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    1. Data och IT
    2. Systemvetenskap och AI
    3. Artificiell intelligens

    Artificial Intelligence for Sustainable Applications

    AvK. Umamaheswari,B. Vinoth Kumar

    Inbunden, Engelska, 2023

    Del i serien Artificial Intelligence and Soft Computing for Industrial Transformation

    2 134 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    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.

    Produktinformation

    • Utgivningsdatum:2023-09-18
    • Mått:237 x 158 x 27 mm
    • Vikt:758 g
    • Format:Inbunden
    • Språk:Engelska
    • Serie:Artificial Intelligence and Soft Computing for Industrial Transformation
    • Antal sidor:368
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781394174584

    Utforska kategorier

    • Artificiell intelligens inom Data och IT

    Mer om författaren

    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.

    Innehållsförteckning

    • Preface xvPart I: Medical Applications 11 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis 3Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan1.1 Introduction 31.2 Prediction of Diseases Using Machine Learning 41.3 Materials and Methods 51.4 Methods 61.5 ML Algorithm and Their Results 71.6 Support Vector Machine (SVM) 111.7 Logistic Regression 111.8 K Nearest Neighbor Algorithm (KNN) 121.9 Naive Bayes 151.10 Finding the Best Algorithm Using Experimenter Application 171.11 Conclusion 181.12 Future Scope 192 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering 23Kavitha S. and Hannah Inbarani2.1 Introduction 232.2 Literature Review 242.3 Dataset Used 262.4 Proposed Method 262.5 Experimental Analysis 292.6 Conclusion 333 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters 37Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen3.1 Introduction 383.2 Literature Review 393.3 Methodology 413.4 Experiment and Results 463.5 Conclusion 514 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer 55L.R. Sujithra and A. Kuntha4.1 Introduction 564.2 Literature Analysis 584.3 Comparison Analysis 664.4 Issues of the Existing Works 704.5 Experimental Results 704.6 Conclusion and Future Work 735 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches 79Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth5.1 Introduction 795.2 Literature Survey 805.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches 815.4 Results and Discussion 835.5 Conclusion 866 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression 89Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen6.1 Introduction 906.2 Background 916.3 Proposed Work 986.4 Experimental Results 1046.5 Discussion and Conclusion 1107 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks 117John Nisha Anita and Sujatha Kumaran7.1 Introduction 1187.2 Literature Survey Based on Brain Tumor Detection Methods 1187.3 Literature Survey Based on WMSN 1227.4 Literature Survey Based on Data Fusion 1237.5 Conclusions 125Part II: Data Analytics Applications 1278 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System 129P. Vasantha Kumari and G. Sujatha8.1 Introduction 1308.2 Related Work 1338.3 Proposed Architecture for Air Quality Prediction System 1348.4 Results and Discussion 1408.5 Conclusion 1459 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks 147R. Tamilselvan, A. Prabhu and R. Rajagopal9.1 Introduction 1489.2 Related Work 1499.3 K-Means Algorithm 1519.4 Data Partitioning 1529.5 Experimental Results 1549.6 Conclusion 15910 An Analysis on Detection and Visualization of Code Smells 163Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gupta and Sandeep Kumar M.10.1 Introduction 16410.2 Literature Survey 16510.3 Code Smells 16810.4 Comparative Analysis 17010.5 Conclusion 17411 Leveraging Classification Through AutoML and Microservices 177M. Keerthivasan and V. Krishnaveni11.1 Introduction 17811.2 Related Work 17911.3 Observations 18111.4 Conceptual Architecture 18111.5 Analysis of Results 19011.6 Results and Discussion 193Part III: E-Learning Applications 19712 Virtual Teaching Activity Monitor 199Sakthivel S. and Akash Ram R.K.12.1 Introduction 19912.2 Related Works 20312.3 Methodology 20612.4 Results and Discussion 21312.5 Conclusions 21513 AI-Based Development of Student E-Learning Framework 219S. Jeyanthi, C. Sathya, N. Uma Maheswari, R. Venkatesh and V. Ganapathy Subramanian13.1 Introduction 22013.2 Objective 22013.3 Literature Survey 22113.4 Proposed Student E-Learning Framework 22213.5 System Architecture 22313.6 Working Module Description 22413.7 Conclusion 22813.8 Future Enhancements 228Part IV: Networks Application 23114 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks 233Arul Jothi S. and Venkatesan R.14.1 Introduction 23414.2 Anomaly Detection in WSN 23614.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms 23714.4 Experimental Results and Challenges of Machine Learning Approaches 23814.5 Performance Evaluation 24414.6 Conclusion 24615 Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network 249S. Venkatesan, M. Ramakrishnan and M. Archana15.1 Introduction 25015.2 Literature Survey 25215.3 Proposed Work 25315.4 Genetic Algorithm (GA) 25315.5 Conclusion 261Part V: Automotive Applications 26516 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles 267R. Arun Chendhuran and J. Senthil Kumar16.1 Introduction 26716.2 Battery State of Charge Prediction Using Non-Recurrent Neural Networks 268 16.3 Evaluation of Charge Prediction Techniques 27216.3 Conclusion 27317 Driver Drowsiness Detection System 275G. Lavanya, N. Sunand, S. Gokulraj and T.G. Chakaravarthi17.1 Introduction 27517.2 Literature Survey 27617.3 Components and Methodology 27717.4 Conclusion 281Part VI: Security Applications 28318 An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning 285Arul Treesa Mathew and Prasanna Mani18.1 Introduction 28518.2 Related Literature 28618.3 Proposed Model 29118.4 Conclusions and Future Works 29219 A Research on Lattice-Based Homomorphic Encryption Schemes 295Anitha Kumari K., Prakaashini S. and Suresh Shanmugasundaram19.1 Introduction 29519.2 Overview of Lattice-Based HE 29619.3 Applications of Lattice HE 29919.4 NTRU Scheme 30119.5 GGH Signature Scheme 30319.6 Related Work 30419.5 Conclusion 30820 Biometrics with Blockchain: A Better Secure Solution for Template Protection 311P. Jayapriya, K. Umamaheswari and S. Sathish Kumar20.1 Introduction 31120.2 Blockchain Technology 31320.3 Biometric Architecture 31720.4 Blockchain in Biometrics 32020.4.1 Template Storage Techniques 32220.5 Conclusion 324References 324Index 329