Artificial Intelligence for Sustainable Applications

  • 4h 50m
  • B. Vinoth Kumar, K. Umamaheswari, S. K. Somasundaram
  • John Wiley & Sons (US)
  • 2023


The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas.

With the advent of recent technologies, the demand for Information and Communication Technology (ICT) based applications such as artificial intelligence (AI), machine learning, Internet of Things (IoT), health care, data analytics, augmented reality / virtual reality, cyber-physical systems, and future generation networks has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications

This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results.


AI researchers as well as engineers in information technology and computer science

About the Author

K. Umamaheswari, PhD, is a professor and head with 27 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India.

B. Vinoth Kumar, PhD, is an associate professor with 19 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India.

S. K. Somasundaram, PhD, is an assistant professor in the Department of Information Technology, PSG College of Technology, Coimbatore, India.

In this Book

  • Predictive Models of Alzheimer's Disease using Machine Learning Algorithms – An Analysis
  • Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering
  • Steering Angle Prediction for Autonomous Vehicles using Deep Learning Model with Optimized Hyperparameters
  • Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer
  • COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches
  • Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression
  • A Systematic Review for Medical Data Fusion over Wireless Multimedia Sensor Networks
  • An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System
  • An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks
  • An Analysis on Detection and Visualization of Code Smells
  • Leveraging Classification Through AutoML and Microservices
  • Virtual Teaching Activity Monitor
  • AI-Based Development of Student E-Learning Framework
  • A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks
  • Unique and Random Key Generation using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication over Wireless Network
  • Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles
  • Driver Drowsiness Detection System
  • An Extensive Study to Devise a Smart Solution for Healthcare IoT Security using Deep Learning
  • A Research on Lattice-Based Homomorphic Encryption Schemes
  • Biometrics with Blockchain: A Better Secure Solution for Template Protection