Details

Autor(en) / Beteiligte

• Umamaheswari, K
• Kumar, B. Vinoth
• Somasundaram, S. K

Titel

Artificial Intelligence for Sustainable Applications

Auflage

1st ed

Ort / Verlag

Newark : John Wiley & Sons, Incorporated,

Erscheinungsjahr

2023

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Chapter 7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks
• Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Medical Applications -- Chapter 1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms - An Analysis -- 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 -- Chapter 2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering -- 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 -- Chapter 3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters -- 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 -- Chapter 4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer -- 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 -- Chapter 5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches -- 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 -- Chapter 6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression -- 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 -- Chapter 7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks -- 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 -- Chapter 8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System -- 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 -- Chapter 9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks -- 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 -- Chapter 10 An Analysis on Detection and Visualization of Code Smells -- 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 -- Chapter 11 Leveraging Classification Through AutoML and Microservices -- 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 -- Chapter 12 Virtual Teaching Activity Monitor -- 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 -- Chapter 13 AI-Based Development of Student E-Learning Framework -- 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 -- Chapter 14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks -- 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 -- Chapter 15 Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network -- 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 -- Chapter 16 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles -- 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 -- Chapter 17 Driver Drowsiness Detection System -- 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 -- Chapter 18 An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning -- 18.1 Introduction -- 18.2 Related Literature -- 18.3 Proposed Model -- 18.3.1 Proposed System Architecture -- 18.4 Conclusions and Future Works -- References -- Chapter 19 A Research on Lattice-Based Homomorphic Encryption Schemes -- 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 -- Chapter 20 Biometrics with Blockchain: A Better Secure Solution for Template Protection -- 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 -- EULA.
• 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.
• Description based on publisher supplied metadata and other sources.