Artificial Intelligence for Sustainable Applications

Artificial Intelligence for Sustainable Applications

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...

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Detalles Bibliográficos
Autor principal: Umamaheswari, K. (-)
Otros Autores: Kumar, B. Vinoth, Somasundaram, S. K.
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2023.
Edición:1st ed
Colección:Artificial Intelligence and Soft Computing for Industrial Transformation Series
Materias:
Artificial intelligence.
Information technology.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811332106719
Tabla de Contenidos:
  • 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.

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