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Applied learning algorithms for intelligent IoT

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied learning algorithms for intelligent IoTedited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan.
other author:	Chelliah, Pethuru Raj.
Published:	Boca Raton, FL :CRC Press,2022.
Description:	1 online resource (369 p.) :ill.
Notes:	"An Auerbach book."
Subject:	Machine learning.
Online resource:	https://www.taylorfrancis.com/books/9781003119838
ISBN:	9781003119838$q(electronic bk.)

Applied learning algorithms for intelligent IoT
Applied learning algorithms for intelligent IoT[electronic resource] /edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan. - 1st ed. - Boca Raton, FL :CRC Press,2022. - 1 online resource (369 p.) :ill.

"An Auerbach book."

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.

ISBN: 9781003119838$q(electronic bk.)Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Applied learning algorithms for intelligent IoT
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245 0 0 $a Applied learning algorithms for intelligent IoT $h [electronic resource] / $c edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan.
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2022.
300 $a 1 online resource (369 p.) : $b ill.
500 $a "An Auerbach book."
520 $a 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.
588 $a Description based on print version record.
650 0 $a Machine learning. $3 188639
650 0 $a Algorithms. $3 184661
650 0 $a Internet of things. $3 670954
700 1 $a Chelliah, Pethuru Raj. $3 878268
700 1 $a Sakthivel, Usha. $3 948251
700 1 $a Nagarajan, Susila. $3 948252
856 4 0 $u https://www.taylorfrancis.com/books/9781003119838