5th International Conference on Wireless, Intelligent and Distributed Environment for Communication: WIDECOM 2022 (Lecture Notes on Data Engineering and Communications Technologies, 174)

Welcome Message from WIDECOM 2022 General Chair
Welcome Message from the WIDECOM 2022 Program Chair
Contents
1 Efficient Fake News Detection Method Using Feature Reduction
	1.1 Introduction
		1.1.1 History of Fake News Detection
		1.1.2 History of Feature Reduction
	1.2 Related Works
		1.2.1 Traditional Machine Learning Approaches
		1.2.2 Deep Learning Approaches
	1.3 Proposed Work
		1.3.1 Formal Definition
		1.3.2 Details
	1.4 Experimentation
	1.5 Results and Discussion
	1.6 Conclusion and Future Scope
	References
2 Using Convolutional Neural Network to Enhance Coronary Heart Disease Predictions in South African Men Living in the Western Cape Region
	2.1 Introduction
	2.2 Literature Review
	2.3 The Proposed Heart Disease Prediction System
		2.3.1 Data Collection
		2.3.2 Data Selection
		2.3.3 Feature Selection
		2.3.4 Data Splitting
		2.3.5 Training the Models
			2.3.5.1 Logistic Regression
			2.3.5.2 Random Forest
			2.3.5.3 K-Nearest Neighbor
			2.3.5.4 Support Vector Machine (SVM)
			2.3.5.5 Convolutional Neural Network
		2.3.6 Assessing Models
	2.4 Experiments
		2.4.1 Experimental Setup
			2.4.1.1 Data Set
			2.4.1.2 Data Type
	2.5 Conclusion
	References
3 On the Performance of Codebook Designs on PD-SCMA Cognitive Radio Networks
	3.1 Introduction
	3.2 System Model
	3.3 Multi-dimensional Constellation Designs
		3.3.1 LDS Codebooks
		3.3.2 TM-QAM Codebooks
		3.3.3 ML-QAM Codebooks
		3.3.4 MC-QAM Codebooks
		3.3.5 Average Symbol Energy-Based Codebooks
	3.4 Performance Evaluation of the MDCs on PD-SCMA
	3.5 Conclusion
	References
4 T-AES- and ECC-Based Secure Data Communication in Peer-to-Peer Networks
	4.1 Introduction
	4.2 Literature Review
	4.3 Proposed Work
		4.3.1 Diffie–Hellman Key Exchange Protocol
		4.3.2 Sender Side
		4.3.3 Receiver Side
		4.3.4 The Proposed T-AES Scheme
		4.3.5 ECC
	4.4 Simulation and Results
		4.4.1 Security Analysis
	4.5 Conclusions
	References
5 Effective Fatigue Driving Detection by Machine Learning
	5.1 Introduction
	5.2 Related Works
		5.2.1 Facial Feature Landmark Model
		5.2.2 Facial Recognition Model: Facenet-Inception-Resnet
		5.2.3 Facial Motion Information Entropy Algorithm
		5.2.4 One-Dimensional Convolutional Neural Network (1D CNN)
		5.2.5 Time Series Analysis Model: LSTM
	5.3 Proposed Method
		5.3.1 Thermal Imaging and MTCNN for Real-Time Face Recognition
		5.3.2 Obtaining Yawning and Eye-Opening/Closing Features
		5.3.3 Facial Rotation Analysis
		5.3.4 Facial Recognition
		5.3.5 Information Entropy of Facial Motion for Data Reduction
	5.4 Machine Learning Models
	5.5 Conclusion
	References
6 Trust-Based Mechanism for Secure Communication in Fog-Based IoT
	6.1 Introduction
	6.2 Literature Review
	6.3 System Model
		6.3.1 Motivation
		6.3.2 Proposed Model
		6.3.3 Algorithm of the Proposed Model
	6.4 Simulation and Analysis
		6.4.1 Results and Analysis
	6.5 Conclusion and Future Work
	References
7 Feature Selections for Detecting Intrusions on the Internet of Medical Things
	7.1 Introduction
	7.2 Related Works
	7.3 Descriptive Statistics on WUSTL-EHMS-2020 Datasets
	7.4 Logistic Regression Model for Feature Selection
	7.5 Predicting Intrusions Using Selected Features
	7.6 Conclusions and Future Work
	References
8 Weighted Voting Stacking Ensemble Method for Highly Skewed Binary Data Distribution
	8.1 Introduction
		8.1.1 Stacking Algorithm
			8.1.1.1 Weighted Voting Method
	8.2 Research Questions and Objectives
		8.2.1 Research Questions
		8.2.2 Research Objectives
	8.3 Research Design
		8.3.1 Stacking Ensemble of Base Models
		8.3.2 Probability Data Dictionary
	8.4 Simulation Results
		8.4.1 In Accuracy of Base Models
		8.4.2 Confusion Matrix
		8.4.3 Optimized Stacking Ensemble Base Models
			8.4.3.1 True Accuracy of Base Models
			8.4.3.2 True Confusion Matrix
		8.4.4 Stacking Ensemble Method
			8.4.4.1 Stacking Ensemble Method Accuracy
			8.4.4.2 Confusion Matrix of Stacking Ensemble
			8.4.4.3 Confusion Matrix Statistics of Stacking Ensemble
	8.5 Results
	8.6 Conclusion
	References
9 Hyperparameter Tuning for an Enhanced Self-Attention-Based Actor-Critical DDPG Framework
	9.1 Introduction
	9.2 Related Works
	9.3 Proposed Work
	9.4 Experiments and Results
		9.4.1 Experimental Setup
		9.4.2 Evaluation Metric
		9.4.3 Implementation Details
		9.4.4 Results
	9.5 Conclusion and Future Scope
	References
10 EBC: Encounter Buffer and Contact Duration-Based Routing Protocol in Opportunistic Networks
	10.1 Introduction
	10.2 Related Work
	10.3 System Model
	10.4 Evaluation
	10.5 Conclusion
	References
11 Socioeconomic Inequality Exacerbated by COVID-19: A Multiple Regression Analysis with Income, Healthcare Insurance, and Mask Use
	11.1 Introduction
	11.2 Related Works
	11.3 Datasets on COVID-19, Mask Use, Income, and Insurance
	11.4 Data Visualization
	11.5 Multiple Regression Modeling
	11.6 Conclusions and Future Work
	Appendix
	References
12 Cellular Communication Network Evolution and the Reliability of System Design from 1G to 6G
	12.1 Introduction
	12.2 Overview of the Cellular Generations
		12.2.1 First Generation (1G)
		12.2.2 Second Generation (2G)
		12.2.3 Third Generation (3G)
		12.2.4 Fourth Generation (4G)
		12.2.5 Fifth Generation (5G)
		12.2.6 Sixth Generation (6G)
	12.3 Dependability Paradigms
		12.3.1 Cell Selection, Reselection, and Handover
		12.3.2 Cell Selection Criteria (S Criterion)
		12.3.3 Intrafrequency and Interfrequency Cell Reselection Process
		12.3.4 Measurement Rules and Power Savings During Cell Reselection
		12.3.5 Adjustments for Speedy UEs
	12.4 Strategies for Reliability and Network Upgradation
		12.4.1 Increased Reliability in Handover Procedure
		12.4.2 Handover Strategy for Improved Reliability in 3G, 4G, and 5G
		12.4.3 Commencement of Handover Process and Requirements
		12.4.4 Different Procedures to Enhance Packet Loss Mitigation
	12.5 Modifications, Calculations, and Multiple Access Techniques
		12.5.1 Beamforming
		12.5.2 New RRC State
		12.5.3 Higher Spectral Efficiency (from FDMA to CDMA)
		12.5.4 Better Utilization of Spectrum (from CDMA to OFDMA)
		12.5.5 Superior Performance and Adaptability from CDMA to OFDMA
		12.5.6 Control over Uplink Power Transmission in OFDMA
	12.6 Software Simulation Results and Discussion
	12.7 Conclusion
	References
13 Artificial Intelligence-Based Method for Smart Manufacturing in Industrial Internet of Things Network
	13.1 Introduction
	13.2 Literature Review of AI-IIoT in Smart Manufacturing
	13.3 Proposed Approach
		13.3.1 Motivation
		13.3.2 Proposed Methodology for AI-IIoT Framework
		13.3.3 Data Flow Cycle for the Proposed Model
		13.3.4 Algorithm for the Proposed Model
	13.4 Experimental Setup and Results
		13.4.1 Tools and Simulators Used
		13.4.2 Build an Engine Analytical Model
		13.4.3 Evaluation of the Proposed Model
		13.4.4 Result Analysis and Discussion
		13.4.5 Results on Varying Number of Sensors
		13.4.6 Results on Varying Number of Records
	13.5 Conclusion and Future Scope
	References
Index