Machine Learning in Cognitive IoT

1. Auflage 2020
ISBN: 978-0-367-35916-4
Verlag: Taylor & Francis Ltd

Buch, Englisch, 318 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 750 g

This book covers the different technologies of 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 real world and 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

- Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics

- Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions

Zielgruppe

Postgraduate and Professional

Autoren/Hrsg.

Kumar, Neeraj

Makkar, Aaisha

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik Mathematik Mathematik Allgemein
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

Chapter 1: Internet of Things Chapter 2: Cognitive Internet of Things Chapter 3: Data mining in IoT Chapter 4: Machine Learning Techniques Chapter 5: R Programming Chapter 6: Machine Learning Paradigms Chapter 7: Different Machine Learning Models Chapter 8: Data Processing Chapter 9: Feature Engineering and Optimization Chapter 10: Evaluation and Validation of Results Chapter 11: Solutions Chapter 12: Data Set Bibliography

Über Autor(innen)

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.