Оглавление
Группа авторов. Handbook of Intelligent Computing and Optimization for Sustainable Development
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Handbook of Intelligent Computing and Optimization for Sustainable Development
Foreword
Preface
Acknowledgment
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
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
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
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
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
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
7. Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane
7.1 Introduction
7.1.1 Motivation, Background and Literature Review
7.1.2 Novelty and Objective of the Chapter
7.1.3 Research Contribution
7.1.4 Organization of the Chapter
7.2 Experimental Procedure
7.3 Methodology. 7.3.1 Generation of Data
7.3.2 Group Method of Data Handling–Based Neural Network
7.3.3 Artificial Neural Network
7.3.4 Normalization of the Data
7.3.5 Analysis of Error
7.3.6 Advantages and Disadvantages of the Study
7.4 Results and Discussions. 7.4.1 Development of GMDH Model
7.4.2 Analysis of Error
7.4.3 Comparative Study
7.4.4 Sensitivity Analysis
7.4.5 Major Findings
7.5 Conclusions
7.5.1 Limitation
7.5.2 Future Research Direction
Acknowledgements
References
8. Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach
8.1 Introduction
8.1.1 Requirements Descriptions are as Follows. 8.1.1.1 Business Requirements
8.1.1.2 Requirements of Users
8.1.1.3 System Requirements
8.1.1.4 Functional Requirements
8.1.1.5 Non-Functional Requirements
8.1.2 Examples of Non-Functional Requirements
8.1.2.1 Benefits of Non-Functional Requirements
8.1.2.2 Drawbacks of Non-Functional Requirements
8.1.3 Problem Statement
8.1.4 Research Objectives
8.1.5 Scope of Work
8.2 Literature Survey
8.3 Methodology
8.3.1 Search String Planning
8.3.2 Classifier Configuration
8.3.2.1 Supervised Machine Learning
8.3.2.2 Supervised Learning Algorithms
8.3.2.2.1 Random Forest Classifier
8.3.2.2.1.1 Advantages of Random Forest
8.3.2.2.1.2 Disadvantages of Random Forest
8.3.2.2.2 Linear Support Vector Machines
8.3.2.2.2.1 Advantages of Support Vector Machines
8.3.2.2.2.2 Drawbacks of Support Vector Machines
8.4 Dataset
8.5 Evaluation
8.6 Conclusion
References
9. Image Classification by Reinforcement Learning With Two-State Q-Learning
9.1 Introduction
9.2 Proposed Approach
9.3 Datasets Used
9.3.1 ImageNet
9.3.2 Cats and Dogs Dataset
9.3.3 Caltech-101
9.4 Experimentation
9.5 Conclusion
References
10. Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process
10.1 Introduction
10.1.1 Programmable Logic Controller
10.1.1.1 Merits of PLC Over Relay Logic
10.1.1.2 Various Modules of PLC
10.1.1.3 Working of PLC
10.2 Distributed Control System
10.2.1 DCS Evolution
10.2.2 DCS Provisions
10.2.3 Main Components of DCS
10.2.4 Different Hierarchy Levels of DCS
10.2.5 Advantages of DCS
10.3 Fuzzy Logic
10.4 Artificial Neural Network
10.4.1 Artificial Neural Network’s Framework
10.5 Neuro-Fuzzy
10.5.1 Neural Network, Fuzzy Logic, and Neuro-Fuzzy Concepts
10.5.2 Design of Neuro-Fuzzy Controller
10.5.3 Software Used
10.6 Case Study
10.6.1 Objective
10.6.2 Description
10.6.3 Formulation of Control Problem
10.6.4 Formulation of Control Strategy
10.6.5 Level of Instrumentation in Case Study
10.7 Software Implementation on Graphical User Interface
10.8 Results and Discussion
10.9 Discussion
10.10 Conclusion
10.11 Scope for Future Work
References
Appendix 10.1 MATLAB Simulation Configuration Using Sugeno
Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model
Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model
11. Artificial Neural Network in the Manufacturing Sectors
11.1 Introduction
11.2 Optimization
11.3 Artificial Neural Network: Optimization of Mechanical Systems
11.3.1 Injection Molding Processes
11.3.2 Additive Manufacturing: 3D Printing
11.3.3 Welding
11.3.4 Foundry and Casting Technology
11.3.5 Machining
11.4 ANN vs. Human Brain
11.5 Architecture of Artificial Neural Networks
11.5.1 Modular Neural Networks
11.5.2 Feed Forward
11.5.3 Convolutional Neural Network
11.5.4 Recurrent or Feedback Neural Network
11.5.5 Radial Neural Network
11.5.6 Multilayer Perceptron
11.5.7 Kohonen Self-Organizing Neural Network
11.5.8 LSTM
11.6 Learning Algorithm(s)
11.6.1 Conjugate Gradient
11.6.2 Quick Propagation
11.6.2.1 Levenberg-Marquardt Method
11.7 Different Type of Data
11.8 Case Study: Hard Machining of EN 31 Steel
11.8.1 Design of Experiments
11.8.2 Construction of ANN Feed Forward Model
11.8.2.1 5-5-1 Feed Forward Network
11.8.2.2 ANN Predicted Values vs. Experimental Values
11.8.3 Major Findings of Above Study
11.9 Advantages of Using ANN in Manufacturing Sectors
11.10 Disadvantages of Using ANN in Manufacturing Sectors
11.11 Applications
11.12 Conclusions
11.13 Future Scope of ANN in Manufacturing Sectors
References
12. Speech-Based Multilingual Translation Framework
12.1 Introduction
12.2 Literature Survey
12.3 Phases of ASR
12.4 Modules of ASR
12.5 Speech Database for ASR
12.5.1 Data Collection
12.6 Developing ASR
12.7 Performance of ASR
12.7.1 Accuracy. 12.7.1.1 Word Error Rate (WER)
12.7.2 Speed
12.8 Application Areas
12.9 Conclusion and Future Work
References
13. Text Summarization: A Technical Overview and Research Perspectives
13.1 Introduction
13.1.1 Motivation
13.1.2 Challenges in ATS
13.2 Summarization Techniques
13.2.1 Feature-Based Summarization Approaches
13.2.1.1 Word Level Features
13.2.1.1.1 Topic-Word Approach
13.2.1.1.2 Word Probability
13.2.1.2 Sentence Level Features
13.2.1.3 Document Level Features
13.2.1.3.1 Summary Generation by Mining Frequent Itemsets
13.2.2 Indicator Representation–Based Approaches
13.2.2.1 Graph-Based Approaches
13.2.3 Machine Learning–Based Approaches
13.2.3.1 Naïve Bayes Methods
13.2.3.2 Decision Tree–Based Methods
13.2.3.3 Neural Network–Based Approach
13.2.4 Deep Learning–Based Approaches
13.2.5 Semantic-Based Approaches
13.3 Evaluating Summaries
13.3.1 Intrinsic Evaluation Measures
13.3.2 Extrinsic Evaluation Measures
13.4 Datasets and Results
13.5 Future Research Directions
13.6 Conclusion
References
14. Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery
14.1 Introduction
14.1.1 Organization of Chapter
14.2 Literature Review
14.3 Understanding the Google Cloud Platform
14.3.1 Outline of CLOUD Platforms Provided by Google
14.3.2 Advantages of Google Cloud Platform
14.3.3 Key Characteristics of Google Cloud Platform
14.4 Using BigQuery in the Google Cloud Console
14.5 Sentiment Analysis
14.6 Turning to Google BigQuery Analysis
14.6.1 Architecture
14.7 Proposed Method
14.7.1 Sub-Modules. 14.7.1.1 Downloading Hashtag Data From Twitter Streaming API
14.7.1.2 Stream Twitter Data into BigQuery With Cloud
14.7.1.3 Deploying the Application on Google App Engine
14.7.1.4 Querying and Loading Large Sets of Tweets onto BigQuery
14.7.1.5 Review Tweet Dataset as Positive, Neutral, and Negative and Employ NLP
14.8 Experimental Setup and Results. 14.8.1 Requirements
14.8.2 Configuration and Setup
14.9 Conclusion
References
15. A Review of Topic Modeling and Its Application
15.1 Introduction
15.1.1 Linguistic
15.1.2 Computer Science
15.2 Objective of Topic Modeling
15.3 Motivations and Contributions
15.4 Detailed Survey of Research Articles. 15.4.1 Methodology—LDA
15.4.1.1 Generative Process for LDA
15.4.2 A Brief Summary of Articles Based on Latent Dirichlet Allocation. 15.4.2.1 Topic Analysis of Climate Change News
15.4.2.2 Evaluation of Document Clustering and Topic Modeling in Online Social Networks: Twitter and Reddit
15.4.2.3 Topic Modeling on Historical Newspaper
15.4.2.4 Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke
15.4.2.5 Hierarchical User Profiles for Interactive Visual User Behavior Analytics
15.4.2.6 Front Page News Selection Algorithm
15.4.2.7 Identifying Spatial Interaction Patterns of Vehicle Movements on Urban Road Networks by Topic Modeling
15.4.2.8 Identifying Topic Relations in Scientific Literature Using Topic Modeling
15.4.2.9 Probabilistic Topic Decomposition of an Eighteenth-Century American Newspaper
15.4.3 A Brief Summary of Articles Based on Support Vector Machines. 15.4.3.1 Predicting Personality Traits of Chinese Facebook Posts
15.4.3.2 Crowdsourcing the Character Level Networks Using Text
15.4.4 A Brief Summary of Articles Based on Visualization Techniques. 15.4.4.1 Termite: Visualization Techniques for Assessing Textual Topic Models
15.4.5 A Brief Summary of Articles Based on Gibbs Sampling. 15.4.5.1 Scan Order in Gibbs Sampling
15.4.5.2 Incorporating Non-Local Information Into Information Extraction Systems by Gibbs Sampling
15.4.6 A Brief Summary of Articles Based on LDA and Markov Chain Monte Carlo Algorithm. 15.4.6.1 Finding Scientific Topics
15.4.7 Other Related Research Articles. 15.4.7.1 Reading Tea Leaves: Human Interpretation of Topic Models
15.4.7.2 Mining Hidden Knowledge for Drug Safety Assessment: A Case Study
15.4.7.3 The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
15.4.7.4 Analysis of Publication Activity of Computational Science Society
15.4.7.5 Studying the History of Ideas Using Topic Models
15.5 Comparison Table of Previous Research
15.6 Expected Future Work
15.7 Conclusion
References
16. ROC Method for Identifying the Optimal Threshold With an Application to Email Classification
16.1 Introduction
16.1.1 ROC Curve
16.2 Related Works
16.3 Methodology
16.3.1 Dataset
16.3.2 Classification
16.3.2.1 Naïve Bayes
16.3.2.2 KNN
16.3.2.3 Support Vector Machine
16.3.2.4 Artificial Neural Network
16.3.2.4.1 Data Normalization
16.3.2.4.2 Training the Neural Network Using NeuralNet
16.3.3 Performance Metrics
16.4 Results and Discussion
16.4.1 Performance Comparison
16.5 Conclusion
References
17. Optimal Inventory System in a Urea Bagging Industry
17.1 Introduction
17.1.1 Reasons for Carrying Inventory
17.1.2 Inventory Metrics
17.1.3 Performance Measures for Inventory Control
17.1.4 Service Performance Measures
17.1.4.1 Service Level
17.1.4.2 Fill Rate
17.1.5 Cost Performance Measures
17.1.5.1 Ordering Cost, A
17.1.5.2 Purchase Cost, P
17.1.5.3 Inventory Holding Cost, h
17.1.5.4 Shortage Cost, β
17.1.5.5 Total Cost
17.1.6 Inventory Models
17.1.7 Deterministic Inventory Model
17.1.8 Stochastic Inventory Model
17.1.9 Inventory Policies
17.2 Continuous Review Policy
17.2.1 Periodic Review Policy
17.3 Inventory Optimization Techniques
17.3.1 Classical Optimization Approaches
17.3.2 Heuristic Optimization Techniques
17.3.3 Simulated Annealing
17.3.4 Genetic Algorithm
17.3.5 Tabu Search
17.3.6 Particle Swarm Optimization
17.3.7 Ant Colony Optimization
17.4 Model Formulation
17.4.1 Model Description
17.4.2 Steady-State Probability Equations
17.4.3 Optimal Inventory Level
17.5 Numerical Calculations
17.6 Conclusion
References
18. Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario
18.1 Introduction
18.2 Mixed Integer Programming Methods
18.3 Introduction to Supply Chain Management System
18.4 Mathematical Model Formulation
18.4.1 Sets an Indices
18.4.2 Parameters
18.4.3 Decision Variables
18.4.4 Constraints. 18.4.4.1 Constraints Related to Inventory
18.4.4.2 Constraints Related to Emergency Orders
18.4.4.3 Constraints Related to Quality
18.4.4.4 Constraints Related to Delivery
18.4.4.5 Constraints Related to Supplier Capacity
18.4.4.6 Non-Negativity Restrictions
18.4.5 Objective Function
18.5 Conclusion
References
19. Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry—A Machine Learning Approach
19.1 Introduction
19.2 Literature Review
19.3 The Aim and Objectives of the Study
19.4 Research Methodology
19.5 Study Results and Discussions. 19.5.1 The Model
19.5.2 The Pricing Calculator
19.6 Conclusions
References
20. A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode
20.1 Introduction
20.2 Designing of 90° SPS
20.3 Designing of Tunable Schiffman Phase Shifter
20.4 Major Finding and Limitation
20.5 Conclusion
References
21. Optimizing Manufacturing Performance Through Fuzzy Techniques
21.1 Introduction
21.1.1 Manufacturing Competency
21.1.2 Green Sustainability
21.1.3 Methodology
21.2 Literature Review
21.2.1 Competency
21.2.1.1 Competency Development
21.2.1.2 Competency Management
21.2.1.3 Economic Effects
21.2.1.4 Technological Competency
21.2.1.5 Competition
21.2.2 Green Sustainability
21.3 Performance Optimization through Fuzzy Techniques. 21.3.1 Fuzzy Logic
21.3.1.1 Fuzzy Logic for Manufacturing Competency
21.3.1.2 Fuzzy Logic for Green Sustainability
21.3.2 Fuzzy AHP
21.3.2.1 Fuzzy AHP for Manufacturing Competency
21.3.2.2 Fuzzy AHP for Green Sustainability
21.3.3 Fuzzy MDEMATEL
21.3.3.1 Fuzzy MDEMATEL for Green Sustainability
21.3.4 Modified Fuzzy TOPSIS. 21.3.4.1 For Manufacturing Competency
21.3.5 Modified Fuzzy VIKOR. 21.3.5.1 For Manufacturing Competency
21.4 Conclusions
21.4.1 Major Findings
21.4.2 Limitations
21.4.3 Suggestions for Future Research
References
22. Implementation of Non-Linear Inventory Optimization Model for Multiple Products
22.1 Introduction
22.2 Literature Review
22.3 Symbols and Assumptions
22.3.1 Symbols
22.3.2 Assumptions
22.4 Model Formulation
22.4.1 The Objective Function for a Single Product
22.4.2 The Service Level Constraint for a Single Product
22.4.3 Mathematical Model for Multiple Products
22.4.4 Solution Procedure. 22.4.4.1 The Solution Without Service Level Constraint (SLC)
22.4.4.2 The Solution With SLC for Multiple Products
22.5 Conclusion
References
23. Pufferfish Optimization Algorithm: A Bioinspired Optimizer
23.1 An Introduction to Optimization
23.2 Optimization and Engineering
23.3 Meta-Heuristic Optimization
23.3.1 Genetic Algorithm
23.4 Torquigener Albomaculosus
23.5 Pufferfish and Circular Structures
23.6 Results
23.7 Conclusion
References
24. A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization
24.1 Introduction
24.2 Background on Sperm Swarm Optimization (SSO) and Grey Wolf Optimizer (GWO)
24.2.1 Sperm Swarm Optimization (SSO)
Algorithm 24.1“Sperm Swarm Optimization (SSO)”
24.2.2 Grey Wolf Optimizer (GWO)
Algorithm 24.2 “Grey Wolf Optimizer (GWO)”
24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization (HGWOSSO)
Algorithm 24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization (HGWOSSO)
24.4 Experiments and Results
24.5 Discussion
24.6 Conclusion
References
25. State-of-the-Art Optimization and metaheuristic Algorithms
25.1 Introduction
25.2 An Overview of Traditional Optimization Approaches
25.3 Properties of Metaheuristics
25.4 Classification of Single Objective Metaheuristic Algorithms
25.4.1 Local and Global Search
25.4.2 Single Solution Approach and Population-Based Algorithm
25.4.2.1 Swarm-Based Optimization Algorithms
25.4.2.2 Evolutionary Algorithms
25.4.2.3 Physics-Based Algorithms
25.4.2.4 Ecology-Based Algorithms
25.4.3 Hybridization of Algorithms and Memetic Algorithms
25.4.4 Parallel metaheuristic Approaches
25.5 Applications of Single Objective metaheuristic Approaches
25.6 Classification of Multi-Objective Optimization Algorithms
25.6.1 Scalarizing
25.6.2 Priori Method
25.6.3 No Preference Method
25.6.4 A Posteriori Method
25.6.5 Interactive Method
25.7 Hybridization of MOPs Algorithms
25.7.1 Low and High Level
25.7.2 Relay or Teamwork
25.8 Parallel Multi-Objective Optimization
25.8.1 Single Walk Parallelism
25.8.2 Multiple Walk Parallelism
25.8.3 The Master/Slave Model
25.8.4 The Distributed Island Model
25.8.5 The Cellular Model
25.8.6 Uncertain Pareto Optimization
25.9 Applications of Multi-Objective Optimization. 25.9.1 Economics
25.9.2 Finance
25.9.3 Optimal Control and Optimal Design
25.9.4 Process Optimization
25.9.5 Radio Resource Management
25.9.6 Inspection of Infrastructure
25.9.7 Electric Power Systems
25.10 Significant Contributions of Researchers in Various Metaheuristic Approaches
25.11 Conclusion
25.12 Major Findings, Future Scope of Metaheuristics and Its Applications
25.13 Limitations and Motivation of Metaheuristics
Acknowledgements
References
26. Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique
26.1 Introduction
26.2 Proposed Methodology
26.3 Simulation Results
26.4 Conclusions
References
27. A New Parameter Estimation Technique of Three-Diode PV Cells
27.1 Introduction
27.2 Problem Statement
27.3 Proposed Method
27.3.1 Exploration Phase
27.3.2 Turning From Exploration to Exploitation
27.3.3 Exploitation Phase
27.4 Simulation Results and Discussions
27.5 Conclusions
References
28. Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network
28.1 Introduction. 28.1.1 State-of-Art
28.1.2 Motivation
28.1.3 Major Contributions
28.2 System Model and Preliminaries
28.2.1 Local Spectrum Sensing
28.2.2 Conventional Decision Fusion Techniques
28.3 Machine Learning Techniques of Decision Fusion
28.3.1 K-Means Clustering
28.3.2 Support Vector Machine
28.4 Optimum Quantization of Decision Statistic and Fusion. 28.4.1 Optimum Quantization
28.4.2 Decision Fusion
28.5 Measurement Setup
28.5.1 SU Nodes
28.5.2 PU Node
28.5.3 Distribution of SU Nodes
28.5.4 Methodology
28.6 Performance Evaluation
28.6.1 ROC Analysis
28.6.2 Effect of SNR and
28.6.3 Effect of Number of Samples
28.6.4 Effect of Number of Cooperative SUs
28.7 Conclusion
28.8 Limitations and Scope for Future Work
References
29. Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies
29.1 Introduction
29.2 Green Approaches: Significance and Motivation
29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control
29.4 Green IoT–Based Smart Irrigation Monitoring
29.5 Technology Enablers for GIoT–Based Irrigation Monitoring
29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation
29.7 Other Recent Developments on GIoT–Based Smart Agriculture
29.8 Literature Review of Edge Computing–Based Irrigation Monitoring
29.9 LPWAN for GIoT–Based Smart Agriculture
29.10 Analysis and Discussion
29.11 Research Gap in GIoT–Based Precision Agriculture
29.12 Analysis of Merits and Shortcomings
29.13 Future Research Scope
29.14 Conclusion
References
30. Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification
30.1 Introduction
30.2 Related Works
30.3 Proposed Approach
30.3.1 Data Collection and Processing
30.3.2 Creation of Sentiment Terms Matrix
30.3.3 Sentiment Classification
30.4 Experimental Details and Results
30.5 Conclusion
References
31. A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission
31.1 Introduction
31.2 Related Work
31.3 Proposed Model
31.3.1 Input Phase
Algorithm 31.1 Fuzzy set distance matching
31.3.2 Deployment Phase
31.3.3 Transmission Phase
Algorithm 31.2 Transmission algorithm (A*)
31.4 Complexity Analysis of Algorithms for Data Transmission
31.5 Experimental Analysis
31.5.1 Deployment-Based Metrics. 31.5.1.1 Coverage vs. Iterations
31.5.1.2 Mean Travel Distance vs. Number of Nodes
31.5.1.3 Energy Utilization
31.5.2 Simulation-Based Metrics. 31.5.2.1 Simulation of Shortest Paths
31.5.3 Deployment and Transmission Testing
31.6 Motivation and Limitations of Research
31.7 Conclusion
31.8 Future Work
References
32. Intelligent Computing for Precision Agriculture
32.1 Introduction
32.1.1 Sampling of Soil
32.1.2 Need for Watering
32.1.3 Composting
32.1.4 Pest Control Technique and Crop Disorder
32.1.5 Harvesting, Monitoring, and Forecasting
32.1.6 Nursery Cultivation
32.1.7 Aquaculture or Tray/Tank Farming
32.1.8 Tools and Mechanism Used
32.2 Technology in Agriculture
References
33. Intelligent Computing for Green Sustainability
33.1 Introduction
33.1.1 Motivation for the Study
33.1.2 Objectives
33.1.3 Background of the Study
33.1.4 Novelty of the Study
33.2 Modified DEMATEL
33.3 Weighted Sum Model
33.4 Weighted Product Model
33.5 Weighted Aggregated Sum Product Assessment
33.6 Grey Relational Analysis
33.7 Simple Multi-Attribute Rating Technique
33.8 Criteria Importance Through Inter-Criteria Correlation
33.9 Entropy
33.10 Evaluation Based on Distance From Average Solution
33.11 MOORA
33.12 Interpretive Structural Modeling
33.12.1 Structural Self-Interaction Matrix
33.12.2 Final Reachability Matrix
33.12.3 Level Partition
33.12.4 MICMAC Analysis
33.13 Conclusions
33.14 Limitations of the Study
33.15 Suggestions for Future Research
References
34. Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease
34.1 Introduction
34.2 Methods
34.2.1 Study Population
34.3 Statistical Analysis
34.4 Results. 34.4.1 Descriptive Characteristics
34.4.2 Clinical Characteristics
34.5 Discussion
34.6 Conclusion
Acknowledgements
References
35. Reconstruction of Dynamic MRI Using Convolutional LSTM Techniques
35.1 Introduction
35.2 Methodologies. 35.2.1 Convolutional Neural Network
35.2.2 Convolutional Long Short-Term Memory
35.3 Problem Formulation
35.4 Network Architecture
35.4.1 Cascaded ConvLSTM Without Dilation
35.4.2 Dense Cascaded ConvLSTM With Dilation
35.5 Results
35.5.1 Datasets
35.5.2 Performance Evaluaton
35.6 Discussion
35.7 Conclusion
References
36. Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion
36.1 Introduction
36.2 Literature Review
36.2.1 Visible Spectrum
36.2.2 Cross-Spectral
36.2.3 Multispectral
36.3 Multispectral Face Database
36.4 Methodology
36.5 Experiments
36.6 Results and Discussion
36.6.1 Observation I—Based on Affine Hull Method
36.6.2 Observation II—Based on Wavelet Average Fusion
36.6.3 Observation III—Comparison of Affine Hull and Wavelet Average Fusion
36.7 Conclusions
Acknowledgments
References
37. Polyp Detection Using Deep Neural Networks
37.1 Introduction
37.2 Literature Survey
37.3 Proposed Methodology
37.3.1 Dataset
37.3.2 Data Pre-Processing
37.3.3 Classification
37.3.3.1 Concept of Transfer Learning and Fine Tuning
37.3.3.2 VGG16 and VGG19 Model Architecture
37.3.4 Polyp Detection
37.4 Implementation and Results
37.5 Conclusion and Future Work
References
38. Boundary Exon Prediction in Human Sequences Using External Information Sources
38.1 Introduction
38.2 Proposed Exon Prediction Model
38.2.1 Splice Site Prediction
38.2.2 Translation Initiation Site Prediction
38.2.3 Coding Region Prediction
38.2.4 Exon Prediction
38.3 Homology-Based Exon Prediction
38.3.1 External Information Used
38.3.2 External Information Collection
38.3.3 Proposed Method
38.3.3.1 Multiple Sequence Alignment
38.3.3.2 Prediction of Coding Regions
38.3.3.3 Merging and Chaining of Predicted Coding Regions
38.3.4 Exon Prediction With Precise Boundaries
38.3.5 Graphical User Interface
38.4 Results and Discussion. 38.4.1 Datasets Used
38.4.2 Results
38.5 Conclusion
38.6 Motivation and Limitations of the Research
38.7 Major Findings of the Research
References
39. Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform
39.1 Introduction
39.1.1 Selection of Wavelength Region
39.1.2 Motivation
39.2 Sample Preparation
39.3 Methodology
39.3.1 Partial Least Square Regression (PLSR)
39.3.2 Backpropagation Artificial Neural Network (BP-ANN)
39.4 Results and Discussion. 39.4.1 Estimation of Glucose Prediction
39.4.2 System Validation
39.4.2.1 Bland-Altman Analysis
39.4.2.2 Clarke Error Grid Analysis (CEGA)
39.4.3 Regression Analysis
39.4.4 Accuracy of the System
39.5 Discussion
39.6 Conclusion
39.7 Future Scope
Acknowledgement
References
40. GIS-Based Geospatial Assessment of Novel Corona Virus (COVID-19) in One of the Promising Industrial States of India—A Case of Gujarat
40.1 Introduction
40.1.1 Major Findings of the Study
40.2 The Rationale of the Study
40.3 Materials and Methodology. 40.3.1 Mapping
40.3.2 Challenges in Using GIS With Spatiotemporal Big Data
40.3.3 Data Acquisition, Sampling Design, and Data Analysis
40.3.4 Methodology
40.3.4.1 Data Collection and Analysis
40.3.4.2 Pre-Processing
40.3.4.3 Model Execution. 40.3.4.3.1 Arc-GIS Used for Novel COVID-19
40.3.4.3.2 Model Development
40.4 GIS and COVID-19 (Corona) Mapping
40.4.1 Limitation of the Study
40.4.2 Advantages of the Study
40.5 Results and Discussion
40.5.1 Future Scope of the Study
40.6 Conclusion
References
41. Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi
41.1 Introduction
41.2 Hardware Design of Monitoring System
41.3 Software Design of Monitoring System
41.4 Working of ZigBee Module
41.5 Developed App for the Monitoring of Health
41.6 Google Fusion Table—Online Database
41.7 Application Developed for Health Monitoring System
41.8 Conclusion and Future Work
References
Index
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