Table of Contents

Cover

Title Page

Dedication

Foreword

Preface

Acknowledgment

Part I INTELLIGENT COMPUTING AND APPLICATIONS

1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models
1. 1.1 Introduction
2. 1.2 Data Acquisition Method
3. 1.3 Feature Extraction
4. 1.4 Deep Learning Models
5. 1.5 Results
6. 1.6 Discussion
7. 1.7 Advantages and Disadvantages of the Study
8. 1.8 Limitations of the Study
9. 1.9 Conclusion
10. References
2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates
1. 2.1 Introduction
2. 2.2 Biological Neurons
3. 2.3 Artificial Neural Networks
4. 2.4 DNA Neural Networks
5. 2.5 DNA Logic Gates
6. 2.6 Advantages and Limitations
7. 2.7 Conclusion
8. Acknowledgment
9. References
3 Intelligent Garment Detection Using Deep Learning
1. 3.1 Introduction
2. 3.2 Literature
3. 3.3 Methodology
4. 3.4 Experimental Results
5. 3.5 Highlights
6. 3.6 Conclusion and Future Works
7. Acknowledgements
8. References
4 Intelligent Computing on Complex Numbers for Cryptographic Applications
1. 4.1 Introduction
2. 4.2 Modular Arithmetic
3. 4.3 Complex Plane
4. 4.4 Matrix Algebra
5. 4.5 Elliptic Curve Arithmetic
6. 4.6 Cryptographic Applications
7. 4.7 Conclusion
8. References
5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies
1. 5.1 Introduction
2. 5.2 Machine/Deep Learning for Future Wireless Communication
3. 5.3 Case Studies
4. 5.4 Major Findings
5. 5.5 Future Research Directions
6. 5.6 Conclusion
7. References
6 Designing of Routing Protocol for Crowd Associated Networks (CrANs)
1. 6.1 Introduction
2. 6.2 Background Study
3. 6.3 CrANs
4. 6.4 Simulation of MANET Network
5. 6.5 Simulation of VANET Network
6. 6.6 CrANs
7. 6.7 Conclusion
8. References
7 Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane
1. 7.1 Introduction
2. 7.2 Experimental Procedure
3. 7.3 Methodology
4. 7.4 Results and Discussions
5. 7.5 Conclusions
6. Acknowledgements
7. References
8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach
1. 8.1 Introduction
2. 8.2 Literature Survey
3. 8.3 Methodology
4. 8.4 Dataset
5. 8.5 Evaluation
6. 8.6 Conclusion
7. References
9 Image Classification by Reinforcement Learning With Two-State Q-Learning
1. 9.1 Introduction
2. 9.2 Proposed Approach
3. 9.3 Datasets Used
4. 9.4 Experimentation
5. 9.5 Conclusion
6. References
10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process
1. 10.1 Introduction
2. 10.2 Distributed Control System
3. 10.3 Fuzzy Logic
4. 10.4 Artificial Neural Network
5. 10.5 Neuro-Fuzzy
6. 10.6 Case Study
7. 10.7 Software Implementation on Graphical User Interface
8. 10.8 Results and Discussion
9. 10.9 Discussion
10. 10.10 Conclusion
11. 10.11 Scope for Future Work
12. References
13. Appendix 10.1 MATLAB Simulation Configuration Using Sugeno
14. Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model.
15. Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model.
11 Artificial Neural Network in the Manufacturing Sectors
1. 11.1 Introduction
2. 11.2 Optimization
3. 11.3 Artificial Neural Network: Optimization of Mechanical Systems
4. 11.4 ANN vs. Human Brain
5. 11.5 Architecture of Artificial Neural Networks
6. 11.6 Learning Algorithm(s)
7. 11.7 Different Type of Data
8. 11.8 Case Study: Hard Machining of EN 31 Steel
9. 11.9 Advantages of Using ANN in Manufacturing Sectors
10. 11.10 Disadvantages of Using ANN in Manufacturing Sectors
11. 11.11 Applications
12. 11.12 Conclusions
13. 11.13 Future Scope of ANN in Manufacturing Sectors
14. References
12 Speech-Based Multilingual Translation Framework
1. 12.1 Introduction
2. 12.2 Literature Survey
3. 12.3 Phases of ASR
4. 12.4 Modules of ASR
5. 12.5 Speech Database for ASR
6. 12.6 Developing ASR
7. 12.7 Performance of ASR
8. 12.8 Application Areas
9. 12.9 Conclusion and Future Work
10. References
13 Text Summarization: A Technical Overview and Research Perspectives
1. 13.1 Introduction
2. 13.2 Summarization Techniques
3. 13.3 Evaluating Summaries
4. 13.4 Datasets and Results
5. 13.5 Future Research Directions
6. 13.6 Conclusion
7. References
14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery
1. 14.1 Introduction
2. 14.2 Literature Review
3. 14.3 Understanding the Google Cloud Platform
4. 14.4 Using BigQuery in the Google Cloud Console
5. 14.5 Sentiment Analysis
6. 14.6 Turning to Google BigQuery Analysis
7. 14.7 Proposed Method
8. 14.8 Experimental Setup and Results
9. 14.9 Conclusion
10. References
15 A Review of Topic Modeling and Its Application
1. 15.1 Introduction
2. 15.2 Objective of Topic Modeling
3. 15.3 Motivations and Contributions
4. 15.4 Detailed Survey of Research Articles
5. 15.5 Comparison Table of Previous Research
6. 15.6 Expected Future Work
7. 15.7 Conclusion
8. References

Part II OPTIMIZATION

16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification
1. 16.1 Introduction
2. 16.2 Related Works
3. 16.3 Methodology
4. 16.4 Results and Discussion
5. 16.5 Conclusion
6. References
17 Optimal Inventory System in a Urea Bagging Industry
1. 17.1 Introduction
2. 17.2 Continuous Review Policy
3. 17.3 Inventory Optimization Techniques
4. 17.4 Model Formulation
5. 17.5 Numerical Calculations
6. 17.6 Conclusion
7. References
18 Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario
1. 18.1 Introduction
2. 18.2 Mixed Integer Programming Methods
3. 18.3 Introduction to Supply Chain Management System
4. 18.4 Mathematical Model Formulation
5. 18.5 Conclusion
6. References
19 Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry—A Machine Learning Approach
1. 19.1 Introduction
2. 19.2 Literature Review
3. 19.3 The Aim and Objectives of the Study
4. 19.4 Research Methodology
5. 19.5 Study Results and Discussions
6. 19.6 Conclusions
7. References
20 A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode
1. 20.1 Introduction
2. 20.2 Designing of 90° SPS
3. 20.3 Designing of Tunable Schiffman Phase Shifter
4. 20.4 Major Finding and Limitation
5. 20.5 Conclusion
6. References
21 Optimizing Manufacturing Performance Through Fuzzy Techniques
1. 21.1 Introduction
2. 21.2 Literature Review
3. 21.3 Performance Optimization through Fuzzy Techniques
4. 21.4 Conclusions
5. References
22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products
1. 22.1 Introduction
2. 22.2 Literature Review
3. 22.3 Symbols and Assumptions
4. 22.4 Model Formulation
5. 22.5 Conclusion
6. References

Part III META-HEURISTICS: APPLICATIONS AND INNOVATIONS

23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer
1. 23.1 An Introduction to Optimization
2. 23.2 Optimization and Engineering
3. 23.3 Meta-Heuristic Optimization
4. 23.4 Torquigener Albomaculosus
5. 23.5 Pufferfish and Circular Structures
6. 23.6 Results
7. 23.7 Conclusion
8. References
24 A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization
1. 24.1 Introduction
2. 24.2 Background on Sperm Swarm Optimization (SSO) and Grey Wolf Optimizer (GWO)
3. 24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization (HGWOSSO)
4. 24.4 Experiments and Results
5. 24.5 Discussion
6. 24.6 Conclusion
7. References
25 State-of-the-Art Optimization and metaheuristic Algorithms
1. 25.1 Introduction
2. 25.2 An Overview of Traditional Optimization Approaches
3. 25.3 Properties of Metaheuristics
4. 25.4 Classification of Single Objective Metaheuristic Algorithms
5. 25.5 Applications of Single Objective metaheuristic Approaches
6. 25.6 Classification of Multi-Objective Optimization Algorithms
7. 25.7 Hybridization of MOPs Algorithms
8. 25.8 Parallel Multi-Objective Optimization
9. 25.9 Applications of Multi-Objective Optimization
10. 25.10 Significant Contributions of Researchers in Various Metaheuristic Approaches
11. 25.11 Conclusion
12. 25.12 Major Findings, Future Scope of Metaheuristics and Its Applications
13. 25.13 Limitations and Motivation of Metaheuristics
14. Acknowledgements
15. References
26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique
1. 26.1 Introduction
2. 26.2 Proposed Methodology
3. 26.3 Simulation Results
4. 26.4 Conclusions
5. References
27 A New Parameter Estimation Technique of Three-Diode PV Cells
1. 27.1 Introduction
2. 27.2 Problem Statement
3. 27.3 Proposed Method
4. 27.4 Simulation Results and Discussions
5. 27.5 Conclusions
6. References

Part IV SUSTAINABLE COMPUTING

28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network
1. 28.1 Introduction
2. 28.2 System Model and Preliminaries
3. 28.3 Machine Learning Techniques of Decision Fusion
4. 28.4 Optimum Quantization of Decision Statistic and Fusion
5. 28.5 Measurement Setup
6. 28.6 Performance Evaluation
7. 28.7 Conclusion
8. 28.8 Limitations and Scope for Future Work
9. References
29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies
1. 29.1 Introduction
2. 29.2 Green Approaches: Significance and Motivation
3. 29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control
4. 29.4 Green IoT–Based Smart Irrigation Monitoring
5. 29.5 Technology Enablers for GIoT–Based Irrigation Monitoring
6. 29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation
7. 29.7 Other Recent Developments on GIoT–Based Smart Agriculture
8. 29.8 Literature Review of Edge Computing–Based Irrigation Monitoring
9. 29.9 LPWAN for GIoT–Based Smart Agriculture
10. 29.10 Analysis and Discussion
11. 29.11 Research Gap in GIoT–Based Precision Agriculture
12. 29.12 Analysis of Merits and Shortcomings
13. 29.13 Future Research Scope
14. 29.14 Conclusion
15. References
30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification
1. 30.1 Introduction
2. 30.2 Related Works
3. 30.3 Proposed Approach
4. 30.4 Experimental Details and Results
5. 30.5 Conclusion
6. References
31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission
1. 31.1 Introduction
2. 31.2 Related Work
3. 31.3 Proposed Model
4. 31.4 Complexity Analysis of Algorithms for Data Transmission
5. 31.5 Experimental Analysis
6. 31.6 Motivation and Limitations of Research
7. 31.7 Conclusion
8. 31.8 Future Work
9. References
32 Intelligent Computing for Precision Agriculture
1. 32.1 Introduction
2. 32.2 Technology in Agriculture
3. References
33 Intelligent Computing for Green Sustainability
1. 33.1 Introduction
2. 33.2 Modified DEMATEL
3. 33.3 Weighted Sum Model
4. 33.4 Weighted Product Model
5. 33.5 Weighted Aggregated Sum Product Assessment
6. 33.6 Grey Relational Analysis
7. 33.7 Simple Multi-Attribute Rating Technique
8. 33.8 Criteria Importance Through Inter-Criteria Correlation
9. 33.9 Entropy
10. 33.10 Evaluation Based on Distance From Average Solution
11. 33.11 MOORA
12. 33.12 Interpretive Structural Modeling
13. 33.13 Conclusions
14. 33.14 Limitations of the Study
15. 33.15 Suggestions for Future Research
16. References

Part V AI IN HEALTHCARE

34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease
1. 34.1 Introduction
2. 34.2 Methods
3. 34.3 Statistical Analysis
4. 34.4 Results
5. 34.5 Discussion
6. 34.6 Conclusion
7. Acknowledgements
8. References
35 Reconstruction of Dynamic MRI Using Convolutional LSTM Techniques
1. 35.1 Introduction
2. 35.2 Methodologies
3. 35.3 Problem Formulation
4. 35.4 Network Architecture
5. 35.5 Results
6. 35.6 Discussion
7. 35.7 Conclusion
8. References
36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion
1. 36.1 Introduction
2. 36.2 Literature Review
3. 36.3 Multispectral Face Database
4. 36.4 Methodology
5. 36.5 Experiments
6. 36.6 Results and Discussion
7. 36.7 Conclusions
8. Acknowledgments
9. References
37 Polyp Detection Using Deep Neural Networks
1. 37.1 Introduction
2. 37.2 Literature Survey
3. 37.3 Proposed Methodology
4. 37.4 Implementation and Results
5. 37.5 Conclusion and Future Work
6. References
38 Boundary Exon Prediction in Human Sequences Using External Information Sources
1. 38.1 Introduction
2. 38.2 Proposed Exon Prediction Model
3. 38.3 Homology-Based Exon Prediction
4. 38.4 Results and Discussion
5. 38.5 Conclusion
6. 38.6 Motivation and Limitations of the Research
7. 38.7 Major Findings of the Research
8. References
39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform
1. 39.1 Introduction
2. 39.2 Sample Preparation
3. 39.3 Methodology
4. 39.4 Results and Discussion
5. 39.5 Discussion
6. 39.6 Conclusion
7. 39.7 Future Scope
8. Acknowledgement
9. 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
1. 40.1 Introduction
2. 40.2 The Rationale of the Study
3. 40.3 Materials and Methodology
4. 40.4 GIS and COVID-19 (Corona) Mapping
5. 40.5 Results and Discussion
6. 40.6 Conclusion
7. References
41 Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi
1. 41.1 Introduction
2. 41.2 Hardware Design of Monitoring System
3. 41.3 Software Design of Monitoring System
4. 41.4 Working of ZigBee Module
5. 41.5 Developed App for the Monitoring of Health
6. 41.6 Google Fusion Table—Online Database
7. 41.7 Application Developed for Health Monitoring System
8. 41.8 Conclusion and Future Work
9. References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Flow chart of the study.
Figure 1.2 Basic neural network.
Figure 1.3 Sigmoid function.
Figure 1.4 Tanh function.
Figure 1.5 ReLU function.
Figure 1.6 Basic Bernoulli’s restricted Boltzmann machine.
Figure 1.7 Accuracy plot for one hidden layer–based ANN.
Figure 1.8 Accuracy plot for two hidden layer–based ANN.
Figure 1.9 Accuracy plot for three hidden layer–based ANN.
Figure 1.10 Accuracy plot for four hidden layer–based ANN.
Figure 1.11 Accuracy vs. hidden layer.

Chapter 2

Figure 2.1 Schematic diagram of biological neuron.
Figure 2.2 Propagation of signal through neurons.
Figure 2.3 Linear threshold function.
Figure 2.4 Schematic diagram of linear threshold gate.
Figure 2.5 ANN model with continuous characteristics.
Figure 2.6 Graphical representation of logistic sigmoid function.
Figure 2.7 Single layer neural network.
Figure 2.8 Multilayer neural network.
Figure 2.9 Formation of axon. (a) Input and output neuron oligonucleotides and t...
Figure 2.10 Formation of output molecule using DNA neural network. (a) Axon mole...
Figure 2.11 Structure of perceptron [5].
Figure 2.12 Catalytic activity of deoxyribozyme.
Figure 2.13 Mechanism to switch on deoxyribozyme logic gate.
Figure 2.14 Mechanism of YES gate.
Figure 2.15 Mechanism of NOT gate.
Figure 2.16 Mechanism of AND gate.
Figure 2.17 Structure of ANDANDNOT gate.
Figure 2.18 Toehold-mediated DNA branch migration and strand displacement [7].
Figure 2.19 Mechanism of two-input AND gate.
Figure 2.20 AND reaction.
Figure 2.21 OR reaction.
Figure 2.22 PROP reaction.

Chapter 3

Figure 3.1 Architecture of proposed framework.
Figure 3.2 GMG background subtraction model [18].
Figure 3.3 Keypoints for pose output [10].
Figure 3.4 Samples from the dataset.
Figure 3.5 Identification of garment of interest in the presence of a single cus...
Figure 3.6 Identification of garments of interest in the presence of a single cu...
Figure 3.7 Identification of garments of interest in the presence of multiple cu...
Figure 3.8 Variation of average confidence score with respect to changes in conf...
Figure 3.9 Average confidence score obtained by garments of interest of particul...
Figure 3.10 Total duration of time for which customers were interested in a garm...

Chapter 4

Figure 4.1 Complex plane Z(n).
Figure 4.2 Adding points (P = M + N).
Figure 4.3 Doubling a point (P = M + M).
Figure 4.4 Experiment on encryption.
Figure 4.5 Experiment on decryption.
Figure 4.6 Visualization of a qubit state.

Chapter 5

Figure 5.1 ML application for communications (re-generated from [24]).
Figure 5.2 Block diagram for the proposed blind identification method.
Figure 5.3 Comparison of overall classification accuracy with benchmark network.
Figure 5.4 Confusion matrix CNN with synthetic data set.
Figure 5.5 Autoencoder model for CSI feedback.
Figure 5.6 Inception block.
Figure 5.7 Encoder and decoder blocks of InceptNet.
Figure 5.8 Pseudo gray plots of (a) original image (b) image recovered by CsiNet...

Chapter 6

Figure 6.1 Transmission process.
Figure 6.2 MANETs routing protocol.
Figure 6.3 FANETs.
Figure 6.4 Mobility model.
Figure 6.5 Random way model.
Figure 6.6 GMM model.
Figure 6.7 Semi-random model.
Figure 6.8 Mission model.
Figure 6.9 Structure of CrANs [27].
Figure 6.10 Proposed structure of CrANS.
Figure 6.11 Single hop.
Figure 6.12 Multihop.
Figure 6.13 Protocol operation.
Figure 6.14 Positioning of sensor nodes.
Figure 6.15 Mesh formation.
Figure 6.16 Minimum spanning tree.
Figure 6.17 Throughput vs. delivery ratio.
Figure 6.18 Ideal placement of nodes.
Figure 6.19 Sensor nodes with faulty points.
Figure 6.20 Delay.
Figure 6.21 Packet delivery ratio.
Figure 6.22 Throughput.
Figure 6.23 Placement of nodes.
Figure 6.24 Good and bad nodes.
Figure 6.25 Fitness function.

Chapter 7

Figure 7.1 Schematic illustration of the setup used for experiments.
Figure 7.2 Comparative plot of actual and predicted value of permeate flux (%) o...
Figure 7.3 Comparative residual plot of training and prediction values.
Figure 7.4 Plot between residual value and number of occurrence.
Figure 7.5 Plot between initial values and predicted values.
Figure 7.6 Sensitivity analysis.

Chapter 8

Figure 8.1 Distribution from NFR catalogs.
Figure 8.2 The dataset definition process.
Figure 8.3 SIG catalog of performance.
Figure 8.4 SIG catalog of performance.
Figure 8.5 The final SIG security catalog.
Figure 8.6 Process of creating a classification.

Chapter 9

Figure 9.1 Flowchart of the proposed RL algorithm.
Figure 9.2 Proposed technique.
Figure 9.3 NNs used after the CNNs, viz., ResNet50 (left) and InceptionV3 (right...

Chapter 10

Figure 10.1 Block diagram of programmable logic controller.
Figure 10.2 Flow chart of programmable logic controller.
Figure 10.3 Fuzzy controller architecture.
Figure 10.4 Basic blocks of fuzzy logic GUI toolbox.
Figure 10.5 Basic neuron structure [25].
Figure 10.6 Artificial neural network’s (ANN) layers [26].
Figure 10.7 ANFIS editor.
Figure 10.8 P & I diagram of the case study (intermediate tank).
Figure 10.9 Electromagnetic flow meter.
Figure 10.10 Resistance temperature device (RTD).
Figure 10.11 Current-to-pressure (I/P) converter.
Figure 10.12 Process control valve.
Figure 10.13 FIS editor.
Figure 10.14 The membership function editor for temperature.
Figure 10.15 The membership function editor for fluid flow.
Figure 10.16 Membership function editor for steam valve.
Figure 10.17 Rule editor.
Figure 10.18 Pictorial representation of rules.
Figure 10.19 Surface viewer.
Figure 10.20 Loaded training data.
Figure 10.21 Loaded checking data.
Figure 10.22 Generation of new FIS from loaded data.
Figure 10.23 ANFIS editor showing results.
Figure 10.24 Modified rules.
Figure 10.25 Modified rules with new tags.
Figure 10.26 Rule viewer with testing data 1.
Figure 10.27 Rule viewer with testing data 2.
Figure 10.28 Rule viewer with testing data 3.
Figure 10.29 Rule viewer with testing data 4.
Figure 10.30 Rule viewer with testing data 5.
Figure 10.31 Modified surface viewer.

Chapter 11

Figure 11.1 Computers usage in modern concept of Manufacturing Industry 4.0.
Figure 11.2 Computer usage in car designing and manufacturing.
Figure 11.3 Optimization methods.
Figure 11.4 Traditional methods and non-traditional methods of optimization.
Figure 11.5 Injection molding process.
Figure 11.6 Three-dimensional printing process.
Figure 11.7 Arc welding process.
Figure 11.8 Casting process.
Figure 11.9 Machining process classification.
Figure 11.10 ANN vs. human brain functioning similarities.
Figure 11.11 Modular Neural Network.
Figure 11.12 Structure of typical neural network 5-k-l-m-1 [1].
Figure 11.13 Convoluted neural networks.
Figure 11.14 Recurrent neural network.
Figure 11.15 Closed-loop control systems.
Figure 11.16 Radial neural structure (self-drawn).
Figure 11.17 Multilayer perceptron.
Figure 11.18 Kohonen self-organizing neural network.
Figure 11.19 LSTM ANN structure.
Figure 11.20 ANN training [55, 56].
Figure 11.21 R or the correlation coefficient = 1.
Figure 11.22 CNC machine.

Chapter 12

Figure 12.1 Block diagram for training phase.
Figure 12.2 Block diagram for testing phase.
Figure 12.3 ASR block diagram.
Figure 12.4 Data collection modes.
Figure 12.5 Block diagram of system.

Chapter 13

Figure 13.1 Extractive summarization model using graph-based approach.
Figure 13.2 Architecture of GRAPHSUM model.

Chapter 14

Figure 14.1 Twitter users’ worldwide data January 2021 [32].
Figure 14.2 The Google Cloud console [6].
Figure 14.3 Sentiment analysis techniques [35].
Figure 14.4 Fetching of data using BigQuery [37].
Figure 14.5 The architecture of Google BigQuery [38].
Figure 14.6 Architectural view of the proposed system [42].
Figure 14.7 Data refining using BigQuery [39].
Figure 14.8 Google App Engine launcher [40].
Figure 14.9 (a) and (b) Twitter for BigQuery result window [41].

Chapter 15

Figure 15.1 Graphical model representation of LDA.
Figure 15.2 Process pipeline.
Figure 15.3 Work flow of the proposed system.
Figure 15.4 Creation of dataset and training of data.

Chapter 16

Figure 16.1 Four-fold plot of predicted class vs. actual class and a ROC curve f...
Figure 16.2 Four-fold plot of predicted class vs. actual class and a ROC curve f...
Figure 16.3 Four-fold plot of predicted class vs. actual class and an ROC curve ...
Figure 16.4 Four-fold plot of predicted class vs. actual class and a ROC curve f...

Chapter 17

Figure 17.1 Inventory management system.
Figure 17.2 Behavior of cost function I with respect to inventory level.
Figure 17.3 Behavior of cost function II with respect to inventory level.

Chapter 18

Figure 18.1 Supply chain network.
Figure 18.2 Mixed integer programming.
Figure 18.3 Inventory in supply chain performance.
Figure 18.4 Closed-loop supply chain management.
Figure 18.5 Supply chain network design under uncertainty.

Chapter 19

Figure 19.1 Architecture of the proposed ensemble model.

Chapter 20

Figure 20.1 Type-A SPS designed using microstrip TL.
Figure 20.2 Simulated result showing PD of Type-A SPS shown in Figure 20.1.
Figure 20.3 Input impedance at port 1 (or 2) of the (a) Type-A SPS has shown in ...
Figure 20.4 Modified Type-A SPS with single stub tuner.
Figure 20.5 Fabricated PS: (a) signal plane and (b) ground plane.
Figure 20.6 Simulated and measured (a) magnitude response, (b) phase response, a...
Figure 20.7 Equivalent spice model of varactor diode (SMV2019).
Figure 20.8 (a) SPS with single varactor diode. (b) Equivalent lumped RLGC model...
Figure 20.9 SPS with (a) two varactor diodes and (b) three varactor diodes.
Figure 20.10 For single varactor diode: (a) RL plot; (b) IL plot; (c) PD plot.
Figure 20.11 For two varactor diodes: (a) RL plot; (b) IL plot; (c) PD plot.
Figure 20.12 For three varactor diodes: (a) RL plot; (b) IL plot; (c) PD plot.

Chapter 21

Figure 21.1 Tools used in Fuzzy logic toolbox.
Figure 21.2 FIS procedure for present study.
Figure 21.3 Empirical transfer function.
Figure 21.4 Transfer function in fuzzy format of PC.
Figure 21.5 Transfer function in fuzzy format of PPC.
Figure 21.6 Transfer function in fuzzy format of QC.
Figure 21.7 Transfer function in fuzzy format of MGT.
Figure 21.8 Transfer function in fuzzy format of result.
Figure 21.9 Fuzzy rules.
Figure 21.10 Fuzzy rules.
Figure 21.11 Rule viewer.
Figure 21.12 Rule viewer.
Figure 21.13 Transfer function in fuzzy format of result.
Figure 21.14 Fuzzy rules.
Figure 21.15 Rule viewer.

Chapter 22

Figure 22.1 Inventory optimization.
Figure 22.2 Multi-product inventory network.
Figure 22.3 Three stage processes.

Chapter 23

Figure 23.1 3D Euclidean space.
Figure 23.2 (a) 3D model of function. (b) Gradient of function.
Figure 23.3 Traveling salesman problem.
Figure 23.4 Wing design parameters.
Figure 23.5 (a) Convex function. (b) Non-convex function.
Figure 23.6 Classification of optimization algorithms.
Figure 23.7 Genetic algorithm.
Figure 23.8 Torquigener albomaculosus [39].
Figure 23.9 Circular structure of pufferfish and zones: (a) sideview and (b) upp...
Figure 23.10 Illustration of circular structures.
Figure 23.11 The modeling of circular structure.
Figure 23.12 Beale function.
Figure 23.13 The change of objective function value versus number of iterations.
Figure 23.14 Change of circular structures for Beale function.

Chapter 24

Figure 24.1 Sperm Swam and the global best value (winner).

Chapter 25

Figure 25.1 Diagram depicting the flow of optimization process.
Figure 25.2 Complete usage of optimization type of problems.
Figure 25.3 Complete framework of metaheuristics approach.

Chapter 27

Figure 27.1 Representation of a three-diode model for a solar cell.
Figure 27.2 Different phases of Harris hawks optimization (HHO) [18].
Figure 27.3 Convergence curve for TDM (Kyocera KC200GT).
Figure 27.4 Convergence curve for TDM (Canadian Solar CS6K-280M).
Figure 27.5 Convergence curve for TDM (Schutten Solar STM6 40-36).
Figure 27.6 Convergence curve for TDM (SolarWorld Pro. SW255).
Figure 27.7 Kruskal-Wallis test performance for TDM (KC200GT).
Figure 27.8 Kruskal-Wallis test results for TDM (CS6K-280M).
Figure 27.9 Kruskal-Wallis test diagram for TDM (STM6 40-36).
Figure 27.10 Kruskal-Wallis test results for TDM (Pro. SW255 model).

Chapter 28

Figure 28.1 System model of the proposed CSS system with M number of secondary u...
Figure 28.2 Illustration showing SVM classifier for two set of data points, mark...
Figure 28.3 Illustration of decision thresholds and quantization points.
Figure 28.4 (a) SU node consisting of an antenna, RTL-SDR, and RPI SBC. (b) Inte...
Figure 28.5 Arrangement of SU nodes. LoS path between PU and SUs is obstructed b...
Figure 28.6 ROC for different fusion techniques applied on soft energy values. V...
Figure 28.7 ROC for different fusion techniques applied on soft energy values. S...
Figure 28.8 ROC plots with EGC decision fusion applied on OQ, UQ, and soft data ...
Figure 28.9 ROC plots with KMC decision fusion applied on OQ, UQ, and soft data ...
Figure 28.10 ROC plots with SVM decision fusion applied on OQ, UQ, and soft data...
Figure 28.11 P_D variation with SNR for different with EGC decision fusion. Val...
Figure 28.12 P_D variation with SNR for different in case of KMC decision fusio...
Figure 28.13 P_D variation with SNR for different in case of SVM decision fusio...
Figure 28.14 P_D variation with SNR for different K in case of EGC decision fusio...
Figure 28.15 P_D variation with SNR for different K in case of KMC decision fusio...
Figure 28.16 P_D variation with SNR for different K in case of SVM decision fusio...
Figure 28.17 P_D variation with number of samples for EGC decision fusion.
Figure 28.18 P_D variation with number of samples for KMC decision fusion.
Figure 28.19 P_D variation with number of samples for SVM decision fusion.
Figure 28.20 P_D variation with SNR for different number of SU for EGC decision f...
Figure 28.21 P_D variation with SNR for different number of SU for KMC decision f...
Figure 28.22 P_D variation with SNR for different number of SU for SVM decision f...

Chapter 29

Figure 29.1 GIoT–based layered architecture for smart irrigation monitoring.
Figure 29.2 Monitored parameters in machine learning–based precision agriculture...
Figure 29.3 Monitored parameters in edge Computing–based precision agriculture.
Figure 29.4 Monitored parameters in GIoT–based precision agriculture.
Figure 29.5 Power, costs, and security optimization in GIoT–based precision agri...
Figure 29.6 LPWAN technologies’ paper distribution used in GIoT–based agricultur...

Chapter 30

Figure 30.1 Block diagram of the proposed model for sentiment analysis.
Figure 30.2 (a) Comparison with time taken to classify data. (b) Result comparis...

Chapter 31

Figure 31.1 Proposed approach based fuzzy and A* star techniques with phase spli...
Figure 31.2 The problem of overlapping sensors.
Figure 31.3 Theoretical description to the overlapping problem present in most w...
Figure 31.4 A hypothetical deployment scenario for the deployment phase.
Figure 31.5 Deployment phase constraint relation.
Figure 31.6 Comparison of coverage through iterations in the deployment phase.
Figure 31.7 Travel distance through number of nodes.
Figure 31.8 Comparison of iterations through energy consumption.
Figure 31.9 Shortest path approach 1.
Figure 31.10 Shortest path approach 2.

Chapter 33

Figure 33.1 Three-dimensional indicators of sustainability performance.
Figure 33.2 MICMAC analysis.
Figure 33.3 ISM-based model.

Chapter 34

Figure 34.1 Procedure of sampling from the NHNES 2015–2016.
Figure 34.2 The posterior distribution of (1) differences of means (μ_M − μ_F), (2...

Chapter 35

Figure 35.1 Convolutional Neural Network. Source: Convolutional Neural Network T...
Figure 35.2 Cascaded Convolutional LSTM architecture without dilation.
Figure 35.3 Cascaded Convolutional LSTM architecture with dilation.
Figure 35.4 Cartesian masks. (a) 4× undersampling. (b) 6× undersampling.
Figure 35.5 Reconstruction results of CNN with a Cartesian undersampling rate of...
Figure 35.6 Reconstruction results of ConvLSTM with a Cartesian undersampling ra...
Figure 35.7 Reconstruction results of dilated ConvLSTM with a Cartesian undersam...

Chapter 36

Figure 36.1 Multispectral face images collected in six fixed illumination condit...
Figure 36.2 Gender classification based on Affine hull method.
Figure 36.3 Gender classification based on wavelet average fusion method.

Chapter 37

Figure 37.1 Total number of new cancer cases of males in 2018 (all ages) [3].
Figure 37.2 Total number of new cancer cases of females in 2018 (all ages) [3].
Figure 37.3 Colonoscopy equipment [4].
Figure 37.4 Polyp present in colon [5].
Figure 37.5 Proposed methodology.
Figure 37.6 Image quality improvement filters.
Figure 37.7 Architecture of VGG16 and VGG19 deep neural networks [24].
Figure 37.8 VGG16 model summary.
Figure 37.9 VGG19 model summary.
Figure 37.10 Polyp detection using VGG19 model.
Figure 37.11 Graphical representation of test accuracy and test loss.
Figure 37.12 Accuracy comparison of CVC-ClinicDB and new test dataset.

Chapter 38

Figure 38.1 Proposed exon prediction model.
Figure 38.2 Approach used for collecting homologous sequences.
Figure 38.3 Steps used for exon prediction.
Figure 38.4 Example of multiple sequence alignment consisting of exons and intro...
Figure 38.5 Steps used in complete gene prediction.
Figure 38.6 Interface for boundary exon predictor.

Chapter 39

Figure 39.1 Normalized spectra of glucose recorded with Jasco V770.
Figure 39.2 System block diagram.
Figure 39.3 A three-layer BP-ANN architecture.
Figure 39.4 Activation function.
Figure 39.5 Flowchart of BP-ANN.
Figure 39.6 MSSL vs. iterations.
Figure 39.7 Bland-Altman plots. (a) PLSR. (b) BP-ANN.
Figure 39.8 CEGA plot. (a) PLSR. (b) BP-ANN.
Figure 39.9 Regression analysis. (a) PLSR model. (b) BP-ANN.

Chapter 40

Figure 40.1 Location map of study area.
Figure 40.2 Flowchart of the methodology.
Figure 40.3 Mapping of novel COVID-19 of Gujarat state, India. (a) Cases tested ...

Chapter 41

Figure 41.1 Roadmap of health monitoring system [2].
Figure 41.2 Basic contigrade temparature sensor (2°C–150°C) [12].
Figure 41.3 A Photoplethysmogram (PPG) waveform (amplitude vs. time) [7].
Figure 41.4 CC2530 Development Kit Contents (courtesy: Texas Instruments) [6].
Figure 41.5 USB UART [13].
Figure 41.6 Overview of ZigBee ports connections [16].
Figure 41.7 Roadmap of Z-Stack [4].
Figure 41.8 Some more basic devices.
Figure 41.9 Screenshot showing the interface of developed app: (a) upside and (b...
Figure 41.10 Flow chart of health monitoring system’s mobile application [15].
Figure 41.11 The health report of patient [1].

List of Tables

Chapter 2

Table 2.1 Truth table for AND gate.
Table 2.2 Truth table for OR gate.
Table 2.3 Truth table for NOT gate.
Table 2.4 Truth table for YES gate.
Table 2.5 Truth table for NOT gate.
Table 2.6 Truth table for ANDANDNOT gate.

Chapter 3

Table 3.1 Layers involved in ResNet-101 architecture.
Table 3.2 Precision and recall of active garment detection.

Chapter 5

Table 5.1 CNN architecture layout for TF images using RML synthetic data set.
Table 5.2 Performance comparison analysis for 100 epochs between CsiNet and Ince...

Chapter 6

Table 6.1 Comparison of proactive reactive and hybrid protocol.
Table 6.2 Comparison of MANETs, VANETs.

Chapter 7

Table 7.1 Characteristics of the feed.
Table 7.2 Advantages and disadvantages of GMDH-NN method.
Table 7.3 Pros and cons of ANN method.
Table 7.4 Error analysis of GMDH-NN model.
Table 7.5 Comparative study between GMDH-NN model and ANN model.

Chapter 8

Table 8.1 Evolution of search string.
Table 8.2 Results after filters.
Table 8.3 Performance keywords.
Table 8.4 Keywords of performance.
Table 8.5 Merge of performance keywords.
Table 8.6 Adaptability keywords.
Table 8.7 Keywords extracted from SIG catalogs.
Table 8.8 Overview of nonfunctional requirements.
Table 8.9 The binary and multi-class metrics.
Table 8.10 Common words.
Table 8.11 The binary and multi-class classification metrics.

Chapter 9

Table 9.1 Distribution of data experimentally.
Table 9.2 Classification accuracy of various approaches on ImageNet (Second Clas...
Table 9.3 Classification accuracy of various approaches on Cats and Dogs dataset...
Table 9.4 Classification accuracy of various approaches on Caltech-101 dataset (...

Chapter 10

Table 10.1 Main instruments used in this process and instrumentation (P & I) dia...
Table 10.2 Comparison table.

Chapter 11

Table 11.1 Percentage relative error in output responses by ANN modeling [53, 54...

Chapter 12

Table 12.1 Corpus detail of automatic speech recognition system.
Table 12.2 Experiment results of speech recognition based on background noise pa...

Chapter 13

Table 13.1 Automatic text summarization techniques.
Table 13.2 Summarization systems ROUGE score.

Chapter 14

Table 14.1 The framework evaluation and administration ML algorithms.

Chapter 16

Table 16.1 The different classifiers based on 0.5 threshold.
Table 16.2 Different classifiers based on the best threshold generated by ROC th...

Chapter 17

Table 17.1 Costs for steady-state equations.
Table 17.2 Total cost and optimum value.

Chapter 19

Table 19.1 Study inquiry matrix.

Chapter 20

Table 20.1 Simulated and measured result (magnitude response) comparison of the ...
Table 20.2 Phase response comparison between simulated and measured result.
Table 20.3 Magnitude and phase response of the tunable phase shifter using singl...
Table 20.4 Magnitude and phase response of the tunable phase shifter using two d...
Table 20.5 Magnitude and phase response of the tunable phase shifter using three...
Table 20.6 Result comparison between the three designs.

Chapter 21

Table 21.1 Range for PC measurement.
Table 21.2 Range for PPC measurement.
Table 21.3 Range for QC measurement.
Table 21.4 Range for MGT measurement.
Table 21.5 Range for result measurement.
Table 21.6 Fuzzy rules for competency strategy.
Table 21.7 Range for SD measurement.
Table 21.8 Range for GM measurement.
Table 21.9 Range for CAM measurement.
Table 21.10 Range for AMN measurement.
Table 21.11 Range for result measurement.
Table 21.12 Fuzzy rules for green sustainability.
Table 21.13 Fuzzy values which are assigned to the different crisp variables.
Table 21.14 Fuzzy AHP decision matrix for manufacturing competency.
Table 21.15 Complete fuzzy AHP matrix for manufacturing competency.
Table 21.16 Fuzzy AHP decision matrix for green sustainability.
Table 21.17 Complete fuzzy AHP matrix for green sustainability.
Table 21.18 Fuzzy numbers for linguistic terms.
Table 21.19 Decision matrix for fuzzy MDEMATEL.
Table 21.20 Ranking matrix based on fuzzy MDEMATEL.
Table 21.21 Fuzzy numbers for linguistic terms.
Table 21.22 Normalized weighted decision matrix.
Table 21.23 Separation from negative ideal solution.
Table 21.24 Separation from positive ideal solution.
Table 21.25 Defuzzification and closeness.
Table 21.26 Ranking based on modified fuzzy TOPSIS.
Table 21.27 Fuzzy numbers for linguistic terms.
Table 21.28 Modified fuzzy VIKOR decision matrix.
Table 21.29 Normalized decision matrix.
Table 21.30 S, R, and Q values for fuzzy matrix.
Table 21.31 Defuzzified values and ranking based on modified fuzzy VIKOR.
Table 21.32 (A) Results of all tools for sustainable green development.
Table 21.32 (B) Results of all tools for manufacturing competency.

Chapter 23

Table 23.1 Circular structure of pufferfish.
Table 23.2 Parameters about meta-heuristic optimization algorithm in the test pr...
Table 23.3 Benchmark functions.
Table 23.4 Benchmark test results.

Chapter 24

Table 24.1 Multimodal and unimodal benchmark problems.
Table 24.2 Multi-modal of fixed dimension benchmarks problems.
Table 24.3 Multi-modal of fixed dimension benchmarks problems.
Table 24.4 Parameters of the approaches.
Table 24.5 SSO, GWO, and HGWOSSO numerical findings of multimodal and unimodal b...
Table 24.6 SSO, GWO, and HGWOSSO statistical findings of multimodal and unimodal...
Table 24.7 SSO, GWO, and HGWOSSO numerical findings of multimodal of fixed-dimen...
Table 24.8 SSO, GWO, and HGWOSSO statistical findings of multimodal of fixed-dim...
Table 24.9 SSO, GWO, and HGWOSSO numerical findings of multimodal of fixed-dimen...
Table 24.10 SSO, GWO, and HGWOSSO statistical findings of multimodal of fixed-di...
Table 24.11 Comparisons between GWO, SSO, and HGWOSSO.

Chapter 25

Table 25.1 Swarm-based intelligent methods, its merits, and demerits.
Table 25.2 Evolutionary algorithms, merits, and demerits.
Table 25.3 Physics-based algorithms, merits, and demerits.
Table 25.4 Merits and demerits of ecology-based algorithms.

Chapter 26

Table 26.1 Reduced systems and the fitness values of the PMSM drive in the discr...
Table 26.2 Some popular time-domain parameters of the reduced PMSM drive model i...
Table 26.3 Some popular performance indices of the reduced PMSM drive system in ...
Table 26.4 Controller gains and the fitness function values of the reduced-order...

Chapter 27

Table 27.1 Datasheet of four marketable PV modules [20, 21].
Table 27.2 Range of decision variables for TDM.
Table 27.3 Optimal parameters of TDM using different algorithms for KC200GT.
Table 27.4 Optimal parameters of TDM using different algorithms for CS6K-280M.
Table 27.5 Optimal parameters of TDM using different algorithms for STM6 40-36.
Table 27.6 Optimal parameters of TDM using different algorithms for Pro. SW255.
Table 27.7 Statistical analysis of error function for TDM.
Table 27.8 Wilcoxon rank sum test results for TDM for different PV models.
Table 27.9 Corrected p-values for TDM for the Wilcoxon test adding Holm-Bonferro...

Chapter 29

Table 29.1 ML algorithms for intelligent decision-making in smart irrigation con...
Table 29.2 Review of power/energy saving for GIoT–based smart irrigation monitor...
Table 29.3 Review of recent GIoT methods to save power/energy for smart agricult...
Table 29.4 Review of different edge computing–based smart weather and irrigation...
Table 29.5 Specifications of LPWAN techniques.

Chapter 30

Table 30.1 An example of sentiment term matrix for datasets.
Table 30.2 Characteristic of the considered training datasets.
Table 30.3 Results with other methods using Sentiment140 Twitter dataset.

Chapter 31

Table 31.1 Comparison of merits and drawbacks of the present approaches to real-...
Table 31.2 Deployment details.
Table 31.3 Complexity comparison of data transmission algorithms.

Chapter 33

Table 33.1 Decision matrix.
Table 33.2 Direct relation coefficient matrix.
Table 33.3 Normalized matrix (X).
Table 33.4 Identity matrix (I).
Table 33.5 (I-X) matrix.
Table 33.6 (I-X)⁻¹ matrix.
Table 33.7 Total relation matrix.
Table 33.8 C and S calculation.
Table 33.9 C and S matrix.
Table 33.10 Ranking based on modified DEMATEL.
Table 33.11 Decision matrix.
Table 33.12 Weighted sum matrix.
Table 33.13 Ranking based on WSM.
Table 33.14 Decision matrix.
Table 33.15 Weighted product matrix.
Table 33.16 Ranking based on WPM.
Table 33.17 Ranking based on WASPAS.
Table 33.18 Decision matrix.
Table 33.19 Normalized decision matrix.
Table 33.20 Max and Min values from normalized matrix.
Table 33.21 Normalized data.
Table 33.22 Deviation sequence matrix.
Table 33.23 Delta matrix.
Table 33.24 Grey relation coefficient matrix.
Table 33.25 Ranking based on GRA.
Table 33.26 Decision matrix.
Table 33.27 Normalized decision matrix.
Table 33.28 Utility calculation.
Table 33.29 Ranking based on SMART.
Table 33.30 Decision matrix.
Table 33.31 Best and worst values.
Table 33.32 Normalized matrix.
Table 33.33 Correlation matrix.
Table 33.34 Criteria weight matrix.
Table 33.35 Ranking based on CRITIC method.
Table 33.36 Decision matrix.
Table 33.37 Normalized matrix.
Table 33.38 Entropy matrix.
Table 33.39 Ranking based on entropy method.
Table 33.40 Decision matrix.
Table 33.41 PDA matrix.
Table 33.42 NDA matrix.
Table 33.43 SP_j matrix.
Table 33.44 SN_j matrix.
Table 33.45 Ranking based on EDAS method.
Table 33.46 Decision matrix.
Table 33.47 Defuzzified score based on sums of square and square root values.
Table 33.48 Normalized and defuzzified rating of alternatives.
Table 33.49 Ranking based on MOORA method.
Table 33.50 Final reachability matrix of factors.
Table 33.51 Iteration 1.
Table 33.52 Iteration 2.
Table 33.53 Iteration 3.
Table 33.54 Iteration 4.
Table 33.55 Iteration 5.
Table 33.56 Iteration 6.
Table 33.57 Iteration 7.
Table 33.58 Iteration 8.
Table 33.59 Iteration 9.
Table 33.60 Iteration 10.

Chapter 34

Table 34.1 Demographic and clinical characteristics of the patients with CAD.
Table 34.2 Classical evaluation of gender differences in association of lipid pr...
Table 34.3 Bayesian evaluation of gender differences in association of lipid pro...

Chapter 35

Table 35.1 Quantitative measures of MSE, SSIM, and PSNR of dynamic MRI for diffe...

Chapter 36

Table 36.1 Average classification accuracy based on Affine hull and wavelet fusi...

Chapter 37

Table 37.1 Comparative analysis of polyp detection techniques.
Table 37.2 Accuracy report of implemented models.
Table 37.3 Classification report for new test dataset.

Chapter 38

Table 38.1 Results of the proposed method at nucleotide (mRNA) level.
Table 38.2 Results of the proposed method at exon (mRNA) level.
Table 38.3 Results of the proposed method at the nucleotide (coding) level on da...
Table 38.4 Results of the proposed method at exon (coding) level on dataset 1.
Table 38.5 Results of the proposed method at the nucleotide (coding) level on da...
Table 38.6 Results of the proposed method at exon (coding) level on dataset 2.

Chapter 39

Table 39.1 Ten typical samples.
Table 39.2 Estimated result with PLSR and BP-ANN models.

Chapter 40

Table 40.1 District-wise COVID-19 cases in Gujarat.

Scrivener Publishing
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Sustainable Computing and Optimization

The objective of the series is to bring together the global research scholars, experts, and scientists in the research areas of sustainable computing and optimization from all over the world to share their knowledge and experiences on current research achievements in these fields. The series aims to provide a golden opportunity for the global research community to share their novel research results, findings, and innovations to a wide range of readers. Data is everywhere and continuing to grow massively, which has created a huge demand for qualified experts who can uncover valuable insights from the data. The series will promote sustainable computing and optimization methodologies in order to solve real life problems mainly from engineering and management systems domains. The series will mainly focus on the real-life problems, which can suitably be handled through these paradigms.

Submission to the series:

Dr. Prasenjit Chatterjee
Department of Mechanical Engineering,
MCKV Institute of Engineering, Howrah - 711204, West Bengal, India
E-mail: dr.prasenjitchatterjee6@gmail.com

Dr. Morteza Yazdani
Department of Management, Universidad Loyola Andalucia, Seville, Spain
E-mail: morteza_yazdani21@yahoo.com

Dr. Dilbagh Panchal
Department of Industrial and Production Engineering,
Dr. B. R. Ambedkar National Institute of Technology (NIT) Jalandhar, Punjab, India
E-mail: panchald@nitj.ac.in

Publishers at Scrivener
Martin Scrivener (martin@scrivenerpublishing.com)
Phillip Carmical (pcarmical@scrivenerpublishing.com)

Handbook of Intelligent Computing and Optimization for Sustainable Development

Edited by

Mukhdeep Singh Manshahia

Punjabi University, Patiala, Punjab, India

Valeriy Kharchenko

Federal Scientific Agroengineering Center, VIM, Moscow, Russia

Elias Munapo

Department of Statistics & Operations Research, NWU, Mafikeng Campus, South Africa

J. Joshua Thomas

UOW Malaysia KDU Penang University College, Malaysia

and

Pandian Vasant

Universiti Teknologi PETRONAS, Malaysia

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