國立虎尾科技大學 |

Applied learning algorithms for intelligent IoT

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Applied learning algorithms for intelligent IoT/ edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan.
其他作者:	Nagarajan, Susila.
出版者:	Boca Raton, FL :CRC Press, : 2022.,
面頁冊數:	1 online resource (369 p.) :ill. :
附註:	"An Auerbach book."
標題:	Internet of things. -
電子資源:	https://www.taylorfrancis.com/books/9781003119838
ISBN:	9781003119838

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: 9781003119838Subjects--Topical Terms:

1023130
Internet of things.

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 Internet of things. $3 1023130
650 0 $a Algorithms. $3 527865
650 0 $a Machine learning. $3 561253
700 1 $a Nagarajan, Susila. $3 1419164
700 1 $a Sakthivel, Usha. $3 1419163
700 1 $a Chelliah, Pethuru Raj. $e author. $3 1309060
856 4 0 $u https://www.taylorfrancis.com/books/9781003119838