Cover Image

Artificial Intelligence for Sustainable Applications.

Bibliographic Details
Main Author: Umamaheswari, K.
Other Authors: Kumar, B. Vinoth, Somasundaram, S. K.
Format: eBook
Language:English
Published: Newark : John Wiley & Sons, Inc., 2023.
Series:Artificial Intelligence and Soft Computing for Industrial Transformation
Subjects:
Artificial intelligence.
Information technology.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Part I: Medical Applications
  • Chapter 1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms
  • An Analysis
  • 1.1 Introduction
  • 1.2 Prediction of Diseases Using Machine Learning
  • 1.3 Materials and Methods
  • 1.4 Methods
  • 1.5 ML Algorithm and Their Results
  • 1.6 Support Vector Machine (SVM)
  • 1.7 Logistic Regression
  • 1.8 K Nearest Neighbor Algorithm (KNN)
  • 1.9 Naive Bayes
  • 1.10 Finding the Best Algorithm Using Experimenter Application
  • 1.11 Conclusion
  • 1.12 Future Scope
  • References
  • Chapter 2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering
  • 2.1 Introduction
  • 2.2 Literature Review
  • 2.3 Dataset Used
  • 2.4 Proposed Method
  • 2.4.1 Histogram Equalization
  • 2.4.2 Threshold-Based Segmentation
  • 2.4.3 K-Means Clustering
  • 2.4.4 Fuzzy-K-Means Clustering
  • 2.5 Experimental Analysis
  • 2.5.1 Results of Histogram Equalization
  • 2.5.2 Findings of Bounding Box Segmentation
  • 2.5.3 Evaluation Metrics
  • 2.6 Conclusion
  • References
  • Chapter 3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Methodology
  • 3.3.1 Architecture
  • 3.3.2 Data
  • 3.3.3 Data Pre-Processing
  • 3.3.4 Hyperparameter Optimization
  • 3.3.5 Neural Network
  • 3.3.6 Training
  • 3.4 Experiment and Results
  • 3.4.1 Benchmark
  • 3.5 Conclusion
  • References
  • Chapter 4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer
  • 4.1 Introduction
  • 4.2 Literature Analysis
  • 4.2.1 Review of Gene Selection Methods in SNP
  • 4.2.2 Review of Classification Methods in SNP
  • 4.2.3 Review of Deep Learning Classification Methods in SNP
  • 4.3 Comparison Analysis
  • 4.4 Issues of the Existing Works
  • 4.5 Experimental Results
  • 4.6 Conclusion and Future Work
  • References
  • Chapter 5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches
  • 5.1 Introduction
  • 5.2 Literature Survey
  • 5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches
  • 5.3.1 Trend Check Analysis Segregation 1 Algorithm
  • 5.3.2 Trend Check Analysis Segregation 2 Algorithm
  • 5.4 Results and Discussion
  • 5.5 Conclusion
  • References
  • Chapter 6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression
  • 6.1 Introduction
  • 6.2 Background
  • 6.2.1 Comprehensive Survey
  • Applications in Healthcare Industry
  • 6.2.2 Comparison of Various Models for Forecasting
  • 6.2.3 Context of the Work
  • 6.3 Proposed Work
  • 6.3.1 Conceptual Architecture
  • 6.3.2 Procedure
  • 6.4 Experimental Results
  • 6.5 Discussion and Conclusion
  • 6.5.1 Future Scope
  • References