J P Patra - Böcker

This book deals with a different research area of cognitive IoT and explains how machine learning algorithms can be applied for cognitive IoT. It deals with applications of cognitive IoT in this pandemic (COVID-19), applications for student performance evaluation, applications for human healthcare for chronic disease prediction, use of wearable sensors and review regarding their energy optimization and how cognitive IoT helps in farming through rainfall prediction and prediction of lake levels.Features:Describes how cognitive IoT is helpful for chronic disease prediction and processing of data gathered from healthcare devicesExplains different sensors available for health monitoringExplores application of cognitive IoT in COVID-19 analysisDiscusses pertinent and efficient farming applications for sustaining agricultural growthReviews smart educational aspects such as student response, performance, and behavior and instructor response, performance, and behaviorThis book aims at researchers, professionals and graduate students in Computer Science and Engineering, Computer Applications and Electronics Engineering, and Wireless Communications and Networking.

This book deals with a different research area of cognitive IoT and explains how machine learning algorithms can be applied for cognitive IoT. It deals with applications of cognitive IoT in this pandemic (COVID-19), applications for student performance evaluation, applications for human healthcare for chronic disease prediction, use of wearable sensors and review regarding their energy optimization and how cognitive IoT helps in farming through rainfall prediction and prediction of lake levels.Features:Describes how cognitive IoT is helpful for chronic disease prediction and processing of data gathered from healthcare devicesExplains different sensors available for health monitoringExplores application of cognitive IoT in COVID-19 analysisDiscusses pertinent and efficient farming applications for sustaining agricultural growthReviews smart educational aspects such as student response, performance, and behavior and instructor response, performance, and behaviorThis book aims at researchers, professionals and graduate students in Computer Science and Engineering, Computer Applications and Electronics Engineering, and Wireless Communications and Networking.

This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields.Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and machine learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various machine learning algorithms, neural net- work algorithms, evolutionary techniques, fuzzy logic techniques, and deep learning techniques through projects, so that the reader can easily understand not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need for developing a foundation of machine learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from machine learning ecosystems to demonstrate the necessary machine learning skills to become a successful practitioner.The primary users for the book include undergraduate and postgraduate students, researchers, academicians, specialists, and practitioners in computer science and engineering.

This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields.Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and machine learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various machine learning algorithms, neural net- work algorithms, evolutionary techniques, fuzzy logic techniques, and deep learning techniques through projects, so that the reader can easily understand not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need for developing a foundation of machine learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from machine learning ecosystems to demonstrate the necessary machine learning skills to become a successful practitioner.The primary users for the book include undergraduate and postgraduate students, researchers, academicians, specialists, and practitioners in computer science and engineering.