Handbook of Intelligent Computing and Optimization for Sustainable Development (eBook, PDF)

Redaktion: Manshahia, Mukhdeep Singh; Vasant, Pandian; Thomas, J. Joshua; Munapo, Elias; Kharchenko, Valeriy

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 944
• Erscheinungstermin: 10. Februar 2022
• Englisch
• ISBN-13: 9781119792635
• Artikelnr.: 63445941

Autorenporträt

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.

Inhaltsangabe

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 17