Subhash K. Shinde – författare

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.

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.

Presenting innovative research-oriented ideas, and the implementation and socioeconomic applications of internet of things-based network, Challenges and Solutions in Internet of Things-Based Smart Applications showcases smart waste management, optical technologies for internet of things and remote patient monitoring and data analysis.

This text is primarily written for graduate students, postgraduate students, professionals and academic researchers working in the fields of Computer Science and Engineering, Information Technology and Electrical Engineering.

Presenting innovative research-oriented ideas, and the implementation and socioeconomic applications of internet of things-based network, Challenges and Solutions in Internet of Things-Based Smart Applications showcases smart waste management, optical technologies for internet of things and remote patient monitoring and data analysis.

This text is primarily written for graduate students, postgraduate students, professionals and academic researchers working in the fields of Computer Science and Engineering, Information Technology and Electrical Engineering.