Green Internet of Things and Machine Learning

Regular price €233.06
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Agriculture and healthcare applications
Artificial Intelligence
Artificial Neural Network
automatic-update
B01=Anil Kumar
B01=Chuan-Ming Liu
B01=Roshani Raut
B01=Sandeep Kautish
B01=Zdzislaw Polkowski
Big Data
Category1=Non-Fiction
Category=UY
Category=UYQ
COP=United States
Deep Learning
Delivery_Delivery within 10-20 working days
Energy consumption
Energy Efficient Computing
Energy-Efficiency
eq_computing
eq_isMigrated=2
eq_non-fiction
Green Internet of Things
Green nanotechnology
Internet-of-Things in Agriculture
KNN
Language_English
Machine Learning
Machine learning for banking industry
Naive Bayes decision tree
PA=Available
Precision Farming
Price_€100 and above
PS=Active
Random forests
Routing Protocol
smart agriculture
smart farming
smart transportation
softlaunch
Support Vector Machine algorithms
Using AdaBoost
WSN IoT

Product details

  • ISBN 9781119792031
  • Weight: 454g
  • Dimensions: 10 x 10mm
  • Publication Date: 04 Feb 2022
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Health Economics and Financing

Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment.

The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier.

Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare.

Audience

The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.

Roshani Raut, PhD is an associate professor in the Department of Information Technology at Pimpri Chinchwad College of Engineering, Pune University, India. She has presented and published more than 70 research communications in national/international conferences and journals and has published 13 patents.

Sandeep Kautish, PhD is a professor & Dean of Academics with LBEF Campus, Kathmandu Nepal. He has published more than 40 papers in international journals.

Zdzislaw Polkowski, PhD is a professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals.

Anil Kumar, PhD is a professor of CSE and Head of Department of Information Technology, DIT University, India. He has published more than 200 research papers.

Chuan-Ming Liu, PhD is a professor in the Department of Computer Science and Information Engineering (CSIE), National Taipei University of Technology (Taipei Tech), Taiwan. He has published more than 100 research article is international journals.