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Handbook of Intelligent Computing and Optimization for Sustainable Development
Читать книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов - Страница 1
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Страница 1
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
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Preface
Страница 14
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1
Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models
1.1 Introduction
1.2 Data Acquisition Method
1.2.1 Data Acquisition Experiment
1.3 Feature Extraction
1.4 Deep Learning Models
1.4.1 Artificial Neural Network
1.4.1.1 Training of a Neural Network
1.4.1.1.1 Sigmoid
1.4.1.1.2 Tanh
1.4.1.1.3 ReLU
1.4.2 Bernoulli’s Restricted Boltzmann Machines
1.5 Results
1.6 Discussion
1.7 Advantages and Disadvantages of the Study
1.8 Limitations of the Study
1.9 Conclusion
References
Страница 35
2
Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates
2.1 Introduction
2.2 Biological Neurons
2.3 Artificial Neural Networks
2.3.1 McCulloch-Pitts Neural Model
2.3.2 The Perceptron
2.3.3 ANN With Continuous Characteristics
2.3.4 Single-Layer Neural Network
2.3.5 Multilayer Neural Network
2.3.6 Learning Process
2.4 DNA Neural Networks
2.4.1 Formation of Axon by DNA Oligonucleotide and Generation of Output Sequence
2.4.2 Design Strategy of DNA Perceptron
2.4.2.1 Algorithm
2.4.2.2 Implementation of the Algorithm
2.5 DNA Logic Gates
2.5.1 Logic Gates Using Deoxyribozymes
2.5.1.1 Catalytic Activity of Deoxyribozyme
2.5.1.2 Controlling Deoxyribozyme Logic Gate
2.5.1.3 YES Gate
2.5.1.4 NOT Gate
2.5.1.5 AND Gate
2.5.1.6 ANDANDNOT Gates
2.5.2 Enzyme-Free DNA Logic Circuits
2.5.2.1 Construction of Enzyme-Free DNA Logic Gate
2.5.2.2 DNA Logic Circuits
2.5.3 DNA Logic Circuits Using DNA Polymerase and Nicking Enzyme
2.5.3.1 AND Reaction
2.5.3.2 OR Reaction
2.5.3.3 PROPAGATE (PROP) Reaction
2.5.4 Applications of DNA Logic Gate
2.5.4.1 Playing Tic-Tac-Toe by DNA
2.5.4.2 Medical Application of the Concept of DNA Logic Gate
2.6 Advantages and Limitations
2.7 Conclusion
Acknowledgment
References
Страница 73
3
Intelligent Garment Detection Using Deep Learning
3.1 Introduction
3.2 Literature
3.3 Methodology
3.3.1 Obtaining the Foreground Information
3.3.1.1 GMG Background Subtraction
3.3.1.2 Person Detection
3.3.2 Detection of Active Garments
3.3.3 Identification of Garments of Interest
3.3.3.1 Centroid Tracking
3.3.3.2 Pose Estimation
3.3.3.3 Calculation of Confidence Score
3.4 Experimental Results
3.4.1 Dataset Used
3.4.2 Experimental Results and Statistics
3.4.3 Analysis of the Proposed Approach
3.5 Highlights
3.6 Conclusion and Future Works
Acknowledgements
References
Страница 94
4
Intelligent Computing on Complex Numbers for Cryptographic Applications
4.1 Introduction
4.2 Modular Arithmetic 4.2.1 Introduction
4.2.2 Congruence
4.2.3 Modular Arithmetic Operations
4.2.4 Inverses
4.3 Complex Plane 4.3.1 Introduction
4.3.2 Complex Number Arithmetic Operations and Inverses
4.4 Matrix Algebra 4.4.1 Introduction
4.4.2 Matrix Arithmetic Operations
4.4.3 Inverses
4.5 Elliptic Curve Arithmetic 4.5.1 Introduction
4.5.2 Arithmetic Operations on E(GF(p))
4.6 Cryptographic Applications 4.6.1 Hill Cipher
4.6.2 Elliptic Curve Cryptography
4.6.2.1 Elliptic Curve Encryption Scheme
4.6.2.2 Elliptic Curve Signature Scheme
4.6.3 Quantum Cryptography
4.7 Conclusion
References
Страница 115
5
Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies
5.1 Introduction
5.2 Machine/Deep Learning for Future Wireless Communication
5.2.1 Automatic Modulation Classification
5.2.2 Resource Allocation (RA)
5.2.3 Channel Estimation/Signal Detection
5.2.4 Millimeter Wave
5.3 Case Studies
5.3.1 Case Study-1: Automatic Modulation Classification
5.3.1.1 System Model
5.3.1.2 CNN Architectures for Modulation Classification
5.3.1.3 Results and Discussion
5.3.2 Case Study 2: CSI Feedback for FDD Massive MIMO Systems
5.3.2.1 Proposed Network Model
5.3.2.2 Results and Discussion
5.4 Major Findings
5.5 Future Research Directions
5.6 Conclusion
References
Страница 135
6
Designing of Routing Protocol for Crowd Associated Networks (CrANs)
6.1 Introduction
6.1.2 Challenges
6.1.2.1 Limitation of Research
6.2 Background Study
6.2.1 AdHoc Network
6.2.2 WSN
6.2.3 MANETs
6.2.4 VANETs
6.2.5 FANETs
6.2.6 Overview of Routing Protocols
6.2.6.1 MANET
6.2.6.1.1 Proactive
6.2.6.1.1.1 Destination Sequenced Distance Vector Routing Protocol (DSDV)
6.2.6.1.1.2 Global State Routing (GSR)
6.2.6.1.2 Reactive
6.2.6.1.2.1 Dynamic Source Routing Protocol (DSR)
6.2.6.1.2.2 AdHoc On-Demand Vector Routing Protocol (AODV)
6.2.6.1.3 Hybrid Routing Protocol
6.2.6.1.3.1 Zone Routing Protocol (ZRP)
6.2.6.2 VANET
6.2.6.2.1 Transmission Base
6.2.6.2.2 Routing Information 6.2.6.2.2.1 Cluster-Based Routing Protocol
6.2.6.2.2.2 COIN: Clustering for Open Network
6.2.6.2.2.3 Cluster-Based Directional Routing Protocol (CBDRP)
6.2.6.3 FANETs
6.2.6.3.1 Load Carry and Deliver Protocol
6.2.6.3.2 Data Centric Routing (DCR)
6.2.6.3.2.1 Static Routing Protocols
6.2.6.3.2.2 Proactive Routing Protocols
6.2.6.3.2.2.1 Optimized Link State Routing (DLSR)
6.2.6.3.2.2.2 Destination-Sequenced Distance Vector (DSDV)
6.2.6.3.3 Reactive Routing Protocols
6.2.6.3.3.1 Dynamic Source Routing (DSR)
6.2.6.3.3.2 AdHoc On-Demand Distance Vector (AODV)
6.2.6.4 MANETs
6.2.6.4.1 Tunneling
6.2.6.4.2 Non-Tunneling
6.2.6.5 VANETs
6.2.6.5.1 Greedy
6.2.6.5.2 V2I/I2V Forwarding
6.2.6.5.3 V2V Forwarding Flooding
6.2.6.5.4 Geographical Forwarding
6.2.6.5.5 Opportunistic Forwarding
6.2.6.5.6 Cluster-Based Forwarding
6.2.6.5.7 Peer-to Peer Forwarding
6.2.6.6 FANETs
6.2.6.6.1 Store-Carry and Forward
6.2.6.6.2 Greedy Forwarding
6.2.6.6.3 Path Discovery
6.2.6.6.3.1 Single Path
6.2.6.6.3.2 Multi Path
6.2.6.6.3.3 Prediction
6.2.7 Mobility Model
6.2.7.1 MANETs
6.2.7.1.1 IP Macro-Mobility Protocols
6.2.7.1.2 IP Micro-Mobility Protocols
6.2.7.2 VANETs
6.2.7.2.1 RWP
6.2.7.2.2 Man-Hattman
6.2.7.2.3 Group Model
6.2.7.2.4 Freeway
6.2.7.3 FANETs
6.2.7.3.1 Random Way Point Mobility Model
6.2.7.3.2 GMM Model
6.2.7.3.3 Semi Random Model
6.2.7.3.4 Mission
6.2.8 Types of Communication
6.2.8.1 MANET
6.2.8.2 VANET
6.2.8.2.1 Inter-Vehicle Communication
6.2.8.2.2 Vehicle-To-Roadside Communication
6.2.8.3 FANET
6.2.8.3.1 Air-to-Air Wireless Communication
6.2.8.3.2 Air to Ground Wireless Communication
6.2.9 QOS
6.2.9.1 Packet Loss
6.2.9.2 Bandwidth
6.2.9.3 Data Rate
6.2.9.4 Frequency
6.2.9.5 Packet Delivery Ratio (PDR)
6.2.9.6 Bit Noise Ratio
6.3 CrANs
6.3.1 Structure of CrANs
6.3.2 Routing
6.3.2.1 Single Hop
6.3.2.2 Multiple Hop
6.3.3 Classification of Routing Protocol
6.3.3.1 Location Base Protocol
6.3.3.2 Route Discovery
6.3.3.3 Protocol Operation
6.3.4 Challenge
6.3.4.1 Latency
6.3.4.2 Reliability
6.3.4.3 Security
6.3.4.4 Power Consumption
6.3.4.5 Energy
6.3.5 Applications
6.3.5.1 Monitoring
6.3.5.2 Control and Automation
6.3.5.3 Safety
6.3.5.4 Nuclear Power Plants
6.3.5.5 Deserts
6.3.5.6 Recovery Structure
6.3.5.7 Earth Quake
6.3.5.8 Road Blasts
6.4 Simulation of MANET Network 6.4.1 Deployment Area
6.4.2 Divide Deployment Area Into Equal Zones
6.4.3 Getting Positions of the Sensor Nodes
6.4.4 Mesh Formation
6.4.5 Getting the Minimum Spanning Tree for the Whole Placement Area
6.4.6 Energy Calculation
6.4.7 Average Delay
6.4.8 Throughput
6.5 Simulation of VANET Network
6.5.1 Placement of Nodes
6.5.2 Sender Node and Receiver Node
6.5.3 Euclidean Distance Between Two Coordinates
6.5.4 Separation of Faulty Nodes
6.5.5 Best Match of the Node
6.5.6 Cases of Simulation
6.5.7 Delay
6.5.8 Packet Delivery Ratio
6.5.9 Throughput
6.6 CrANs 6.6.1 Deploy of Nodes
6.6.2 Info Transfer 1
6.6.3 Calculate the Fitness Function
6.6.4 Routing Nodes
6.7 Conclusion
References
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