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Big Data Analytics for Cyber-Physical Systems - Machine Learning for the Internet of Things (Paperback)

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science.
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Product Description

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science.

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Product Details

General

Imprint

Elsevier Science Publishing Co Inc

Country of origin

United States

Release date

July 2019

Availability

Expected to ship within 18 - 22 working days

First published

2019

Editors

, ,

Dimensions

229 x 152 x 19mm (L x W x T)

Format

Paperback

Pages

396

ISBN-13

978-0-12-816637-6

Barcode

9780128166376

Categories

LSN

0-12-816637-1

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