Applied Learning Algorithms for Intelligent IoT
Language: English
Published by Taylor and Francis Ltd, GB, 2024
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
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Add to basketItem description from seller
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. …
Seller Inventory # LU-9781032113210
- Title
- Applied Learning Algorithms for Intelligent IoT
- Author
- Pethuru Raj Chelliah
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2024
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1032113219
- ISBN 13
- 9781032113210
- Item weight
- 560 grams
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.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Pethuru Raj is Chief Architect and Vice President of the Site Reliability Engineering (SRE) Division of Reliance JioInfocomm. Ltd., Bangalore, India.
Dr. Usha Sakthivel is the dean of research, Department of Computer Engineering, RajaRajeswari College of Engineering, Bangalore, India.
Dr. Susila N is a professor and head of the Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India.
"About the title" may belong to another edition of this title.
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