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 (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.
Audience
AI researchers as well as engineers in information technology and computer science.
Table of Contents
Preface xv
Part I: Medical Applications 1
1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis 3
Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan
1.1 Introduction 3
1.2 Prediction of Diseases Using Machine Learning 4
1.3 Materials and Methods 5
1.4 Methods 6
1.5 ML Algorithm and Their Results 7
1.6 Support Vector Machine (SVM) 11
1.7 Logistic Regression 11
1.8 K Nearest Neighbor Algorithm (KNN) 12
1.9 Naive Bayes 15
1.10 Finding the Best Algorithm Using Experimenter Application 17
1.11 Conclusion 18
1.12 Future Scope 19
2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering 23
Kavitha S. and Hannah Inbarani
2.1 Introduction 23
2.2 Literature Review 24
2.3 Dataset Used 26
2.4 Proposed Method 26
2.5 Experimental Analysis 29
2.6 Conclusion 33
3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters 37
Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen
3.1 Introduction 38
3.2 Literature Review 39
3.3 Methodology 41
3.4 Experiment and Results 46
3.5 Conclusion 51
4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer 55
L.R. Sujithra and A. Kuntha
4.1 Introduction 56
4.2 Literature Analysis 58
4.3 Comparison Analysis 66
4.4 Issues of the Existing Works 70
4.5 Experimental Results 70
4.6 Conclusion and Future Work 73
5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches 79
Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth
5.1 Introduction 79
5.2 Literature Survey 80
5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches 81
5.4 Results and Discussion 83
5.5 Conclusion 86
6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression 89
Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen
6.1 Introduction 90
6.2 Background 91
6.3 Proposed Work 98
6.4 Experimental Results 104
6.5 Discussion and Conclusion 110
7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks 117
John Nisha Anita and Sujatha Kumaran
7.1 Introduction 118
7.2 Literature Survey Based on Brain Tumor Detection Methods 118
7.3 Literature Survey Based on WMSN 122
7.4 Literature Survey Based on Data Fusion 123
7.5 Conclusions 125
Part II: Data Analytics Applications 127
8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System 129
P. Vasantha Kumari and G. Sujatha
8.1 Introduction 130
8.2 Related Work 133
8.3 Proposed Architecture for Air Quality Prediction System 134
8.4 Results and Discussion 140
8.5 Conclusion 145
9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks 147
R. Tamilselvan, A. Prabhu and R. Rajagopal
9.1 Introduction 148
9.2 Related Work 149
9.3 K-Means Algorithm 151
9.4 Data Partitioning 152
9.5 Experimental Results 154
9.6 Conclusion 159
10 An Analysis on Detection and Visualization of Code Smells 163
Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gupta and Sandeep Kumar M.
10.1 Introduction 164
10.2 Literature Survey 165
10.3 Code Smells 168
10.4 Comparative Analysis 170
10.5 Conclusion 174
11 Leveraging Classification Through AutoML and Microservices 177
M. Keerthivasan and V. Krishnaveni
11.1 Introduction 178
11.2 Related Work 179
11.3 Observations 181
11.4 Conceptual Architecture 181
11.5 Analysis of Results 190
11.6 Results and Discussion 193
Part III: E-Learning Applications 197
12 Virtual Teaching Activity Monitor 199
Sakthivel S. and Akash Ram R.K.
12.1 Introduction 199
12.2 Related Works 203
12.3 Methodology 206
12.4 Results and Discussion 213
12.5 Conclusions 215
13 AI-Based Development of Student E-Learning Framework 219
S. Jeyanthi, C. Sathya, N. Uma Maheswari, R. Venkatesh and V. Ganapathy Subramanian
13.1 Introduction 220
13.2 Objective 220
13.3 Literature Survey 221
13.4 Proposed Student E-Learning Framework 222
13.5 System Architecture 223
13.6 Working Module Description 224
13.7 Conclusion 228
13.8 Future Enhancements 228
Part IV: Networks Application 231
14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks 233
Arul Jothi S. and Venkatesan R.
14.1 Introduction 234
14.2 Anomaly Detection in WSN 236
14.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms 237
14.4 Experimental Results and Challenges of Machine Learning Approaches 238
14.5 Performance Evaluation 244
14.6 Conclusion 246
15 Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network 249
S. Venkatesan, M. Ramakrishnan and M. Archana
15.1 Introduction 250
15.2 Literature Survey 252
15.3 Proposed Work 253
15.4 Genetic Algorithm (GA) 253
15.5 Conclusion 261
Part V: Automotive Applications 265
16 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles 267
R. Arun Chendhuran and J. Senthil Kumar
16.1 Introduction 267
16.2 Battery State of Charge Prediction Using Non
-Recurrent Neural Networks 26816.3 Evaluation of Charge Prediction Techniques 272
16.3 Conclusion 273
17 Driver Drowsiness Detection System 275
G. Lavanya, N. Sunand, S. Gokulraj and T.G. Chakaravarthi
17.1 Introduction 275
17.2 Literature Survey 276
17.3 Components and Methodology 277
17.4 Conclusion 281
Part VI: Security Applications 283
18 An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning 285
Arul Treesa Mathew and Prasanna Mani
18.1 Introduction 285
18.2 Related Literature 286
18.3 Proposed Model 291
18.4 Conclusions and Future Works 292
19 A Research on Lattice-Based Homomorphic Encryption Schemes 295
Anitha Kumari K., Prakaashini S. and Suresh Shanmugasundaram
19.1 Introduction 295
19.2 Overview of Lattice-Based HE 296
19.3 Applications of Lattice HE 299
19.4 NTRU Scheme 301
19.5 GGH Signature Scheme 303
19.6 Related Work 304
19.5 Conclusion 308
20 Biometrics with Blockchain: A Better Secure Solution for Template Protection 311
P. Jayapriya, K. Umamaheswari and S. Sathish Kumar
20.1 Introduction 311
20.2 Blockchain Technology 313
20.3 Biometric Architecture 317
20.4 Blockchain in Biometrics 320
20.4.1 Template Storage Techniques 322
20.5 Conclusion 324
References 324
Index 329
