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Artificial Intelligence for Sustainable Applications

Artificial Intelligence and Soft Computing for Industrial Transformation 1. Aufl.

von: K. Umamaheswari, B. Vinoth Kumar, S. K. Somasundaram
168,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	22.08.2023
ISBN/EAN:	9781394175239
Sprache:	englisch
Anzahl Seiten:	368

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Beschreibungen

Titelbeschreibung

ARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS 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.

Inhaltsverzeichnis

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 268 16.3 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

Autorenportrait

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.

Back cover copy

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.