(Ebook) Applied Learning Algorithms for Intelligent IoT by Pethuru Raj Chelliah (editor), Usha Sakthivel (editor), Susila Nagarajan (editor) ISBN 9780367635947, 9781003119838, 9781032113210, 0367635941, 1003119832, 1032113219

(Ebook) Applied Learning Algorithms for Intelligent IoT by Pethuru Raj Chelliah (editor), Usha Sakthivel (editor), Susila Nagarajan (editor) ISBN 9780367635947, 9781003119838, 9781032113210, 0367635941, 1003119832, 1032113219

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.