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Artificial Intelligence in Industry 4.0 and 5G Technology


Artificial Intelligence in Industry 4.0 and 5G Technology


1. Aufl.

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

115,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 30.06.2022
ISBN/EAN: 9781119798781
Sprache: englisch
Anzahl Seiten: 352

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Beschreibungen

<p><b>Artificial Intelligence in Industry 4.0 and 5G Technology</b></p> <p><b>Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors</b></p> <p>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.</p> <p><i>Artificial Intelligence in Industry 4.0 and 5G Technology</i> 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).</p> <ul> <li>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.</li> <li>Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor.</li> <li>Provides guidance on implementing metaheuristics in different applications and hybrid technological systems.</li> <li>Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement.</li> </ul> <p><i>Artificial Intelligence in Industry 4.0 and 5G Technology</i> 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.</p>
<p>List of Contributors xv</p> <p>Preface xix</p> <p>Profile of Editors xxvii</p> <p>Acknowledgments xxx</p> <p><b>1 Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 4.0 1<br /></b><i>Subhash Mondal, Swapnoj Banerjee, Soumodipto Halder, and Diganta Sengupta</i></p> <p>1.1 Introduction 1</p> <p>1.2 Literature Review 2</p> <p>1.3 Proposed Framework 5</p> <p>1.3.1 Enrolment Phase 5</p> <p>1.3.2 Authentication Phase 7</p> <p>1.3.2.1 Pre-processing 7</p> <p>1.3.2.2 Minutiae Extraction and False Minutiae Removal 12</p> <p>1.3.2.3 Key Generation from extracted Minutiae points 13</p> <p>1.3.2.4 Encrypting the Biometric Fingerprint Image Using AES 14</p> <p>1.4 Comparative Analysis 18</p> <p>1.5 Conclusion 19</p> <p>References 19</p> <p><b>2 Decision Support Methodology for Scheduling Orders in Additive Manufacturing 25<br /></b><i>Juan Jes&uacute;s Tello Rodr&iacute;guez and Lopez-I Fernando</i></p> <p>2.1 Introduction 25</p> <p>2.2 The Additive Manufacturing Process 26</p> <p>2.3 Some Background 28</p> <p>2.4 Proposed Approach 30</p> <p>2.4.1 A Mathematical Model for the Initial Printing Scheduling 32</p> <p>2.4.1.1 Considerations 32</p> <p>2.4.1.2 Sets 32</p> <p>2.4.2 Parameters 33</p> <p>2.4.2.1 Orders 33</p> <p>2.4.2.2 Parts 33</p> <p>2.4.2.3 Printing Machines 33</p> <p>2.4.2.4 Process 33</p> <p>2.4.3 Decision Variables 33</p> <p>2.4.4 Optimization Criteria 33</p> <p>2.4.5 Constrains 34</p> <p>2.5 Results 35</p> <p>2.5.1 Orders 35</p> <p>2.6 Conclusions 39</p> <p>References 39</p> <p><b>3 Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 43<br /></b><i>Y. Bevish Jinila, Cinthia Joy, J. Joshua Thomas, and S. Prayla Shyry</i></p> <p>3.1 Introduction 43</p> <p>3.2 Background and RelatedWork 47</p> <p>3.3 Challenges in Smart Cities 49</p> <p>3.3.1 Data Acquisition 49</p> <p>3.3.2 Data Analysis 50</p> <p>3.3.3 Data Security and Privacy 50</p> <p>3.3.4 Data Dissemination 50</p> <p>3.4 Need for AI and Data Analytics 50</p> <p>3.5 Applications of AI in Smart Cities 51</p> <p>3.5.1 Road Condition Monitoring 51</p> <p>3.5.2 Driver Behavior Monitoring 52</p> <p>3.5.3 AI-Enabled Automatic Parking 53</p> <p>3.5.4 Waste Management 53</p> <p>3.5.5 Smart Governance 53</p> <p>3.5.6 Smart Healthcare 54</p> <p>3.5.7 Smart Grid 54</p> <p>3.5.8 Smart Agriculture 55</p> <p>3.6 AI-based Modeling for Smart Cities 55</p> <p>3.6.1 Smart Cities Deployment Model 55</p> <p>3.6.2 AI-Based Predictive Analytics 57</p> <p>3.6.3 Pre-processing 58</p> <p>3.6.4 Feature Selection 58</p> <p>3.6.5 Artificial Intelligence Model 58</p> <p>3.7 Conclusion 60</p> <p>References 60</p> <p><b>4 Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 63<br /></b><i>Vladimir A. Kozub, Alexander B. Murynin, Igor S. Litvinchev, Ivan A. Matveev, and Pandian Vasant</i></p> <p>4.1 Introduction 63</p> <p>4.2 Methodology for Constructing a Digital Terrain Model 64</p> <p>4.3 Image Segmentation Problem 65</p> <p>4.4 Segmentation Quality Assessment 67</p> <p>4.5 Existing Segmentation Methods and Algorithms 68</p> <p>4.6 Classical Methods 69</p> <p>4.7 Neural Network Methods 72</p> <p>4.7.1 Semantic Segmentation of Objects in Satellite Images 74</p> <p>4.8 Segmentation with Neural Networks 76</p> <p>4.9 Convolutional Neural Networks 79</p> <p>4.10 Batch Normalization 83</p> <p>4.11 Residual Blocks 84</p> <p>4.12 Training of Neural Networks 85</p> <p>4.13 Loss Functions 85</p> <p>4.14 Optimization 86</p> <p>4.15 Numerical Experiments 88</p> <p>4.16 Description of the Training Set 88</p> <p>4.17 Class Analysis 90</p> <p>4.18 Augmentation 90</p> <p>4.19 NN Architecture 92</p> <p>4.20 Training and Results 93</p> <p>4.21 Conclusion 97</p> <p>Acknowledgments 97</p> <p>References 97</p> <p><b>5 The Impact of Data Security on the Internet of Things 101<br /></b><i>Joshua E. Chukwuere and Boitumelo Molefe</i></p> <p>5.1 Introduction 101</p> <p>5.2 Background of the Study 102</p> <p>5.3 Problem Statement 103</p> <p>5.4 Research Questions 103</p> <p>5.5 Literature Review 103</p> <p>5.5.1 The Data Security on IoT 103</p> <p>5.5.2 The Security Threats and Awareness of Data Security on IoT 105</p> <p>5.5.3 The DifferentWays to Assist with Keeping Your IoT Device Safer from Security Threats 105</p> <p>5.6 Research Methodology 106</p> <p>5.6.1 Population and Sampling 106</p> <p>5.6.2 Data Collection 107</p> <p>5.6.3 Reliability and Validity 108</p> <p>5.7 Chapter Results and Discussions 108</p> <p>5.7.1 The Demographic Information 109</p> <p>5.7.1.1 Age, Ethnic Group, and Ownership of a Smart Device 109</p> <p>5.7.2 Awareness of Users About Data Security of the Internet of Things 109</p> <p>5.7.3 The Security Threats that are Affecting the Internet of Things Devices 111</p> <p>5.7.3.1 The Architecture of IoT Devices 112</p> <p>5.7.3.2 The botnets Attack 112</p> <p>5.7.4 The Effects of Security Threats on IoT Devices that are Affecting Users 112</p> <p>5.7.4.1 The Slowness or Malfunctioning of the IoT Device 112</p> <p>5.7.4.2 The Trust of Users on IoT 113</p> <p>5.7.4.3 The Safety of Users 113</p> <p>5.7.4.4 The Guaranteed Duration of IoT Devices 114</p> <p>5.7.5 DifferentWays to Assist with Keeping IoT Smart Devices Safer from Security Threats 114</p> <p>5.7.5.1 The Change Default Passwords 114</p> <p>5.7.5.2 The Easy or Common Passwords 114</p> <p>5.7.5.3 On the Importance of Reading Privacy Policies 114</p> <p>5.7.5.4 The Bluetooth and Wi-Fi of IoT Devices 115</p> <p>5.7.5.5 The VPN on IoT 115</p> <p>5.7.5.6 The Physical Restriction 115</p> <p>5.7.5.7 Two-Factor Authentication 116</p> <p>5.7.5.8 The Biometric Authentication 116</p> <p>5.8 Answers to the Chapter Questions 116</p> <p>5.8.1 Objective 1: Awareness on Users About Data Security of Internet of Things (IoT) 116</p> <p>5.8.2 Objective 2: Determine the Security Threats that are Involved in the Internet of Things (IoT) 117</p> <p>5.8.3 Objective 3: The Effects of Security Threats on IoT Devices that are Affecting Users 117</p> <p>5.8.4 Objective 4: DifferentWays to Assist with Keeping IoT Devices Safer from Security Threats 117</p> <p>5.8.5 Other Descriptive Analysis (Mean) 118</p> <p>5.8.5.1 Mean 1 &ndash; Awareness on Users About Data Security on IoT 118</p> <p>5.8.5.2 The Effects of Security Threats on IoT Devices that are Affecting Users 118</p> <p>5.8.5.3 DifferentWays to Assist with Keeping an IoT Device Safer 122</p> <p>5.9 Chapter Recommendations 122</p> <p>5.10 Conclusion 122</p> <p>References 124</p> <p><b>6 Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution 129<br /></b><i>Choo K. Chin and Deng H. Xiang</i></p> <p>6.1 Introduction 129</p> <p>6.2 Literature Review 131</p> <p>6.2.1 Energy Efficiency 135</p> <p>6.2.2 Waste Minimization 136</p> <p>6.2.3 Water Consumption 137</p> <p>6.2.4 Eco-Procurement 137</p> <p>6.2.5 Communication 138</p> <p>6.2.6 Awareness 138</p> <p>6.2.7 Sustainable and Renewable Energy Development 138</p> <p>6.3 Conceptual Framework 139</p> <p>6.4 Conclusion 139</p> <p>6.4.1 Energy Efficiency 140</p> <p>6.4.2 Waste Minimization 140</p> <p>6.4.3 Water Consumption 140</p> <p>6.4.4 Eco-Procurement 141</p> <p>6.4.5 Communication 141</p> <p>6.4.6 Sustainable and Renewable Energy Development 141</p> <p>Acknowledgment 142</p> <p>References 142</p> <p><b>7 Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks 147<br /></b><i>Denis Y. Nartsev, Alexander N. Gneushev, and Ivan A. Matveev</i></p> <p>7.1 Introduction 147</p> <p>7.2 Problem Statement 149</p> <p>7.3 Modifications of the Adam Optimization Method for Training a Regression Model 151</p> <p>7.4 Computational Experiments 155</p> <p>7.4.1 Model for Evaluating the Eye Image Blurring Degree 155</p> <p>7.4.2 Facial Rotation Angle Estimation Model 158</p> <p>7.5 Conclusion 160</p> <p>Acknowledgments 161</p> <p>References 161</p> <p><b>8 Application of Integer Programming in Allocating Energy Resources in Rural Africa 165<br /></b><i>Elias&nbsp;Munapo</i></p> <p>8.1 Introduction 165</p> <p>8.1.1 Applications of the QAP 165</p> <p>8.2 Quadratic Assignment Problem Formulation 166</p> <p>8.2.1 Koopmans&ndash;Beckmann Formulation 166</p> <p>8.3 Current Linearization Technique 167</p> <p>8.3.1 The General Quadratic Binary Problem 167</p> <p>8.3.2 Linearizing the Quadratic Binary Problem 169</p> <p>8.3.2.1 Variable Substitution 169</p> <p>8.3.2.2 Justification 169</p> <p>8.3.3 Number of Variables and Constraints in the Linearized Model 170</p> <p>8.3.4 Linearized Quadratic Binary Problem 171</p> <p>8.3.5 Reducing the Number of Extra Constraints in the Linear Model 171</p> <p>8.3.6 The General Binary Linear (BLP) Model 171</p> <p>8.3.6.1 Convex Quadratic Programming Model 172</p> <p>8.3.6.2 Transforming Binary Linear Programming (BLP) Into a Convex/Concave Quadratic Programming Problem 172</p> <p>8.3.6.3 Equivalence 173</p> <p>8.4 Algorithm 174</p> <p>8.4.1 Making the Model Linear 175</p> <p>8.5 Conclusions 176</p> <p>References 176</p> <p><b>9 Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society 179<br /></b><i>Sadia S. Ali, Rajbir Kaur, and Haidar Abbas</i></p> <p>9.1 Introduction 179</p> <p>9.1.1 Technology and Business 181</p> <p>9.1.2 Technological Revolution of the Twenty-first Century 181</p> <p>9.2 An Overview of Drone Technology and Its Future Prospects in Indian Market 182</p> <p>9.2.1 Utilities 183</p> <p>9.2.1.1 Delivery 183</p> <p>9.2.1.2 Media/Photography 183</p> <p>9.2.1.3 Agriculture 184</p> <p>9.2.1.4 Contingency and Disaster Management Scenarios 184</p> <p>9.2.1.5 Civil and Military Services: Search and Rescue, Surveillance,Weather, and Traffic Monitoring, Firefighting 185</p> <p>9.2.2 Complexities Involved 185</p> <p>9.2.3 Drones in Indian Business Scenario 186</p> <p>9.3 Literature Review 187</p> <p>9.3.1 Absorption and Diffusion of New Technology 188</p> <p>9.3.2 Leadership for Innovation 188</p> <p>9.3.3 Social and Economic Environment 189</p> <p>9.3.4 Customer Perceptions 190</p> <p>9.3.5 Alliances with Other National and International Organizations 190</p> <p>9.3.6 Other Influencers 191</p> <p>9.4 Methodology 191</p> <p>9.5 Discussion 193</p> <p>9.5.1 Market Module 195</p> <p>9.5.2 Technology Module 196</p> <p>9.5.3 Commercial Module 198</p> <p>9.6 Conclusions 199</p> <p>References 200</p> <p><b>10 Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure 209<br /></b><i>Isidro Soria-Arguello, Rafael Torres-Esobar, and Pandian Vasant</i></p> <p>10.1 Introduction 209</p> <p>10.2 New Distribution System 211</p> <p>10.3 The Mathematical Model to Design the Distribution Network 214</p> <p>10.4 Solution Technique 216</p> <p>10.4.1 Lagrangian Relaxation 216</p> <p>10.4.2 Methods for Finding the Value of Lagrange Multipliers 216</p> <p>10.4.3 Selecting the Solution Method 216</p> <p>10.4.4 Used Notation 217</p> <p>10.4.5 Proposed Relaxations of the Distribution Model 218</p> <p>10.4.5.1 Relaxation 1 218</p> <p>10.4.5.2 Relaxation 2 219</p> <p>10.4.6 Selection of the Best Lagrangian Relaxation 219</p> <p>10.5 Heuristic Algorithm to Restore Feasibility 220</p> <p>10.6 Numerical Analysis 222</p> <p>10.6.1 Scenario 2020 223</p> <p>10.6.2 Scenario 2021 224</p> <p>10.6.3 Scenario 2022 225</p> <p>10.6.4 Scenario 2023 226</p> <p>10.7 Conclusions 228</p> <p>References 228</p> <p><b>11 Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education 231<br /></b><i>Arrieta-M Luisa F and Lopez-I Fernando</i></p> <p>11.1 Introduction 231</p> <p>11.1.1 Background 232</p> <p>11.2 Proposed Approach 233</p> <p>11.3 Case Study 234</p> <p>11.3.1 Intelligent Phase 234</p> <p>11.3.2 Design Phase 235</p> <p>11.3.3 Choice Phase 236</p> <p>11.4 Results 238</p> <p>11.5 Conclusion 240</p> <p>References 240</p> <p><b>12 A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology 243<br /></b><i>Timothy Ganesan, Irraivan Elamvazuthi, Pandian Vasant, and J. Joshua Thomas</i></p> <p>12.1 Background 243</p> <p>12.2 Computational Optimization Frameworks 246</p> <p>12.3 Internet of Things (IoT) Systems 248</p> <p>12.4 Smart Grids Data Systems 250</p> <p>12.5 Supply Chain Management 252</p> <p>12.6 Healthcare Data Management Systems 255</p> <p>12.7 Outlook 257</p> <p>References 258</p> <p><b>13 Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority 267<br /></b><i>Paulina A. Avila-Torres and Nancy M. Arratia-Martinez</i></p> <p>13.1 Introduction 267</p> <p>13.2 Problem Description 270</p> <p>13.3 Mathematical Formulation 273</p> <p>13.4 Computational Experiments 275</p> <p>13.4.1 Numerical Example 276</p> <p>13.4.1.1 The Inventory Routing Problem Under Certainty 279</p> <p>13.4.1.2 The Inventory Routing Problem Under Uncertainty in the Consumption Rate of Product 279</p> <p>13.5 Conclusions and FutureWork 280</p> <p>References 281</p> <p><b>14 Comparison of Defuzzification Methods for Project Selection 283<br /></b><i>Nancy M. Arratia-Martinez, Paulina A. Avila-Torres, and Lopez-I Fernando</i></p> <p>14.1 Introduction 283</p> <p>14.2 Problem Description 286</p> <p>14.3 Mathematical Model 286</p> <p>14.3.1 Sets and Parameters 287</p> <p>14.3.2 Decision Variables 287</p> <p>14.3.3 Objective Functions 287</p> <p>14.4 Constraints 288</p> <p>14.5 Methods of Defuzzification and Solution Algorithm 289</p> <p>14.5.1 <i>k</i>-Preference Method 289</p> <p>14.5.2 Integral Value 291</p> <p>14.5.3 SAUGMECON Algorithm 291</p> <p>14.6 Results 292</p> <p>14.6.1 Results of <i>k</i>-Preference Method 292</p> <p>14.6.2 Results of Integral Value Method 295</p> <p>14.7 Conclusions 299</p> <p>References 300</p> <p><b>15 Re-Identification-Based Models for Multiple Object Tracking 303<br /></b><i>Alexey D. Grigorev, Alexander N. Gneushev, and Igor S. Litvinchev</i></p> <p>15.1 Introduction 303</p> <p>15.2 Multiple Object Tracking Problem 305</p> <p>15.3 Decomposition of Tracking into Filtering and Assignment Tasks 306</p> <p>15.4 Cost Matrix Adjustment in Assignment Problem Based on Re-Identification with Pre-Filtering of Descriptors by Quality 310</p> <p>15.5 Computational Experiments 313</p> <p>15.6 Conclusion 315</p> <p>Acknowledgments 315</p> <p>References 316</p> <p>Index 319</p>
<p><b>PANDIAN VASANT</b>&nbsp;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).</p> <p><b>ELIAS MUNAPO</b>, 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.</p> <p><b>J. JOSHUA THOMAS</b>&nbsp;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&rsquo;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.</p> <p><b>GERHARD-WILLIAM WEBER</b>, 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.</p>
<p><b>Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors </b></p> <p>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. <p><i>Artificial Intelligence in Industry 4.0 and 5G Technology</i> 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). <ul><li>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.</li> <li>Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor.</li> <li>Provides guidance on implementing metaheuristics in different applications and hybrid technological systems.</li> <li>Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement.</li></ul> <p><i> Artificial Intelligence in Industry 4.0 and 5G Technology</i> 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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