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Handbook of Research on Machine Learning: Foundations and Applications de Monika Mangla

Handbook of Research on Machine Learning: Foundations and Applications

Editat de Monika Mangla, Subhash K. Shinde, Vaishali Mehta, Nonita Sharma, Sachi Nandan Mohanty
en Limba Engleză Hardback – 4 aug 2022
This volume takes the reader on a technological voyage of machine learning 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 book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension 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.
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Specificații

ISBN-13: 9781774638682
ISBN-10: 1774638681
Pagini: 594
Ilustrații: 36 Tables, black and white; 25 Line drawings, color; 182 Line drawings, black and white; 13 Halftones, color; 30 Halftones, black and white; 38 Illustrations, color; 212 Illustrations, black and white
Dimensiuni: 156 x 234 x 33 mm
Greutate: 0.74 kg
Ediția:1
Editura: Apple Academic Press Inc.
Colecția Apple Academic Press

Notă biografică

Monika Mangla, PhD, is Associate Professor in the Department of Information Technology at Dwarkadas J. Sanghvi College of Engineering, Mumbai, India. She has over 18 years of teaching experience and holds two patents. She has guided many student projects and has published research papers and book chapters with reputed publishers.
Subhash K. Shinde, PhD, is Professor and Vice Principal at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai, India. He has over 20 years of teaching experience and has published many research papers in national and international conferences and journals. He has also 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.
Vaishali Mehta, PhD, is Professor in the Department of 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. She has published research articles and books and has also reviewed research papers for reputed journals and conferences.
Nonita Sharma, PhD, is Assistant Professor at the National Institute of Technology, Jalandhar, India. She has more than 10 years of teaching experience. She has published papers in international and national journals and conferences and has also written book chapters. She has authored a book titled XGBoost: The Extreme Gradient Boosting for Mining Applications.
Sachi Nandan Mohanty, PhD, is Associate Professor in the Department of Computer Science & Engineering at Vardhaman College of Engineering, India. He is actively involved in the activities of several professional societies. He has received awards for his work as well as international travel funds. Dr. Mohanty is currently acting as a reviewer of many journals and has also published four edited books and three authored books.

Cuprins

PART 1: RUDIMENTS OF MACHINE LEARNING APPROACHES, 1. Ethics in AI in Machine Learning, 2. Advances in Artificial Intelligence Models for Providing Security and Privacy Using Machine Learning Techniques, 3. A Systematic Review of Deep Learning Techniques for Semantic Image Segmentation: Methods, Future Directions, and Challenges, 4. Covariate Shift in Machine Learning, 5. Understanding and Building Generative Adversarial Networks, PART 2: APPLICATION OF MACHINE LEARNING IN HEALTHCARE, 6. Machine Learning in Healthcare: Applications, Current Status, and Future Prospectus, 7. Employing Machine Learning for Predictive Data Analytics in Healthcare, 8. Prediction of Heart Disease Using Machine Learning, 9. Detection of Infectious Diseases in Human Bodies by Using Machine Learning Algorithms, 10. Medical Review Analytics Using Social Media, 11. Time Series Forecasting Techniques for Infectious Disease Prediction, PART 3: TOWARDS INDUSTRIAL AUTOMATION THROUGH MACHINE LEARNING, 12. Machine Learning in the Steel Industry, 13. Experiments Synergizing Machine Learning Approaches with Geospatial Big Data for Improved Urban Information Retrieval, 14. Garbage Detection Using SURF Algorithm Based on Merchandise Marker, 15. Evolution of Long Short-Term Memory (LSTM) in Air Pollution Forecasting, 16. Application of Machine Learning in Stock Market Prediction, 17. Deep Learning Model for Stochastic Analysis and Time-Series Forecasting of Indian Stock Market, 18. Enhanced Fish Detection in Underwater Video Using Wavelet-Based Color Correction and Machine Learning, 19. Fake News Predictor Model-Based on Machine Learning and Natural Language Processing, 20. Machine Learning on Simulation Tools for Underwater Sensor Network, 21. Prediction and Analysis of Heritage Monuments Images Using Machine Learning Techniques

Descriere

An exploration of machine learning advancements. It covers many aspects in machine learning, along with the applications in healthcare, 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.