Handbook of Research on Machine Learning

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.

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