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Submit a ProposalArtificial Intelligence for Sustainable Applications
 | Edited by K. Umamaheswari, B. Vinoth Kumar and S. K. Somasundaram
Series: Artificial Intelligence and Soft Computing for Industrial Transformation Copyright: 2023 | Status: Published ISBN: 9781394174584 | Hardcover | 356 pages | 115 illustrations Price: $225 USD  |
One Line Description
AI researchers as well as engineers in information technology and computer science.
Audience
AI researchers as well as engineers in information technology and computer science.
AI researchers as well as engineers in information technology and computer science.
Description
With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), 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. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore.
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.
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With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), 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. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore.
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.
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Author / Editor Details
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.
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.
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Table of Contents
Preface
Part I: Medical Applications
1. Predictive Models of Alzheimer’s Disease Using Machine Learning Algorithms – An Analysis
Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan
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
2. Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering
Kavitha S. and Hannah Inbarani
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
3. Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters
Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen
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
4. Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer
L.R. Sujithra and A. Kuntha
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
5. COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches
Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth
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
6. Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression
Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen
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
7. A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks
John Nisha Anita and Sujatha Kumaran
7.1 Introduction
7.1.1 Survey on Brain Tumor Detection Methods
7.1.2 Survey on WMSN
7.1.3 Survey on Data Fusion
7.2 Literature Survey Based on Brain Tumor Detection Methods
7.3 Literature Survey Based on WMSN
7.4 Literature Survey Based on Data Fusion
7.5 Conclusions
References
Part II: Data Analytics Applications
8. An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System
P. Vasantha Kumari and G. Sujatha
8.1 Introduction
8.1.1 Air Pollutants
8.1.2 AQI (Air Quality Index)
8.2 Related Work
8.3 Proposed Architecture for Air Quality Prediction System
8.3.1 Data Splitting Layer
8.3.2 Data Layer
8.4 Results and Discussion
8.5 Conclusion
References
9. An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks
R. Tamilselvan, A. Prabhu and R. Rajagopal
9.1 Introduction
9.2 Related Work
9.3 K-Means Algorithm
9.4 Data Partitioning
9.5 Experimental Results
9.5.1 Datasets
9.5.2 Performance Analysis
9.5.3 Approximation on Real-World Datasets
9.6 Conclusion
Acknowledgments
References
10. An Analysis on Detection and Visualization of Code Smells
Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gupta and Sandeep Kumar M.
10.1 Introduction
10.2 Literature Survey
10.2.1 Machine Learning-Based Techniques
10.2.2 Code Smell Characteristics in Different Computer Languages
10.3 Code Smells
10.4 Comparative Analysis
10.5 Conclusion
References
11. Leveraging Classification Through AutoML and Microservices
M. Keerthivasan and V. Krishnaveni
11.1 Introduction
11.2 Related Work
11.3 Observations
11.4 Conceptual Architecture
11.5 Analysis of Results
11.6 Results and Discussion
References
Part III: E-Learning Applications
12. Virtual Teaching Activity Monitor
Sakthivel S. and Akash Ram R.K.
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.3.1 Head Movement
12.3.2 Drowsiness and Yawn Detection
12.3.3 Attendance System
12.3.4 Network Speed
12.3.5 Text Classification
12.4 Results and Discussion
12.5 Conclusions
References
13. AI-Based Development of Student E-Learning Framework
S. Jeyanthi, C. Sathya, N. Uma Maheswari, R. Venkatesh and V. Ganapathy Subramanian
13.1 Introduction
13.2 Objective
13.3 Literature Survey
13.4 Proposed Student E-Learning Framework
13.5 System Architecture
13.6 Working Module Description
13.6.1 Data Preprocessing
13.6.2 Driving Test Cases
13.6.3 System Analysis
13.7 Conclusion
13.8 Future Enhancements
References
Part IV: Networks Application
14. A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks
Arul Jothi S. and Venkatesan R.
14.1 Introduction
14.1.1 Data Aggregation in WSNs
14.1.2 Anomalies
14.2 Anomaly Detection in WSN
14.2.1 Need for Anomaly Detection in WSNs
14.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms
14.3.1 Data Dimension Reduction
14.3.2 Adaptability with Dynamic Data Changes
14.3.3 Correlation Exploitation
14.4 Experimental Results and Challenges of Machine Learning Approaches
14.4.1 Data Exploration
14.4.1.1 Pre-Processing and Dimensionality Reduction
14.4.1.2 Clustering
14.4.2 Outlier Detection
14.4.2.1 Neural Network
14.4.2.2 Support Vector Machine (SVM)
14.4.2.3 Bayesian Network
14.5 Performance Evaluation
14.6 Conclusion
References
15. Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network
S. Venkatesan, M. Ramakrishnan and M. Archana
15.1 Introduction
15.2 Literature Survey
15.3 Proposed Work
15.4 Genetic Algorithm (GA)
15.4.1 Selection
15.4.2 Crossover
15.4.3 Mutation
15.4.4 ECDH Algorithm
15.4.5 ECDH Key Exchange
15.4.6 DCNN
15.4.7 Results
15.5 Conclusion
References
Part V: Automotive Applications
16. Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles
R. Arun Chendhuran and J. Senthil Kumar
16.1 Introduction
16.2 Battery State of Charge Prediction Using Non‑Recurrent Neural Networks
16.2.1 Feed-Forward Neural Network
16.2.2 Radial Basis Function Neural Network
16.2.3 Extreme Learning Machine
16.2.4 Support Vector Machine
16.3 Evaluation of Charge Prediction Techniques
16.3 Conclusion
References
17. Driver Drowsiness Detection System
G. Lavanya, N. Sunand, S. Gokulraj and T.G. Chakaravarthi
17.1 Introduction
17.2 Literature Survey
17.2.1 Reports on Driver’s Fatigue Behind the Steering Wheel
17.2.2 Survey on Camera-Based Drowsiness Classification
17.2.3 Survey on Ear for Drowsy Detection
17.3 Components and Methodology
17.3.1 Software (Toolkit Used)
17.3.2 Hardware Components
17.3.3 Logitech C270 HD Webcam
17.3.4 Eye Aspect Ratio (EAR)
17.3.5 Mouth Aspect Ratio (MAR)
17.3.6 Working Principle
17.3.7 Facial Landmark Detection and Measure Eye Aspect Ratio and Mouth Aspect Ratio
17.3.8 Results Obtained
17.4 Conclusion
References
Part VI: Security Applications
18. An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning
Arul Treesa Mathew and Prasanna Mani
18.1 Introduction
18.2 Related Literature
18.3 Proposed Model
18.3.1 Proposed System Architecture
18.4 Conclusions and Future Works
References
19. A Research on Lattice-Based Homomorphic Encryption Schemes
Anitha Kumari K., Prakaashini S. and Suresh Shanmugasundaram
19.1 Introduction
19.2 Overview of Lattice-Based HE
19.3 Applications of Lattice HE
19.4 NTRU Scheme
19.5 GGH Signature Scheme
19.6 Related Work
19.5 Conclusion
References
20. Biometrics with Blockchain: A Better Secure Solution for Template Protection
P. Jayapriya, K. Umamaheswari and S. Sathish Kumar
20.1 Introduction
20.2 Blockchain Technology
20.3 Biometric Architecture
20.4 Blockchain in Biometrics
20.4.1 Template Storage Techniques
20.5 Conclusion
References
Index
Back to Top
Preface
Part I: Medical Applications
1. Predictive Models of Alzheimer’s Disease Using Machine Learning Algorithms – An Analysis
Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan
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
2. Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering
Kavitha S. and Hannah Inbarani
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
3. Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters
Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen
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
4. Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer
L.R. Sujithra and A. Kuntha
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
5. COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches
Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth
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
6. Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression
Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen
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
7. A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks
John Nisha Anita and Sujatha Kumaran
7.1 Introduction
7.1.1 Survey on Brain Tumor Detection Methods
7.1.2 Survey on WMSN
7.1.3 Survey on Data Fusion
7.2 Literature Survey Based on Brain Tumor Detection Methods
7.3 Literature Survey Based on WMSN
7.4 Literature Survey Based on Data Fusion
7.5 Conclusions
References
Part II: Data Analytics Applications
8. An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System
P. Vasantha Kumari and G. Sujatha
8.1 Introduction
8.1.1 Air Pollutants
8.1.2 AQI (Air Quality Index)
8.2 Related Work
8.3 Proposed Architecture for Air Quality Prediction System
8.3.1 Data Splitting Layer
8.3.2 Data Layer
8.4 Results and Discussion
8.5 Conclusion
References
9. An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks
R. Tamilselvan, A. Prabhu and R. Rajagopal
9.1 Introduction
9.2 Related Work
9.3 K-Means Algorithm
9.4 Data Partitioning
9.5 Experimental Results
9.5.1 Datasets
9.5.2 Performance Analysis
9.5.3 Approximation on Real-World Datasets
9.6 Conclusion
Acknowledgments
References
10. An Analysis on Detection and Visualization of Code Smells
Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gupta and Sandeep Kumar M.
10.1 Introduction
10.2 Literature Survey
10.2.1 Machine Learning-Based Techniques
10.2.2 Code Smell Characteristics in Different Computer Languages
10.3 Code Smells
10.4 Comparative Analysis
10.5 Conclusion
References
11. Leveraging Classification Through AutoML and Microservices
M. Keerthivasan and V. Krishnaveni
11.1 Introduction
11.2 Related Work
11.3 Observations
11.4 Conceptual Architecture
11.5 Analysis of Results
11.6 Results and Discussion
References
Part III: E-Learning Applications
12. Virtual Teaching Activity Monitor
Sakthivel S. and Akash Ram R.K.
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.3.1 Head Movement
12.3.2 Drowsiness and Yawn Detection
12.3.3 Attendance System
12.3.4 Network Speed
12.3.5 Text Classification
12.4 Results and Discussion
12.5 Conclusions
References
13. AI-Based Development of Student E-Learning Framework
S. Jeyanthi, C. Sathya, N. Uma Maheswari, R. Venkatesh and V. Ganapathy Subramanian
13.1 Introduction
13.2 Objective
13.3 Literature Survey
13.4 Proposed Student E-Learning Framework
13.5 System Architecture
13.6 Working Module Description
13.6.1 Data Preprocessing
13.6.2 Driving Test Cases
13.6.3 System Analysis
13.7 Conclusion
13.8 Future Enhancements
References
Part IV: Networks Application
14. A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks
Arul Jothi S. and Venkatesan R.
14.1 Introduction
14.1.1 Data Aggregation in WSNs
14.1.2 Anomalies
14.2 Anomaly Detection in WSN
14.2.1 Need for Anomaly Detection in WSNs
14.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms
14.3.1 Data Dimension Reduction
14.3.2 Adaptability with Dynamic Data Changes
14.3.3 Correlation Exploitation
14.4 Experimental Results and Challenges of Machine Learning Approaches
14.4.1 Data Exploration
14.4.1.1 Pre-Processing and Dimensionality Reduction
14.4.1.2 Clustering
14.4.2 Outlier Detection
14.4.2.1 Neural Network
14.4.2.2 Support Vector Machine (SVM)
14.4.2.3 Bayesian Network
14.5 Performance Evaluation
14.6 Conclusion
References
15. Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network
S. Venkatesan, M. Ramakrishnan and M. Archana
15.1 Introduction
15.2 Literature Survey
15.3 Proposed Work
15.4 Genetic Algorithm (GA)
15.4.1 Selection
15.4.2 Crossover
15.4.3 Mutation
15.4.4 ECDH Algorithm
15.4.5 ECDH Key Exchange
15.4.6 DCNN
15.4.7 Results
15.5 Conclusion
References
Part V: Automotive Applications
16. Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles
R. Arun Chendhuran and J. Senthil Kumar
16.1 Introduction
16.2 Battery State of Charge Prediction Using Non‑Recurrent Neural Networks
16.2.1 Feed-Forward Neural Network
16.2.2 Radial Basis Function Neural Network
16.2.3 Extreme Learning Machine
16.2.4 Support Vector Machine
16.3 Evaluation of Charge Prediction Techniques
16.3 Conclusion
References
17. Driver Drowsiness Detection System
G. Lavanya, N. Sunand, S. Gokulraj and T.G. Chakaravarthi
17.1 Introduction
17.2 Literature Survey
17.2.1 Reports on Driver’s Fatigue Behind the Steering Wheel
17.2.2 Survey on Camera-Based Drowsiness Classification
17.2.3 Survey on Ear for Drowsy Detection
17.3 Components and Methodology
17.3.1 Software (Toolkit Used)
17.3.2 Hardware Components
17.3.3 Logitech C270 HD Webcam
17.3.4 Eye Aspect Ratio (EAR)
17.3.5 Mouth Aspect Ratio (MAR)
17.3.6 Working Principle
17.3.7 Facial Landmark Detection and Measure Eye Aspect Ratio and Mouth Aspect Ratio
17.3.8 Results Obtained
17.4 Conclusion
References
Part VI: Security Applications
18. An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning
Arul Treesa Mathew and Prasanna Mani
18.1 Introduction
18.2 Related Literature
18.3 Proposed Model
18.3.1 Proposed System Architecture
18.4 Conclusions and Future Works
References
19. A Research on Lattice-Based Homomorphic Encryption Schemes
Anitha Kumari K., Prakaashini S. and Suresh Shanmugasundaram
19.1 Introduction
19.2 Overview of Lattice-Based HE
19.3 Applications of Lattice HE
19.4 NTRU Scheme
19.5 GGH Signature Scheme
19.6 Related Work
19.5 Conclusion
References
20. Biometrics with Blockchain: A Better Secure Solution for Template Protection
P. Jayapriya, K. Umamaheswari and S. Sathish Kumar
20.1 Introduction
20.2 Blockchain Technology
20.3 Biometric Architecture
20.4 Blockchain in Biometrics
20.4.1 Template Storage Techniques
20.5 Conclusion
References
Index
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