Handbook of Research on Machine Learning Foundations and Applications Editors: Monika Mangla, PhD Subhash K. Shinde, PhD Vaishali Mehta, PhD Nonita Sharma, PhD Sachi Nandan Mohanty, PhD Ordering Info/Buy Book

Published. Available now. Pub Date: August 2022 Hardback Price: see ordering info Hard ISBN: 9781774638682 E-Book ISBN: 9781003277330 Pages: 594pp w/index Binding Type: Hardback / ebook Notes: 38 color and 212 b/w illustrations

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Reviews
“Comprehensive, research-oriented, and well-articulated coverage of concepts of machine learning. The book articulates the basics of ML with foundation topics, the use cases, and real-life applications that lays out the roadmap of transformations through ML and, hence, succeeds in presenting the topics in a self-contained manner. . . . Many examples of ML projects from various industries, academics and research labs. . . . Caters to the demand of a naïve user by presenting the necessary rudiments while providing a solid foundation for research, foundation, and applications to cater the needs of researchers focusing in domains related to ML. Further, the book also provides vast perspective and methodical suggestions on where to start, what to look for, and overall what are the best practices in the context of machine learning.”
—Dr. Suneeta Satpathy, Associate Professor, Faculty of Emerging Technologies, Sri Sri University, India

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With over 250 figures, tables, and charts, this volume takes the reader on a technological voyage of machine learning (ML) advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation.

The first section the Handbook of Research on Machine Learning: Foundations and Applications provides an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning. The section also presents a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks.

The second section of the volume focuses on the applications of machine learning in healthcare, emphasizing its current status, analytics, and future prospects. Chapters explore predictive data analytics for health issues, the detection of infectious diseases in human bodies, time series forecasting techniques for infectious disease prediction, as well as a review of medical analytics using social media.

Section 3 adds a macro dimension to the book by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.

The plethora of topics covered in the Handbook of Research on Machine Learning: Foundations and Applications will give readers a thorough look into the vast applications of machine learning. It will familiarize researchers and scientists as well as faculty and students with the latest trends in machine learning starting from rudiments and then delving into its applications in healthcare and other industries.

CONTENTS:

Preface

PART I: RUDIMENTS OF MACHINE LEARNING APPROACHES
1. Ethics in AI in Machine Learning
Shilpa Kapse

2. Advances in Artificial Intelligence Models for Providing Security and Privacy Using Machine Learning Techniques
R. S. M. Lakshmi Patibandla and V. Lakshman Narayana

3. A Systematic Review of Deep Learning Techniques for Semantic Image Segmentation: Methods, Future Directions, and Challenges
Reena, Amanpratap Singh Pall, Nonita Sharma, K. P. Sharma, and Vaishali Wadhwa

4. Covariate Shift in Machine Learning
Santosh Chapaneri and Deepak Jayaswal

5. Understanding and Building Generative Adversarial Networks
Harsh Jalan and Dakshata Panchal

PART II: APPLICATION OF MACHINE LEARNING IN HEALTHCARE
6. Machine Learning in Healthcare: Applications, Current Status, and Future Prospectus
Rohini Patil and Kamal Shah

7. Employing Machine Learning for Predictive Data Analytics in Healthcare
Rakhi Akhare, Monika Mangla, Sanjivani Deokar, and Hardik Deshmukh

8. Prediction of Heart Disease Using Machine Learning
Subasish Mohapatra, Jijnasee Dash, Subhadarshini Mohanty, and Arunima Hota

9. Detection of Infectious Diseases in Human Bodies by Using Machine Learning Algorithms
Snehlata Beriwal, Thirunavukkarasu K, Shahnawaz Khan, and Satheesh Abimannan

10. Medical Review Analytics Using Social Media
Dipen Chawla, Sujay Varma, and Sujata Khedkar

11. Time Series Forecasting Techniques for Infectious Disease Prediction
Jaiditya Dev, Monika Mangla, Nonita Sharma, and K. P. Sharma

PART III: TOWARD INDUSTRIAL AUTOMATION THROUGH MACHINE LEARNING
12. Machine Learning in the Steel Industry
Sushant Rath

13. Experiments Synergizing Machine Learning Approaches with Geospatial Big Data for Improved Urban Information Retrieval
Kavach Mishra, Asfa Siddiqui, and Vinay Kumar

14. Garbage Detection Using Surf Algorithm Based on Merchandise Marker
Lalit Gupta, Samarth Jain, Dhruv Bansal, and Princy Randhawa

15. Evolution of Long Short-Term Memory (LSTM) in Air Pollution Forecasting
Satheesh Abimannan, Deepak Kochhar, Yue-Shan Chang, and K. Thirunavukkarasu

16. Application of Machine Learning in Stock Market Prediction
P. S. Sheeba and Subhash K. Shinde

17. Deep Learning Model for Stochastic Analysis and Time-Series Forecasting of the Indian Stock Market
Sourabh Yadav

18. Enhanced Fish Detection in Underwater Video Using Wavelet-Based Color Correction and Machine Learning
Jitendra P. Sonawane, Mukesh D. Patil, and Gajanan K. Birajdar

19. Fake News Predictor Model Based on Machine Learning and Natural Language Processing
Priyanka Bhartiya, Sourabh Yadav, Vaishali Wadhwa, and Poonam Mittal

20. Machine Learning on Simulation Tools for Underwater Sensor Network
Mamta and Nitin Goyal

21. Prediction and Analysis of Heritage Monuments Images Using Machine Learning Techniques
Gopal Sakarkar, Nilesh Shelke, Ayon Moitra, Manoj Shanti, and Pravin Ghatode


Index

About the Authors / Editors:
Editors: Monika Mangla, PhD
Associate Professor, Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering, Mumbai, Maharashtra, India

Monika Mangla, PhD, is Associate Professor in the Department of Information Technology at Dwarkadas J. Sanghvi College of Engineering, Mumbai, India. She has two patents to her credit as well as over 18 years of teaching experience at undergraduate and postgraduate levels. She has guided many student projects. Her interest areas include IoT, cloud computing, network security, algorithms and optimization, location modeling, and machine learning. She has published several research papers and book chapters (SCI and Scopus-indexed) with reputed publishers. She has also been associated with several SCI-indexed journals, including the Turkish Journal of Electrical Engineering & Computer Sciences (TUBITAK), Industrial Management & Data Systems, etc., as a reviewer. She is a life member of the Computer Society of India and the Institution of Electronics and Telecommunication Engineers. Dr. Mangla received her PhD from Thapar Institute of Engineering & Technology, Patiala, Punjab, India.

Subhash K. Shinde, PhD
Professor and Vice Principal, Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai, India

Subhash K. Shinde, PhD, is a Professor and Vice Principal at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai, India. He has over 20 years of rich teaching experience at both the undergraduate and graduate levels. His research areas include machine learning, computer networks, network security, data warehousing, and mining. He has also been guiding research scholars at the University of Mumbai. He has published numerous research papers at various reputed national and international conferences and journals (SCI and Scopus Indexed) with reputed publishers, including Elsevier and Inderscience and has authored many books. He has also worked as Chairman of the Board of Studies in Computer Engineering under the Faculty of Technology at the University of Mumbai. Dr. Shinde earned his PhD in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering and Technology, India.

Vaishali Mehta, PhD
Professor, Department Information Technology, Panipat Institute of Engineering and Technology, Panipat, Haryana, India

Vaishali Mehta, PhD, is Professor in the Department Information Technology at Panipat Institute of Engineering and Technology, Panipat, Haryana, India. She has two patents published to her credit. She has over 17 years of teaching experience at undergraduate and postgraduate levels. Her research interests include approximation algorithms, location modeling, IoT, cloud computing, and machine learning. She has published research articles in quality journals (SCI and Scopus-indexed), national and international conferences, and books of reputed publishers. She has also reviewed research papers of reputed journals and conferences. Dr. Mehta has a PhD in facility location problems from Thapar University, India.

Nonita Sharma, PhD
Assistant Professor, National Institute of Technology, Jalandhar, India

Nonita Sharma, PhD, is an Assistant Professor at the National Institute of Technology, Jalandhar, India. She has more than 10 years of teaching experience. Her major area of interest includes data mining, bioinformatics, time series forecasting, and wireless sensor networks. She has published several papers in the international and national journals and conferences and has written book chapters also. She received a best paper award for her research paper at the Mid-Term Symposium organized by CSIR, Chandigarh, India. She has authored a book titled XGBoost: The Extreme Gradient Boosting for Mining Applications. Dr. Sharma received her BTech degree in Computer Science Engineering, her MTech degree in Computer Science Engineering, and her PhD degree in Wireless Sensor Network from the National Institute of Technology, Jalandhar, India.

Sachi Nandan Mohanty, PhD
Associate Professor, Department of Computer Engineering, College of Engineering Pune, India
Sachi Nandan Mohanty, PhD, is Associate Professor in the Department of Computer Engineering at College of Engineering Pune, India. Professor Mohanty’s research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence. He has received three best paper awards during his PhD studies and has since published 20 papers in SCI journals. As a Fellow of the Indian Society Technical Education, The Institute of Engineering and Technology, and the Computer Society of India, and as a Fellow of the Institute of Engineers and Senior member of IEEE Computer Society, he is actively involved in the activities of professional societies. He has received the Best Researcher Award from the Biju Pattnaik University of Technology in 2019, Best Thesis Award (first prize) from Computer Society of India in 2015, and Outstanding Faculty in Engineering Award from Dept. of Higher Education, Govt. of Odisha in 2020. He has also received international travel funds from SERB, Dept of Science and Technology, Govt. of India, for chairing a session at an international conference USA, 2020. Dr. Mohanty is currently acting as a reviewer of many journals, including Robotics and Autonomous Systems, Computational and Structural Biotechnology, Artificial Intelligence Review, and Spatial Information Research. He has also published four edited books and three authored books.

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