Author Description

"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 f