Nonita Sharma – 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.

Cyber-Physical Systems: A Comprehensive Guide explores the complete sys-tem perspective, underlying theories, modelling, and the applications of Cyber Physical Systems (CPS). It aims to cover all topics ranging from discussion of ru-diments of the system, efficient management, to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and to achieve this, the book has been edited to include all the aspects of CPS. The book fo-cuses on the concept map of CPS including latest technological interventions; issues, challenges, and the integration of CPS with IoT & Big Data Analytics. This aims to bring together unique contributions on cyber-physical systems research and education with applications in industrial, agriculture, and medical domains. The main aim of the book is to provide a roadmap to the latest advancements to provide optimal solutions in the field of CPS.

Features

• Coverage of rudiments of the subject• Discussion of recent advancements in the associated field• Considers an audience of diverse domains• Suitable for students (both UG and PG level) and researchers in the field of CPS

This book aims to present the emergence of Cyber Physical Systems in response to revolutionary advancements in IoT. While discussing the associated challenges, it also endeavors to devise efficient models which are competent to address these challenges. This book aims to cater to researchers and academicians working in the related field of CPS.

Cyber-Physical Systems: A Comprehensive Guide explores the complete sys-tem perspective, underlying theories, modelling, and the applications of Cyber Physical Systems (CPS). It aims to cover all topics ranging from discussion of ru-diments of the system, efficient management, to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and to achieve this, the book has been edited to include all the aspects of CPS. The book fo-cuses on the concept map of CPS including latest technological interventions; issues, challenges, and the integration of CPS with IoT & Big Data Analytics. This aims to bring together unique contributions on cyber-physical systems research and education with applications in industrial, agriculture, and medical domains. The main aim of the book is to provide a roadmap to the latest advancements to provide optimal solutions in the field of CPS.

Features

• Coverage of rudiments of the subject• Discussion of recent advancements in the associated field• Considers an audience of diverse domains• Suitable for students (both UG and PG level) and researchers in the field of CPS

This book aims to present the emergence of Cyber Physical Systems in response to revolutionary advancements in IoT. While discussing the associated challenges, it also endeavors to devise efficient models which are competent to address these challenges. This book aims to cater to researchers and academicians working in the related field of CPS.

“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques.

The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions.

This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.

“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques.

The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions.

This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.