John Wiley & Sons Inc Emerging Technologies for Healthcare: Internet of Things and Deep Learning Models (Machine Learning in Biomedical Science and Healthcare Informatics)

“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. From the Inside Flap The book aims to devise new machine learning paradigms to address prevalent challenges in the field of healthcare from multiple perspectives.Internet of Things (IoT) refers to the computer network consisting of ‘things’ or physical objects. These things comprise sensors or software or a method to connect and exchange data with other devices. This book, Emerging Technologies for Healthcare, focuses primarily on the use and applications of IoT and deep learning approaches for providing automated healthcare solutions. It gives insightful information of data and provides feasible solutions through various approaches of machine learning and its applicability 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 to this, providing healthcare solutions for post COVID-19 outbreaks through various suitable approaches is also highlighted. Furthermore, a detailed detection mechanism is discussed which is used to come up with solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions. This book covers not only theoretical approaches and algorithms, but also contains a sequence of steps to analyze problems with data, process, reports, and optimization techniques. The book serves to be a single source for various problem-solving by machine learning algorithms. It begins with IoT-based solutions for the automated healthcare sector and extends to providing solutions of deep learning as an advanced technology. Audience The book will be used by research scholars, engineers, IT professionals, IT manufacturing industries involved in the associated healthcare fields, network administrators, health care practitioners, cybersecurity experts, and government research agencies. From the Back Cover The book aims to devise new machine learning paradigms to address prevalent challenges in the field of healthcare from multiple perspectives.Internet of Things (IoT) refers to the computer network consisting of ‘things’ or physical objects. These things comprise sensors or software or a method to connect and exchange data with other devices. This book, Emerging Technologies for Healthcare, focuses primarily on the use and applications of IoT and deep learning approaches for providing automated healthcare solutions. It gives insightful information of data and provides feasible solutions through various approaches of machine learning and its applicability to disease