Handbook of Intelligent Computing and Optimization for Sustainable Development E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1488

Veröffentlichungsjahr: 2022

Beschreibung

HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Dedication

Foreword

Preface

Acknowledgment

Part I INTELLIGENT COMPUTING AND APPLICATIONS

1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models

1.1 Introduction

1.2 Data Acquisition Method

1.3 Feature Extraction

1.4 Deep Learning Models

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

2.4 DNA Neural Networks

2.5 DNA Logic Gates

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

3.4 Experimental Results

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

4.3 Complex Plane

4.4 Matrix Algebra

4.5 Elliptic Curve Arithmetic

4.6 Cryptographic Applications

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

5.3 Case Studies

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

6.2 Background Study

6.3 CrANs

6.4 Simulation of MANET Network

6.5 Simulation of VANET Network

6.6 CrANs

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

7.2 Experimental Procedure

7.3 Methodology

7.4 Results and Discussions

7.5 Conclusions

Acknowledgements

References

8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach

8.1 Introduction

8.2 Literature Survey

8.3 Methodology

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

9.4 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

10.2 Distributed Control System

10.3 Fuzzy Logic

10.4 Artificial Neural Network

10.5 Neuro-Fuzzy

10.6 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

11.4 ANN vs. Human Brain

11.5 Architecture of Artificial Neural Networks

11.6 Learning Algorithm(s)

11.7 Different Type of Data

11.8 Case Study: Hard Machining of EN 31 Steel

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

12.6 Developing ASR

12.7 Performance of ASR

12.8 Application Areas

12.9 Conclusion and Future Work

References

13 Text Summarization: A Technical Overview and Research Perspectives

13.1 Introduction

13.2 Summarization Techniques

13.3 Evaluating Summaries

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

14.2 Literature Review

14.3 Understanding the Google Cloud Platform

14.4 Using BigQuery in the Google Cloud Console

14.5 Sentiment Analysis

14.6 Turning to Google BigQuery Analysis

14.7 Proposed Method

14.8 Experimental Setup and Results

14.9 Conclusion

References

15 A Review of Topic Modeling and Its Application

15.1 Introduction

15.2 Objective of Topic Modeling

15.3 Motivations and Contributions

15.4 Detailed Survey of Research Articles

15.5 Comparison Table of Previous Research

15.6 Expected Future Work

15.7 Conclusion

References

Part II OPTIMIZATION

16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification

16.1 Introduction

16.2 Related Works

16.3 Methodology

16.4 Results and Discussion

16.5 Conclusion

References

17 Optimal Inventory System in a Urea Bagging Industry

17.1 Introduction

17.2 Continuous Review Policy

17.3 Inventory Optimization Techniques

17.4 Model Formulation

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

18.5 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

19.6 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

21.2 Literature Review

21.3 Performance Optimization through Fuzzy Techniques

21.4 Conclusions

References

22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products

22.1 Introduction

22.2 Literature Review

22.3 Symbols and Assumptions

22.4 Model Formulation

22.5 Conclusion

References

Part III META-HEURISTICS: APPLICATIONS AND INNOVATIONS

23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer

23.1 An Introduction to Optimization

23.2 Optimization and Engineering

23.3 Meta-Heuristic Optimization

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)

24.3 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

25.5 Applications of Single Objective metaheuristic Approaches

25.6 Classification of Multi-Objective Optimization Algorithms

25.7 Hybridization of MOPs Algorithms

25.8 Parallel Multi-Objective Optimization

25.9 Applications of Multi-Objective Optimization

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

27.4 Simulation Results and Discussions

27.5 Conclusions

References

Part IV SUSTAINABLE COMPUTING

28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network

28.1 Introduction

28.2 System Model and Preliminaries

28.3 Machine Learning Techniques of Decision Fusion

28.4 Optimum Quantization of Decision Statistic and Fusion

28.5 Measurement Setup

28.6 Performance Evaluation

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

30.4 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

31.4 Complexity Analysis of Algorithms for Data Transmission

31.5 Experimental Analysis

31.6 Motivation and Limitations of Research

31.7 Conclusion

31.8 Future Work

References

32 Intelligent Computing for Precision Agriculture

32.1 Introduction

32.2 Technology in Agriculture

References

33 Intelligent Computing for Green Sustainability

33.1 Introduction

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

33.13 Conclusions

33.14 Limitations of the Study

33.15 Suggestions for Future Research

References

Part V AI IN HEALTHCARE

34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease

34.1 Introduction

34.2 Methods

34.3 Statistical Analysis

34.4 Results

34.5 Discussion

34.6 Conclusion

Acknowledgements

References

35 Reconstruction of Dynamic MRI Using Convolutional LSTM Techniques

35.1 Introduction

35.2 Methodologies

35.3 Problem Formulation

35.4 Network Architecture

35.5 Results

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

36.3 Multispectral Face Database

36.4 Methodology

36.5 Experiments

36.6 Results and Discussion

36.7 Conclusions

Acknowledgments

References

37 Polyp Detection Using Deep Neural Networks

37.1 Introduction

37.2 Literature Survey

37.3 Proposed Methodology

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

38.3 Homology-Based Exon Prediction

38.4 Results and Discussion

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

39.2 Sample Preparation

39.3 Methodology

39.4 Results and Discussion

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

40.2 The Rationale of the Study

40.3 Materials and Methodology

40.4 GIS and COVID-19 (Corona) Mapping

40.5 Results and Discussion

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

End User License Agreement

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

Foreword

Preface

Acknowledgments

Begin Reading

Index

End User License Agreement

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.

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

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 ChatterjeeDepartment of Mechanical Engineering,MCKV Institute of Engineering, Howrah - 711204, West Bengal, IndiaE-mail: [email protected]

Dr. Morteza YazdaniDepartment of Management, Universidad Loyola Andalucia, Seville, SpainE-mail: [email protected]

Dr. Dilbagh PanchalDepartment of Industrial and Production Engineering,Dr. B. R. Ambedkar National Institute of Technology (NIT) Jalandhar, Punjab, IndiaE-mail: [email protected]

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

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

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

© 2022 Scrivener Publishing LLC

For more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-79182-9

Cover images: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Dedicated toGreen Earth

Foreword

Emerging cutting-edge methodologies of smart technology models are thoroughly investigated holistically reflected, comprehensively explained and meticulously illustrated in this book. Included are highly important fields of investigation and facilitation of original, innovative and novel real-life application of smart-technology models currently being used today, related to novel up-and-coming areas of business, industry and personal life. During previous decades, the analytical toolbox and heuristic methods of mathematics and statistics, computer science and analytics, and exploration and information have attracted the attention of numerous researchers and practitioners worldwide. This development has also had a strong impact on science, engineering, economics, the IT sector and, recently, the branches of hospitality and recreation, leisure, travel and the arts. Here, analysis and calculus, algebra and arithmetic, statistics and stochastics, optimization and optimal control, and neuro- and quantum physics have become key technologies of support from an integrated viewpoint, and are closely related and supportive to modern operational research (OR), including OR for development of developing countries, social complexity and ethics, to appropriately address aspects of living conditions, life perspectives, education, security, freedom and peace in relation to new and fast-growing industries, the environment, and towards future generations.