Applied Learning Algorithms for Intelligent IoT

Applied Learning Algorithms for Intelligent IoT 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 devicesCyber physical systems (CPS)The Internet of Things (IoT) and industrial use casesThe industry 4.0 for smarter manufacturingPredictive and prescriptive insights for smarter systemsMachine vision and intelligenceNatural interfacesK-means clustering algorithmSupport vector machine (SVM) algorithmA priori algorithmsLinear and logistic regression The book 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 newer 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.

Author: Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan

Publisher: CRC Press, Taylor & Francis Group

Publication Date: Oct 04, 2024

Number of Pages: NA pages

Language: English

Binding: Paperback

ISBN-10: 1032113219

ISBN-13: 9781032113210