Machine Learning in Cognitive IoT

Name: Machine Learning in Cognitive IoT
Brand: Taylor & Francis
SKU: 9780367359164
Price: 205.0 NZD
Availability: InStock

By Neeraj Kumar and Aaisha Makkar

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Summary

Machine Learning in Cognitive IoT provides a comprehensive overview of integrating machine learning technologies within the Internet of Things (IoT), focusing on Cognitive IoT (CIoT). Starting with the fundamentals of IoT, its history, architecture, and applications, the book then delves into the capabilities of CIoT and the role of machine learning in data analytics and decision making. It covers various machine learning paradigms, data pre-processing, feature engineering, and includes practical implementations using R, Python, and Matlab alongside sample questions for deeper understanding.
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Format: Hardback

$205⁰⁰

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Machine Learning in Cognitive IoT

$205⁰⁰

Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

This book is ideal for technology analysts, developers, and students interested in data analytics, machine learning, and emerging IoT technologies. It offers practical insights suitable for those seeking to enhance their skills in data processing, Big Data, and robotics within the IoT domain.

This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things.

Description

This book covers the different technologies of the Internet and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps.

It initiates with IoT basics, its history, architecture, and applications followed by capabilities of CIoT in the real world and a description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications.

Explains integration of Machine Learning in IoT for building an efficient decision support system.
Covers IoT, CIoT, machine learning paradigms and models.
Includes implementation of machine learning models in R.
Helps analysts and developers work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics.
Includes programming codes in Python/Matlab/R along with practical examples, questions, and multiple-choice questions.

Book Details

INFORMATION

ISBN: 9780367359164

Publisher: Taylor & Francis Ltd

Format: Hardback

Date Published: 28 May 2020

Country: United Kingdom

Imprint: CRC Press

Illustration: 43 Tables, black and white; 90 Illustrations, black and white

Audience: Tertiary education, Professional and scholarly

DIMENSIONS

Width: 156.0mm

Height: 234.0mm

Weight: 620g

Pages: 318

Collections

Aaisha Makkar Computing & Technology Education & Reference Engineering, Technology & Agriculture Neeraj Kumar Non-Fiction

About the Author

Dr. Neeraj Kumar is working as Full Professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala (Pb.), India. Prof. Neeraj is an internationally renowned researcher in the areas of VANET & CPS Smart Grid & IoT Mobile Cloud computing & Big Data and Cryptography. He has published more than 300 technical research papers in leading journals and conferences from IEEE, Elsevier, Springer, John Wiley, and Taylor and Francis. He has guided many research scholars leading to Ph.D. and M.E./M.Tech. He is member of the Cyber-Physical Systems and Security (CPSS) research group. He has research funding from DST, CSIR, UGC, and TCS. He has won best papers awards from IEEE ICC and IEEE Systems Journals 2018. He is a senior member of IEEE and is in the editorial board of various journals of repute.

Dr. Aaisha Makkar received her Bachelor of Computer Applications degree from Panjab University, Chandigarh, India in 2010 and Master of Computer Applications from National Institute of Technology (NIT), Kurukshetra, India in 2013. She had worked as an Assistant Professor in Computer Application Department of NIT, Kurukshetra. She obtained her Ph.D. degree from Computer Science and Engineering Department in Thapar Institute of Engineering &Technology, Patiala (Punjab), India. Her research interests in data mining, web mining, algorithms, machine learning and Internet of thing. She has experience of more than 10 years in teaching and research. He has more than 10 research publications in good journals of repute.