Computational Intelligence in Recent Communication Networks (EAI/Springer Innovations in Communication and Computing)

Preface
Contents
About the Editors
1 An Overview of Blockchain and 5G Networks
	1.1 Introduction
	1.2 Background
		1.2.1 Blockchain
			1.2.1.1 Blockchain Taxonomy
			1.2.1.2 Blockchain Platform Types
			1.2.1.3 Blockchain Consensus
			1.2.1.4 Blockchain Smart Contract
			1.2.1.5 Blockchain Sharding
			1.2.1.6 Blockchain Oracle
	1.3 5G Networks and Beyond: An Overview
		1.3.1 Software-Defined Networking (SDN)
		1.3.2 Network Function Virtualization (NFV)
		1.3.3 Network Slicing
		1.3.4 Multi-Access Edge Computing (MEC)
		1.3.5 Device to Device (D2D)
		1.3.6 Cloud Computing (CC)
	1.4 Blockchain for 5G
		1.4.1 Blockchain Integration with 5G Networks
		1.4.2 Opportunities Brought by Blockchain Integration with 5G Networks
			1.4.2.1 Security Improvements
			1.4.2.2 Performance Enhancements
	1.5 A Scalable and Secure Blockchain Suitable for 5G
		1.5.1 A Scalable and Secure Blockchain Architecture Suitable for 5G
		1.5.2 Architecture
			1.5.2.1 Shared Blockchain
			1.5.2.2 Peer-to-Peer Oracle Network
		1.5.3 Design Components
			1.5.3.1 Initialization
			1.5.3.2 Reward
	1.6 Challenges and Future Research Directions
		1.6.1 Scalability and Performance
		1.6.2 Standardization and Regulations
		1.6.3 Resource Constraints
		1.6.4 Interoperability
		1.6.5 Security
		1.6.6 Infrastructure Costs
	1.7 Conclusion
	References
2 Deep Learning Approach for Interference Mitigation in MIMO-FBMC/OQAM Systems
	2.1 Introduction
	2.2 MIMO-FBMC/OQAM System Model
	2.3 Problem Formulation
	2.4 Deep Neural Network for Blind Detection and Interference Mitigation in MIMO-FBMC/OQAM Systems
		2.4.1 Data Set
		2.4.2 Learning Rule
	2.5 Simulation Results
		2.5.1 Deep Neural Network Performance: RMSE and Loss
		2.5.2 Bit Error Rate
	2.6 Conclusion
	References
3 Deep Learning-Based Non-orthogonal Multiple Access for 5G and Beyond Networks
	3.1 Introduction
	3.2 System Model
		3.2.1 Principle
		3.2.2 NOMA for Downlink
		3.2.3 NOMA for Uplink
		3.2.4 Imperfection in NOMA
		3.2.5 Spectral and Energy Efficiency
	3.3 Overview of Deep Learning Models
		3.3.1 Deep Neural Network
		3.3.2 Convolutional Neural Network
		3.3.3 Recurrent Neural Network
	3.4 Deep Learning-Based NOMA
	3.5 Conclusion
	References
4 Traffic Sign Detection: A Comparative Study Between CNN and RNN
	4.1 Introduction
	4.2 Materials and Methods
		4.2.1 Convolutional Neural Networks
			4.2.1.1 Multilayer Neural Networks
			4.2.1.2 Feed-Forward Neural Network
			4.2.1.3 Learning and Training
			4.2.1.4 Structure of a Convolutional Neural Network
			4.2.1.5 Convolutional Layers
			4.2.1.6 Shared Weights
			4.2.1.7 Multiple Filters
			4.2.1.8 Subsampling Layers
			4.2.1.9 Fully Connected Layers
			4.2.1.10 Correction Layers (ReLU)
			4.2.1.11 Loss Layer (LOSS)
		4.2.2 Recurrent Neural Networks
			4.2.2.1 Applications of RNNs
			4.2.2.2 Loss Function
			4.2.2.3 Temporal Back Propagation
			4.2.2.4 Activation Functions
			4.2.2.5 Long Short-Term Memory
	4.3 Proposed System
		4.3.1 Comparison Between CNN and RNN
		4.3.2 Our System
	4.4 Results Obtained
	4.5 Conclusion
	References
5 Merging Attack-Defense Tree and Game Theory to Analyze Vehicular Ad Hoc Network Security
	5.1 Introduction
	5.2 System Model
	5.3 Fundamental Attack-Defense Tree
	5.4 Attack-Defense Tree for VANET Availability
	5.5 ROI and ROA for Attack-Defense Tree
	5.6 VANET Availability Attack-Defense Game
		5.6.1 Basics of the Game Theory
		5.6.2 Modeling VANET Availability Attack-Defense Game
	5.7 Conclusion
	References
6 A Secure Vehicle to Everything (V2X) Communication Model for Intelligent Transportation System
	6.1 Introduction
	6.2 Overview of Vehicular Networks
		6.2.1 Vehicular Networks Components
		6.2.2 Communication Architectures
			6.2.2.1 Centralized Architecture: Vehicle-to-Infrastructure Communication
			6.2.2.2 Distributed Architecture: Vehicle-to-Vehicle Communication
			6.2.2.3 Hybrid Architecture
	6.3 V2X Communications
	6.4 Security Requirements
		6.4.1 Authentication
			6.4.1.1 Authentication of the ID
			6.4.1.2 Property Authentication
		6.4.2 Integrity
		6.4.3 Confidentiality
		6.4.4 Non-repudiation
		6.4.5 Availability
		6.4.6 Access Control
	6.5 Preliminaries
		6.5.1 Encrypt Using Elliptical Curves
			6.5.1.1 Exchange of Keys by Elliptical Curves
			6.5.1.2 Transmission of Messages
		6.5.2 Attribute-Based Signature
			6.5.2.1 Computational Assumption
			6.5.2.2 Lagrange Interpolation
			6.5.2.3 Attribute-Typed Signature
	6.6 Proposed Scheme
	6.7 Validation and Evaluation
		6.7.1 Verification Environment
			6.7.1.1 High-Level Protocol Specification Language (HLPSL)
			6.7.1.2 Verification Hypotheses
			6.7.1.3 Properties to Check
		6.7.2 Security Analysis
		6.7.3 Performance Evaluation
	6.8 Conclusion
	References
7 A Novel Unsupervised Learning Method for Intrusion Detection in Software-Defined Networks
	7.1 Introduction
	7.2 Related Work
	7.3 IDS-IF: An Overview
	7.4 IDS-IF
	7.5 Evaluation of IDS-IF
		7.5.1 Experimental Environment
		7.5.2 Performance Evaluation
	7.6 Conclusion
	References
8 Deep Reinforcement Learning Modeling of a V2V Communication-Based Bike Avoidance Protocol for Increased Vehicular Flow
	8.1 Introduction
	8.2 Related Work
	8.3 Overview
		8.3.1 DBDA Protocol
		8.3.2 Deep Reinforcement Learning
	8.4 Markov Decision Process Modeling for DRL-Based DBDA Protocol
	8.5 Deep Q-Learning Architecture for DRL-Based DBDA Protocol
		8.5.1 Optimal Driving Policy Estimation
		8.5.2 Deep Neural Network Architecture for Optimal Driving Learning
	8.6 Discussion
	8.7 Conclusion
	References
9 Deep Learning-Based Modeling of Pedestrian Perception and Decision-Making in Refuge Island for Autonomous Driving
	9.1 Introduction
	9.2 Related Work
	9.3 Overview
		9.3.1 APC Protocol
		9.3.2 Deep Machine Learning
			9.3.2.1 Conventional Neural Network
			9.3.2.2 Long Short-Term Memory
	9.4 Contribution
		9.4.1 P-LPN-Based Architecture for Pedestrian Perception
		9.4.2 LSTM Application for Real-Time Decision-Making
	9.5 Discussion
	9.6 Conclusion
	References
10 Machine Learning for Hate Speech Detection in Arabic Social Media
	10.1 Introduction
	10.2 Overview of Hate Speech Detection on Arabic Social Media
	10.3 Natural Language Processing
		10.3.1 Stemming
		10.3.2 Bag of Words and Term Frequency
		10.3.3 Term Frequency-Inverse Document Frequency (TF-IDF)
	10.4 Dataset
		10.4.1 YouTube
		10.4.2 Alakrot\'s YouTube Comments Collection [10]
	10.5 Methodology
		10.5.1 Preprocessing
		10.5.2 Algorithms
			10.5.2.1 Logistic Regression (LR)
			10.5.2.2 Random Forests (RF)
			10.5.2.3 Support Vector Machines (SVM)
			10.5.2.4 Long Short-Term Memory (LSTM)
		10.5.3 Evaluation Metrics
	10.6 Experimental Results
	10.7 Conclusion and Perspectives
	References
11 PDDL Planning and Ontologies, a Tool for Automatic Composition of Intentional-Contextual Web Services
	11.1 Introduction
	11.2 Background and Related Work
		11.2.1 Context and Context Awareness
		11.2.2 Intention
		11.2.3 Service Composition and Related Work
			11.2.3.1 Service Composition Categories
			11.2.3.2 Technical Classification of Service Composition Approaches
			11.2.3.3 Service Composition Approaches
		11.2.4 Summary
	11.3 Intention and Context Modelling
		11.3.1 Proposed Meta-Model
		11.3.2 Ontology-Based Intention and Context Modeller
		11.3.3 Intention Ontology
		11.3.4 Context Ontology
		11.3.5 OWL-S Extension for the Semantic Integration of Context and Intention
	11.4 PDDL and OWL Interaction
		11.4.1 Planning and PDDL
		11.4.2 Mapping OWL to PDDL
	11.5 Proposed Architecture Overview
		11.5.1 CISCA Architecture
		11.5.2 CISCA Architecture Features
	11.6 Service Composition Module
	11.7 A Walk-through Example
	11.8 Conclusion and Future Work
	References
12 QSAR Anti-HIV Feature Selection and Prediction for Drug Discovery Using Genetic Algorithm and Machine Learning Algorithms
	12.1 Introduction
	12.2 Proposed Techniques
		12.2.1 Genetic Algorithms
		12.2.2 Machine Learning Methods in QSAR Problem
	12.3 Proposed Approach and Validation
		12.3.1 Data
		12.3.2 Proposed Approach
		12.3.3 Results and Validation
	12.4 Conclusion
	References
13 Mining Electronic Health Records of Patients Using Linked Data for Ranking Diseases
	13.1 Introduction
	13.2 Related Work
		13.2.1 Mining Electronic Health Records Using Linked Data
		13.2.2 Diseases Identification Based on Clustering RDF Dataset
	13.3 Results and Discussion
		13.3.1 Dataset
		13.3.2 Validation Metrics
		13.3.3 Quality of Results and Discussion
	13.4 Conclusion and Future Work
	References
14 The COVID-19 Pandemic\'s Impact on Stock Markets and Economy: Deep Neural Networks Driving the Alpha Factors Ranking
	14.1 Introduction
	14.2 Theoretical Background
		14.2.1 Investment Factors
		14.2.2 Cross-Sectional Investment
	14.3 Cross-Sectional Investment-Based Clustering and Intelligent Delay
	14.4 The COVID-19 Influence on the Markets and Economy
		14.4.1 COVID-19 Growth Analysis
		14.4.2 2008 Crisis Comparison
		14.4.3 The Influence of COVID-19 on Companies According to their Geographic Revenue
		14.4.4 Delay
	14.5 Discussion
	14.6 Conclusion
	Appendix
	Original and Customized Factors Utilized in this Work
	References
15 An Artificial Immune System for the Management of the Emergency Divisions
	15.1 Introduction
	15.2 Review of the Literature
		15.2.1 Disruptions in Healthcare Facilities
		15.2.2 The Status of Stress Within the Emergency Divisions
		15.2.3 Improvement Actions to Deal with Situations of Tension
		15.2.4 White Plan: The Guiding Standards
		15.2.5 Improving the Capacity of Reception Within Hospitals
	15.3 Artificial Immune System Techniques: An Overview
		15.3.1 Key Conception
		15.3.2 Negative Selection
		15.3.3 Clonal Selection
		15.3.4 Immunity Networks
		15.3.5 Training Methods
	15.4 Scrutiny of the Arriving Patients
	15.5 Prioritizing Patients at Emergency Divisions
	15.6 Projection of Filtering Methodology
		15.6.1 Overview of the System
		15.6.2 Distance Metrics Function
		15.6.3 Choosing Manhattan Distance
	15.7 Manhattan Distance: The Matching Method
		15.7.1 Defining the Problem
		15.7.2 Framework of Optimization
	15.8 System Skeleton
		15.8.1 Gathering Traces
		15.8.2 Scrutinizing Traces
			15.8.2.1 Basic Concept of Negative Selectivity NSA
	15.9 Outcomes of Computation
		15.9.1 Alimenting the Database
	15.10 Conclusion
	References
Index