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Machine Learning Paradigm for Internet of Things Applications

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Challenges in data gathering and processing
Data Analysis Automation
Deep learning concepts aiding smart city applications
eHealth or mHealth
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eq_computing
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eq_nobargain
eq_non-fiction
eq_tech-engineering
From Big Data to Smart Data
Health care solution and data analytics in hospital environment
Home automation
Intelligent Transportation Systems
Interconnectivity
IoT without ML
Major ML techniques implemented in IoT
Microsoft Azure
Predictive Power of ML
privacy and trust in the IoT context
Prototypes for new applications for ML and IoT
Role of AI and bio inspired computing in decision making
Role of Data Analytics in smart environment
Security
Smart applications: Case study
Smart cities
Smart education
Vehicular communication networks

Product details

  • ISBN 9781119760474
  • Weight: 454g
  • Dimensions: 10 x 10mm
  • Publication Date: 04 Mar 2022
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description
MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.
About

Audience

Scholars and scientists working in artificial intelligence and electronic engineering, industry engineers, software and computer hardware specialists.

Shalli Rani, PhD is an associate professor in the Department of CSE, Chitkara University, Punjab, India.

R. Maheswar, PhD is the Dean and associate professor, School of EEE, VIT Bhopal University, Madya Pradesh, India.

G. R. Kanagachidambaresan, PhD associate professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India.

Sachin Ahuja, PhD is a professor in the Department of CSE, Chitkara University, Punjab, India.

Deepali Gupta, PhD is a professor, Department of CSE, Chitkara University, Punjab, India.