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Handbook of Intelligent Computing and Optimization for Sustainable Development

1. Aufl.

von: Mukhdeep Singh Manshahia, Valeriy Kharchenko, Elias Munapo, J. Joshua Thomas, Pandian Vasant
275,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	11.02.2022
ISBN/EAN:	9781119792628
Sprache:	englisch
Anzahl Seiten:	944

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Titelbeschreibung

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.

Inhaltsverzeichnis

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

Autorenportrait

Mukhdeep Singh Manshahia, PhD, is an assistant professor at Punjabi University Patiala, India. He has published more than 40 international and national research papers and edited 1 book. Valeriy Kharchenko, PhD, is the Chief Scientific Officer at the Federal Scientific Agro Engineering Center VIM, Moscow, Russia. Elias Munapo, PhD, is a full professor in the Department of Statistics & Operations Research, North West University, South Africa. He has published more than 100 research articles and book chapters and has edited several volumes. J. Joshua Thomas, PhD, is a senior lecturer at UOW Malaysia KDU Penang University College, Malaysia. Currently, he is working with machine learning, big data, data analytics, deep learning, specifically targeting convolutional neural networks (CNN) and bi-directional recurrent neural networks (RNN) for image tagging with embedded natural language processing, end-to-end steering learning systems, and GAN. He has published more than 40 papers in leading international conference proceedings and peer-reviewed journals. Pandian Vasant, PhD, is a professor at Universiti Teknologi PETRONAS, Malaysia. He has co-authored more than 250 research articles in journals, conference proceedings, presentations, special issues guest editor, book chapters, and is the Editor-in-Chief of International Journal of Energy Optimization & Engineering.

Back cover copy

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.