Handbook of Intelligent Computing and Optimization for Sustainable Development
Description
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, 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.
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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.
Content
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
Preface
The editors are immensely gratified to introduce this Handbook of Intelligent Computing and Optimization for Sustainable Development, with the intent of compiling information that advances sustainability as a fundamental factor for intelligent computing and optimization.
Intelligent and smart technologies are at the forefront of technological advances that represent potential transformations in smart living. These technologies and their applications are already rapidly impacting many industries and occupations. With applications in a number of fields such as the Internet of Things (IoT), optimization, renewable energy, manufacturing, agriculture, healthcare and smart cities, research on the developments of artificial intelligence is essential for sustainable development. The recent rise of emerging networking technologies such as 5G networks, social networks, IoT networks, etc., have attracted significant attention from academia as well as industry professionals looking to utilize these technologies for efficient communication purposes. Future citizens of the world will face increasing sustainability issues and need to be better prepared for energy transformation and sustainable future economic development in a smart world. Advances in artificial intelligence (AI) paradigms and smart informatics systems domains highlight the need for systems that aim to improve the quality and security of human life; therefore, this book discusses applications of artificial intelligence in sustainable development.
Recently, optimization has also received enormous attention along with the rapidly increasing use of communication technology and 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 optima.
This book provides 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 the IoT, manufacturing, optimization and healthcare. In general, the book is intended as a presentation of a wide range of publicly advanced achievements in the field of intelligent computing and optimization, which will be useful for a wide range of readers and will doubtless have a positive impact on a solution to the problem of sustainable development.
This book is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on emerging perspectives in the field of artificial intelligence to improve their products or research. Therefore, it will be a great resource for both Master's and research students of computing sciences, engineering, environmental sciences, medical sciences, mathematics, statistics, economics and agricultural sciences. Moreover, industry experts from telecommunications, the energy sector, manufacturing industry, health sector, and finance and planning sectors can use this book as a reference resource.
Based on double-blind review processes, the 41 chapters were accepted for publication according to their suitability for the five parts of the book. A brief description of each chapter is given below.
Part I: Intelligent Computing and Applications
Chapter 1 proposes a mental workload prediction model using a neural network and the Bernoulli Boltzmann machine. For measuring mental workload, eye movement metrics were considered. The eye metrics were computed from raw eye movement data, which were recorded using an eye tracking device while solving coding problems. The authors found that the Bernoulli Boltzmann machine provides better accuracy in predicting mental workload from eye metrics.
Chapter 2 discusses the development of an artificial neural network (ANN) using short DNA strands, i.e., oligonucleotides. The short sequences of DNA molecules can be used to code input and output signals and to build the basic architecture of the neuron. The authors also illustrate the design methodology of DNA logic gates and DNA logic circuits. Because of a few drawbacks, viz. immense energy consumption, vast memory requirement and heat dissipation, the traditional computation approaches the limitations of its processing power and design strategy. The aim of the authors is a paradigm shift in the computational world; from silicon to carbon. The design strategies discussed in this chapter are essential for effective development of a practical DNA computer.
Chapter 3 focuses on a novel framework for detection of garments of interest from the footage of a surveillance camera. The video frames are processed using the GMG background subtraction model to obtain relevant foreground information along with foreground masks. The Mask R-CNN object detection model is used to identify customers and multiple image processing techniques are used to obtain the active garments in these frames. The detected customers are tracked and the OpenPose human pose estimation framework is utilized on them to obtain useful landmarks. The garments of interest are then determined based on the filtration of confidence scores calculated for each active garment. The framework was tested on a CCTV video dataset and was found to be effective despite facing arduous obstacles such as background noise and occlusions.
Chapter 4 studies matrix algebra and elliptic curve arithmetic computing based on the integration of modular arithmetic and complex number arithmetic. It describes the intelligent computing of nonlinear transformations based on the residue matrices and the elliptic curve arithmetic over complex plane that can be applied in the computer science fields, which deal with cryptographic applications for more security. Their mathematical properties over complex plane are applied to create the cryptographic nonlinear transformation techniques in traditional ciphers, elliptic curve cryptography and quantum cryptography.
Chapter 5 highlights the trends in fifth generation (5G) wireless communication and beyond. Further discussed is the use of machine learning algorithms in future wireless networks (5G and B5G). ML-based signal processing algorithms have the potential to solve network issues with 5G networks, thereby paving the way to the development of future intelligent wireless networks (B5G). This chapter presents a unified application of data science for wireless communication and analyzes the physical layer challenges for automatic modulation classification (AMC), resource allocation, channel estimation and millimeter wave communication. Further case studies have been carried out on AMC and CSI feedback in massive multi-input and multi-output (MIMO) systems. The performance of the proposed deep learning methods has been presented through extensive simulation results.
Chapter 6 envisions how all present networks can be compiled into a single crowd associated network called a CrAN. This chapter contains a routing protocol for the proposed network. All applications for the proposed network are also discussed. The authors highlight the importance of creating this network and how it tackles transmission problems better than other networks. Some limitations of the proposed network are also mentioned.
Chapter 7 demonstrates the application of a neural network (NN)-based group method of data handling (GMDH) for prediction of permeate flux (%) in disc-shaped membrane. The permeate flux is predicted using three parameters for this study such as pore size, operating pressure, and feed velocity. Different statistical techniques, such as mean absolute error, root mean square error (RMSE), RMSE-observation standard deviation error, and Pearson's correlation coefficient, are analyzed in order to show the precision of the GMDH-NN models. To demonstrate the performance of the GMDH-NN model, the total error values are compared with the developed artificial neural network model. The study illustrates that the GMDH model predicts permeate flux of disc membrane with high accuracy.
Chapter 8 introduces a new approach to identify nonfunctional needs by using nonfunctional requirements (NFR) catalogs via machine-learning methods, and proposes a process to acquire these catalogs by using a systemic mapping study of "lightweight." The authors analysis provides a way of generating data sets used to classify non-functional specifications by NFR catalogs extracted from the mapping research in order to address the circumstance. They focus on the definition of the various NFR forms, with an emphasis on usability, security, performance and adaptability.
Chapter 9 proposes an efficient and simple image recognition classification system, which consists of components from both reinforcement learning and deep learning. More specifically, Q-Learning is used with an agent having 2 states, and 2 to 3 actions. This classifier is different from others, because the latter use...
