Artificial Intelligence for Sustainable Applications

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1. Cover
2. Table of Contents
3. Series Page
4. Title Page
5. Copyright Page
6. Preface
7. Part I: Medical Applications
1. 1 Predictive Models of Alzheimer’s Disease Using Machine Learning Algorithms – An
Analysis
1. 1.1 Introduction
2. 1.2 Prediction of Diseases Using Machine Learning
3. 1.3 Materials and Methods
4. 1.4 Methods
5. 1.5 ML Algorithm and Their Results
6. 1.6 Support Vector Machine (SVM)
7. 1.7 Logistic Regression
8. 1.8 K Nearest Neighbor Algorithm (KNN)
9. 1.9 Naive Bayes
10. 1.10 Finding the Best Algorithm Using Experimenter Application
11. 1.11 Conclusion
12. 1.12 Future Scope
13. References
2. 2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by
Thresholding and Clustering
1. 2.1 Introduction
2. 2.2 Literature Review
3. 2.3 Dataset Used
4. 2.4 Proposed Method
5. 2.5 Experimental Analysis
6. 2.6 Conclusion
7. References
3. 3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with
Optimized Hyperparameters
1. 3.1 Introduction
2. 3.2 Literature Review
3. 3.3 Methodology
4. 3.4 Experiment and Results
5. 3.5 Conclusion
6. References
4. 4 Review of Classification and Feature Selection Methods for Genome-Wide Association
SNP for Breast Cancer
1. 4.1 Introduction
2. 4.2 Literature Analysis
3. 4.3 Comparison Analysis
4. 4.4 Issues of the Existing Works
5. 4.5 Experimental Results
6. 4.6 Conclusion and Future Work
7. References

, 5. 5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches
1. 5.1 Introduction
2. 5.2 Literature Survey
3. 5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches
4. 5.4 Results and Discussion
5. 5.5 Conclusion
6. References
6. 6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression
1. 6.1 Introduction
2. 6.2 Background
3. 6.3 Proposed Work
4. 6.4 Experimental Results
5. 6.5 Discussion and Conclusion
6. References
7. 7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor
Networks
1. 7.1 Introduction
2. 7.2 Literature Survey Based on Brain Tumor Detection Methods
3. 7.3 Literature Survey Based on WMSN
4. 7.4 Literature Survey Based on Data Fusion
5. 7.5 Conclusions
6. References
8. Part II: Data Analytics Applications
1. 8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air
Quality Prediction System
1. 8.1 Introduction
2. 8.2 Related Work
3. 8.3 Proposed Architecture for Air Quality Prediction System
4. 8.4 Results and Discussion
5. 8.5 Conclusion
6. References
2. 9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks
1. 9.1 Introduction
2. 9.2 Related Work
3. 9.3 K-Means Algorithm
4. 9.4 Data Partitioning
5. 9.5 Experimental Results
6. 9.6 Conclusion
7. Acknowledgments
8. References
3. 10 An Analysis on Detection and Visualization of Code Smells
1. 10.1 Introduction
2. 10.2 Literature Survey
3. 10.3 Code Smells
4. 10.4 Comparative Analysis
5. 10.5 Conclusion
6. References
4. 11 Leveraging Classification Through AutoML and Microservices
1. 11.1 Introduction
2. 11.2 Related Work
3. 11.3 Observations

, 4. 11.4 Conceptual Architecture
5. 11.5 Analysis of Results
6. 11.6 Results and Discussion
7. References
9. Part III: E-Learning Applications
1. 12 Virtual Teaching Activity Monitor
1. 12.1 Introduction
2. 12.2 Related Works
3. 12.3 Methodology
4. 12.4 Results and Discussion
5. 12.5 Conclusions
6. References
2. 13 AI-Based Development of Student E-Learning Framework
1. 13.1 Introduction
2. 13.2 Objective
3. 13.3 Literature Survey
4. 13.4 Proposed Student E-Learning Framework
5. 13.5 System Architecture
6. 13.6 Working Module Description
7. 13.7 Conclusion
8. 13.8 Future Enhancements
9. References
10. Part IV: Networks Application
1. 14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in
Wireless Sensor Networks
1. 14.1 Introduction
2. 14.2 Anomaly Detection in WSN
3. 14.3 Summary of Anomaly Detections Techniques Using Machine Learning
Algorithms
4. 14.4 Experimental Results and Challenges of Machine Learning Approaches
5. 14.5 Performance Evaluation
6. 14.6 Conclusion
7. References
2. 15 Unique and Random Key Generation Using Deep Convolutional Neural Network and
Genetic Algorithm for Secure Data Communication Over Wireless Network
1. 15.1 Introduction
2. 15.2 Literature Survey
3. 15.3 Proposed Work
4. 15.4 Genetic Algorithm (GA)
5. 15.5 Conclusion
6. References
11. Part V: Automotive Applications
1. 16 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries
of Electric Vehicles
1. 16.1 Introduction
2. 16.2 Battery State of Charge Prediction Using Non-Recurrent Neural Networks
3. 16.3 Evaluation of Charge Prediction Techniques
4. 16.4 Conclusion
5. References
2. 17 Driver Drowsiness Detection System
1. 17.1 Introduction