Handbook of Research on Machine Learning: Foundations and Applications

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.