Applied Learning Algorithms for Intelligent IoT (Paperback)

Pethuru Raj Chelliah

ISBN 10: 1032113219 / ISBN 13: 9781032113210

Published by Taylor & Francis Ltd, London, 2024

About this Item

Paperback. This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights.This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The books detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research. Connectivity through the Internet of Things (IoT) processes and platforms combined with AI create the foundation of developing smart systems exhibiting intelligent behavior. The book illustrates promising and potential machine learning (ML) and deep learning (DL) algorithms through real-world and real-time business use cases. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781032113210

Bibliographic Details

• Title: Applied Learning Algorithms for Intelligent IoT (Paperback)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2024
• Language: English
• ISBN 10: 1032113219
• ISBN 13: 9781032113210
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics:

• Cognitive machines and devices
• Cyber physical systems (CPS)
• The Internet of Things (IoT) and industrial use cases
• Industry 4.0 for smarter manufacturing
• Predictive and prescriptive insights for smarter systems
• Machine vision and intelligence
• Natural interfaces
• K-means clustering algorithm
• Support vector machine (SVM) algorithm
• A priori algorithms
• Linear and logistic regression

Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights.

This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.