU-MlMO-80 ms SS) to model and simulate wireless LANs based on the IEEE 802.11ac standard. The simulation result shows that 4x4 SU-MlMO outperforms MU-MlMO for both (20 and 80 ms) repetition periods of channel calibration, and the highest throughput is acquired at the 4×4 SU-MIMO scenario. The performance of MU-MlMO highly depends on the overhead of channel calibrations. When the channel sounding (calibration) repetition period is reduced (from 80 to 20 ms), the throughput is decreased, due to the more frequently repeated overhead channel sounding or calibration. In comparison with 4×4 MU-MIMO-20 ms, 4×4 MU-MIMO-80 ms can support a larger number of beamformees, due to the reduced overhead of the calibration operation. It is also concluded that for a large number of nodes (45) scenario, the repetition calibration or sounding channel periods along with collision probability and interference among streams have a significant effect on the throughput performance as well as limiting the scalability of the MUMIMO WLANs.

References 1. Gast, M. 802. 11ac : A Survival Guide. 2. Al-Heety, A. T., Islam, M. T., Rashid, A. H., Abd Ali, H. N., Fadil, A. M., & Arabian, F. (2020). Performance evaluation of wireless data traffic in mm wave massive MIMO communication. Indonesian Journal of Electrical Engineering and Computer Science, 20(3), 1342–1350. https:/ /doi.org/10.11591/ijeecs.v20.i3.pp1342-1350 3. Su, S., Tan, W. T., Zhu, X., Liston, R. (2019) Client pre-screening for MU-MIMO in commodity 802.11ac networks via online learning. Proceedings of IEEE INFOCOM (vol. 2019, pp. 649– 657) https://doi.org/10.1109/INFOCOM.2019.8737646 4. Ali, M. Z., Misic, J., & Misic, V. B. (2017). Performance analysis of downlink MU-TXOP sharing in IEEE 802.11ac. IEEE Transactions on Vehicular Technology, 66(10), 9365–9380. https://doi.org/10.1109/TVT.2017.2701351 5. Charfi, E., Chaari, L., & Kamoun, L. (2013). PHY/MAC enhancements and qos mechanisms for very high throughput WLANs: A survey. IEEE Communications Surveys & Tutorials, 15(4), 1714–1735. https://doi.org/10.1109/SURV.2013.013013.00084

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Author Index

A Abdulhussain, Zahraa N., 503 Abdullah, 281 Abid, Salah H., 183, 235 Acharya, Ashish, 371, 381, 419 Adhikari, Saurabh, 21, 37, 51, 65, 79, 281 Adrian, Ngui, 461 Akila, D., 21, 79 Alex, Suja A., 125 Ali, Omer Mohammed, 541 Alkhafaji, Mohammed Ayad, 51, 65, 391 Altawil, Jumana A., 183, 235 Alwan, Adil Abbas, 271 Ashfaq, Farzeen, 165, 503, 513 Ashraf, Humaira, 165 Asirvatham, David, 503 Ayoub, Razouk, 199 Azman, Amal Danish, 261

Chourashia, Khusbhu, 1 Chu, Thi Minh Chau, 451 Cuong, Ton Quang, 303, 451

B Balaganesh, D., 21, 37 Balakrishnan, Sumathi, 261, 427, 461 Banerjee, Saurabh, 485 Basu, Nilanjana G., 317 Bhowmick, Partha, 317 Bhuvana, R., 21 Biswas, Manajat Ali, 371, 419 Brayyich, Mohammed, 79

F Farej, Ziyad Khalaf, 331, 541 Fiza, Inbasat, 165 Fountis, Anastasios, 271, 391, 525 Ftaiet, Adnan Allwi, 281

C Chaini, Najihah, 407 Cheng, Phung Shun, 261 Choo, Jer Lyn, 461

D Dang, Tuan Minh, 107 Das, Arijit, 211, 291 Dash, Santanu Kumar, 95 Das, Shampa Rani, 503, 513 Das, Shamp Rani, 261 Devika, S., 79 Dey, Niti, 281 Díaz, Vicente García, 65 E Ejodame, Osezua Ehizogie, 261 Elangovan, V. R., 21

G Ghorai, Santu, 211, 291 Ghosh, Anup Kumar, 439 Guan, Low Jun, 427 H Hachimi, Hanaa, 51

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S.-L. Peng et al. (eds.), Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science, Advances in Intelligent Systems and Computing 1450, https://doi.org/10.1007/978-981-99-3611-3

555

556 Hanaa, Hachimi, 199 Hanoon, Falah H., 37 Ha, Vu Thi Thu, 451 Hazra, Sudipta, 485 Hoang Van, Pham, 451 Huda, Mahfuzul, 281, 493 Hung Van, Nguyen, 219 Huong Thi, Nguyen, 219 Hussain, Manzoor, 345, 359, 427, 461

I Ibeh, Lawrence, 11 Ikram, Hafsah, 165

J Jabbar, Kadim A., 359 Jeyalaksshmi, S., 37 Jhanjhi, Noor Zaman, 125, 503, 513 Jin, Pu Kai, 461 Juin, Tan Vern, 427

K Kaushik, Ruchi, 37 Kavikumar, J., 407 Khan, Naveed Ali, 513 Kin, Tang Wai, 261 Kiritsi, Anastasia, 271, 391 Koley, Santanu, 95 Kumar, Bishwajeet, 485 Kumar, Sunil, 155

M Mahata, Animesh, 371, 381, 419 Maity, Saikat, 51, 65 Majumder, Subhashis, 317 Mali, Prakash Chandra, 419 Mandal, Bijoy, 439 Manna, Balaram, 381 Mehdi, Falloul Moulay, 199 Mohamud, Sulekha, 11 Mondal, Subhabrata, 211, 291, 381 Mukherjee, Dibyendu, 439 Mukherjee, Supriya, 371, 419

N Nagarajan, D., 1, 407 Naqvi, Mehmood, 345, 359 Narayan, Lalit Kumar, 135, 143 Ngo, Lien Thi, 107

Author Index Nguyen, Hoang Vu, 451 Nguyen, Huong Thi, 107 Nisar, Jinan, 261 O Obaid, Ahmed J., 37 Ooi, Jing Kai, 461 P Pal, Souvik, 51, 65, 493 Pan, Sonjoy, 211, 291 Parihar, Shefali, 471 Paul, Subrata, 371, 381, 419 Peng, Lee Yun, 427 Pham, Tiep Quang, 107 Phuong, Ta Duy, 451 R Raina, Sneha, 251 Ray, Sayan Kumar, 125, 165 Revathi, S., 79 Rohit, Kumar, 493 Roy, Banamali, 371, 381, 419 S Sabah, Hawraa Ali, 345 Saeed, Soobia, 345, 359 Sagaladinov, Sultan, 427 Sakar, Bikramjit, 21, 79 Sarkar, Bikramjit, 493 Sarkar, Biman, 211, 291 Sharma, Priya, 291 Shrotriya, Neha, 471 Shyam, Rahul, 439 Swami, Kailash Chand, 95 T Tai, Qiao Hui, 461 Talab, Azhar Waleed, 331 Thakore, Parthivi, 471 Thuy Le, Tran, 451 Ti, Shin Kir, 461 Tiep Quang, Pham, 219 Trivedi, Chetan, 155 Trivedi, Kshma, 95 Tuan Minh, Dang, 219 U Udhayakumar, A., 1

Author Index V

Vishwakarma, Virendra Prasad, 135, 143

557 Y Yen, Pham Thi Hai, 303 Yi, Yeo Jia, 261 Youness, Saoudi, 199