Artificial Intelligence in Industry 4.0 and 5g Technology

Herausgegeben:Vasant, Pandian; Munapo, Elias; Thomas, J. Joshua; Weber, Gerhard-Wilhelm

• Gebundenes Buch

Produktbeschreibung

Artificial Intelligence in Industry 4.0 and 5G Technology

Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors

As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise.

Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT).
_ Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks.
_ Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor.
_ Provides guidance on implementing metaheuristics in different applications and hybrid technological systems.
_ Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement.

Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119798760
• 1. Auflage
• Seitenzahl: 352
• Erscheinungstermin: 20. Juli 2022
• Englisch
• Abmessung: 235mm x 157mm x 23mm
• Gewicht: 622g
• ISBN-13: 9781119798767
• ISBN-10: 1119798760
• Artikelnr.: 63047145

Autorenporträt

PANDIAN VASANT is Research Associate at MERLIN Research Centre, TDTU, HCMC, Vietnam, and Editor in Chief of International Journal of Energy Optimization and Engineering (IJEOE). He holds PhD in Computational Intelligence (UNEM, Costa Rica), MSc (University Malaysia Sabah, Malaysia, Engineering Mathematics) and BSc (Hons, Second Class Upper) in Mathematics (University of Malaya, Malaysia). He has co-authored research articles in journals, conference proceedings, presentations, special issues Guest Editor, chapters and General Chair of EAI International Conference on Computer Science and Engineering in Penang, Malaysia (2016) and Bangkok, Thailand (2018). ELIAS MUNAPO, PhD, currently heads the Department of Business Statistics and Operations research at North West University-Mafikeng, South Africa. He has published 50+ articles and contributed to five chapters on industrial engineering and management texts. J. JOSHUA THOMAS is an Associate Professor at UOW Malaysia KDU Penang University College. He obtained his PhD (Intelligent Systems Techniques) from University Sains Malaysia, Penang and master's degree from Madurai Kamaraj University, India. He is working with Deep Learning algorithms, specially targeting on Graph Convolutional Neural Networks (GCNN) and Bi-directional Recurrent Neural Networks (RNN) for drug target interaction and image tagging with embedded natural language processing. His work involves experimental research with software prototypes and mathematical modelling and design. GERHARD-WILLIAM WEBER, PhD, is Professor and Chair of Marketing and Economic Engineering at Poznan University of Technology, Poland. He is also an Adjunct Professor at Department of Industrial and Systems Engineering, College of Engineering at Istinye University, Istanbul, Turkey.

Inhaltsangabe

List of Contributors xv

Preface xix

Profile of Editors xxvii

Acknowledgments xxx

1 Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 4.0 1
Subhash Mondal, Swapnoj Banerjee, Soumodipto Halder, and Diganta Sengupta

1.1 Introduction 1

1.2 Literature Review 2

1.3 Proposed Framework 5

1.3.1 Enrolment Phase 5

1.3.2 Authentication Phase 7

1.3.2.1 Pre-processing 7

1.3.2.2 Minutiae Extraction and False Minutiae Removal 12

1.3.2.3 Key Generation from extracted Minutiae points 13

1.3.2.4 Encrypting the Biometric Fingerprint Image Using AES 14

1.4 Comparative Analysis 18

1.5 Conclusion 19

References 19

2 Decision Support Methodology for Scheduling Orders in Additive Manufacturing 25
Juan Jesús Tello Rodríguez and Lopez-I Fernando

2.1 Introduction 25

2.2 The Additive Manufacturing Process 26

2.3 Some Background 28

2.4 Proposed Approach 30

2.4.1 A Mathematical Model for the Initial Printing Scheduling 32

2.4.1.1 Considerations 32

2.4.1.2 Sets 32

2.4.2 Parameters 33

2.4.2.1 Orders 33

2.4.2.2 Parts 33

2.4.2.3 Printing Machines 33

2.4.2.4 Process 33

2.4.3 Decision Variables 33

2.4.4 Optimization Criteria 33

2.4.5 Constrains 34

2.5 Results 35

2.5.1 Orders 35

2.6 Conclusions 39

References 39

3 Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 43
Y. Bevish Jinila, Cinthia Joy, J. Joshua Thomas, and S. Prayla Shyry

3.1 Introduction 43

3.2 Background and RelatedWork 47

3.3 Challenges in Smart Cities 49

3.3.1 Data Acquisition 49

3.3.2 Data Analysis 50

3.3.3 Data Security and Privacy 50

3.3.4 Data Dissemination 50

3.4 Need for AI and Data Analytics 50

3.5 Applications of AI in Smart Cities 51

3.5.1 Road Condition Monitoring 51

3.5.2 Driver Behavior Monitoring 52

3.5.3 AI-Enabled Automatic Parking 53

3.5.4 Waste Management 53

3.5.5 Smart Governance 53

3.5.6 Smart Healthcare 54

3.5.7 Smart Grid 54

3.5.8 Smart Agriculture 55

3.6 AI-based Modeling for Smart Cities 55

3.6.1 Smart Cities Deployment Model 55

3.6.2 AI-Based Predictive Analytics 57

3.6.3 Pre-processing 58

3.6.4 Feature Selection 58

3.6.5 Artificial Intelligence Model 58

3.7 Conclusion 60

References 60

4 Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 63
Vladimir A. Kozub, Alexander B. Murynin, Igor S. Litvinchev, Ivan A. Matveev, and Pandian Vasant

4.1 Introduction 63

4.2 Methodology for Constructing a Digital Terrain Model 64

4.3 Image Segmentation Problem 65

4.4 Segmentation Quality Assessment 67

4.5 Existing Segmentation Methods and Algorithms 68

4.6 Classical Methods 69

4.7 Neural Network Methods 72

4.7.1 Semantic Segmentation of Objects in Satellite Images 74

4.8 Segmentation with Neural Networks 76

4.9 Convolutional Neural Networks 79

4.10 Batch Normalization 83

4.11 Residual Blocks 84

4.12 Training of Neural Networks 85

4.13 Loss Functions 85

4.14 Optimization 86

4.15 Numerical Experiments 88

4.16 Description of the Training Set 88
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