Intelligent Systems: Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data (ICMIB 2023) (Lecture Notes in Networks and Systems, 728) [1st ed. 2024] 9819939313, 9789819939312

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Lecture Notes in Networks and Systems 728

Siba K. Udgata Srinivas Sethi Xiao-Zhi Gao   Editors

Intelligent Systems Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data (ICMIB 2023)

Lecture Notes in Networks and Systems Volume 728

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

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

Siba K. Udgata · Srinivas Sethi · Xiao-Zhi Gao Editors

Intelligent Systems Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data (ICMIB 2023)

Editors Siba K. Udgata School of Computer and Information Sciences University of Hyderabad Hyderabad, Telangana, India

Srinivas Sethi Department of Computer Science and Engineering Indira Gandhi Institute of Technology Dhenkanal, Odisha, India

Xiao-Zhi Gao Department of Computer Science University of Eastern Finland Kuopio, Finland

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-99-3931-2 ISBN 978-981-99-3932-9 (eBook) https://doi.org/10.1007/978-981-99-3932-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 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

Preface

This Springer LNNS volume contains the papers presented at the 3rd International Conference on Machine Learning, Internet of Things and Big Data (ICMIB-2023) held during March 10 to 12, 2023, organized by Indira Gandhi Institute of Technology (IGIT), Sarang, Odisha, India. A lot of challenges at us and no words of appreciation are enough for the organizing committee who could still pull it off successfully. The conference draws some excellent technical keynote talks and papers. Two tutorial talks by Prof. Deepak Tosh (Senior Member, IEEE), University of Texas at El Paso, and Prof. Sanjay Kumar Panda, National Institute of Technology, Warangal, and an Innovative Project Showcase are planned on March 10, 2023. The overwhelming response for the tutorial talks is worth mentioning. Apart from the tutorial sessions, seven keynote talks by Prof. Gheorghita (George) Ghinea, Professor in Mulsemedia Computing, Department of Computer Science, Brunel University, UK, Dr. Padmanabh Kumar, Senior Researcher, EBTIC (a research laboratory of British Telecom), Prof. Amit Mishra (University of Cape Town, South Africa), Prof.(Dr.) Saroj K. Meher, Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, Dr. Haesik Kim, Head/Coordinator of 5G-HEART, VTT Technical Research Centre, Finland, Prof. Xiao-Zhi Gao, University of Eastern Finland, and Dr Bijaya Kumar Sahu, Regional Manager and Head, NRDC-Intellectual Property Facilitation Centre and Technology Innovation Centre are delivered. We are grateful to all the speakers for accepting our invitation and sparing their time to deliver the talks. We received 209 full paper submissions, and we accepted only 53 papers with an acceptance rate of 25%, which is considered very good in any standard. The contributing authors are from different parts of the globe that includes UAE, Nepal, Sweden, UK, Turkey, Norway, Bangladesh, Czech Republic and India. The conference also received papers from distinguished authors from the length and breadth of the country including 12 states and many premier institutes. All the papers are reviewed by at least three independent reviewers and in some cases by as many as five reviewers. All the papers are also checked for plagiarism and similarity score. It was really a tough job for us to select the best papers out of so many good papers for presentation in the conference. We had to do this unpleasant task, keeping the v

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Springer guidelines and approval conditions in view. We take this opportunity to thank all the authors for their excellent work and contributions and also the reviewers who have done an excellent job. On behalf of the technical committee, we are indebted to Prof. L. M. Patnaik, General Chair of the Conference, for his timely and valuable advice. We cannot imagine the conference without his active support at all the crossroads of decisionmaking process. The management of the host institute, particularly Director Prof. Satyabrata Mohanta, Prof. (Mrs.) Sasmita Mishra, HOD, CSEA, Program Co-Chair and Convenor Prof. Srinivas Sethi, and Organizing Chair Prof. S. N. Mishra have extended all possible support for the smooth conduct of the conference. Our sincere thanks to all of them. We would also like to place on record our thanks to all the keynote speakers, tutorial speakers, reviewers, session chairs, authors, technical program committee members, various chairs to handle finance, accommodation, and publicity, and above all to several volunteers. Our sincere thanks to all press, print, and electronic media for their excellent coverage of this conference. We are also thankful to Springer Nature publication house for agreeing to publish the accepted papers in their Lecture Notes in Networks and Systems (LNNS) series. Please take care of yourself, your loved ones, and stay safe. March 2023

Siba Kumar Udgata Srinivas Sethi Xiao-Zhi Gao

Organization

Reviewers List Abhinav Tomar Alok Tripathy Anisha Kumari Anitha A. Ashima Rout Atish Nanda Bibhudatta Sahoo Bibudhendu Pati Bichitra Mandal Bunil Balabantaray Chinmayee Rout Debabrata Dansana Debasis Mohapatra Deepak Singh Devesh Bandil Sudhansu Patra Jui Pattnaik Gopal Behera J. Chandrkanta Badajena Jitendra Kumar Jitendra Rout Kaibalya Panda Kali Rath kallam suresh Kalyan Kumar Jena Kauser Ahmed Kshira Sahoo Lakhmi Das

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Lalatendu Muduli Lalitha K. Manmath Bhuyan Manoj Das Manoj Kumar Patra Niranjan Panigrahi Nitin Bommi Om Prakash Jena Prahallad Sahu Prajna Nanda Prasant Dhal Pratap Sekhar Preeti Chandrakar Pritam Raul Swarup Roy Punyaban Patel Purna Sethi Pushkar Kishore Rabindra Behera Rajendra Nayak Rajiv Senapati Rakesh Chandra Balabantaray Ramesh Sahoo Rohit Kumar Bondugula S. Gopal Krishna Patro Sambit Mishra Sampa Sahoo Sandhya Sahoo Sangharatna Godboley Sangita Pal Sanjaya Panda Sanjib Nayak Santanu Dash Saroj Panigrahy Sasmita Acharya Siba Udgata Sohan Pande Sonali Jena Sourav Bhoi Srichandan Sobhanayak Srinivas Sethi Subasish Mohapatra Subhasish Pani Suman Paul Sumit Kar

Organization

Organization

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Sunita Dhalbisoi Suraj Sharma Surya Das Suvendra Jayasingh Swarupananda Bissoyi Tirimula Rao Benala Umakanta Samantsinghar Umesh Sahu V. Ramanjaneyulu Yannam

Committee Members Patron Satyabrata Mohanto (Director) IGIT Sarang

General Chair Lalit Mohan Patnaik National Institute of Advanced Studies and Indian Institute of Science, Bangalore

Program Chair Siba K. Udgata University of Hyderabad, India

Program Co-chairs Srinivas Sethi IGIT Sarang Xiao-Zhi Gao University of Eastern Finland, Finland

Organizing Chairs S. N. Mishra IGIT Sarang Sasmita Mishra IGIT Sarang

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Organization

Convenors Srinivas Sethi IGIT Sarang Sanjaya Kumar Patra IGIT Sarang

Publicity Chairs Sourav Roy B. P. Panigrahi Ashima Rout S. K. Tripathy Subhrashu Das

Sikim University IGIT Sarang IGIT Sarang IGIT Sarang GCE, Keunjhar

Hospitality Chairs Biswanath Sethi Anshuman Padhy P. R. Dhal Sujit Kumar Pradhan

IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang

Finance Chairs Ashima Rout IGIT Sarang Sanjaya Kumar Patra IGIT Sarang

Logistic Chairs Dillip Kumar Swain Medimi Srinivas Priyabrat Sahoo Niroj Kumar Pani Rabi Nayan Sethi Anand Gupta

IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang

Organization

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Accommodation Chairs Rabindra Kumar Behera Monoj K. Choudhury Manoj Kumar Muni K. D. Sa Supriya Sahoo

IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang

Organizing Committee Jogendra Mahi Sandeep Sahoo Kashinath Barik Rabinarayan Murmu July Randhari Ritesh Patel S. R. Pradhan Gaurab Ghose Deepak Suna Ashok Pradhan Himanshu Dash Sangita Pal Sangram Nayak Sushant Kumar Sahoo Anupama Sahoo Subhendu Bhusan Rout Suvendu Kumar Jena Ramesh Kumr Sahoo Binaya Kumar Patra Supriya Lenka Bapuji Rao

IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang

Technical Program Committee Manas Ranjan Patra Siba Kumar Udgata O. B. V. Ramanaiah G. Suvarna Kumar G. Sandhya R. Hemalatha

Berhampur University University of Hyderabad OBV, JNTU Hyderabad MVGRCE, Vijayanagaram MVGRCE, Vijayanagaram University College of Engineering, Osmania University

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Amit Kumar Mishra R. Thangarajan Birendra Biswal Somanath Tripathy A. K. Turuk B. D. Sahoo D. P. Mohapatra P. M. Khilar P. G. Sapna R. K. Dash B. K. Tripathy Moumita Patra S. N. Das Ram Kumar Dhurkari Chitta Ranja Hota A. Kavitha Subasish Mohapatra Sanjaya Kumar Panda S. Mini S. Nagender Kumar Lalit Garg Lalitha Krishna C. Poongodi Sumanth Yenduri Shaik Shakeel Ahamad K. Srujan Raju R. Hemalatha P. Sakthivel Pavan Kumar Mishra Tapan Kumar Gandhi Annappa B. Prafulla Kumar Behera Nekuri Naveen Ch. Venkaiah Rajendra Lal Dillip Singh Sisodia Pradeep Singh Jay Bagga

Organization

University of Cape Town, South Africa Kongu Engineering College, Tamil Nadu Gayatri Vidya Parishad College of Engineering, Vishakhapatnam IIT Patna NIT Rourkela NIT Rourkela NIT Rourkela NIT Rourkela CIT, Coimbatore NIC, Mizoram VIT, Vellore IIT, Guahati GIET University, Gunupur, Odisha IIM, Sirmaur, Himachal Pradesh BITS Pilani, Hyderabad JNTU, Hyderabad CET Bhubaneswar NIT, Warangal NIT, Goa University of Hyderabad University of Malta, Malta Kongu Engineering College, Tamil Nadu Kongu Engineering College, Tamil Nadu Kennesaw University, USA Majmaah University, Saudi Arabia CMR Technical Campus, Hyderabad University College of Engineering, Osmania University, Hyderabad Anna University NIT, Raipur Department of Electrical Engineering, IIT, Delhi NIT Surathkal Utkal University, Bhubaneswar, Odisa School of Computer and Information Sciences, University of Hyderabad School of Computer and Information Sciences, University of Hyderabad School of Computer and Information Sciences, University of Hyderabad Department of Computer Science and Engineering, NIT, Raipur Department of Computer Science and Engineering, NIT, Raipur Ball State University, USA

Organization

Sumagna Patnaik Ajit K. Sahoo Atluri Rahul Samrat L. Sabat

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JB Institute of Engineering and Technology, Hyderabad University of Hyderabad Neurolus Systems, Hyderabad Center for Advanced Studies in Electronic Science and Technology (CASEST), University of Hyderabad Nihar Satapathy Sambalpur University Susil Kumar Mohanty Department of Computer Science and Engineering, IIT, Patna Kagita Venkat NIT, Warangal Sanjay Kuanar GIET University, Gunupur, Orissa College of Engineering, Bhubaneswar Bhabendra Biswal Padmalaya Nayak GR Institute of Engineering and Technology, Hyderabad Bhibudendu Pati R.D Womens University, Bhubaneswar Chabi Rani Panigrahi R.D Womens University, Bhubaneswar Rajesh Verma Infosys Ltd, Hyderabad Arun Avinash Chauhan School of Computer and Information Sciences, University of Hyderabad Khusbu Pahwa Delhi Technological University, New Delhi DRDL, Hyderabad Soumen Roy Tech Mahindra, Hyderabad Satyajit Acharya BITS Pilani, Hyderabad Subhrakanta Panda Vineet P. Nair School of Computer and Information Sciences, University of Hyderabad Subash Yadav Department of Computer Science, Central University of Jharkhand, Ranchi Layak Ali Central University Karnataka, Gulbarga Deepak Kumar NIT, Meghalaya NIT, Meghalaya Bunil Balabantaray Sumanta pyne NIT Rourkela Asis Tripathy VIT, Vellore NIT, Durgapur Mousumi Saha Abhijit Sharma NIT, Durgapur MNNIT, Allahabad Mayukh Sarkar Oishila Bandyopadhyay IIIT, Kalayani Subrat Kumar Mohanty IIIT, Bhubaneswar Ramesh Chandra Mishra IIIT Manipur Kolaghat Engineering College Hirak Maity Sandeep Kumar Panda ICFAI, Hyderabad Swain NOU, Odisha Prasanta Kumar Ashim Rout IGIT Sarang Srinivas Sethi IGIT Sarang IGIT Sarang S. N. Mishra Urmila Bhanja IGIT Sarang D. J. Mishra IGIT Sarang S. Mishra IGIT Sarang

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Sangita Pal Sanjaya Patra Biswanath Sethi Niroj Pani Dillip Kumar Swain Pranati Dash B. P. Panigrahy Rabindra Behera L. N. Tripathy B. B. Choudhary Dhiren Behera R. N. Sethi Anand Gupta Ayaskanta Swain Gayadhr Panda S. K. Tripathy B. B. Panda Md. N. Khan Anukul Padhi Debakanta Tripathy S. K. Maity Devi Acharya Sanjaya Kumar V. Patle Saurov Bhoi Kalyan Kumar Jena Alok Ranjan Prusty Babita Majhi Subhrashu Das Puspalata Pujahari Tirimula Rao Kshirsagar Sahoo Maheswar Behera Niranjan Panigrahy Trilochon Rout P. K. Panigrahy Mihir Kumar Sutar Sukanta Besoi Gopal Behera Rajendra Prasad Nayak Kaliprasan Sethi Jitendra Kumar Rout

Organization

IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang CET Bhubaneswar IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang NIT Rourkela NIT, Meghalaya IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang IGIT Sarang VIT, Vellore PRSU, Raipur, India PRSU, Raipur, India PMEC Berhampur PMEC Berhampur Skill Development, Delhi GGU, Chatisgarh GCE, Keunjhar GGU, Chatisgarh JNTU Kakinada Umea University, Sweden IGIT Sarang PMEC Berhampur PMEC Berhampur GIET, India UCE Burla CVRCE, Bhubaneswar GCEK, Bhawanipatna GCEK, Bhawanipatna GCEK, Bhawanipatna NIT, Raipur

Contents

Effect of the Longitudinal Strain of PM Fiber on the Signal Group Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karel Slavicek, David Grenar, Jiri Vavra, Martin Kyselak, Jan Radil, and Jakub Frolka Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priyanka Kaushik Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smrutiranjan Nayak, Subhransu Sekhar Dash, Sanjeeb Kumar Kar, Ananta Kumar Sahoo, and Ashwin Kumar Sahoo

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Detecting Depression Using Quality-of-Life Attributes with Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Premalatha, S. Aswin, D. JaiHari, and K. Karamchand Subash

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Patient Satisfaction Through Interpretable Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Anandamurugan, P. Jayaprakash, S. Mounika, and R. Narendranath

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Predicting the Thyroid Disease Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lalitha Krishnasamy, M. Aparnaa, G. Deepa Prabha, and T. Kavya

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An Automatic Traffic Sign Recognition and Classification Model Using Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajalaxmi Padhy, Alisha Samal, Sanjit Kumar Dash, and Jibitesh Mishra

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An Artificial Intelligence Enabled Model to Minimize Corona Virus Variant Infection Spreading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dipti Dash, Isham Panigrahi, and Prasant Kumar Pattnaik

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Contents

SoundMind: A Machine Learning and Web-Based Application for Depression Detection and Cure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Madhusha Shete, Chaitaya Sardey, and Siddharth Bhorge

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Japanese Encephalitis Symptom Prediction Using Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Piyush Ranjan, Sushruta Mishra, Tridiv Swain, and Kshira Sagar Sahoo

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Smart Skin-Proto: A Mobile Skin Disorders Recognizer Model . . . . . . . . 113 Sushruta Mishra, Shubham Suman, Aritra Nandi, Smaraki Bhaktisudha, and Kshira Sagar Sahoo Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Kazi Kutubuddin Sayyad Liyakat Real Time Air-Writing and Recognition of Tamil Alphabets Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 S. Preethi, T. Meeradevi, K. Mohammed Kaif, S. Hema, and M. Monikraj A Fuzzy Logic Based Trust Evaluation Model for IoT . . . . . . . . . . . . . . . . . 147 Rabindra Patel and Sasmita Acharya Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Riyam Patel, Borra Sivaiah, Punyaban Patel, and Bibhudatta Sahoo Solar Powered Smart Home Automation and Smart Health Monitoring with IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Atif Afroz, Sephali Shradha Khamari, and Ranjan Kumar Behera Seasonal-Wise Occupational Accident Analysis Using Deep Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 N. Nandhini and A. Anitha MLFP: Machine Learning Approaches for Flood Prediction in Odisha State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Subasish Mohapatra, Kunaram Tudu, Amlan Sahoo, Subhadarshini Mohanty, and Chandan Marandi Vision-Based Cyclist Travel Lane and Helmet Detection . . . . . . . . . . . . . . . 207 Jyoti Madake, Shripad Bhatlawande, and Madhusha Shete Design and Experimental Analysis of Spur Gear–A Multi-objective Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 S. Panda and Jawaz Alam Chest X-Ray Image Classification for COVID-19 Detection Using Various Feature Extraction Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Sareeta Mohanty and Manas Ranjan Senapati

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Computer Vision and Image Segmentation: LBW Automation Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Jeebanjyoti Nayak, Jyotsnarani Jena, Hrushikesh Pradhan, Jyotiprakash Das, and Surendra N. Bhagat A Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Gopal Behera, Ramesh Kumar Mohapatra, and Ashok Kumar Bhoi Hybrid Clustering-Based Fast Support Vector Machine Model for Heart Disease Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Chaitanya Datta Maddukuri and Rajiv Senapati Forecasting and Analysing Time Series Data Using Deep Learning . . . . . 279 Snigdha Sen, V. T. Rajashekar, and N. Dharshan Intelligent Blockchain: Use of Blockchain and Machine Learning Algorithm for Smart Contract and Smart Bidding . . . . . . . . . . . . . . . . . . . . 293 Jyotiranjan Rout, Susmita Pani, Sibashis Mishra, Bhagyashree Panda, Satya Swaroop Kar, and Sanjay Paramanik Weed Detection in Cotton Production Systems Using Novel YOLOv7-X Object Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 G. V. S. Narayana, Sanjay K. Kuanar, and Punyaban Patel Smart Healthcare System Management Using IoT and Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 P. Sudam Sekhar, Gunamani Jena, Shubhashish Jena, and Subhashree Jena Automatic Code Clone Detection Technique Using SDG . . . . . . . . . . . . . . . 327 Akash Bhattacharyya, Jagannath Singh, and Tushar Ranjan Sahoo Simulated Design of an Autonomous Multi-terrain Modular Agri-bot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Safwan Ahmad, Shamim Forhad, Mahmudul Hasan Shuvo, Sadman Saifee, Md Shahadat Hossen, Kazi Naimul Islam Nabeen, and Mahbubul Haq Bhuiyan Customer Segmentation Analysis Using Clustering Algorithms . . . . . . . . 353 Biyyapu Sri Vardhan Reddy, C. A. Rishikeshan, VishnuVardhan Dagumati, Ashwani Prasad, and Bhavya Singh SP: Shell-Based Perturbation Approach to Localize Principal Eigen Vector of a Network Adjacency Matrix . . . . . . . . . . . . . . . . . . . . . . . . 369 Baishnobi Dash and Debasis Mohapatra Development of a Robust Dataset for Printed Tamil Character Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 M. Arun, S. Arivazhagan, and R. Ahila Priyadharshini

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An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Abhishek Bajpai, Harshvardhan Rai, and Naveen Tiwari Multi-class Pathogenic Microbes Classification by Stochastic Gradient Descent and Discriminative Fine-Tuning on Different CNN Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Nirajan Jha, Dibakar Raj Pant, Jukka Heikkonen, and Rajeev Kanth Early Prediction of Thoracic Diseases Using Rough Set Theory and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Radhanath Hota, Sachikanta Dash, Sujogya Mishra, P. K. Pattnaik, and Sipali Pradhan Predicting Liver Disease from MRI with Machine Learning-Based Feature Extraction and Classification Algorithms . . . . . . . . . . . . . . . . . . . . 435 Snehal V. Laddha, Manish Yadav, Dhaval Dube, Mahansa Dhone, Madhav Sharma, and Rohini S. Ochawar An Improved Genetic Algorithm Based on Chi-Square Crossover for Text Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Gyananjaya Tripathy and Aakanksha Sharaff Tuna Optimization Algorithm-Based Data Placement and Scheduling in Edge Computing Environments . . . . . . . . . . . . . . . . . . . . 457 P. Jayalakshmi and S. S. Subashka Ramesh Frequency Control of Single Area Hybrid Power System with DG . . . . . . 471 Ashutosh Biswal, Prakash Dwivedi, and Sourav Bose Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Abdullah Al Maruf, Aditi Golder, Abdullah Al Numan, Md. Mahmudul Haque, and Zeyar Aung Verifiable Secret Image Sharing with Cheater Identification . . . . . . . . . . . 493 Franco Debashis Ekka, Sourabh Debnath, Jitendra Kumar, and Ramesh Kumar Mohapatra An ECC-Based Lightweight CPABE Scheme with Attribute Revocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Avinash Chandel, Sourabh Debnath, Jitendra Kumar, and Ramesh Kumar Mohapatra Prediction of Schizophrenia in Patients Using Fuzzy AHP and TOPSIS Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 R. Anoop, Impana Anand, Mohammed Rehan, R. Yashvanth, Ashwini Kodipalli, Trupthi Rao, and Shoaib Kamal

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Sports Activity Recognition - Shot Put, Discus, Hammer and Javelin Throw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Swati Shilaskar, Gayatri Aurangabadkar, Chinmayee Awale, and Sakshi Awale User Acceptance of Contact Tracing Apps: A Study During the Covid-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Inger Elisabeth Mathisen, Kanika Devi Mohan, Tor-Morten Grønli, Tacha Serif, and Gheorghita Ghinea Digital Watermark Techniques and Its Embedded and Extraction Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Satya Narayan Das and Mrutyunjaya Panda Galvanic Skin Response-Based Mental Stress Identification Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 Padmini Sethi, Ramesh K. Sahoo, Ashima Rout, and M. Mufti A Federated Learning Based Connected Vehicular Framework for Smart Health Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Biswa Ranjan Senapati, Sipra Swain, Rakesh Ranjan Swain, and Pabitra Mohan Khilar ELECTRE I-based Zone Head Selection in WSN-Enabled Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 Sengathir Janakiraman, M. Deva Priya, A. Christy Jeba Malar, and Suma Sira Jacob Fabrication of Metal Oxide Based Thick Film pH Sensor and Its Application for Sweat pH Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Vandana Pagar, Shweta Jagtap, Arvind Shaligram, and Pravin Bhadane Reliable Data Delivery in Wireless Sensor Networks with Multiple Sinks and Optimal Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 Vasavi Junapudi and Siba K. Udgata

Effect of the Longitudinal Strain of PM Fiber on the Signal Group Velocity Karel Slavicek , David Grenar , Jiri Vavra , Martin Kyselak , Jan Radil , and Jakub Frolka

Abstract The polarization of the light can be used as the core principle of fiber optic sensors. One of the physical quantities which can be detected or measured this way is the longitudinal tension of the fiber. A set of measurements leading to approval of the suitability of polarization for this purpose was performed. This paper analyzes the dependency of differential group delay of the signal in slow and fast axes of the birefringent optical fiber on the longitudinal tension. Keywords Birefringent fiber · Polarization · Differential group delay · Sensors

1 Introduction and Motivation Our research group has been studying polarization properties of the fiber optic for a long time [1–4]. The main aim is to utilize polarization for sensing purposes. The key sensing element of our sensors is a birefringent fiber - usually a Panda one. The basic K. Slavicek Masaryk University, Brno, Czech Republic e-mail: [email protected] D. Grenar (B) · J. Frolka Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Brno, Czech Republic e-mail: [email protected] J. Frolka e-mail: [email protected] J. Vavra · M. Kyselak University of Defence, University of Defence, Czech republic, Czech Republic e-mail: [email protected] M. Kyselak e-mail: [email protected] J. Radil Department of Computer Science, Faculty of Science, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_1

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Linearly Polarized Light

Detector slow axis

Sensitive segment

Fig. 1 The basic structure of polarization based sensors.

principle of the sensor is denoted in Fig. 1. The sensing system consists of a source of linearly polarized light which is inserted into birefringent (commonly Panda type) fiber used as the sensing element. The input signal should be evenly distributed into slow and fast axes of the Panda fiber. The measured physical quantity causes a change in the polarization state or a change in the differential group delay. The change can be detected by a polarimeter (in the case of lab experiments) or by a dedicated optical detector (in the case of a production sensor system). The main advantage of polarization-based sensors is the speed of the detector. This type of sensor is very suitable for the detection of rapid changes in the measured physical quantity. Frequently it is used just to detect that a change has occurred and provides binary output only. In the case of tension measurement, the longitudinal tension has an impact on the differential group delay [5]. In this paper, we study the influence of longitudinal tension on the differential group delay. Our goal is to check the ability of polarizationbased sensors to provide not only the binary output but provide information about the intensity of the tension power.

2 Physical Principles The light beam in the optical fiber propagates in two orthogonal polarization planes. Commonly, we denote as .z axis the direction of wave propagation, the horizontal plane as the .x or fast axis direction and the vertical plane as the . y or slow axis, see Fig. 2. Optical signal propagation velocity differs in both polarization planes. A light pulse is propagated throughout the fiber line. At the input, the pulse is projected into both the slow and fast axis of the fiber. Signal in both axes is propagated throughout the fiber with different velocities. This causes different times of the arrival of pulse projection into the slow and into the fast axis at the end of measuring fiber. See Fig. 3. The situation denoted in Fig. 3 is a bit simplified and is valid for the polarization maintaining fiber only. In the case of a legacy single-mode fiber (G.652) we have a set of segments with different orientations of the slow and fast axes The reason for

Effect of the Longitudinal Strain of PM Fiber ...

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Slow Axis

Electrical Field Vector Optical Fiber Fast Axis

Fig. 2 Model of the light polarization inside fiber optic. VSlow

PANDA PM FIBER

VFast

VSlow DGD

VFast

Fig. 3 Signal propagation throughout a polarization maintaining fiber.

this situation is differences in the intensity and orientation of external stress causing fiber microbending, differences in thermal stress, etc. Moreover, the final differential group delay (DGD) at the far end of the fiber varies according to variations of the external influences alongside the fiber. These differences in the signal group velocity can cause some distortion of the optical pulses which is very meaningful in telecommunications. As the DGD causes some variation, in this case, we use an average value called PMD - Polarization Mode Dispersion instead. There is a close relation between DGD and PMD so the measuring equipment designed for PMD measurement in telecommunication networks can be used for DGD measurement on polarization maintaining fiber as well [6]. The PMD, its influence on telecommunications networks, and methods of its measurement and analysis are discussed in several research papers and whitepaper prepared by measurement equipment manufacturers [7–9].

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3 DGD Measurement There are several methods for DGD and PMD measurement documented in the ITU-T Recommendation G650.2 [10]. The most widely used one in contemporary telecommunication networks is the GINTY - Generalized Interferometric Method. GINTY measurement setup consists of a polarized light source, fiber under test (FUT), the input analyzer at the output of the FUT, the interferometer, polarization beam splitter, and two photodetectors as shown in Fig. 4. The light velocity in the fiber line depends among other parameters on the wavelength. This is the reason for a broadband light source. The polarizer in the polarimeter source serves to set several different states of polarization of the light source entering the FUT. The analyzer in the polarimeter detector serves to bring two orthogonal states of polarization of the light to the interferometer. The polarization beamsplitter is used to split the output light into two orthogonal polarization planes. The optical power on two orthogonal polarization planes is then presented on outputs . Px and . Py . These outputs are subsequently used to calculate the DGD and PMD coefficients. In the case of birefringent fiber, the DGD can be read directly from the interferogram.

4 Experimental Results The aim of our experimental measurement was to study the dependency of DGD in polarization maintaining fiber on the longitudinal tensile force. In the first step, we employed a proof tester commonly used to ensure good mechanical properties of fiber lines after manufacturing like writing FBG (Fibre Brag Grating), splicing, or similar. In our measurement setup, the light source EXFO FTB-5800A and the analyzer EXFO FTB-5700 were used.

INTEFEROMETER POLARIMETER SOURCE FUT

BROUDBAND LIGHT SOURCE

POLARIZER

ANALYZER

PBS POLARMETER RECIVER

PX

Fig. 4 The GINTY PMD measurement setup.

PY

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4.1 Measurement on Proof Tester The proof tester used was SFO Proof Tester from NWG company. The proof tester can apply a tensile force from 0 N up to 70 N to the fiber under test (FUT) with step 0.1 N. It uses a 45mm mandrel to hold the FUT. The FUT is wounded on both mandrels and fixed by small magnetic holders. We have set up the measurement as depicted in Fig. 5. After the first measurement without applying tensile force, which was used as a kind of calibration, we applied a tensile force from 1 N up to 25 N with step 1 N. As expected, the DGD increases with increasing tensile force. The DGD dependence on tensile force is linear as can be easily read from the graph in Fig. 6. The interferograms for tensile force from 1 N up to 7 N look almost like they were copied from a textbook on PMD measurement, see Fig. 7. Since force about 8 N additional peaks occurred in the interferogram, see Fig. 8. Even if the distance of the main peaks from the center corresponds to our expectations, we intended to explain this behavior. Our assumption was, that the additional peaks were caused by crosswise stress. This stress is caused by the mechanical stress of the fiber on the proof tester’s mandrels. To verify this assumption, we performed two sets of additional measurements documented in the following subsections.

PM FUT

GINTY SIGNAL SOURCE

SMF

SMF

GINTY SIGNAL RECEIVER

REEL HOLDER

Fig. 5 Measurement setup with the proof tester.

3.1

DGD [ps]

3.05 3 2.95 2.9 2.85

0

5

15 10 Tensile Force [N]

Fig. 6 The DGD dependence on tensile force.

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Fig. 7 Interferogram of applied tensile force 1 N.

Fig. 8 Interferogram of applied tensile force 8 N.

4.2 Straining Fiber on Connectors To get rid of the crosswise strain, we decided to stress the fiber by pulling it by the connectors. In this case, we have used the fact, that the tested fiber was equipped with FC/PC connectors. We have constructed a special holder holding the FC adapter. One of these holders was affixed on top of the rack in our lab. The second one was simply hanging on the fiber under test, and a proper weight was connected to this holder. The measurement setup is denoted in Fig. 9.

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Fig. 9 Measurement setup using connectors as a fiber holder.

In this measurement setup, we were not able to prove our assumptions. The mechanical strength of the fiber is not sufficient to perform this experiment. With the tensile force above 5 N, the fiber was snapped. For this reason, we have prepared the next measurement setup.

4.3 Usage of Fiber Clamps The aim of the next setup is to avoid variation of the crosswise stress on the fiber caused by the longitudinal tension applied to the fiber. The usage of connectors, which avoids the crosswise stress proved to be not usable. Another option is to apply constant crosswise stress in all cases, which means in the whole range of applied longitudinal tension. The constant crosswise stress can be achieved by the usage of clamps holding the fiber under test. The situation is denoted in Fig. 10. In this setup, additional peaks

Fig. 10 Measurement setup using clamps to hold the FUT.

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Fig. 11 Interferogram of applied tensile force 2 N.

occurred in all applied tensile forces. In this setup, we were not able to perform the measurement in the whole range of applied tensile force as in the case of the proof tester. The reason is the mechanical properties of used clamps. The fiber under test was in 250 .µm primary jacket. Common clamps are designed for regular fiber cables with a diameter 2 mm and above. The holding of these clamps is firm enough to apply longitudinal tension of up to 6 N. The clamps make almost constant crosswise strain on the measured fiber, independent of the applied longitudinal tension. This crosswise strain causes peaks in the interferogram very similar to the case of proof tester usage. In this case, the “additional” peaks occur for all applied values of longitudinal tension force and are almost constant, see Fig. 11.

5 Funding This work was supported by the grant project of the Ministry of Interior of the Czech Republic, No VK01030060.

6 Conclusion The measurements performed have approved, that the polarization is usable for the measurement of longitudinal tension. In the case of a fixed length of birefringent fiber (what’s the case of the expected sensor system), the dependence of the DGD on longitudinal tension force is linear. Even though the uneven crosswise tension force applied causes changes in the interferogram on PMD measurement, it doesn’t

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influence the overall DGD. That means polarization proved sufficient robustness for longitudinal tension measurement. The next step in the practical utilization of this principle is to transform any physical quantity of interest into longitudinal tension of the measuring fiber.

References 1. Kyselák M, Slavicek K, Grenar D, Bohrn M, Vavra J (2022) Fiber optic polarization temperature sensor for biomedical and military security systems. In: Smart biomedical and physiological sensor technology XIX, vol 12123, pp 121230A. https://doi.org/10.1117/12.2618911 2. Kyselák M, Vyležich Z, Vávra J, Grenar D, Slavíˇcek K (2021) The long fiber optic paths to power the thermal field disturbance sensor. In: Optical components and materials XVIII, vol 11682, p 116821A. https://doi.org/10.1117/12.2575832 3. Kyselak M, Maschke J, Panasci M, Slavicek K, Dostal O, Grenar D, Cucka M, Filka M (2020) Birefringence influence on polarization changes and frequency on optical fiber. In: Electrooptical remote sensing XIV, vol 11538, p 115380F. https://doi.org/10.1117/12.2573696 4. Kyselak M, Dvorak F, Maschke J, Vlcek C (2018) Optical birefringence fiber temperature sensors in the visible spectrum of light. Adv Electr Electron Eng 15:885–889. https://doi.org/ 10.15598/aeee.v15i5.2419 5. Grenar D, Cucka M, Filka M, Slavicek K, Vavra J, Kyselak M (2022) Optical sensor based on birefringent fiber type PANDA used for tensile detection. In: 2022 IEEE international conference on internet of things and intelligence systems (IoTaIS), pp 57–63 6. https://www.c3comunicaciones.es/Documentacion/fiberguide2_bk_fop_tm_ae.pdf 7. Gordon J, Kogelnik H (2000) PMD fundamentals: polarization mode dispersion in optical fibers. Proc Natl Acad Sci USA 97:4541–4550 8. https://www.corning.com/media/worldwide/coc/documents/Fiber/white-paper/WP5051-12_ 12.pdf 9. Jurdana I, Pilinsky S, Batagelj B (2006) PMD Measurements in Telecom Networks 10. Recommendation ITU-T G650.2. https://www.itu.int/rec/T-REC-G.650.2-201508-I/en

Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf Priyanka Kaushik

Abstract The range of diseases that can affect grape leaves has made it vital to analyze them. High-end data analytics and predictive analysis are required for a number of diseases, including black rot esca black measles, blight isariopsis, and others, in order to predict disease occurrence. For the prediction of leaf diseases, convolution neural networks combined with data augmentation have increased the degree of verification. For illness predictive analytics, a proper confusion matrix for support vector machines driven by CNN was created. Along with k-mean clustering, fuzzy logic with accurate feature extraction, and color moment definition, we also compared our results with these techniques. The findings indicate a higher effectiveness of up to 95% in correctly predicting grapes leaf disease. Keywords Leaf diseases · DI · Pests · Classification · Detection · Forecasting · SVM · Convolution neural network · k mean clustering · Fuzzy logic

1 Introduction In India, China, and several nations in the southeast pacific region, grape output reached a total of 20.083 million tons in 2022, making it one of the most well-known significant, and prosperous fruit sectors. On the other side, diseases that harm grape leaves have stunted the grape industry’s expansion and caused significant monetary losses. This has led to a great lot of effort being put forth by orchard workers and experts in the prevention and treatment of diseases and pests to identify and diagnose illnesses that harm grape leaves. To begin with, in order for CNN models to be successfully trained, a sizable amount of input data is required. However, because each illness that damages the grape leaf occurs at a distinct period, there is only a limited window of opportunity P. Kaushik (B) Computer Science Engineering Department (AIT CSE (AIML)), Chandigarh University, Gharuan, Punjab, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_2

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for taking pictures of sickness. Since there aren’t enough images of grape leaves with damage, the model can’t be taught from them. Second, transfer learning-trained models have difficulty achieving adequate levels of performance because of the challenging nature of the fine-grained image categorization for diseases of the grape leaves. Making the most accurate diagnosis of diseases that harm grape leaves for the CNN structure is therefore a difficult task. The main innovation of this study is to detect disease on grape leaves using a reform CNN algorithm [20]. The other significant advancements are enumerated as follows: The compiling of a data set on the disease of the grape leaf lays a crucial foundation for the generalization of the model. As a prediction tool, it guides people in recognizing grape diseases and will be grateful for its average accuracy rate of 98.57%. In order to increase the model’s robustness, images of ill grape leaves are first gathered with both intricate and homogeneous backgrounds. Additionally, the raw images of the diseased grape leaves are run via a data augmentation technology and then used to build a suitable amount of training photos in order to avoid the issue of the model being too accurate.

2 Literature Review The prevention of disease spread and maintenance of the grape industry’s health depends on prompt diagnosis and accurate treatment. This study by Bin Liu et al. [1] offers a distinct recognition method for vine leaf diseases. The analysis results show that the suggested approach can reliably diagnose grape leaf diseases. According to Xiaoyue Xie, et al. [2], the absence of a real-time diagnosis tool for diseases that damage grape leaves prevents grape plants from developing normally. It is suggested grape leaf disease detector by deep learning CNN. Zhaohua Huang, et al. [3] speak for Pets and grapevine illnesses can result in significant financial losses for farmers and grape output if they are not identified and treated quickly. Using a developed grape leaf dataset, four updated deep-learning models for diagnosing and categorizing grape leaf diseases are created. According to Miaomiao Ji, et al. [4], creatingan automated detection tool for diseases of grape leaves is essential. It’s possible to extract complementary discriminative features using the proposed United Model. Yun Peng and others [5] for grape yield and quality, grape leaf disease must be quickly and accurately identified. This article suggests a way for recognizing vine leaf disorders based on combined deep neural network characteristics from convolutions (CNN) and AVM (SVM). Real-time disease identification at the press would be possible because of the employment of segmentation and classification algorithms on low-powered devices, according to Lucas Mohimont et al. [6]. Using a hierarchical technique, the backdrop can be removed and photographs with grape diseases can be found. A pertained MobileNet-V2 model had an F1 score of 94% for classifying illnesses.

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According to Carlos S. Pereira et al. [7], shots of grapevine leaves and bunches taken during harvest seasons have a low picture volume, a high degree of resemblance among grape species, signs ofleaf senescence, and significant changes. In two different geographical locations and throughout two different harvest seasons, natural vineyard images were gathered. Mathilde Chen and others [8] Regarding the environment and public health, minimizing the actual treatments is a major problem. Finding vineyards might be one approach. The algorithms under consideration use the date the disease began as well as/or monthly average temperatures and precipitation as input variables. According to Tanmay A. Wagh et al. [9], the disease on grape plants frequently starts on the leaf before spreading to the stem, root, and fruit. The entire plant perishes when the spreads to the fruit. In our proposed system, a type of neural network called a CNN is referred to as a “deep learning model.” The accuracy is 98.23% when comparing bacterial spots to powdery mildew. According to S.M. Jaisakthi et al., Crop diseases are largely a natural occurrence due to variables like climate change and environmental alterations. An autonomous system for diagnosing ailments in grape plants was created using image processing and machine learning techniques. They were able to achieve a 93% testing accuracy using SVM. Ujjwal Singh et al. [11] Regular and proactive monitoring, the use of disease detection techniques, and the reduction of aesthetic and financial losses brought on by plant diseases are all necessary for the production of grapes. An automated computer vision technique for identifying Black Measles disease has been built using grape leaf image samples. Y. Nagaraju et al. [12] Fruits like grapes and apples are the most lucrative but also the most prone to disease. Here, the outcome layer of the prior source is deleted, and a new output layer is attached to it in order to fine-tune the VGG-16 Network. With a 97.87% accuracy rate, the deep- convolutional neural network performs remarkably well in the diagnosis of apple and grape leaf diseases. Khaing Zin Thet, among others [13] Grape plants become infected from the leaves to the stem, fruit, and root. The VGG16network, one of CNN Architecture’s networks, is tuned using this system to detect illnesses on grape leaves. Instead of the two completely connected layers of VGG16, the system used a GAP layer before the final classification. Sandy Lauguico and others [14]. It is essential to identify various disorders in leaves in order to boost crop output. The comparison has been done to predict which of the 3 pre-trained networks—AlexNet, GoogleLeNet, and ResNet-18—performed best. The accuracy of the other two models, GoogLeNet and ResNet-18, was only 92.29% and 89.49%, respectively. Among others, Stefania Barburiceanu [15] extractors for new rotation, illumination, and observation scale invariant color texture categorization features. Comparing the suggested feature extractors to traditional grayscale LBP-based techniques reveals a considerable improvement in terms of accuracy, correctness, and recall. Data is categorized using the findings of the experimental section of the study that used Support Vector Machines (SVMs).

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[16] Changjian Zhou et al. early sickness onset identification is essential for practical usage since similar treatments might be used when a plant disease is still in its early stages. A fine-grained- GAN based on five cutting-edge deep learning models, most notably ResNet-50, was suggested as a way to further strengthen the generalization categorization models’ power. This strategy greatly increased identification accuracy. Aravind K R and others [18] One of the most important fruit crops that suffer from illness is the grape. The classification accuracy of the model was 97.62%. The Support uses feature data acquired from different layers of the same network. When MSVM was combined with AlexNet’s Rectified Linear Unit (ReLu) layer, the highest classification accuracy of 99.23% was attained. Early disease detection is essential to preventing crop damage, according to Suviksha Poojari, et al. [19] because these diseases cause significant agricultural damage and financial loss. Choosing a categorizing system is never easy when there are so many available, especially since the output consistency varies depending on the input data. Arie Moh, et al. [20] An accurate identification of the plant disease is necessary in order to implement suitable management measures as a tool for the diagnosis to identify and group grape leaf diseases, a CNN network was employed [17]. According to the literature, the feature selection procedure needs to be revised. CNN and support vector machines are two deep learning techniques that could be used (SVM). For precise disease prediction, a comparison between CNN and support vector machines should be conducted.

3 Diagnosis of Grape Leaf Diseases Major vine shoots diseases are discussed in this study The following major vine shoots or leaf diseases were chosen for the prediction: 1. Spanish measles: Figure 1 is a true photograph of a grape leaf infected with Esca Black Measles disease. The surface spots of the fruit are referenced by the name “measles”. Over the course of the season, the dots could mix over the skin’s surface, rendering the berries black. Between the time of fruit set and a few days before harvest, spotting may emerge. During fruit set, berries that are harmed do not develop and instead wilt and dry off. Later in the season, fruit that has been damaged will also have a bad flavor. Figure 1 illustrates the “tiger stripe” pattern that characterizes leaf symptoms when infections are strong from year to year [10]. 2. Black rot - Leaf symptoms first appear as small, round, reddish-brown spots: The condition is depicted in real life in Fig. 2. Black rot, a deadly disease of both cultivated and wild grapes, is brought on by the fungus Guignardiabidwellii. The worst effects of this condition occur during warm, wet months. The fruit leaves, fruit stems, tendrils, shoots, leaf stems, and all other green parts of the vine are all attacked [9].

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Fig. 1 Grape leaf and Grapes infected by Spanish Measles disease Fig. 2 Grape leaf and Grapes infected by Black rot disease

3. Leaf blight Isariopsis Leaf Spot: This is identified with distributed purple brownish marks on the leaf surface. Figure 3 depicts a real-world example of this condition. Pseudocercospora vitis fungi cause infected leaves to turn yellow with brown spots [23].

Fig. 3 Grape leaf infected by Leaf blight Isariopsis Leaf Spot disease

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4 Data Analysis and Augmentation Several processing digital picture technologies are used to give data expansion all types of image intensity interference are used to mimic how the weather affects photography. The pixels’ red, green, and blue values are randomly increased or decreased to alter the brightness of each image [22]. Assume that V represents the changed value, the brightness transformation factor, and that represents the original RGB value [20]. The way involved in transforming RGB values are: V = V 0 + (1 + d)

(1)

The image’s contrast value is modified by raising the bigger RGB values and reducing the smaller RGB values based on the brightness’s median value. The RGB values are transformed in the following way: V = i + (V 0 − i )(1 + d) To alter the sharpness value, the Laplacian template is applied to the picture. Assume that a pixel in an image RGB picture [20] is represented by asc(x, y) = [R(x, y), G(x, y), B(x, y)] The formula is as follows: ∇2[c(x, y)] = [∇2R(x, y)∇2G(x, y)∇2B(x, y)]

(2)

Images are rotated by moving each pixel around the center at the same degree. Assume that P(x,y) is a random point in the picture and that after clockwise rotation by 90°, its new coordinate is (x,h-y). The two points’ computed coordinates are written as [20]. {x = r cos αy = r sin{X = r cos(α − θ ) = x cos θ + yr sin θ Y = r sin(α − θ ) = −x cos θ + yr cos θ

(3)

Cascade Dense Inception Module Varieties of grape leaves differ substantially in the prevalence of the disease spots. The ultimate recognition accuracy greatly depends on the model’s capacity to extract features at various scales. It is common practice to evaluate the temporal complexity of the CNN model using floating-point operations. One-way to represent a single convolutional layer’s temporal complexity is as follows: T imes ∼ (M2 ∗ K 2 ∗ Cin ∗ Count)

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The temporal complexity of the Inception structure may be represented as the total of the operation times of all the convolutional layers: T imes ∼

(

Mi2 ∗ Pi ∗ Qi ∗ C(i, in) ∗ C(i, out)Di = 1

 (4)

where D = Inception structure convolutional layers, Pi denotes the convolution kernel’s length, and Qi denotes the convolution kernel’s width and Qi != Pi when asymmetric. The model’s recognition accuracy is greatly harmed by this loss of features. The dense connection technique was presented in Dense Net as a way to improve the movement of data between layers even further. As shown in Equation, the l layer collects feature maps from all previous layers: x = ([xo, x1, . . . xλ − 1]) where [x_0,x_1,…,x_(λ-1)] denotes the concatenation of the preceding layers’ maps [21]. Adaptive Connectivity Strategy The classification of grape leaf diseases is done by CNN Technique. The training results are significantly influenced by the optimization technique employed. Adaptive moment estimation (Adam) was used as the model’s optimization method as opposed to the more common stochastic gradient descent (SGD). The data from the previous iteration is used to compute the new weights, and the following is how the weight optimization process is described: gt = ∇(θ t − 1)mt = β1.mt − 1 + (1 − β1).gt vt = β2.vt − 1 + (1 − β2).gt2mt = mt/(1 − β1t) √ v tˆ = vt/(1 − β2t)θ t = θ t − 1 − α.m tˆ/( v tˆ + ε) where a represents the learning rate, β1 and β2 the following are the formulas for calculating the recognized performance of each indicator [24]. Pr ecision = T P T P + F P

(5)

Recall = T P T P + F N

(6)

F1Scor e = 2 × Pr ecision × Recall Pr ecision × Recall = 2 × T P2 × T P + F N + F P

(7)

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5 Simulation Result Using training and testing datasets for grape leaf disease, the accuracy of grape leaf disease prediction for the chosen diseases has been examined. Initially, grape leaf disease accuracy was demonstrated using CNN technology; however, after monitoring the results, SVM technology has emerged as a potentially useful tool. Convolutional Neural Network The confusion matrix is a matrix that represents this accuracy. The accuracy score for CNN is 0.375. The results show that deep learning algorithms not only take a lot of time but also make bad predictions (Fig. 4). Support Vector Machine (SVM) With a classification accuracy of 0.5 for the 4b transcript variables and four fewer factors than logistic regression. The linear SVM model appears to be the most promising for usage in clinical settings, as this result shows a desirable sensitivity and specificity (Figs. 5 and 6).

Fig. 4 a Accuracy Graph of Convolutional Neural Network (CNN) b Loss Graph of Convolutional NeuralNetwork (CNN)

Fig. 5 a Accuracy Graph of SVM (CNN) b Loss Graph of SVM (SVM)

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Fig. 6 a Confusion Matrix for CNN b Confusion Matrix for SVM

6 Conclusion Grape leaf disease analysis has grown in importance as a part of the industry because of how different plants may detect illness. Effective illness prediction requires sophisticated data analytics and predictive analysis. This category includes naming a few, illnesses like black rot, esca black measles, and blight isariopsis. There is now a greater level of verification for the prediction of leaf diseases thanks to the deployment of a convolutional neural network and data augmentation. For accurate illness prediction analytics, a suitable confusion matrix for support vector machines driven by CNN was developed. Additionally, we contrasted our findings with those obtained using the k-mean clustering method, fuzzy logic with precise feature extraction, and color moment definition. The execution time increases along with the network’s complexity. The main obstacle in accurately predicting grape leaf disease is to increase classification accuracy and execution speed.

References 1. Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082. https://doi.org/10. 3389/fpls.2020.01082. ISSN1664-462X 2. Xie X, Ma Y, Liu B, He J, Li S, Wang H (2020) A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front Plant Sci 11:751. https://doi.org/10.3389/fpls.2020.00751. ISSN 1664-462X 3. Huang Z, Qin A, Lu J, Menon A, Gao J (2020) Grape leaf disease detection and classification using machine learning, pp 870–877. https://doi.org/10.1109/iThings-GreenCom-CPSComSmartData-Cybermatics50389.2020.00150 4. Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Inf Process Agric 7(3):418–426. ISSN 2214-3173 5. Peng Y, Zhao S, Liu J (2021) Fused-deep-features based grape leaf disease diagnosis. Agronomy 11:2234. https://doi.org/10.3390/agronomy11112234

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6. Mohimont L, Alin F, Gaveau N, Steffenel LA (2022) Lite CNN models for real-time postharvest grape disease detection. In: Workshop on edge AI for smart agriculture (EAISA 2022), Biarritz, France. ffhal-03647740f 7. Pereira CS, Morais R, Reis MJCS (2019) Deep learning techniques for grape plantspecies identification in natural images. Sensors 19:4850. https://doi.org/10.3390/s19224850 8. Chen M, Brun F, Raynal M, Makowski D (2020) Forecasting severe grape downymildew attacks using machine learning. PLoS ONE 15(3):e0230254 9. Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020). Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082. https://doi.org/10.3389/ fpls.2020.0108 10. Wagh TA, Samant RM, Gujarathi SV, Gaikwad SB (2019) Grapes leaf disease detection using convolutional neural network. Int J Comput Appl 178(20):7–11. https://doi.org/10.5120/ijca20 19918982 11. Jaisakthi SM, Mirunalini P, Thenmozhi D (2019) Grape leaf disease identification using machine learning techniques. In IEEE 2019 international conference on computational intelligence in data science (ICCIDS), Chennai, India, pp 1–6. https://doi.org/10.1109/ICCIDS.2019. 8862084 12. Singh U, Srivastava A, Chauhan D, Singh A (2020) Computer vision technique for detection of grape esca (black measles) disease from grape leaf samples. In: IEEE 2020 international conference on contemporary computing and applications (IC3A) - Lucknow, India, pp 110–115. https://doi.org/10.1109/IC3A48958.2020.233281 13. Nagaraju Y, Swetha S, Stalin S (2020) Apple and grape leaf diseases classification using transfer learning via fine-tuned classifier. In: 2020 IEEE international conference on machine learning and applied network technologies (ICMLANT). https://doi.org/10.1109/icmlant50963.2020. 9355991 14. Thet KZ, Htwe KK, Thein MM (2020) Grape leaf diseases classification using convolutional neural network. In: 2020 international conference on advanced information technologies (ICAIT). https://doi.org/10.1109/icait51105.2020.9261801 15. Lauguico S, Concepcion R, Tobias RR, Bandala A, Vicerra RR, Dadios E (2020) Grape leaf multi- disease detection with confidence value using transfer learning integrated to regions with convolutional neural networks. In: 2020 IEEE region 10 conference (TENCON). https:// doi.org/10.1109/tencon50793.2020.9293866 16. Barburiceanu S, Terebes R, Meza S (2020) Grape leaf disease classification using LBP-derived texture operators and colour. In: IEEE 2020 IEEE international conference on automation, quality and testing, robotics (AQTR) - Cluj- Napoca, Romania, pp 1–6. https://doi.org/10.1109/ AQTR49680.2020.9130019. Zhou C, Zhang Z, Zhou S, Xing J, Wu Q, Song J (2021) Grape leaf spot identification under limited samples by fine grained-GAN. IEEE Access 9:100480– 100489. https://doi.org/10.1109/access.2021.3097050 17. Ali A, Ali S, Husnain M, Saad Missen MM, Samad A, Khan M (2022) Detection of deficiency of nutrients in grape leaves using deep network. Math Probl Eng 2022, Article ID 3114525, 12 p 18. Aravind KR, Raja P, Aniirudh R, Mukesh KV, Ashiwin R, Vikas G (2019) Grape Crop Disease Classification Using Transfer Learning Approach. https://www.researchgate.net/publication/ 331634971 19. Poojari S, Sahare D, Pachpute B, Patil M (2020) Identification and solutions for grape leaf disease using convolutional neural network (CNN). In: 2nd international conference on communication & information processing (ICCIP). https://ssrn.com/abstract=3648108. http://dx.doi. org/https://doi.org/10.2139/ssrn.3648108 20. Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082 21. Rathore R (2022) A study on application of stochastic queuing models for control of congestion and crowding. IJGASR 1(1):1–6 22. Kaushik P (2022) Role and application of artificial intelligence in business analytics: a critical evaluation. Int J Glob Acad Sci Res 1(3):1–11. https://doi.org/10.55938/ijgasr.v1i3.15

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23. Vijayaganth V, Krishnamoorthi M (2022) Soft computing-based ensemble learning model for multi-disease classification of plant leaves. https://doi.org/10.1080/10106049.2022.2112300 24. Hasan M, Riana D, Swasono S, Priyatna A, Pudjiarti E, Prahartiwi L (2020) Identification of grape leaf diseases using convolutional neural network. J Phys: Conf Ser 2020(1641):012007. https://doi.org/10.1088/1742-6596/1641/1/012007

Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed Power System Smrutiranjan Nayak, Subhransu Sekhar Dash, Sanjeeb Kumar Kar, Ananta Kumar Sahoo, and Ashwin Kumar Sahoo

Abstract In this article improved fuzzy PI regulator is stated for frequency regulation of Automatic Control of distributed power systems. Originally, a two-region nonwarm framework is utilized. The advantage of the stated fuzzy PI regulator is shown with the help of contrasting the outputs. All real structure shows nonstraight nature, subsequently, traditional regulators are not generally ready to give great and precise outcomes. So fuzzy-logic controller can be utilized to get more exact outcomes. The primacy of the stated hybrid particle swarm optimization & pattern search (hPSO-PS) approach adjusted fuzzy-PI selector over PS changed fuzzy PI selector, PSO changed fuzzy PI selector, hBFOA-PS changed PI selector, Differential Evolution (DE) changed PI selector and Bacteria Foraging optimization algorithm (BFOA) adjusted PI selector is demonstrated. It is seen that the Fuzzy PI regulator is more effective for controlling frequency relative to the PI regulator. Keywords Nonlinearity · Distributed power system · PI regulator · Integral time absolute error · Area control error

S. Nayak (B) · S. K. Kar Department of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, India e-mail: [email protected] S. K. Kar e-mail: [email protected] S. S. Dash Department of Electrical Engineering, GCE, Keonjhar, Odisha, India e-mail: [email protected] A. K. Sahoo · A. K. Sahoo Department of Electrical Engineering, CV Raman Global University, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_3

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1 Introducion In present systems, the goal of AGC is to keep on the balance in the middle of creation & peak loads, therefore reducing the recurrence variation. It additionally controls the tie-line-power trade over different control regions consequently guaranteeing solid activity of the appropriated power framework [1, 2]. Generation control recognizes the organization’s repeat and tie-line flows and replaces the put situation of the generator’s interior space to stay the time usual of Area-Control-Error at the very least worth. As the ACE is changed under zero by the AGC, the two recurrence and tie-line power blunders will become zero [3, 4]. In target work utilizing Integral-Time-Absolute-Error (ITAE), damping proportion of prevailing Eigen-values & settling-time is suggested where the PI regulator boundaries are redesigned utilizing Differential-Evolution (DE) calculation & outcomes stood out from BFOA and PSO improved ITAE based PI regulator to appear its benefit [5–11]. In Artificial-Bee-Colony calculation is registered to update the PI and PID regulators for inter-connected warm nuclear energy framework and shows that better powerful execution is accomplished by ABC adjusted regulators differentiated to PSO adjusted regulators [12, 13]. Fuzzy-Logic regulator refines the shut circle execution of PI regulator and can deal with trade in working point or framework boundary by internet refreshing the regulator boundaries. Various control loops are operating to carry on the system frequency at its set point. Most supply–demand balancing is attained by controlling the outputs of dispatchable generating units. Fuzzy PI means the union of fuzzy and PI controller. To lessen rise time Proportional gain is used & to continue error as little as possible integral gains are used. It is seen that the Fuzzy PI regulator is more productive in controlling the frequency relative to the PI regulator.

2 System Model and Controller Structure Two-locale power systems as given in Fig. 1. The structure of the regulator has appeared in Fig. 2. The Area-Control-Errors is given by: ACE1 = B1 ΔF1 + ΔPTie

(1)

ACE2 = B2 ΔF2 − ΔPTie

(2)

B1 & B2 are frequency bias parameters, ΔF1 & ΔF2 are small changes in frequency and ΔPtie is variance in power in the tie-line. Three-sided participation capacities are utilized with five fuzzy phonetic factors like negative large, negative little, zero, positive little, and positive enormous for both the information sources and the yield. Mamdani fuzzy interface motor is chosen for this work [14, 15].

Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed …

25

Fig. 1 Two area’s power-system

Fig. 2 Structure Fuzzy-Logic selector

2.1 Objective Function ITAE minimizes the settling time & also reduces the peak overshoot. The main outcome is given by {

t

J = ITAE =

(|ΔF1 | + |ΔF2 | + |ΔPtie |).t. dt

(3)

0

So ΔF = small change in frequency, ΔPTie = variance in power in tie-line, and t = simulation in time.

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3 Overview of Fusion PSO and PS Calculation Every one of these calculations is portrayed underneath. The PSO technique is an individual from a wide class of a multitude of knowledge strategies for taking care of advancement issues. In PSO every molecule endeavor to work on itself by copying qualities from its fruitful companions. The area relating to the best wellness is pbest & the general best out of the multitude of molecules in the populace is called g-best. . All specialists step by step draw near to the worldwide ideal utilizing the various headings of p-best and g-best. . The strategy is registered to the constant issue. Be that as it may, the strategy is applied to the separate issue utilizing networks for X–Y position and its speed. . There is no irregularity in looking through methods regardless of whether constant and discrete state factors are used. Pattern-Search The P-S enhancement procedure is a subsidiary-free transformative calculation to tackle an assortment of advancement issues such lie the extent of the quality improvement techniques. It is adaptable and even administrators to improve and adjust the neighborhood search. The Pattern-Search estimation processes a succession of focuses that might move toward the ideal point. The calculation begins with a bunch of focuses called networks, around the underlying focuses. The underlying focuses are given by the PSO strategy. The cross-section is made by putting on the current highlight a scalar numerous of a bunch of vectors called an example.

4 Results and Discussions Fuzzy-PI regulators are thought about for each area. The target work is determined in the m-document and utilized in the enhancement calculation. With the PI regulator structure, a more modest ITAE esteem is gotten with hPSO-PS upgraded fuzzy PI regulator contrasted with PSO advanced fuzzy PI regulator and PS enhanced fuzzy PI regulator. Recurrence changes and tie-line power change results are displayed in Fig. 3 to Fig. 4. Therefore, better framework execution as far as the least settling time in recurrence & tie-line-power variations is accomplished for the expressed hPSO-PS streamlined fuzzy-PI regulator contrasted with different methodologies.

Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed …

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Fig. 3 Frequency change of region-1 for 0.1 SL rise in region-1

Fig. 4 Tie-line power change for 0.1 SL rise in region-1

5 Conclusion In this article, the fuzzy-PI regulator is stated for programmed age control of manyregion frameworks. At first, a two-region nuclear energy framework is thought of & the information scaling components and gains of the fuzzy-PI regulator are all the while advance utilized. The stated half-and-half procedure exploits the worldwide investigation potential of PSO and the nearby misuse capacity of PS. The primacy of the stated hPSO-PS approach adjusted fuzzy-PI selector over PS finetuned fuzzy PI selector, PSO fine-tuned fuzzy PI selector, hBFOA-PS fine-tuned PI selector, DE fine-tuned PI selector, and BFOA fine-tuned PI selector are demonstrated. Most developments are attained for the stated approach contrasted to some recently reported approaches. The expressed methodology is additionally reached out to a two-region four-unit aqueous force framework with/without HVDC connects. Further, an affectability examination is done to show the power of the regulator. It is expressed that hPSO-PS represents proposed approaches and may turn into an extremely encouraging calculation for taking care of more mind-boggling designing advancement issues in future exploration.

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References 1. Parmar KPS, Majhi S, Kothari DP (2012) Load frequency control of a realistic power system with multi-source power generation. Int J Electr Power Energy Syst 42:426–433 2. Nayak S, Kar SK, Dash SS, Das MC, Swain SC (2022) PIDA regulator for frequency limitation of conventional power systems, intelligent systems. Lecture notes in networks and systems, vol 431. Springer, Singapore, pp 11–17 3. Chandrakala KRMV, Balamurugan S, Sankar Narayanan K (2013) Variable structure fuzzy gain scheduling-based load frequency controller for the multi-source multi-area hydrothermal system. Int J Electr Power Energy Syst 53:375–381 4. Nayak SR, Kar SK, Dash SS (2021) Performance comparison of the hSGA-PS procedure with PIDA regulator in AGC of the power system. In: ODICON-2021, pp 1–4 5. Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm-based load frequency controller for an interconnected power system. Int J Electr Power Energy Syst 33:633–638 6. Nayak S, Kar SK, Dash SS (2021) A Hybrid search group algorithm & pattern search optimized PIDA controller for AGC of the interconnected power system. In: ICICA, pp 309–322 7. Patel NC, Debnath MK, Sahu BK, Dash SS, Bayindir R (2018) Multi-staged PID controller tuned by an invasive weed optimization algorithm for LFC issues. In: 7th international conference on renewable energy research and applications 8. Nayak S, Kar SK, Dash SS (2022) Change detection filter technique of HVDC transmission link fed by a wind farm. In: 4th international conference on energy, power, and environment (ICEPE), pp 1–6 9. Parida SM, Rout PK, Kar SK (2020) Fuzzy multi-objective approach-based small signal stability analysis and optimal control of a PMSG-based wind turbine. Int J Comput Aided Eng Technol 12(4):513–534 10. Nayak S, Dash SS, Kar SK (2021) Frequency regulation of hybrid distributed power systems integrated with renewable sources by optimized type-2 fuzzy PID controller. In: 9th international conference on smart grid, smart-grid, pp 259–263 11. Padhi JR, Debnath MK, Pal S, Kar SK (2019) AGC investigation in wind-thermal-hydrodiesel power system with 1 plus fractional order integral plus derivative controller. Int J Recent Technol Eng 8(1):281–286 12. Nayak S, Kar SK, Dash SS, Vishnuram P, Thanikanti SB, Nastasi B (2022) Enhanced salp swarm algorithm for multimodal optimization and fuzzy based grid frequency controller design. Energies 15(9):3210 13. Nayak S, Kar SK, Dash SS, Das MC (2022) Synchronization and its use in communication network with frequency control, intelligent systems. Lecture notes in networks and systems, vol 431. Springer, Singapore, pp 19–29 14. Sahoo BP, Panda S (2018) Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control. Sustain Energy Grids Netw 278–299 15. Khamari D, Sahu RK, Panda S (2019) Application of search group algorithm for automatic generation control of interconnected power system. In: Computational intelligence in data mining. Springer, Singapore, pp 557–568

Detecting Depression Using Quality-of-Life Attributes with Machine Learning Techniques J. Premalatha, S. Aswin, D. JaiHari, and K. Karamchand Subash

Abstract Worldwide, depression affects millions of individuals even without their knowledge and is a crippling affliction. Primary care physicians frequently discover that they must treat mental health problems like depression despite having little or no formal training in how to do so. There is proof that an integrated strategy, where doctors regularly screen patients for mental health issues and collaborate with psychologists and other mental health specialists to treat patients, results in lower costs and improved patient outcomes. In order to handle and study the heterogeneous data and understand the correlation between aspects of quality of life and depression, this paper uses machine learning techniques. Machine learning is used to predict people who might have depression based on data that is found in CDC National Health and Examination Survey (NHAES) website. These forecasts could be used to more quickly and easily connect patients with qualified mental health specialists. Keywords Depression · Affliction · Quality of life · CDC NHAES

1 Introduction No matter the situation of a nation, healthcare is one of the biggest issues that is faced by almost every nation. Smart and effective healthcare systems are considered as the most important thing in enhancing the quality of life globally. Finding a patient’s mental health issues remains difficult for healthcare organizations and J. Premalatha · S. Aswin · D. JaiHari · K. Karamchand Subash (B) Department of IT, Kongu Engineering College, Erode, India e-mail: [email protected] J. Premalatha e-mail: [email protected] S. Aswin e-mail: [email protected] D. JaiHari e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_4

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doctors, especially with younger patients. The ability of machine learning and deep learning to identify people’s psychiatric problems and understand the effects of those disorders on lifestyle has recently been demonstrated. The phrase “quality of life” highlights various aspects of a person’s existence, including their emotional, physical, and psychological health. These characteristics explain how an individual experiences life, and various researchers and health professionals are taking them into consideration. Machine learning has the capacity to search for Quality-of-Life (QoL) variables in a wider method in order to identify the relationship between depression and QoL aspects. One of the most dynamic subfields of artificial intelligence is machine learning. Its learning approach is allegedly employed in various intelligent environments, including self-driving cars, healthcare speech recognition services, and it also offers recommendations based on google searches. This paper mainly focuses in healthcare perspective. Below mentioned are the important contributions of this paper: • For the purpose of integrating the heterogeneous data, a data consolidation technique is developed. • Along with more established statistical methods, data pertaining to quality of life is examined through machine learning techniques.

2 Related Work M Masood Habib et al. (2022) [1] describes an experimental study to investigate the relationship between depression and variables linked to quality of life. Several machine learning methods were used to conduct the investigation. The consolidation strategy provided a foundation for the development and verification of the research idea. L. Castelli et al. (2020) [2] assessed the results indicating a common agreement between the MADRS and the HADS (K-test: 0.44), however selecting a cut-off of 11 for the HADS resulted in a much higher underestimating of depressive patients. There were 151 participants in the research with mixed cancer pathology. M. Milic et al. (2020) [3] compared the effects of smoking on students’ health in two different university contexts. When sociodemographic, behavioral, and health characteristics were taken into account, smoking was linked to lower Mental Composite Scores (MCS) and Physical Composite Scores (PCS). Ibrahim Aljarah et al. (2019) [4] suggested a hybrid strategy built on the Grasshopper optimization method (GOA). A recent algorithm called GOA was motivated by the biological behaviour of swarms of grasshoppers. The proposed strategy aims to improve the SVM model’s parameters. I.C. Passos et al. (2019) [5] looked into the research on use of big data techniques for diagnosing and treating mental illness. These includes discussion of different types of mental illnesses such as anxiety, depression, and personality problems. The impact of users’ mental health on behaviours like drug abuse and suicidal thoughts is highlighted (Fig. 1).

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Fig. 1 Proposed methodology

3 Proposed Methodology Different quality-of-life datasets was collected from National Health and Nutrition Examination Survey (https://www.cdc.gov/nchs/nhanes/index.htm) such as drug usage, occupation, alcohol usage and sleep disorders. During this study, the datasets collected through past 7 years under each quality-of-life attribute were separately assessed to identify the factors that work effectively with fresh new, untested data. In this paper, the methods that were used to develop the models for predicting depression in a technical manner is explained.

3.1 Data Pre-processing Data Cleaning – In data analytics, data cleaning is crucial because it eliminates unused and noisy data. Datasets are checked for null values and ambiguous values are replaced with null values. This is carried out for all the quality-of-life attributes datasets.

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3.2 K-means Clustering The clustering issues in machine learning or data science are resolved using the unsupervised learning algorithm K-Means Clustering. The data will be clustered using K-Means and added as a feature for modelling. It is hoped that by using this method, the model will gain some accuracy without the need for additional data collection.

3.3 Classification Three different classification algorithms are used in this paper: Random Forest - The subsection’s discussion of the decision tree’s overfitting issue shows how random forests can fix this issue. It is an ensemble made up of numerous independent decision trees working together. The core tenet of random forest is straightforward but effective. The class with the most votes determines the forecast for our model. Support Vector Machine - The SVM approach determines the best decision boundary or solution line for categorising n-features. As a result, we can group the points in the new dataset appropriately. The best boundary is referred to as a hyperplane. Logistic Regression - The likelihood of the target attribute is ascertained using the supervised approach technique of LR. Ordinal, interval, or ratio-level independent variables are either true or false, 0 or 1, etc. as a result of this regression. The distinction between a dependent variable and additional independent variables is described and explained using LR.

4 Results and Discussion 4.1 Parameters for Evaluation Three prediction models were used in this experiment to predict depression. Here, Random Forest, logistic regression, and SVM are employed as classification algorithms. The parameters that were considered are Precision, Recall, F1-Score and Accuracy. Precision - Precision is the ratio of appropriately assessed samples tested to all favorably classified samples.

Detecting Depression Using Quality-of-Life Attributes with Machine …

Precision = TP/TP + FP

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(1)

TP: True Positive - Observation rightly detected as positive. FP: False Positive - Observation wrongly detected as positive. Recall - The percentage of Positive samples that were accurately labelled as Positive relative to all Positive samples is how the recall is calculated. The recall increases as more positive samples are found. Recall = TP/TP + FN

(2)

FN: False Negative - Observation wrongly detected as negative. F1-Score – Mean of precision and recall. F1 − Score = 2 ∗ (Recall ∗ Precision) / (Recall + Precision)

(3)

Accuracy - The accuracy performance metric, which is just the ratio of rightly predicted observations to total observations, is the simplest to comprehend. If our model is correct, then it must be the best, right? Accuracy is a great indication, but only if the dataset’s false positive and false negative rate values are about comparable. As a result, while evaluating the performance of the model, additional things must be taken into account. Accuracy = TP + TN/TP + FP + FN + TN TN: True Negative - Observation rightly detected as negative.

4.2 Performance of Random Forest See Figs. 2 and 3.

4.3 Performance of Support Vector Machine See Figs. 4 and 5.

4.4 Performance of Logistic Regression See Figs. 6 and 7.

(4)

34 Fig. 2 Confusion matrix – Random Forest

Fig. 3 ROC Curve – Random Forest

Fig. 4 Confusion matrix – SVM

J. Premalatha et al.

Detecting Depression Using Quality-of-Life Attributes with Machine … Fig. 5 ROC Curve – SVM

Fig. 6 Confusion matrix – LR

Fig. 7 ROC Curve – LR

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Table 1 Comparing three algorithms Algorithm

Accuracy (%)

Precision (%)

Recall (%)

F1-Score (%)

SVM

75

97

76

85

RF

89

94

94

94

LR

75

97

97

85

The Table 1 shows the accuracy, precision, recall and F1-Score of various classifier models. It indicates that the maximum accuracy was achieved by the Random Forest classification model.

5 Conclusion It is not unexpected that predicting depression is a challenging subject to model because it is a complicated, multifaceted problem. It could be possible to find a tactic that would be ideal for this assignment by looking at additional model kinds. The apparent possibility in pursuing this work would be to conduct additional feature evaluation to eliminate those that are not useful and perhaps experiment to see if new features might be introduced that would turn out to be beneficial. It’s obviously easier said than done to strike the correct balance between the acceptable level of inaccuracy and the amount of data required for an accurate model. An alternate scoring measure, such as maximising simply for recollection or weighting recall much more highly in a customised F-statistic scoring item, could possibly be used because the depressed class was so challenging to categorise. We observed that Logistics Regression and Support vector machine gave almost same results but based on accuracy, the random forest algorithm can be given higher than others. However, for a higher number of features in the feature set, Random Forest and decision tree are better options. Finally, we conclude that for detecting depression Random Forest Algorithm is more accurate than the support vector machine and logistic regression.

References 1. Habib M, Wang Z, Qiu S, Zhao H, Murthy AS (2022) Machine learning based healthcare system for ınvestigating the association between depression and quality of life. J Biomed Health Inform 26(5):1–12 2. Qiu S et al (2022) Multi-sensor information fusion based on machine learn ing for real applications in human activity recognition: state-of-the-art and research challenges. Inf Fusion 80:241–265 3. Daniel SC, Azuero A, Gutierrez OM, Heaton K (2021) Examining the relationship between nutrition, quality of life, and depression in hemodialysis patients. Qual Life Res 30(3):759–768

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4. Hazarika A, Abraham A, Kandar D, Maji AK (2021) An im proved lenet-deep neural network model for Alzheimer’s disease clas sification using brain magnetic resonance images. IEEE Access 9:161194–161207 5. Niu S, Liu M, Liu Y, Wang J, Song H (2021) Distant domain transfer learning for medical imaging. IEEE J Biomed Health Informat 25(10):3784–3793 6. Riemann D, Krone LB, Wulff K, Nissen C (2020) Sleep, insomnia, and depression. Neuropsychopharmacology 45(1):74–89 7. Darimont T, Karavasiloglou N, Hysaj O, Richard A, Rohrmann S (2020) Body weight and self-perception are associated with depression: Results from the national health and nutrition examination survey (NHANES) 2005–2016. J Affect Disorders 274:929–934 8. Cahuas A, He Z, Zhang Z, Chen W (2020) Relationship of physical activity and sleep with depression in college students. J Amer College Health 68(5):557–564 9. Castelli L, Torta R, Mussa A, Caldera P, Binaschi L (2020) Fast screening of depres-sion in cancer patients: the effectiveness of the HADS. Eur J Cancer Care 20(4):528–533 10. Milic M et al (2020) Tobacco smoking and health-related quality of life among university students: Mediating effect of depression. PLoS ONE 15(1):1–18 11. Kim SY et al (2020) Gender and age differences in the association between work stress and incident depressive symptoms among Korean employees: a cohort study. Int Arch Occup Environ Health 93(4):457–467 12. Kandola A, Ashdown-Franks G, Hendrikse J, Sabiston CM, Stubbs B (2019) Physical activity and depression: towards un derstanding the antidepressant mechanisms of physical activity. Neurosci Biobehavioral Rev 107:525–539 13. Francis HM, Stevenson RJ, Chambers JR, Gupta D, Newey B, Lim CK (2019) A brief diet intervention can reduce symptoms of depression in young adults – a randomised controlled trial. PLoS ONE 14(10):1–17 14. Dong Y, Dragut EC, Meng W (2019) Normalization of duplicate records from multiple sources. IEEE Trans Knowl Data Eng 31(4):769–782 15. Passos IC, Ballester P, Pinto JV, Mwangi B, Kapczinski F (2019) Big data and machine learning meet the health sciences. In: Personalized psychiatry, vol 81. Springer, Cham, pp 1–13 16. Shatte ABR, Hutchinson DM, Teague SJ (2019) Machine learning in mental health: a scoping review of methods and applications. Psychol Med 49(9):1426–1448 17. Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput 10(3):478–495 18. Wolohan JT, Hiraga M, Mukherjee A, Sayyed ZA (2018) Detecting linguistic traces of depression in topic-restricted text: attending to self-stigmatized depression with NLP. In: Workshop, pp 11–21 19. Chen S, Conwell Y, Cerulli C, Xue J, Chiu HFK (2018) Primary care physicians’ perceived barriers on the management of depression in China primary care settings. Asian J Psychiatry 36:54–59 20. González-Blanch C, Hernández-de-Hita F, Muñoz-Navarro R, Ruíz-Rodríguez P, Medrano LA, Cano-Vindel A (2018) The association between different domains of quality of life and symptoms in primary care patients with emotional disorders. Sci Rep 8(1):11180 21. Dwyer DB, Falkai P, Koutsouleris N (2018) Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 14(1):91–118 22. Ledesma S, Ibarra-Manzano MA, Cabal-Yepez E, Almanza-Ojeda DL, Avina-Cervantes JG (2018) Analysis of data sets with learning conflicts for machine learning. IEEE Access 6:45062– 45070 23. Srividya M, Mohanavalli S, Bhalaji N (2018) Behavioral modeling for mental health using machine learning algorithms. J Med Syst 42:1–12 24. Yazdavar AH et al (2017) Semi-supervised approach to monitoring clinical depressive symptoms in social media. In: Proceedings of IEEE/ACM international conference on advances in social networks analysis and mining, pp 1191–1198

Patient Satisfaction Through Interpretable Machine Learning Approach S. Anandamurugan, P. Jayaprakash, S. Mounika, and R. Narendranath

Abstract In Patient satisfaction, the most important factor in assessing the quality is patient happiness. The happiness key factor impacts the health policy decisions. An individual’s specific health requirements, individualised treatment, and desired health results are of the utmost importance in the period of patient-centered care. Across the past decade, treatment delivery, management, and reimbursement practices have all been impacted by patient satisfaction as a clear insight and quality management of patient experiences. Using machine learning algorithms, the most relevant factors for patient satisfaction are founds. Keywords Synthetic minority oversampling technique · Random forest · XGBoost · K-nearest neighbour · Support vector machine · Patient satisfaction

1 Introduction In healthcare industry one important factor in assessing the quality of healthcare is patient happiness. The key factors like cleanliness, doctor’s experience, exact diagnosis, modern equipments, etc. influences health policy decisions. An individual’s specific health requirements, individualized treatment, and desired health results are of the utmost importance in the period of patient-centered care. The Centers for Medicare & Medicaid Services introduced the Hospital Value-Based Purchasing programme (CMS). Patient happiness is one of these criteria, and poor performance S. Anandamurugan · P. Jayaprakash · S. Mounika (B) · R. Narendranath Kongu Engineering College, Erode, India e-mail: [email protected] S. Anandamurugan e-mail: [email protected] P. Jayaprakash e-mail: [email protected] R. Narendranath e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_5

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on it can put hospitals at greater financial risk. To reduce the financial risks faced by the hospitals they need to focus on improving the factors which affects the patient satisfaction. Choosing the most relevant factors for patient satisfaction is the most confusing one. In past years, many methods were used to know the most important factors but most of them failed. To find most relevant factors machine learning algorithms are used here. The results were interpreted and best factors were taken into account.

2 Related Work Eunbi kim (2022) and colleagues created predicted models for hospitalisation based on machine learning, including as XGBoost. Emergency department (ED) overcrowding has long been an issue for the safety and satisfaction of patients worldwide. Overcrowding is frequently brought on by delays in ED patients’ boarding times as they wait for inpatient beds. Patients’ hospitalizations can be expected in EDs early enough to allow for the preparation of inpatient beds and a shorter waiting period. Jing Yu et al. [1] designed a method to boost medical effectiveness and patient happiness, suggested the use of a multi-patient treatment modality (MTM). The patients were categorized based on common disease symptoms. Thereafter selected the best patient matching method from the groups. The development of a mathematical model of the DPCMP involved the application of numerous ant colony optimization approaches. techniques were improved. methods were created, in order to address the aforementioned issue. The use of applied regression models to measure patient satisfaction as well as correlation approaches is one of the crucial considerations made by G. Sabarmathi et al. [4]. When developing the higher quality of health care application models. It helps in coming to a decision by taking into account the workplace and administrative traits relevant to patient satisfaction. Gavin Tsang [3] suggested a methodology for chronic, incurable diseases like dementia. A machine learning technique called ensemble deep neural networks (ECNN) uses entropy regularisation to provide high hospitalisation prediction accuracy for dementia patients while also making it possible to understandably analyse the model architecture using heuristics that can spot specific features that matter within a broad feature domain space. Berk Ustun et al. (2016) developed a novel technique called as Supersparse Linear Integer Model for developing data-driven scoring systems (SLIM). Due to the direct control, it has over these parameters, SLIM can build acceptable models without parameter adjustment while accommodating a variety of accuracy and sparsityrelated operational constraints. We establish restrictions on constraints on the SLIM scoring systems’ testing and training accuracy as well as a cutting-edge data reduction technique which can increase scalability by getting rid of some of the training data in advance.

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The three essential elements of explainable machine learning were assessed by Ribana Roscher et al. (2020) in light of applications in the natural sciences. such as transparency, interpretability, and explainability. Regarding these fundamental concepts, they included an overview of contemporary scientific studies that use machine learning as well as examples of how explainable machine learning is combined with application-specific domain knowledge. Based on the positive assessments of the nurses’ work performance in the hospitals and the comfort of the patients, Salman Alsaqri [2] claimed that this study had demonstrated a very high level of nursing care quality. Results of the relationship between patient satisfaction and ward size, past health, and marital status were shown to be statistically significant. Significant correlations between the means of admission, past admission, and staff nurses’ work performance were also discovered. Youness Frichi et al. identified the scope of hospital logistics includes a number of issues that affect patient satisfaction, including as waiting times, hospitality services, and healthcare workers’ satisfaction with their jobs, as well as the availability of resources and effective planning. The creation of an association matrix that establishes the dependent relationship between satisfaction criteria in healthcare facilities and hospital logistics activities sheds further light on the relationship between satisfaction and hospital logistics. Athar Mohd et al. suggested that patients’ satisfaction should be regularly assessed because it is a useful indicator of the quality of healthcare. The objective was to evaluate and compare the degree of patient satisfaction in a hospital’s outpatient department. The outpatient department (OPD) is the hospital’s first point of contact with patients and acts as a storefront for all community-based healthcare services.

3 Proposed System The proposed methodology presented here will help the hospitals to know what are the places they need to improve to gain patient’s satisfaction using machine learning. Choice of data is crucial for choosing meaningful documents for analysis and to acquire and generate useful knowledge. The dataset was downloaded from the kaggle website to study about the patient satisfaction. The acquired dataset was cleared of any potential irregular data and pre-processed with several methods followed by feature selection and model building. The classifiers employed here were SVM, Random Forest, XG boost and KNN. The performances of all the classifiers were assessed based on the accuracy scores, confusion matrices, and classification report. The work flow diagram is shown in the following Fig. 1

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Fig. 1 Proposed System

3.1 Dataset Description The dataset was downloaded from kaggle data repository. The link for the dataset is https://www.kaggle.com/datasets/vdimitrievska/patient-satisfaction-dataset. In this dataset, patients answered to the questions asked in an online survey which was conducted by an organization. The data is aggregated online for a period of 3 months using Likert scale from 1–5 scores. This dataset is in CSV format. There were 453 records. More specifically there are 17 attributes namely . . . . . . . . . . . . . .

Satisfaction in overall Time wating Check up appointment Admin procedures Time of appointment Hygiene and cleaning Quality/experience of Doctor Communication with Doctor Specialists available Exact diagnosis Friendly health care workers Modern equipment Lab services Waiting Rooms

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. Availability of Drugs . Parking, Playing rooms & Caffes . Hospital rooms quality

3.2 Data Pre-processing If the data in dataset is in wrong format or having missing values leads to fault in classification. The format of the data in machine learning projects must be correct in order to get better results from the applied model. For instance, null values are not accepted as input for the Random Forest method., hence null values must be handled from the original raw dataset in order to run the Random Forest model. Some specific Machine Learning models require data in a specific format. Rows or columns with all cells having null values can be removed. The loss of data can be dealt using this strategy. Snake case is a writing style in which the first letter of each word is written in lowercase and each whitespace is substituted by the underscore (_) character. It is a naming convention that is frequently used in computers, such as for filenames, variables, and subroutine names so that we can access easily.

3.3 Balancing Dataset with SMOTE The synthetic minority oversampling technique or SMOTE, is one of the most popular methods of oversampling to handle the imbalance problem. It attempts to balance the distribution of classes by randomly replicating examples of minority classes. SMOTE synthesises previously existing minority instances to produce new minority instances. Since the dependent variable was in unbalanced state we used SMOTE here to oversample it in a balanced way. The process involved in SMOTE: . It first identifies the minority class vector. . Then it decides the k (nearest neighbours to be chosen) value. It uses k-NN to find the nearest neighbours. . Then compute the line between the chosen data points based on k and create the new point in the link. . The previous step is repeated for all minority data points till the dataset is balanced.

3.4 Feature Selection Feature selection is one of the major part in machine learning. Since the input variable and output variable both of them were categorical values here we used Chi-square. Using the Chi-square approach, categorical features in a dataset were assessed. The

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Chi-square between each feature and the dependent variable was calculated and the features with the highest Chi-square scores were then selected. This test’s objective is to determine whether a disparity between observed and anticipated data was due to Σchance or a correlation between the variables. Formula for Chi-square is χ^2 = (O-E)^2/E. Here in this formula the square difference between the observed value O and expected value E and then divided by expected value E to obtain the Chi-square score.

4 Models XGBoost Classifier XGBoost (Extreme Gradient Boosting) algorithm designed for supervised learning tasks. XGBoost is an ensemble learning technique, which means it makes predictions by combining the output of numerous base learners (models). It comes under the type boosting. XGBoost employs DTs as their base learners, just like Random Forests. Then, a robust and precise model is created by combining these various classifiers/ predictors. It has numerous parameters so it can be altered to see which gets better accuracy. SVM Classifier SVM is a popular and effective machine learning technique. The SVM approach appears to determine the best decision boundary or solution line for categorising nfeatures. The best choice boundary is referred to as a hyperplane. The support vector machine, which chooses the high data points, creates the hyperplane. But generally SVM doesn’t support multiclass classification. So to do that we used OVR (One Vs Rest). Here 3 classes which means 1 class vs other class the hyperplane is drawn. Random Forest Classifier It is an ensemble classifier. It comes under the type Bagging. The class with the most votes determines the forecast for our model. In a conventional decision tree, just one decision tree was produced. Several decision trees were produced during the random forest process. Two criteria were used to classify data frames: Variables and Observation. When a large number of decsion trees are produced and used, a forest was established. In this Random Forest we used 100 Decision trees and some other parameters as input to predict the outcome. KNN Classifier The K-nearest Neighburs (KNN) technique is a kind of supervised machine learning algorithm that may be applied to challenges involving classification and regression predictive modelling. The next two characteristics would suitably characterise KNN: Lazy learning algorithm - KNN uses all of the data for training while classifying, making it a lazy learning algorithm since it lacks a dedicated training phase. Non-parametric learning algorithm - A non-parametric learning algorithm, KNN makes no assumptions regarding the underlying data.

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5 Results and Discussion The dataset contains 17 variables and among them 5 significant features that were helpful in evaluating the system were chosen. These features represent the expected characteristics resulting in patient satisfaction. If all the features were taken into account, the creator receives a less efficient system. Attribute selection was carried out to improve efficiency. In this case, 5 characteristics were chosen in order to evaluate the model that provides greater accuracy. Some dataset features had virtually equal correlations, so they were eliminated. The efficiency significantly declines if all the features in the dataset were considered. Before using classifier models, SMOTE was used here because the dataset was imbalanced. This oversampling was done because of very low number of records present. If the dataset was heavily imbalanced then it affects the model by reducing accuracy. We used four machine learning models here and interpreted their performance and results. XGBoost and SVM more or less gave the similar performance score. Since K-Nearest Neighbors is based on grouping the individual data points, it didn’t give that much expected result. Probably because of the low number of records, Random Forest also didn’t give expected result. After using machine learning approach for training and testing, we discovered that the XGBoost algorithm gave higher accuracy when compared to other algorithms. The confusion matrix for each algorithm was used for the conclusion. In this case, the number count for TP, TN, FP, and FN was provided and value was obtained using the accuracy equation. The accuracy of each of the four machine learning techniques is evaluated, from which one prediction model is created. Therefore, the objective was to employ a variety of evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and f1-score, which effectively predicted patient satisfaction. The extreme gradient boosting classifier has the highest accuracy of 89.66% when compared to the other three and the confusion matrix for each algorithm was shown below (Figs. 2, 3, 4, 5 and Table 1). Fig. 2 Confusion Matrix – Random Forest

46 Fig. 3 Confusion Matrix – XGBoost

Fig. 4 Confusion Matrix – SVM

Fig. 5 Confusion Matrix – KNN

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Table 1 Comparing four classifiers Classifiers

Accuracy (%)

Precision (%)

Recall (%)

F1-Score (%)

XGBoost

89.66

90

90

90

SVM

88.97

89

89

89

Random Forest

85.52

86

86

85

KNN

84.83

86

85

84

6 Conclusion and Future Works Using supervised machine learning techniques, they were concentrated on improving predictive models to achieve better accuracy in predicting and also to find successful attributes. It’s obviously easier said than done to strike the correct balance between the acceptable level of inaccuracy and the amount of data required for an accurate model. In SVM, the accuracy was more or less equals to the XGBoost classifier. In Random Forest and KNN the accuracy, precision, recall and f1-score were all less compared to SVM and XGBoost. So XGBoost had the amazing capacity to raise categorization and forecasting precision. Thus, XGBoost model gave higher accuracy than the other three algorithms compared here with the accuracy of 89.66%. For future works, to improve accuracy various modern models can be used. Here 5 attributes were selected based on chi-square test. So various feature selection can also be used to select even more attributes which may tend to increase the prediction even more accurately for patient satisfaction. But sometimes lesser attributes have more chance of gaining accuracy. It depends on the dataset. Furthermore, Randomized Search can be used to improve the performance of the classification models.

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References 1. Yu J, Xing L, Tan X, Ren T, Li Z (2019) Doctor-patient combined matching problem and its solving algorithms. IEEE Access 7:177723–177733 2. Alsaqri S (2016) Patient satisfaction with quality of nursing care at governmental hospitals, Ha’il City, Saudi Arabia. J Biol Agric Healthc 6(10):128–142 3. Tsang G, Zhou SM, Xie X (2020) Modeling large sparse data for feature selection: hospital admission predictions of the dementia patients using primary care electronic health records. IEEE J Transl Eng Health Med 9:1–13 4. Sabarmathi G., Chinnaiyan R (2019) Reliable machine learning approach to predict patient satisfaction for optimal decision making and quality health care. In: Proceedings of the fourth international conference on communication and electronics systems (ICCES 2019). IEEE Xplore. ISBN 978-1-7281-1261-9

Predicting the Thyroid Disease Using Machine Learning Techniques Lalitha Krishnasamy, M. Aparnaa, G. Deepa Prabha, and T. Kavya

Abstract An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesn’t provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease.

L. Krishnasamy Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be) University, Kengeri Campus, Bengaluru 560074, India e-mail: [email protected] M. Aparnaa (B) · G. Deepa Prabha · T. Kavya Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India e-mail: [email protected] G. Deepa Prabha e-mail: [email protected] T. Kavya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_6

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1 Introduction The healthcare sector uses computational biology advancements. It made it possible to gather patient data that had been saved to forecast medical diseases. Various intelligent prediction algorithms are available to diagnose a disease in its early stages. The medical information system contains a variety of data types, but no advanced algorithms exist that can quickly analyze disorders. Machine learning algorithms significantly increase the size of a prediction model over time by dealing with challenging and complex tasks. Any illness prediction model needs to have characteristics that can be quickly and accurately utilized to classify healthy patients using data from a range of datasets.. Otherwise, misclassification can force a healthy patient to receive needless care. As a result, it’s important to anticipate any diseases that might develop in addition to thyroid issues. The thyroid is a neck-based endocrine gland. It develops in the more constrained region called the neck in the human body, beneath the Apple of Adams, and helps the thyroid gland produce thyroid hormones that affect how quickly proteins are synthesized and how quickly metabolism occurs. The body’s metabolism is controlled by thyroid hormones in a variety of ways, including by monitoring the rate at which the heart beats and the rate at which calories are expended. In order to determine if a patient has hypothyroidism, hyperthyroidism, or euthyroid disease, this study suggests a method based on machine learning techniques that takes advantage of thyroid hormone measurements as well as other clinical information about the patient. A simple classification of hyperthyroidism, hypothyroidism, and euthyroid is made possible with the aid of the three proposed algorithms. Finally, measure the evaluation accuracy increases with time built model training set. On the basis of the patient’s historical and present data, the author of the study [5] utilized it to forecast the treatment trend of thyroid illness. Gradient Boosting Classifier, Decision Tree, and Nave Bayes are the algorithms that were utilized to predict the course of treatment. According to the patient’s tumor kind and stage at the time of diagnosis, the study in [8] describes the treatment for thyroid cancer in patients. The main objective of this paper is to identify various types of thyroid diseases in patients using machine learning techniques like SVM, Random Forest and Logistic regression.

2 Related Work Multivariable Logistic Regression was suggested by Machen et al. [1] as a way to predict the cure which is biochemical for patients affected with medullary thyroid cancer. The most current data demonstrates that after the compartmentoriented surgery done for node-positive Medullary Thyroid Cancer (MTC), the number of node metastases influences the biochemical cure. the challenge of eliminating cancers that have been surgically removed yet are still present in a scarred area

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that has been surgically removed yet are still present in a scarred area. following an initial operation may be reflected in the drawback of repeat surgery as a predictor of failure to obtain the biochemical cure. In comparison to patients who did not achieve a biochemical cure, patients who underwent surgery had a centroid of 2 versus 16 metastases during the initial procedure and 4 versus 12 metastases at the subsequent surgery. In research [2] published by Gyanendra Chau bey and colleagues, the accuracy of three machine learning (ML) algorithms—logistic regression, decision trees, and KNN—was evaluated in the prediction of thyroid illness. This study primarily illustrates intuition on how to forecast thyroid problems. In this technique, the thyroid dataset is used to predict the disease. The author, however, makes no mention of the patient having a particular thyroid condition, such as hyperthyroidism or hypothyroidism. In contrast to this paper, we present a dataset on the thyroid conditions that influence patients. In paper [3], Ritesh Jha and colleagues used the spark platform to carry out all experiments in a distributed setting. The thyroid dataset is employed, and ML methods like PCA, Decision Tree, and KNN assist in delivering the results. Utilizing several approaches to dimension reduction, the features discovered by these techniques were then fed into the classifiers. Two classifiers employ the methods for dimension reduction and data enrichment. This document presents the experiments’ comprehensive results. The accuracy of the feature reduction and data augmentation strategies used in this paper is 98.7% and 99.95%, respectively. It is suggested in [4] by Lerina Aversanoa et al. that the prognosis of the course of treatment for the patient with hypothyroidism, as well as the early diagnosis of a potential malfunction, plays a key role. This information can be very helpful to physicians who are treating patients and additionally to anticipate therapy for thyroid disease. Gradient Boosting Classifier, Decision Tree, and Naive Bayes are the machine learning (ML) methods that are utilized in this work to predict. In this work, a real-time dataset from patients at a hospital in Naples is employed. The primary phase entails preparing the data that will be used in the classifier in relation to the application of the SMOTE method. Early predictions of the spread of thyroid disease in women were made by Dhyan Chandra Yadav et al. [5]. With early sickness discovery, the dangerous condition of thyroid cancer can be prevented. The findings of these classifiers can also be obtained using decision trees, random forests, and classification and regression trees (CART), and this paper enhanced the results using the bagging ensemble technique. The stored datasets are mined for hidden patterns using the decision tree. In a paper [6], Eun Joo Lee et al. explain that the type of tumor and its stage at the time of diagnosis determine the course of treatment for thyroid cancer patients. Many thyroid tumors are still tiny, stable, and asymptomatic. Patients with thyroid cancer who have a total thyroidectomy had higher survival rates and lower recurrence rates [7, 8]. The process of treating patients involves the use of a logistic regression method [9]. This research largely focuses on treating thyroid cancer patients based on stage, which increases mortality.

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Fig. 1 Proposed System

3 Proposed System This article’s recommended technique identifies the patient’s thyroid conditions, including hypothyroidism, hyperthyroidism, and euthyroidism. Logistic Regression, Random Forest, and (SVM) were the classifiers utilized. Before being chosen for usage in the model and its features, the collected dataset is cleaned of any potential irregular data and pre-processed using a variety of techniques. The effectiveness of the classifiers is assessed using confusion matrices and accuracy scores. Selecting meaningful documents for analysis is essential if you want to employ different machinelearning approaches to learn and generate usable knowledge. Here, the downloaded Kaggle dataset is used to make predictions on hypothyroidism, hyperthyroidism, and euthyroidism. The work flow is shown in the following Fig. 1.

3.1 Dataset Description a. Age

b. Sex

c. Thyroxine

d. Query on thyroxine

e. On antithyroid medication

f. Sick

g. Pregnant

h. Thyroid Surgery (continued)

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(continued) i. 1131 Treatment

j. Query Hypothyroid

k. Query Hyperthyriod

l. Lithium

m. Goitre

n. Tumor

o. Hypopituitary

p. Psyco

q. TSH measured

r. TSH

s. T3 measured

t. T3

u. TT4 measured

v. TT4

w. T4U measures

x. T4U

y. T3

z. FTI

aa. TBG Measured

ab. TBG

ac. Referral source

ad. category

3.2 Data Pre-processing The processes that are used to modify or encrypt data such that a machine can swiftly and easily decode it are referred to as data preparation. In this paper three types of preprocessing are used, they are label Binarize, Min-MaxScaler, and Label Encoder. Binarization divides the data into two groups and assigns one of two values to each group. All data characteristics are scaled by Min-MaxScaler in the range [0, 1]. In order for machines to read labels, they must be transformed into a numeric representation.

3.3 Models SVM Classifier SVM is a highly used and run-in efficient ML method or algorithm. A model that can be used for regression and classifications. The SVM method appears to determine the best decision boundary or solution line for categorizing n-features. As a result, we may place the new datasets points in the appropriate groupings. The optimal choice boundary is referred to as a hyperplane. The hyperplane is created using the support vector machine, which selects the high data points. The decision boundary or hyperplane is used to classify three separate groups: Both for non-linear and linear data, the SVM model is used. The major preparation information is converted into a greater measurement via a nonlinear mapping. Random Forest Classifier It is an ensemble classifier. It comes under the type Bagging. The class with the most votes determines the forecast for our model. In a conventional decision tree, just one

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DT is produced. Several DTs are produced during the random forest process. Two criteria are used to classify data frames: Variables and Observation. When a large number of DT are produced and used, a forest is established. In this Random Forest we used 100 Decision trees as input to predict the outcome. Logistic Regression Classifier The most popular machine learning algorithm in the supervised learning area is logistic regression. This method makes use of a group of independent factors to forecast the category-dependent variable. By using logistic regression, a dependent categorical variable’s output is predicted. Therefore, the outcome should be discrete or of categorical value. Rather than delivering an exact value between 0 and 1, it delivers probabilistic values that are in the range of 0 and 1. It can be either Yes or No, 0 or 1, true or false, etc. Logistic regression and linear regression have many characteristics but are used Differently. Classification issues are resolved using logistic regression, whereas regression-related issues are resolved using linear regression.

4 Results and Discussion Classifiers have been used to build the intended framework. The accuracy scores obtained from the confusion matrix are used to evaluate the efficiency of the classifiers. The accuracy of the created classifiers can be predicted by, Accuracy =

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

TP: True Positive—Observation correctly predicted that the device must store energy TN: True Negative—Observation correctly predicted that the device must not store energy FP: False Positive—Observation incorrectly predicted that the device must store energy FN: False Negative—Observation incorrectly predicted that the device must not store energy Support Vector Machine model produced the following confusion matrix using test data of 806 instances with the target variable being the class values hypothyroid, hyperthyroid and euthyroid. The confusion matrix evidently depicts that 618 instances have been correctly classified while 188 instances were not and that this classifier model’s accuracy is 78.28% (Figs. 2 and 3). The logistic regression model produced the following confusion matrix using test data of 806 instances with the target variable being the class values hypothyroid, hyperthyroid and euthyroid. The confusion matrix evidently depicts that 686

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Fig. 2 Confusion matrix – SVM

Fig. 3 Confusion matrix – Logistic Regression

instances have been correctly classified while 120 instances were not and that this classifier model’s accuracy is 85.24% (Fig. 4). Random Forest model produced the following confusion matrix using Test data of 806 instances with the target variable being the class values hypothyroid, hyperthyroid and euthyroid. The confusion matrix evidently depicts that 680 instances have been correctly classified while 126 instances were not and that this classifier model’s accuracy is 84.37% and all the three algorithm’s accuracy level comparison is given in Table 1.

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Fig. 4 Confusion matrix – Random Forest

Table 1 Comparing three algorithms

Classifiers

Accuracy (%)

SVM

78.28%

Logistic Regresson

85.24%

Random Forest

84.37%

5 Conclusion and Future Works The healthcare sector generates enormous amounts of data that are used in daily life. This study used machine learning approaches to analyze the raw data and come to a novel conclusion on hypothyroid, hyperthyroid, and euthyroid conditions. However, in this work, we suggest a technique to identify individuals affected by all hypothyroid, hyperthyroid, and euthyroid conditions. Various papers have proposed various solutions to identify thyroid-affected people or any one specific thyroid. In the sphere of medicine, this prediction is tough and significant. This study makes use of a thyroid dataset that was collected from Kaggle. There are 806 instances and 31 attributes accessible in the dataset. The Support Vector Machine (SVM), Logistic Regression, and Random Forest are combined in the suggested approach. These algorithms help to identify which type of thyroid disease is affected by people. This also includes the count of male and female affected people, pregnant people who are all affected by the thyroid, and the graphical representation of these counts is represented to easily identify them. Finally, the accurate results obtained by SVM, Logistic Regression, and Random Forest are 78.28%, 85.24%, and 84.37% respectively. From this project, we can able to know the number of male and female people affected and also predict the age group of people who must be affected by which type of thyroid disease.

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By comparing the three algorithms, Logistic Regression provides the most accurate results in identifying disease.

References 1. Machens A, Lorenz K, Dralle H (2020) Prediction of biochemical cure in patients with medullary thyroid cancer. Br J Surg 107:695–704 2. Chaubey G, Bisen D, Arjaria S, Yadav V (2020) Thyroid disease prediction using machine learning approaches. Natl Acad Sci Lett 44:233–238 3. Jha R, Bhattacharjee V, Mustafi A (2021) Increasing the prediction accuracy for thyroid disease: a step towards better health for society. Wirel Pers Commun 122:1921–1938 4. Aversano L, Bernardi ML, Cimitile M, Iammarino M, Macchia PE, Nettore IC, Verdone C (2021) Thyroid disease treatment prediction with machine learning approaches. Procedia Comput Sci 192:1031–1040 5. Yadav DC, Pal S (2020) Prediction of thyroid disease using decision tree ensemble method. Hum-Intell Syst Integr 2:89–95 6. Nguyen QT, Lee EJ, Huang MG, Park YI, Khullar A, Plodkowski RA (2015) Diagnosis and treatment of patients with thyroid cancer. Am Health Drug Benefits 8(1):30 7. Elliott Range DD, Dov D, Kovalsky SZ, Henao R, Carin L, Cohen J (2020) Application of a machine learning algorithm to predict malignancy in thyroid cytopathology. Cancer Cytopathol 128(4):287–295 8. Yadav DC, Pal S (2020) Discovery of hidden pattern in thyroid disease by machine learning algorithms. Indian J Public Health Res Dev 11(1):61–66 9. Selwal A, Raoof I (2020) A multi-layer perceptron based intelligent thyroid disease prediction system. Indones J Electr Eng Comput Sci 17(1):524–533

An Automatic Traffic Sign Recognition and Classification Model Using Neural Networks Rajalaxmi Padhy, Alisha Samal, Sanjit Kumar Dash, and Jibitesh Mishra

Abstract The significance of traffic symbol recognition technologies, which have played a key role in street security, has been the subject of much interest to researchers. To accomplish their assessment, specialists employed Artificial Intelligence, deep learning, and image processing tools. Convolutional Neural Networks (CNN) are deep learning-based designs that have sparked a new and ongoing research into traffic symbol classifications and recognition frameworks. The objective of this paper is to establish a CNN model that is suitable for insertion purposes and has a high level of order exactness. For the series of street symbols, we used an upgraded LeNet5 model. The German Traffic Sign Recognition Benchmark (GTSRB) information base will function as the framework for our model architecture, which outperformed existing models. GTSRB will have 99.84 percent accuracy. We decided to use a camera to verify the proposed model for an implanted application because of its softness and reduced number of boundaries (0.38 million) based on the improved LeNet-5 structure. The outcomes are advantageous, demonstrating the effectiveness of the discussed strategy. Keywords Convolutional Neural Network · Deep Learning · Image Preprocessing · Feature Extraction · Normalization · Gray Scaling · Local Histogram Equalization · Data Augmentation

1 Introduction In India, approximately 400 road accidents occur every day, according to government figures. Road symbols aid in the prevention of traffic accidents by assuring the safety of both drivers and pedestrians. The high frequency of traffic deaths has resulted in numerous personal injuries and property damages [1]. Furthermore, traffic signals ensure that road users follow specified regulations, reducing the chances of traffic offences. The usage of traffic symbols also helps with route guidance. All road users, R. Padhy · A. Samal · S. K. Dash (B) · J. Mishra Odisha University of Technology and Research, Bhubaneswar, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_7

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including automobiles and pedestrians, should give priority to road symbols. For a variety of reasons, such as focus issues, tiredness, and sleep deprivation, we overlook traffic signs. The detection and categorization of traffic symbols are the two sub-tasks of traffic sign recognition systems [2]. The objective is to keep a strategic distance from accidents using both manual and automated approaches, with all actions being executed in accordance with the identified traffic symbols. We’re interested in using CNN for traffic symbol identification because of the success of deep learning-based classification and recognition approaches in several fields. As a solution, a convolutional neural network is used in the suggested system. This paper is mainly inspired by the work of various scientists, which is summarized in Sect. 2. The German Traffic Sign datasets, which are mainly illustrated in Sect. 3 of this paper, besides this, Sect. 3 also focuses on the system design, data preprocessing part of the project and the model architecture and basic components of the CNN architecture such as convolutional layers, subsampling layers, dropouts etc. It also explains the training and testing aspects of the project. Finally, Sect. 4 describes the future scope and the conclusion of the project, mentioning the learning curve and the project accuracy.

2 Literature Review Wu et al. [3] developed their methodology using a combination of color transformation and a fixed layer CNN that can help to reduce the number of locations that the classifier must deal with, thereby speeding up the detection process. Zhu et al. [4] suggested an innovative and effective method for detecting and recognizing traffic signs which uses a fully convolutional network, which significantly decreases the search area for traffic signs while maintaining detection rates. Lee et al. [5] introduced a system that is used to predict the location and precise boundaries of traffic signs at the same time. Supraja and Kumar [6] proposed that the primary purpose for mechanizing traffic-sign stock management is to address a large number of traffic sign categories. The location and acknowledgment are displayed using a strategy based on the Squeeze net CNN. Narejo et al. [7] implemented a system for recognizing and interpreting warning traffic signs where color info and the geometric property of the road signs are used to classify the recognized traffic signs. Alexander et al. [8] considered an implementation of the method for traffic sign classification which was combined with earlier work of preprocessing and localization procedures. Cao et al. [9] presented an enhanced traffic symbol recognition algorithm for automated vehicles to address issues such as the poor real-time performance of traffic sign recognition approaches. Zuo et al. [10] represents the highest level in object recognition, as it eliminates the need to manually extract picture attributes and can automatically segment images to generate candidate region recommendations. Radzak et al. [11] introduced a system which aims to identify the regions of interest (ROI) using several techniques and methodologies, such as binarization, region of interest, and pixel classification. Prasad et al. [12] approached a system where the photos of the road scene

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were first converted to grayscale images. Second; they used the maximally stable external regions approach to extract the region of interest. Finally, they employed convolution neural networks using simplified Gabor feature maps. Luo et al. [13] suggested video sequences, containing both symbol-based and textbased indicators, taken by an automobile camera which includes areas of interest (ROIs), refining and categorization of ROIs, and post-processing. Akshata. [14] covered the three stages of the real-time traffic sign recognition and classification system, which include image segmentation, traffic sign detection, and classification based on the input image. Mammeri et al. [15] demonstrated how to construct a TSDR system by going over some of the strategies that can be employed at each stage of the process. These strategies were organized into distinct groups for each level. Anuraag Velamati [16] developed a model that is stored as a h5 file. They were also successful in designing the GUI and using TensorFlow, CNN, and OpenCV. Shukla et al. [17] proposed a software technology that would aid in the detection and identification of traffic signs. For that, A deep learning-based traffic sign recognition algorithm is proposed. Akhil Sharma [18] pointed out that after using data augmentation provided by the deep learning package, it was possible to discriminate between distinct types of traffic signs. Zhang et al. [2] developed a multi-scale attention technique that uses dot-product and softmax to create weighted multi-scale features, which are then finetuned to highlight traffic sign characteristics and increase traffic sign identification accuracy. Santos et al. [33] implemented a system with voice alert using python and four preprocessing. Yadav et al. proposed an approach for detecting and recognizing traffic signs from a video sequence that takes into account all of the challenges associated with object recognition in outdoor contexts [20]. Saha et al. [21] devised a system that needs the fewest learnable parameters and the least amount of training time. M. M. Sruti [22] discussed the design and implementation of a Traffic Sign Recognition (TSR) system for Bangladeshi traffic signs utilizing CNNs as both a feature extractor and a classifier. Vennelakanti et al. [23] presented a detection and recognition system separated into two parts. Wali et al. [24] divided their TSR system into three primary steps: detection, tracking, and classification. Zang et al. [25] solved the problem of unmanned autonomous vehicle traffic sign recognition by using the Faster R-CNN algorithm that is utilized to recognize traffic signs in each frame of the input image sequences. Veliˇckovi´c et al. [26] trained and assessed CNN numerous times, and the accuracy averaged approximately 80%. They discovered that the CNN had trouble discriminating between signs with similar shapes. Zhao et al. [27] discussed a faster R-CNN target detection algorithm, which is based on the migration learning concept, extracts image features using pre-trained neural network models and is suitable for training a target detection model system with a small amount of data. Jain et al. [28] suggest a novel technique for the TSR system which focuses on the concept of domain transfer learning for each layer of the pre-trained CNN model (VGG-16). López and Guzman [29] proposed a model that uses CNN and visual processing to improve autonomous traffic sign identification. Zhang et al. [2] proposed a cascaded R-CNN. Except for the initial layer, each layer of the cascaded network fuses the output bounding box of the preceding layer for joint training. Shafiei et al. [30] proposed different deep learning models that

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were examined in terms of classification accuracy as well as prediction speed. They have suggested implementation with VGG-19. Cao et al. [31] improved a Sparse R-CNN, a neural network model that is being inspired by Transformer. K. Prakash [32] presented a technique for classification, where the author employed CNN and DCNN. VGG16, VGG19, and AlexNet are used to implement the model. Haque et al. [33] suggested a simple CNN model for traffic sign recognition that does not require the utilization of a GPU. The authors presented a deep thin architecture with three modules: input processing, learning, and prediction. Ellahyani et al. proposed a rapid approach of selecting the observed candidates for random forests classifier with a mix of HOG and LSS features [34]. Jacopo Credi [35] used Conv.NET, which is the first attempt to create a lightweight toolbox for deep learning inside the.NET framework. The Conv.NET class library was built from the ground up for this project. It utilizes Open CLTM, an open platform for heterogeneous parallel computing. Hatolkar et al. proposed an improved technique for identifying road traffic indicators. Short, low-quality videos are captured by a camera mounted on a car [36]. The fuzzy classification module is an optimizer that improves CNN’s results. Carl Ekman [37] worked on the Mobile Net V2, a CNN architecture that is specifically designed to be computationally efficient. For complete feature extraction, the authors used CNN instead of the Hough transform, or local binary patterns [38]. Bangquan and Xiong [39] developed two new efficient TSC networks, ENet (efficient network) and EmdNet. Matoš et al. [40] proposed the Hough Circle feature for traffic detection. It locates circles within images using the Hough transformation and has used the SVM classifier for training and testing. Song et al. [41] proposed a CNN model for small traffic sign detection. For the evaluation, the Tsinghua-Tencent data set was employed as a raw data set. Chauhan et al. [42] developed a highly successful approach for TSR that uses CLAHE (Contrast Limited Adaptive Histogram Equalization), SVM, KNN classifier, and artificial neural network. Robertson [43] used the existing state-of- the-art YOLO (You Only Look Once) framework. The key advantages of YOLO are that it is incredibly fast, produces fewer background mistakes than other approaches. Bichkar et al. [44] suggested the categorization of traffic signs using CNN with multiple filters. For detection of traffic signs in the environment and classifying the image, the detection model used YOLO and BLOB analysis. Rachapudi et al. [45] suggested a model where the complexity of the photos has decreased after they applied gray scaling to the dataset images and the accuracy has improved. They continued to use normalizing approaches, and the accuracy grew by a tiny amount. Wan et al. [46] discussed a CNN model named TS-Yolo for achieving accuracy in traffic identification during extreme weather situations.

3 System Model Traffic-sign recognition system evaluates photos captured by a car’s camera in real time to recognize symbols. It provides assistance to the driver by delivering warnings. The recognition module acknowledges the symbol region found in the image/video by

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Fig. 1 Block Diagram of System Model

the detection module. During the detection step, the symbol areas with the maximum possibility are selected and transmitted to the recognition system for classification. So, with respect to the above context, we design the model with 6 stages as shown in the Fig. 1.

3.1 Image Preprocessing The goal of preprocessing is to increase the image quality so that we can better analyse it. To acquire the best possible outcome, we applied several image preprocessing methods such as shuffling, gray-scaling the image, normalization, and local histogram equalization. 1. Grayscaling: Grayscale images fall in between binary and color images. It’s the only part that doesn’t get cut off. According to a survey, using grayscale images rather than colored images enhances ConvNet accuracy. Therefore, we have converted the training images to grayscale using OpenCV and the. cvtColor function. 2. Local Histogram Equalization: This method mainly spreads out the most common intensity values in an image, resulting in low-contrast images being enhanced or adjusted. To apply local histogram equalization to the training photos, we are using a function, i.e., equalizeHist. 3. Normalization: It basically adjusts the range of pixel intensity values. Therefore, we applied normalization to the data in the range of (0,1), which was accomplished by setting the rescale argument using the line of code (img = (img/255)). The mean of the resulting dataset was not precisely zero, but it was lowered from around 81.65 to around 0.0039. Thus, it benefits during training because

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it eliminates the chance of having an outspread distribution in the data, which makes it more difficult to train with a single learning rate. 4. Image Data Augmentation: Image data augmentation used for enhancing the training dataset in order to improve the model’s execution and generalisation capability. For that, we made various changes to the dataset by changing the width_shift_range to 0.1, height_shift_range to 0.1, zoom_range to 0.2, shear_ range to 0.1, and rotation range to 10 with the help of the Image Data Generator, and then finally we called the data-generator to augment the images in real time with a particular layout. As a result, data augmentation aids in reducing the gap between training and validation loss and accuracy, hence reducing overfitting. The steps involved in image pre-processing stage are as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Import the required libraries i.e., numpy, pandas, random, matplot. Initialize the images with coordinates (32,32,3) i.e., width, height, RGB value. Load the image from a specified path. Use the. cvtcolor method for converting the colored image into a grey-scaled image. Apply simple contrasting to the grayscale image by equalisation method. Apply image pixel normalization to the image to range it between 0 and 1. Call the Data Generator to augment images in real time. Resize the image by changing the axes value where 20 and 5 represent the width and height. Apply data augmentation. Save the image for further classification.

3.2 Deep Learning Model CNN is a deep learning model that works in a near way similar to traditional neural networks. The inputs are received by the neural network, which then conducts a dot operation on the input before applying a nonlinear function (ReLU Activation). ConvNets and CNNs work in a similar way: they take an image as input and apply weights and biases to various characteristics of that picture. Preprocessing is often not necessary in ConvNets because it has the potential to learn features of an image. ConvNets minimizes the image to make it easier to process while preserving the image’s features. Each image in CNN is processed through a set of convolutional layers, which include filters, pooling, and fully connected layers. The output layer uses a softmax function to classify the objects with probabilities between 0 and 1 as shown in Fig. 2. From the standpoint of work, the images input into CNN are grayscale images with pixel values recorded in a data frame. The first step is to get the photos out of the data frame and separate the data from the labels. Splitting the data into train and validation sets, as well as their labels, is the second stage. After that, the image’s pixel values are adjusted before being fed to CNN. When normalized photos are sent into CNN, they are processed through the various layers as shown in Fig. 3.

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Fig. 2 General CNN Architecture

Fig. 3 CNN Model Architecture

3.3 Feature Extraction Convolution layer consists of different sets of learnable kernels. Each kernel is small but extends throughout the image. The filter is slid across the width and height of the image to compute the dot product between the filter values and image input. Convolution is to extract features such as edges. The result we achieve after this operation is either the convolved feature is reduced in dimensionality or the convolved feature remains the same. In this case, the latter has been used which is termed as the Same Padding. Relu-activation was used to introduce non-linearity in our convolution layer. Then the pooling layer reduces the computational power required for data processing by reducing the size of the convolved feature. It also helps in extracting

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the necessary features which are uniform to rotational and positional changes. We used max-pooling as it acts as a better noise reducer as well as dimensionality reducer.

3.4 Classification A fully connected neural network usually needs a huge number of layers and neurons in the network for classifying images, which enhances the model parameters and causes over-fitting. As all neurons are interconnected to each other, the input image may lose its pixel correlation properties. CNN has emerged as an approach to these problems which uses kernel filters to extract the key features from an input image and then inject them into a fully connected network to classify the class. In proposed system model, we’ve chosen the LeNet-5 convolutional neural network, which was originally trained to recognize handwritten digits. It has six layers: four levels of convolution and refinement functions constructed with 3 × 3 kernel filters, and a 2 × 2 max pooling filter to reduce the 32 × 32 input image to 60 5 × 5 maps. The feature images provide the most crucial characteristics that describe a specific object.

3.5 Training and Testing of Data To train the model, we started with the normalized dataset, which had a validation accuracy of 92.7 percent. Although this accuracy was satisfactory, it was insufficient for the development of a robust classifier. Running the same model with gray-scaled and normalized inputs as stated in the LeNet lab using the default architecture (batch size: 128; epochs: 10; rate: 0.001; mu: 0; sigma: 0.1). The accuracy of validation was approximately 94.4 percent, which was a significant improvement. However, as we started working with the real model, we began tweaking the rate, epoch, and batch size in the hopes of improving the model. We discovered that when the model was either turned too much or too little, it only improved a little (and in some cases, it acted significantly worse). Despite the fact that this paper on using LeNet Architecture for traffic signs was written a long time ago, it was still reliable enough to analyse traffic symbols with 99 percent accuracy. As a result, we decided to scrap the traditional architecture in favour of the architecture described in the article. The optimizer, Adam Optimizer, which was previously implemented in the LeNet Lab, and the mu and sigma values, which are 0 and 0.1 respectively, were two items that we did not change throughout implementation. The remaining hyper parameters were tweaked across several tests by setting the batch size to 2000, epochs value to 10, learning rate to 0.0009 and dropout keep probability to 0.5 which was used to achieve highest accuracy as shown in table 1.

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Table 1 The different models obtained after the modification of the default models Models

Inputs to 1st Convolutional layer

Inputs to 2nd Convolutional layer

Neurons present in epochs fully connected layer

Batch size

Accuracy

Model 1 28 × 28x60

24 × 24x60

480

10

2000

99.84

Model 2 28 × 28x60

24 × 24x60

480

10

600

99.2

Model 3 28 × 28x60

24 × 24x60

480

10

400

98.6

4 Data Set Analysis The dataset used is German Traffic Sign Benchmark (GTSRB) which is obtained from Kaggle (Source: “https://bitbucket.org/jadslim/german-traffic-sig ns.git”) whose samples are shown in Fig. 4. For the simplicity of work, we have also referred to Bitbucket where the extracted images of this dataset are already present. There are 43 classes in this picture collection (many unique traffic symbol images). There are about 34,799 photos in the training set, 12,630 images in the test set, and 4410 images in the validation set. Thus the size of training set(i.e. basically used for training the network) becomes 34,799, the size of the validation set(i.e. used for the supervision of network performance while training) becomes 4410, The size of test set(i.e. basically used for evaluation of final network) becomes 12,630. Moreover, the pattern of an image of a traffic sign so far (32, 32, 3) (3 because of R,G,B{Red, Green, Blue} Channels). The majority of the images have a distinct appearance, primarily in terms of contrast and brightness. If we want to train models with great precision, this might not be the best option. To gain substantial accuracy, we can use some form of histogram equalisation (increases contrast for lower contrast image) (In ML terms this will be

Fig. 4 Sample datasets depicting various classes

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Fig. 5 Distribution of Training Dataset

good for better feature extraction). The proper distribution of data is being predicted through a graph illustrated in Fig. 5.

5 Results and Discussions The learning curve showing performance comparison of the above model are shown in Fig. 6 and Fig. 7 considering batch size 1000. We’ll now utilize the testing set to assess the model’s accuracy when applied to unknown samples. We were successful in attaining a Test accuracy of 96.8 percent, which is an outstanding result. For checking more efficiency, we even tried changing the batch size to 2000 and try to plot the graphs for learning curves as shown in Fig. 8 and Fig. 9. It is observed that on changing the value of the batch size we are getting very slight differences in the point values and thus able to achieve 97.6 percent testing accuracy which was closer to previous achievement. Fig. 6 Training vs Validation Accuracy with batch size 1000

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Fig. 7 Training vs Validation Loss with batch size 1000

Fig. 8 Training vs Validation Accuracy with batch size 2000

Fig. 9 Training vs Validation Loss with batch size 2000

To demonstrate the new architecture’s efficiency, we try to forecast images that the trained model architecture has never seen before. For that we imported pictures from the internet and tried to test it with the proposed model. And when we tried processing this image, we get the resultant output as Predicted Category as 28 and Predicted sign as Children crossing which was very accurate as shown in the above Fig. 10. There are several applications for automatic traffic sign categorization, However, when

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Fig. 10 Flow diagram of Traffic Sign Classification

compared to previous research, the system’s accuracy was exceptional and among the best. The framework has been developed and changed after being influenced by the well-known LeNet-5. The advancements enable us to achieve 99.84 percent accuracy while using less training parameters in comparison to the depth of the model. We can test the model using a webcam-enabled embedded application because of its lightweight. The categorization is likewise extremely accurate in this example.

6 Conclusion In this paper, we introduce a more efficient model having the highest accuracy in classifying traffic symbols using various preprocessing techniques such as greyscaling, local histogram equaliser, normalization, data augmentation, and convolutional neural networks. The proposed methodology uses the LeNet architecture for classification, which is made up of 7 layers. The use of four preprocessing techniques helps in removing the noise present in the data and thus gives us more accuracy while classifying it. While the model provided in this research takes us one bit closer to the ideal Advanced Driver Assistance System or perhaps a self-driving vehicle, there is still much to be improved. This paper uses the color and geometry of a sign to determine its identity. If there is a reflection on the sign that alters its color, then it might create an issue. This application can also have a text-to-speech module. The driver here has to read the text that is printed on the classified sign in the existing application, however, further comfort is provided by using a speech module. With more datasets and from multiple countries, overall performance could be increased and modified.

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References 1. Li W, Li D, Zeng S (2019) Traffic sign recognition with a small convolutional neural network. IOP Conf Ser Mater Sci Eng 688(4):044034. https://doi.org/10.1088/1757-899X/688/4/044034 2. Zhang J, Xie Z, Sun J, Zou X, Wang J (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754 3. Wu Y, Liu Y, Li J, Liu H, Hu X (2013). Traffic sign detection based on convolutional neural networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1–7 4. Zhu Y, Zhang C, Zhou D, Wang X, Bai X, Liu W (2016) Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing 214:758–766 5. Lee HS, Kim K (2018) Simultaneous traffic sign detection and boundary estimation using convolutional neural network. IEEE Trans Intell Transp Syst 19(5):1652–1663 6. Supraja S, Kumar P (2021) An intelligent traffic signal detection system using deep learning. SSRG Int J VLSI Signal Process 8(1):5–9 7. Narejo S, Talpur S, Memon M, Rahoo A (2020) An automated system for traffic sign recognition using convolutional neural network. 3C Tecnología_Glosas de innovación aplicadas a la pyme 9:119–135. https://doi.org/10.17993/3ctecno.2020.specialissue6.119-135 8. Shustanov A, Yakimov P (2017) CNN design for real-time traffic sign recognition. Proc Eng 201:718–725 9. Cao J, Song C, Peng S, Xiao F, Song S (2019) Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors 19(18):4021 10. Zuo Z, Yu K, Zhou Q, Wang X, Li T (2017) Traffic signs detection based on faster R-CNN. In: 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, pp. 286–288 11. Radzak MY, Alias MF, Arof S, Ahmad MR, Muniandy I (2015) Study on traffic sign recognition. Int J Res Stud Comput Sci Eng (IJRSCSE) 2(6):33–39 12. Prasad S, Desai S, Kumar S, Adarsha MD (2021) Traffic sign detection using CNN. Int J Adv Res Comput Commun Eng 10(6):2021 13. Luo H, Yang Y, Tong B, Wu F, Fan B (2017) Traffic sign recognition using a multi-task convolutional neural network. IEEE Trans Intell Transp Syst 19(4):1100–1111 14. Akshata VS, Panda S (2019) Traffic sign recognition and classification using convolutional neural networks. J Emerg Technol Innov Res 6(2):132–147 15. Mammeri A, Boukerche A, Almulla M (2013) Design of traffic sign detection, recognition, and transmission systems for smart vehicles. IEEE Wirel Commun 20(6):36–43 16. Velamati A (2021) Traffic sign classification using convolutional neural networks and computer vision. Turk J Comput Math Educ (TURCOMAT) 12(3):4244–4250 17. Shukla SK, Dubey S, Pandey AK, Mishra V, Awasthi M, Bhardwaj V (2021) Image caption generator using neural networks. Int J Sci Res Comput Sci Eng Inf Technol. 1–7 https://doi. org/10.32628/CSEIT21736 18. Sharma A (2019). Traffic Sign Recognition & Detection using Transfer learning. Doctoral dissertation, Dublin, National College of Ireland 19. Santos A, Abu PAR, Oppus C, Reyes RS (2020) Real-Time Traffic Sign Detection and Recognition System for Assistive Driving 20. Yadav S, Patwa A, Rane S, Narvekar C (2019) Indian traffic signboard recognition and driver alert system using machine learning. Int J Appl Sci Smart Technol 1(1):1–10 21. Saha S, Kamran SA, Sabbir AS (2018) Total recall: understanding traffic signs using deep convolutional neural network. In: 2018 21st International Conference of Computer and Information Technology (ICCIT). IEEE, pp. 1–6 22. Surti MM. Real time traffic sign detection and classification system on Jetson TX1 Embedded Development Board 23. Vennelakanti A, et al (2019) Traffic sign detection and recognition using a CNN ensemble. In: 2019 IEEE International Conference on Consumer Electronics (ICCE). IEEE, pp. 1–4

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An Artificial Intelligence Enabled Model to Minimize Corona Virus Variant Infection Spreading Dipti Dash, Isham Panigrahi, and Prasant Kumar Pattnaik

Abstract Many nations including India are being very badly affected by the second wave of the COVID-19 infections. The critical situation prevails in some states and cities of India. The mortality rate varies state to state depending on the health care facilities, immunological response of the individuals & comorbidities and vaccination status of that particular state. The multiclass prediction model is developed based on the status of data available from the different states of India considering their level of population density, intensity economic activities, education level, vaccination status and timing of lockdown or shut down. Based on this prediction model we can develop an application to motivate the internet of health things (IoHT), which can monitor the state and help in governing. This paper uses a multi class prediction model using Deep Neural Network (DNN) and validates the data set up to the year 2022, with accuracy level 98%. In this architecture, we have used 4 hidden layers between input and output layer. We have collected data from JHU CSSE Covid-19 and also follow our own algorithm to create our own dataset. We have taken 80% of data for training purposes and 20% of the dataset as validation purposes. Keywords COVID · Deep Neural Network · Multi class Prediction Model · Vaccination and IoHT

D. Dash (B) · I. Panigrahi · P. K. Pattnaik Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, India e-mail: [email protected] I. Panigrahi e-mail: [email protected] P. K. Pattnaik e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 S. K. Udgata et al. (eds.), Intelligent Systems, Lecture Notes in Networks and Systems 728, https://doi.org/10.1007/978-981-99-3932-9_8

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1 Introduction People in different countries on the earth are now fighting against a common enemy. These are the different variants of Corona Viruses. Many human beings have been severely infected by these viruses and many have also died. This pandemic also leads to big losses to business houses, service sectors and industries across the world. It is greatly affecting the economic development goals of all the nations. The livelihood of many are at risk [1]. This pandemic is considered to be the worst challenge faced by mankind in recent times. Now, many nations like India are being very badly affected by the second wave of the COVID-19 infections in the year 2021 and the most dangerous and critical situation prevails in some states and cities of India [2]. The mortality rate varies state to state depending on the health care facilities, immunological response of the individuals, comorbidity [3] and vaccination status of that particular area. During the first wave in the year 2020 of the pandemic, we had no vaccine available to prevent or counter these viruses. But, at present, we have a number of vaccines at our disposal. The vaccinations are considered to be the most effective means to prevent or counter its spread across the world. Vaccines reduce the severity of infections, need for hospitalization and controls the overall mortality rate [4, 5]. The vaccination drives have not been able to achieve their desired momentum till the onset of the second wave. This was because of vaccine hesitancy among some sections of the population. This paper tries to find out the positive impact of vaccination on reducing severity of infection, hospitalization and mortality rate. At the same time this paper tries to find out the different possible causes of vaccine hesitancy in India. The present vaccination status of different states and cities of India are studied. The slow vaccination rate is strongly related to active infection cases. The results are found out from the available data, at present from different continents, countries, and states of India, using statistical tools [6] and mathematical prediction models [7]. In India, two vaccines were approved in the year 2021 and administered to the public [8]. In the first phase of the vaccination drive all the front line health workers were eligible to take the jab, it is seen that the infection and mortality rate of doctors and nurses are much less in the second wave as compared to the first wave. But, still some of them are critical about the efficacy of these vaccines and some of them are apprehensive about their probable side effects in the long term. In the second phase the elderly citizens were being vaccinated, this category of people is considered to be the most susceptible to COVID virus infections but the pace of vaccinations is very slow and not up to the mark yet that time probably leads to adverse implications. This paper tries to analyse the available vaccination data of different states of India and compares these with similar studies of vaccination data and results of other countries. We have collected data from JHU CSSE Covid-19 and also follow our own algorithm to create our own dataset. The prediction model is validated with the existing data and the data from other countries; those are already gone through the second wave of infections in the year 2021. Here we have used the DNN architecture

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model to validate the dataset because it can solve more real complex problems like classification. It concluded that level of vaccination plays a very important role in reducing the number and severity of infections. Beside the vaccination level, the timing of the shutdown also played a very important role in reducing the cases of infections in different states of India.

1.1 Brief History of Corona Many nations, including India, are affected by COVID-19 infections. Even if COVID19 infection was first diagnosed in Wuhan, China in late 2019, and the World Health Organization (WHO) declared the eruption a global public health emergency in Jan 2020. It is not a new infection. It has evolved from the human coronavirus disease, first detected in 2002 in Asia SAR-CoV. It leads to severe breathing problems [23]. Coronaviruses range drastically in hazard issues [21]. It can cause colds to have important symptoms like fever, sore throat, etc., [22]. It can also lead to pneumonia and bronchitis. In 2003, severe breathing problems had started globally, and WHO revealed a new virus SARS-CoV-1. Around 800 people died and more than 8000 people from different places around the globe had been infected [24, 25]. New version of Coronavirus was recognized in Sept 2012 and officially given the name MERS-CoV [26, 27]. The Ministry of the health of France 2013 confirmed that it was a spreading infection from person to person. In 2015, a plague of MERS-CoV happened in the Republic of Korea causing the most important eruption of MERS-CoV out of the door in the middle east [28, 29]. Till December 2019, by using tests in the laboratory around 2500 cases of MERS-CoV have been confirmed and out of this number 851 have been lethal, with a mortality rate of about 34.5% [30]. 2020 onwards, the whole world has been badly affected by the COVID-19, caused by SARS-CoV-2, which is another strain of coronavirus. Provisionally this infection was called novel coronavirus (2019-nCov). It was first detected in Wuhan city, China and then the whole world came under the grip of this infection. As of 12 July 2022, there were a minimum 6,354,564 [31] confirmed deaths and extra than 55,341,787 [31] cases in this pandemic. Now a research concern is whether this disease came from bats or any other animal. It is a contagious disease, human to human transmission and spreading rate is also high. As this type of pandemic may come in future, we should always be aware of it and try to reduce the spreading rate by analyzing properly. We have to live with corona taking certain precautions and restrictions.

1.2 Pandemic Challenges This pandemic affected India and all other countries. It has created mental health issues of children, adults and old aged people. The pandemic has equally impacted poor and rich countries. Initially the mortality rate was very high in India because

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of high population density and lower medical resources, the condition became more critical. In the pandemic period, people in India were facing problems with less supplies of important medicines, lack of proper information, financial loss, stigma and infection fear. India is one of the densest countries in the world. Here it is not unusual for more than one family, families with multiple generations to live together. So the biggest challenge for all physical distancing mandates. Some people can access advanced solid care. But in general here in India the health infrastructure is not adequate for example they do not have enough beds and equipment such as ventilators. Especially in rural areas hospital capacity is a biggest concern. The high rates of comorbidity conditions that are contributing to COVID-19 spikes. India is a low income country, so it is very difficult for all to follow a better developed hygiene standard. People were unaware of how long people to be quarantined. An infected person has a potential to infect hundreds, as some of the COVID virus strains are highly contagious. The social media misinformation on COVID. Nowadays with growing social media intake by the public leads to many wrong procedures and practices. The conduction of mass gathering emerges as a super spreader event with high rate increase of cases. The potential super spreader events were religious, personal and political gatherings. From the available data, It has been observed through statistical analysis, there is a direct influence of these gatherings on the increase in overall number of COVID-19 cases in some cities and localities.

2 Background We have considered five important parameters for all the Indian states and three levels based on the stages of infection and their present trend and probable situation in near future. The deep learning technique is used to predict the caseload for different levels of vaccination and timing of lockdown for different combinations of data sets and further validate it from the actual data available from different states of India. The results of finding are very closely agreeing with the actual infections reported. The data of countries like America, U.K and Israeli with better vaccination level and its effects on the caseloads are considered in giving extra weight to vaccination and lockdown in reducing infection level. This gives a better understanding of beneficial effects of vaccination in curbing the COVID virus induced pandemic in other countries [4, 5, 10]. It concludes that universal vaccination is the better alternative instead of implementing strict lockdowns. The lockdowns lead to big economic loss to the country and great hardship to the general public. Now the most pressing issue is to address vaccine hesitancy among some sections of the public. This methodology and the same mathematical model may also be used to predict the caseload in similar situations, in other countries. This will help governments in swift planning of their responses and better management of limited resources available with them.

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We consider five important factors in the following order. First, the population density, which increases probability of more proximity, between the peoples [8, 11]. Second, the business activities which required a high level of mobility and more close contact between peoples [9]. Third, the education levels of individuals also play a very important role in compliance of COVID appropriate behavior. Fourth, the vaccination level plays the most important role [4, 13–15]. Finally, the fifth important point is timing of lockdown or shutdown implementations [8, 9]. All these above five parameters have significant correlations and bearing on confirmed caseloads. In turn, these five factors also show the major effects in determining the caseloads or conformed cases count, which were being reported from different states of India, in the months of February, March, April and May 2021.This is the important period of second waves in India. In Fig. 1 is clearly showing, the new cases are rising at an alarming rate in Asia. The significant contributions to new cases are coming from India only because of its population density, new mutants of COVID viruses and low vaccination status. The new variants are found to be more infectious then the original COVID-19 strain. The total confirmed cases are 21,491,598, the recovered cases are17612351 and the death cases are 234,083 as per the data available till first week of May 2021. It can be clearly seen in the Fig. 2 death cases are rising very fast. The high mortality rate in the year 2021 is due to the high caseloads. Hospitals have been over stretched and over loaded with limited medical facilities. The health workers are overburdened too. The big cities like Mumbai, Delhi and Bengaluru are also reeling under severe resource constraints in this second wave in India. In the plot shown in Fig. 3, we can clearly see the top five states of India in terms of confirmed cases of infections in million, incidentally these states are also the states having high population densities. Some have a high level of industrial activities or business activities too, which demands very close contacts in shop floors and offices. Some have also delayed lockdown or shutdown implementations because of elections or political rallies, religious congregations and economic considerations. Besides, some of the states have low or medium vaccination levels till now. The Table 1 clearly shows how the infection levels are being directly affected by these

Fig. 1 Continent wise new cases in second wave of COVID

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Fig. 2 Plot of cumulative death cases in the year 2021 in India

five features in ten Indian states during the peak time period of the second waves in the year 2021. In the Fig. 4 the latest treads are shown and it can be seen the cases are rising slowly again. The total score for finding out the infection levels of different states are calculated using the weights assigned to five features in the Table 2. For example taking the case of Maharashtra, population density was high there so weight of population density over there is 3. Accordingly we can find the weight of the other 4 features. The target predicted output infection levels as per the summation of all the five features and their level’s weight scores are categorized as per their range, low (5–9), medium (10–14) and high (15–19) respectively. We have elaborated the algorithm below. In total 243 possible data sets are considered for DNN modelling.

Fig. 3 Top five states of India in terms of confirmed cases during 2nd wave in the year 2021

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Table 1 Five factors affecting COVID caseloads in ten states Name of State or City

COVID Cases

Population Density

Industry or Business activities or Mobility

Education status or Appropriate Behaviour

Vaccination Status

Lockdown /Shutdown enforced time /Period

Maharashtra

High

High

High

Medium

Medium

Delayed

Kerala

High

High

High

High

Medium

Delayed

West Bengal

Medium

High

Medium

High

Medium

Delayed

Karnataka

High

High

High

High

Medium

Timely

Delhi

High

High

High

Medium

Low

Timely

Bihar

Medium

High

Low

Low

Low

Promptly

Odisha

Medium

Medium

Medium

Medium

Medium

Timely

U Pradesh

High

High

Low

Low

Low

Delayed

H Pradesh

Low

Low

Low

Medium

High

Promptly

Gujrat

Medium

High

High

High

High

Timely

Trend of death cases in India 1000 900 800 700 600 500 400 300 200 100 0 Jan2022

Feb2022

Mar2022

Apr2022

May2022

June2022

July2022

Fig. 4 death cases in the year 2022 in India Table 2 Weights assigned to five features for three different levels

Features

Weights Assigned Low

Medium

High

Population Density

1

2

3

Education Level

1

2

3

Business Activity

1

2

3

Vaccination Status

5

3

1

Lockdown Timing

5

3

1

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Algorithm /* Output: Infection Level(IL) */ /*Input: Population Density( PD)[Low=1,Medium=2, High=3], Education Level( EL)[Low=1,Medium=2, High=3], Business Activity( BA)[Low=1,Medium=2, High=3], Vaccination Status (VS)[Low=5,Medium=3, High=1], Lockdown Timing (LT)[Low=5,Medium=3, High=1] */ Input PD, EL, BA, VS, LT IL=(PD+EL+BA+VS+LT) if (IL>=5 and =10 and IL