Applied Learning Algorithms for Intelligent IoT

Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan

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English

Auerbach
04 October 2024

Electrical engineering; WAP (wireless) technology; Environmental science, engineering & technology; Computing: general; Computer science

Summary
Details

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.

Edited by: Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan
Imprint: Auerbach
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 680g
ISBN: 9781032113210
ISBN 10: 1032113219
Pages: 356
Publication Date: 04 October 2024
Audience: College/higher education , Primary
Format: Paperback
Publisher's Status: Active