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.

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

Pandian Vasant

Universiti Teknologi PETRONAS, Malaysia

Foreword xxxi

Preface xxxv

Acknowledgment xlv

Part I: Intelligent Computing and Applications 1

1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models 3
Souvik Das, Kintada Prudhvi and J. Maiti

1.1 Introduction 3

1.2 Data Acquisition Method 4

1.3 Feature Extraction 4

1.4 Deep Learning Models 5

1.5 Results 8

1.6 Discussion 10

1.7 Advantages and Disadvantages of the Study 11

1.8 Limitations of the Study 11

1.9 Conclusion 11

References 12

2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates 13
Mandrita Mondal and Kumar S. Ray

2.1 Introduction 13

2.2 Biological Neurons 15

2.3 Artificial Neural Networks 17

2.4 DNA Neural Networks 22

2.5 DNA Logic Gates 28

2.6 Advantages and Limitations 45

2.7 Conclusion 47

Acknowledgment 47

References 47

3 Intelligent Garment Detection Using Deep Learning 49
Aniruddha Srinivas Joshi, Savyasachi Gupta, Goutham Kanahasabai and Earnest Paul Ijjina

3.1 Introduction 49

3.2 Literature 50

3.3 Methodology 52

3.4 Experimental Results 59

3.5 Highlights 64

3.6 Conclusion and Future Works 65

Acknowledgements 65

References 66

4 Intelligent Computing on Complex Numbers for Cryptographic Applications 69
Ni Ni Hla and Tun Myat Aung

4.1 Introduction 69

4.2 Modular Arithmetic 70

4.3 Complex Plane 71

4.4 Matrix Algebra 71

4.5 Elliptic Curve Arithmetic 73

4.6 Cryptographic Applications 74

4.7 Conclusion 78

References 79

5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies 81
Satyendra Singh Yadav, Shrishail Hiremath, Pravallika Surisetti, Vijay Kumar and Sarat Kumar Patra

5.1 Introduction 82

5.2 Machine/Deep Learning for Future Wireless Communication 83

5.3 Case Studies 87

5.4 Major Findings 95

5.5 Future Research Directions 95

5.6 Conclusion 96

References 96

6 Designing of Routing Protocol for Crowd Associated Networks (CrANs) 101
Rabia Bilal and Bilal Muhammad Khan

6.1 Introduction 101

6.2 Background Study 103

6.3 CrANs 117

6.4 Simulation of MANET Network 123

6.5 Simulation of VANET Network 126

6.6 CrANs 130

6.7 Conclusion 132

References 132

7 Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane 135
Anirban Banik, Mrinmoy Majumder, Sushant Kumar Biswal and Tarun Kanti Bandyopadhyay

7.1 Introduction 135

7.2 Experimental Procedure 138

7.3 Methodology 139

7.4 Results and Discussions 142

7.5 Conclusions 146

Acknowledgements 147

References 147

8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach 149
M. Sunil Kumar, A. Harika, C. Sushama and P. Neelima

8.1 Introduction 149

8.2 Literature Survey 153

8.3 Methodology 156

8.4 Dataset 165

8.5 Evaluation 166

8.6 Conclusion 169

References 170

9 Image Classification by Reinforcement Learning With Two-State Q-Learning 171
Abdul Mueed Hafiz

9.1 Introduction 171

9.2 Proposed Approach 173

9.3 Datasets Used 174

9.4 Experimentation 176

9.5 Conclusion 178

References 178

10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process 183
Pankaj Mohindru, Pooja and Vishwesh Akre

10.1 Introduction 184

10.2 Distributed Control System 187

10.3 Fuzzy Logic 192

10.4 Artificial Neural Network 193

10.5 Neuro-Fuzzy 194

10.6 Case Study 197

10.7 Software Implementation on Graphical User Interface 203

10.8 Results and Discussion 212

10.9 Discussion 214

10.10 Conclusion 214

10.11 Scope for Future Work 215

References 215

Appendix 10.1 MATLAB Simulation Configuration Using Sugeno 217

Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model 218

Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model 218

11 Artificial Neural Network in the Manufacturing Sector 219
Navriti Gupta

11.1 Introduction 219

11.2 Optimization 221

11.3 Artificial Neural Network: Optimization of Mechanical Systems 223

11.4 ANN vs. Human Brain 228

11.5 Architecture of Artificial Neural Networks 229

11.6 Learning Algorithm(s) 235

11.7 Different Type of Data 237

11.8 Case Study: Hard Machining of EN 31 Steel 238

11.9 Advantages of Using ANN in Manufacturing Sectors 242

11.10 Disadvantages of Using ANN in Manufacturing Sectors 242

11.11 Applications 242

11.12 Conclusions 243

11.13 Future Scope of ANN in Manufacturing Sectors 244

References 245

12 Speech-Based Multilingual Translation Framework 249
Saloni and Williamjeet Singh

12.1 Introduction 249

12.2 Literature Survey 250

12.3 Phases of ASR 252

12.4 Modules of ASR 253

12.5 Speech Database for ASR 253

12.6 Developing ASR 255

12.7 Performance of ASR 256

12.8 Application Areas 257

12.9 Conclusion and Future Work 258

References 258

13 Text Summarization: A Technical Overview and Research Perspectives 261
Korrapati Sindhu and Karthick Seshadri

13.1 Introduction 262

13.2 Summarization Techniques 263

13.3 Evaluating Summaries 279

13.4 Datasets and Results 281

13.5 Future Research Directions 281

13.6 Conclusion 282

References 282

14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery 287
Sitendra Tamrakar, B. K. Madhavi and V. Mohan

14.1 Introduction 287

14.2 Literature Review 289

14.3 Understanding the Google Cloud Platform 291

14.4 Using BigQuery in the Google Cloud Console 294

14.5 Sentiment Analysis 294

14.6 Turning to Google BigQuery Analysis 295

14.7 Proposed Method 297

Streaming API 298

14.8 Experimental Setup and Results 300

14.9 Conclusion 302

References 303

15 A Review of Topic Modeling and Its Application 305
R. Sandhiya, A. M. Boopika, M. Akshatha, S. V. Swetha and N. M. Hariharan

15.1 Introduction 305

15.2 Objective of Topic Modeling 306

15.3 Motivations and Contributions 307

15.4 Detailed Survey of Research Articles 308

Information Extraction Systems by Gibbs Sampling 316

Monte Carlo Algorithm 316

15.5 Comparison Table of Previous Research 319

15.6 Expected Future Work 320

15.7 Conclusion 320

References 321

Part II: Optimization 323

16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification 325
Fasanya, Oluwafunmibi O., Adediran, Adetola A., Ewemooje, Olusegun S. and Adebola, Femi B.

16.1 Introduction 325

16.2 Related Works 326

16.3 Methodology 328

16.4 Results and Discussion 334

16.5 Conclusion 337

References 338

17 Optimal Inventory System in a Urea Bagging Industry 339
C. Vijayalakshmi, R. Subramani and N. Anitha

17.1 Introduction 339

17.2 Continuous Review Policy 345

17.3 Inventory Optimization Techniques 345

17.5 Numerical Calculations 353

17.6 Conclusion 354

References 354

18 Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario 357
C. Vijayalakshmi

18.1 Introduction 357

18.2 Mixed Integer Programming Methods 359

18.3 Introduction to Supply Chain Management System 359

18.4 Mathematical Model Formulation 362

18.5 Conclusion 368

References 368

19 Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry—A Machine Learning Approach 371
Blanche Mabusela-Motsosi, Senzosenkosi Myeni and Elias Munapo

19.1 Introduction 372

19.2 Literature Review 373

19.3 The Aim and Objectives of the Study 374

19.4 Research Methodology 374

19.5 Study Results and Discussions 376

19.6 Conclusions 381

References 382

20 A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode 385
Partha Kumar Deb, Tamasi Moyra and Bidyut Kumar Bhattacharyya

20.1 Introduction 385

20.2 Designing of 90° SPS 386

20.3 Designing of Tunable Schiffman Phase Shifter 391

20.4 Major Finding and Limitation 398

20.5 Conclusion 398

References 399

21 Optimizing Manufacturing Performance Through Fuzzy Techniques 401
Chandan Deep Singh, Harleen Kaur and Rajdeep Singh

21.1 Introduction 401

21.2 Literature Review 403

21.3 Performance Optimization through Fuzzy Techniques 408

21.4 Conclusions 441

References 443

22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products 447
Thiripura Sundari P.R. and Vijayalakshmi C.

22.1 Introduction 447

22.2 Literature Review 448

22.3 Symbols and Assumptions 449

22.4 Model Formulation 451

22.5 Conclusion 459

References 459

Part III: Meta-Heuristics: Applications and Innovations 461

23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer 463
Mehmet Cem Catalbas and Arif Gulten

23.1 An Introduction to Optimization 463

23.2 Optimization and Engineering 465

23.3 Meta-Heuristic Optimization 469

23.4 Torquigener Albomaculosus 471

23.5 Pufferfish and Circular Structures 471

23.6 Results 475

23.7 Conclusion 483

References 483

24 A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization 487
Hisham A. Shehadeh and Nura Modi Shagari

24.1 Introduction 487

24.2 Background on Sperm Swarm Optimization (SSO) and Grey

Wolf Optimizer (GWO) 489

24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization

(HGWOSSO) 493

24.4 Experimental and Results 494

24.5 Discussion 504

24.6 Conclusion 505

References 505

25 State-of-the-Art Optimization and Metaheuristic Algorithms 509
Vineet Kumar, R. Naresh, Veena Sharma and Vineet Kumar

25.1 Introduction 509

25.2 An Overview of Traditional Optimization Approaches 511

25.3 Properties of Metaheuristics 512

25.4 Classification of Single Objective Metaheuristic Algorithms 514

25.5 Applications of Single Objective Metaheuristic Approaches 519

25.6 Classification of Multi-Objective Optimization Algorithms 519

25.7 Hybridization of MOPs Algorithms 521

25.8 Parallel Multi-Objective Optimization 521

25.9 Applications of Multi-Objective Optimization 525

25.10 Significant Contributions of Researchers in Various

Metaheuristic Approaches 526

25.11 Conclusion 528

25.12 Major Findings, Future Scope of Metaheuristics and Its Applications 529

25.13 Limitations and Motivation of Metaheuristics 529

Acknowledgements 530

References 530

26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique 537
Souvik Ganguli, Tanya Srivastava, Gagandeep Kaur and Prasanta Sarkar

26.1 Introduction 538

26.2 Proposed Methodology 541

26.3 Simulation Results 542

26.4 Conclusions 545

References 546

27 A New Parameter Estimation Technique of Three-Diode PV Cells 549
Shilpy Goyal, Parag Nijhawan, Yashonidhi Srivastava and Souvik Ganguli

27.1 Introduction 549

27.2 Problem Statement 551

27.3 Proposed Method 553

27.4 Simulation Results and Discussions 555

27.5 Conclusions 603

References 603

Part IV: Sustainable Computing 605

28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network 607
Saikat Majumder and Mukhdeep Singh Manshahia

28.1 Introduction 607

28.2 System Model and Preliminaries 610

28.3 Machine Learning Techniques of Decision Fusion 613

28.4 Optimum Quantization of Decision Statistic and Fusion 618

28.5 Measurement Setup 621

28.6 Performance Evaluation 623

28.7 Conclusion 633

28.8 Limitations and Scope for Future Work 633

References 634

29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies 637
Parijata Majumdar, Sanjoy Mitra and Diptendu Bhattacharya

29.1 Introduction 638

29.2 Green Approaches: Significance and Motivation 638

29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control 639

29.4 Green IoT–Based Smart Irrigation Monitoring 639

29.5 Technology Enablers for GIoT–Based Irrigation Monitoring 642

29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation 642

29.7 Other Recent Developments on GIoT–Based Smart Agriculture 643

29.8 Literature Review of Edge Computing–Based Irrigation Monitoring 645

29.9 LPWAN for GIoT–Based Smart Agriculture 646

29.10 Analysis and Discussion 647

29.11 Research Gap in GIoT–Based Precision Agriculture 649

29.12 Analysis of Merits and Shortcomings 650

29.13 Future Research Scope 651

29.14 Conclusion 651

References 652

30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification 655
B. Bazeer Ahamed and Z. A. Feroze Ahamed

30.1 Introduction 655

30.2 Related Works 656

30.3 Proposed Approach 657

30.4 Experimental Details and Results 660

30.5 Conclusion 662

References 662

31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission 665
R. Deepa, Revathi Venkataraman and Soumya Snigdha Kundu

31.1 Introduction 665

31.2 Related Work 666

31.3 Proposed Model 667

31.4 Complexity Analysis of Algorithms for Data Transmission 671

31.5 Experimental Analysis 672

31.6 Motivation and Limitations of Research 675

31.7 Conclusion 675

31.8 Future Work 675

References 675

32 Intelligent Computing for Precision Agriculture 677
Priyanka Gupta, Kavita Jhajharia and Pratistha Mathur

32.1 Introduction 677

32.2 Technology in Agriculture 684

References 691

33 Intelligent Computing for Green Sustainability 693
Chandan Deep Singh and Harleen Kaur

33.1 Introduction 693

33.2 Modified DEMATEL 697

33.3 Weighted Sum Model 706

33.4 Weighted Product Model 708

33.5 Weighted Aggregated Sum Product Assessment 709

33.6 Grey Relational Analysis 712

33.7 Simple Multi-Attribute Rating Technique 717

33.8 Criteria Importance Through Inter-Criteria Correlation 721

33.9 Entropy 726

33.10 Evaluation Based on Distance From Average Solution 731

33.11 MOORA 739

33.12 Interpretive Structural Modeling 739

33.13 Conclusions 748

33.14 Limitations of the Study 749

33.15 Suggestions for Future Research 749

References 750

Part V: AI in Healthcare 753

34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease 755
Vivek Verma, Anita Verma, Ashwani Kumar Mishra, Hafiz T.A. Khan, Dilip C. Nath and Rajiv Narang

34.1 Introduction 756

34.2 Methods 757

34.3 Statistical Analysis 757

34.4 Results 759

34.5 Discussion 761

34.6 Conclusion 767

Acknowledgements 767

References 767

35 Reconstruction of Dynamic MRI Using Convolutional LSTM Technique 771
Shashidhar V. Yakkundi and Subha D. Puthankattil

35.1 Introduction 771

35.2 Methodologies 773

35.3 Problem Formulation 774

35.4 Network Architecture 776

35.5 Results 778

35.6 Discussion 780

35.7 Conclusion 782

References 784

36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion 785
Narayan Vetrekar, Aparajita Naik and R. S. Gad

36.1 Introduction 785

36.2 Literature Review 787

36.3 Multispectral Face Database 791

36.4 Methodology 792

36.5 Experiments 794

36.6 Results and Discussion 794

36.7 Conclusions 796

Acknowledgments 797

References 797

37 Polyp Detection Using Deep Neural Networks 801
Nancy Rani, Rupali Verma and Alka Jindal

37.1 Introduction 801

37.2 Literature Survey 803

37.3 Proposed Methodology 806

37.4 Implementation and Results 810

37.5 Conclusion and Future Work 812

References 813

38 Boundary Exon Prediction in Humans Sequences Using External Information Sources 815
Neelam Goel, Shailendra Singh and Trilok Chand Aseri

38.1 Introduction 815

38.2 Proposed Exon Prediction Model 817

38.3 Homology-Based Exon Prediction 819

38.4 Results and Discussion 827

38.5 Conclusion 830

38.6 Motivation and Limitations of the Research 831

38.7 Major Findings of the Research 831

References 832

39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform 835
Jivan Parab, M. Sequeira, M. Lanjewar, C. Pinto and G.M. Naik

39.1 Introduction 835

39.2 Sample Preparation 837

39.3 Methodology 839

39.4 Results and Discussion 842

39.5 Discussion 845

39.6 Conclusion 846

39.7 Future Scope 846

Acknowledgement 847

References 847

40 GIS-Based Geospatial Assessment of Novel Corona Virus (COVID-19) in One of the Promising Industrial States of India—A Case of Gujarat 849
Azazkhan I. Pathan, Pankaj J. Gandhi , P.G. Agnihotri and Dhruvesh Patel

40.1 Introduction 849

40.2 The Rationale of the Study 852

40.3 Materials and Methodology 854

40.4 GIS and COVID-19 (Corona) Mapping 859

40.5 Results and Discussion 860

40.6 Conclusion 865

References 866

41 Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi 869
Munish Manas and Shivam Kumar

41.1 Introduction 869

41.2 Hardware Design of Monitoring System 870

41.3 Software Design of Monitoring System 873

41.4 Working of ZigBee Module 874

41.5 Developed App for the Monitoring of Health 874

41.6 Google Fusion Table—Online Database 875

41.7 Application Developed for Health Monitoring System 876

41.8 Conclusion and Future Work 877

References 877

Index 879 

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