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<p>Preface xvii</p> <p><b>1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning 1</b><br /><i>P. Anbalagan, S. Saravanan and R. Saminathan</i></p> <p>1.1 Introduction 2</p> <p>1.2 Comprehensive Study 3</p> <p>1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 7</p> <p>1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 9</p> <p>1.5 Conclusion 12</p> <p><b>2 Impact of AI in 5G Wireless Technologies and Communication Systems 15</b><br /><i>A. Sivasundari and K. Ananthajothi</i></p> <p>2.1 Introduction 16</p> <p>2.2 Integrated Services of AI in 5G and 5G in AI 18</p> <p>2.3 Artificial Intelligence and 5G in the Industrial Space 23</p> <p>2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 25</p> <p>2.5 Conclusion 28</p> <p><b>3 Artificial Intelligence Revolution in Logistics and Supply Chain Management 31</b><br /><i>P.J. Sathish Kumar, Ratna Kamala Petla, K. Elangovan and P.G. Kuppusamy</i></p> <p>3.1 Introduction 32</p> <p>3.2 Theory--AI in Logistics and Supply Chain Market 35</p> <p>3.3 Factors to Propel Business Into the Future Harnessing Automation 40</p> <p>3.4 Conclusion 43</p> <p><b>4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning 47</b><br /><i>M. P. Vaishnnave and R. Manivannan</i></p> <p>4.1 Introduction 47</p> <p>4.2 An Overview of Reinforcement Learning in Agriculture 49</p> <p>4.3 Reinforcement Learning Startups for Crop Prediction 52</p> <p>4.4 Conclusion 57</p> <p><b>5 Cost Optimization for Inventory Management in Blockchain and Cloud 59</b><br /><i>C. Govindasamy, A. Antonidoss and A. Pandiaraj</i></p> <p>5.1 Introduction 60</p> <p>5.2 Blockchain: The Future of Inventory Management 62</p> <p>5.3 Cost Optimization for Blockchain Inventory Management in Cloud 66</p> <p>5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 71</p> <p>5.5 Conclusion 72</p> <p><b>6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases 75</b><br /><i>G. Gangadevi and C. Jayakumar</i></p> <p>6.1 Introduction 75</p> <p>6.2 Literature Review 76</p> <p>6.3 Proposed Idea 82</p> <p>6.4 Reference Gap 86</p> <p>6.5 Conclusion 87</p> <p><b>7 Generating Art and Music Using Deep Neural Networks 91</b><br /><i>A. Pandiaraj, S. Lakshmana Prakash, R. Gopal and P. Rajesh Kanna</i></p> <p>7.1 Introduction 91</p> <p>7.2 Related Works 92</p> <p>7.3 System Architecture 94</p> <p>7.4 System Development 96</p> <p>7.5 Algorithm-LSTM 100</p> <p>7.6 Result 100</p> <p>7.7 Conclusions 101</p> <p><b>8 Deep Learning Era for Future 6G Wireless Communications--Theory, Applications, and Challenges 105</b><br /><i>S.K.B. Sangeetha and R. Dhaya</i></p> <p>8.1 Introduction 106</p> <p>8.2 Study of Wireless Technology 108</p> <p>8.3 Deep Learning Enabled 6G Wireless Communication 113</p> <p>8.4 Applications and Future Research Directions 117</p> <p><b>9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks 121</b><br /><i>J. Banumathi, S.K.B. Sangeetha and R. Dhaya</i></p> <p>9.1 Introduction 122</p> <p>9.2 Spectrum Sensing in Cognitive Radio Networks 122</p> <p>9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments 124</p> <p>9.4 Cooperative Sensing Among Cognitive Radios 125</p> <p>9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems 128</p> <p>9.6 Spectrum Agile Radios: Utilization and Sensing Architectures 128</p> <p>9.7 Some Fundamental Limits on Cognitive Radio 130</p> <p>9.8 Cooperative Strategies and Capacity Theorems for Relay Networks 131</p> <p>9.9 Research Challenges in Cooperative Communication 133</p> <p>9.10 Conclusion 135</p> <p><b>10 Natural Language Processing 139</b><br /><i>S. Meera and S. Geerthik</i></p> <p>10.1 Introduction 139</p> <p>10.2 Conclusions 152</p> <p>References 152</p> <p><b>11 Class Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval 155</b><br /><i>D. Sujatha, M. Subramaniam and A. Kathirvel</i></p> <p>11.1 Introduction 156</p> <p>11.2 Literature Review 158</p> <p>11.3 Class Level Semantic Similarity-Based Retrieval 159</p> <p>11.4 Results and Discussion 164</p> <p><b>12 Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling With Diurnal Changes 175</b><br /><i>J.V. Anand, T.R. Ganesh Babu, R. Praveena and K. Vidhya</i></p> <p>12.1 Introduction 176</p> <p>12.2 Literature Survey 176</p> <p>12.3 Proposed Work 177</p> <p>12.4 Results 180</p> <p>12.5 Conclusion and Future Work 190</p> <p><b>13 Multi-Layer UAV Ad Hoc Network Architecture, Protocol and Simulation 193</b><br /><i>Kamlesh Lakhwani, Tejpreet Singh and Orchu Aruna</i></p> <p>13.1 Introduction 194</p> <p>13.2 Background 196</p> <p>13.3 Issues and Gap Identified 197</p> <p>13.4 Main Focus of the Chapter 198</p> <p>13.5 Mobility 199</p> <p>13.6 Routing Protocol 201</p> <p>13.7 High Altitude Platforms (HAPs) 202</p> <p>13.8 Connectivity Graph Metrics 204</p> <p>13.9 Aerial Vehicle Network Simulator (AVENs) 206</p> <p>13.10 Conclusion 207</p> <p><b>14 Artificial Intelligence in Logistics and Supply Chain 211</b><br /><i>Jeyaraju Jayaprakash</i></p> <p>14.1 Introduction to Logistics and Supply Chain 212</p> <p>14.2 Recent Research Avenues in Supply Chain 217</p> <p>14.3 Importance and Impact of AI 222</p> <p>14.4 Research Gap of AI-Based Supply Chain 224</p> <p><b>15 Hereditary Factor-Based Multi-Featured Algorithm for Early Diabetes Detection Using Machine Learning 235</b><br /><i>S. Deepajothi, R. Juliana, S.K. Aruna and R. Thiagarajan</i></p> <p>15.1 Introduction 236</p> <p>15.2 Literature Review 237</p> <p>15.3 Objectives of the Proposed System 244</p> <p>15.4 Proposed System 245</p> <p>15.5 HIVE and R as Evaluation Tools 246</p> <p>15.6 Decision Trees 247</p> <p>15.7 Results and Discussions 250</p> <p>15.8 Conclusion 252</p> <p><b>16 Adaptive and Intelligent Opportunistic Routing Using Enhanced Feedback Mechanism 255</b><br /><i>V. Sharmila, K. Mandal, Shankar Shalani and P. Ezhumalai</i></p> <p>16.1 Introduction 255</p> <p>16.2 Related Study 258</p> <p>16.3 System Model 259</p> <p>16.4 Experiments and Results 264</p> <p>16.5 Conclusion 267</p> <p><b>17 Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing 269</b><br /><i>R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumar</i></p> <p>and R. Mahaveerakannan</p> <p>17.1 Introduction 270</p> <p>17.2 New Development of Artificial Intelligence 271</p> <p>17.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing 271</p> <p>17.4 Current Status and Problems of Green Manufacturing 272</p> <p>17.5 Artificial Intelligence for Green Manufacturing 276</p> <p>17.6 Detailed Description of Common Encryption Algorithms 280</p> <p>17.7 Current and Future Works 282</p> <p>17.8 Conclusion 283</p> <p><b>18 Deep Learning in 5G Networks 287</b><br /><i>G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani</i></p> <p>18.1 5G Networks 287</p> <p>18.2 Artificial Intelligence and 5G Networks 291</p> <p>18.3 Deep Learning in 5G Networks 293</p> <p><b>19 EIDR Umpiring Security Models for Wireless Sensor Networks 299</b><br /><i>A. Kathirvel, S. Navaneethan and M. Subramaniam</i></p> <p>19.1 Introduction 299</p> <p>19.2 A Review of Various Routing Protocols 302</p> <p>19.3 Scope of Chapter 307</p> <p>19.4 Conclusions and Future Work 311</p> <p><b>20 Artificial Intelligence in Wireless Communication 317</b><br /><i>Prashant Hemrajani, Vijaypal Singh Dhaka, Manoj Kumar Bohra and Amisha Kirti Gupta</i></p> <p>20.1 Introduction 318</p> <p>20.2 Artificial Intelligence: A Grand Jewel Mine 318</p> <p>20.3 Wireless Communication: An Overview 320</p> <p>20.4 Wireless Revolution 320</p> <p>20.5 The Present Times 321</p> <p>20.6 Artificial Intelligence in Wireless Communication 321</p> <p>20.7 Artificial Neural Network 324</p> <p>20.8 The Deployment of 5G 326</p> <p>20.9 Looking Into the Features of 5G 327</p> <p>20.10 AI and the Internet of Things (IoT) 328</p> <p>20.11 Artificial Intelligence in Software-Defined Networks (SDN) 329</p> <p>20.12 Artificial Intelligence in Network Function Virtualization 331</p> <p>20.13 Conclusion 332</p> <p>References 332</p> <p>Index 335</p>

<p><b>R. Kanthavel</b>, PhD is a Professor in the Department of Computer Engineering, King Khalid University Abha, Kingdom of Saudi Arabia. He has published more than 150 research articles in reputed journals and international conferences as well as published 10 engineering books. He specializes in communication systems engineering and information and communication engineering.</p> <p><b>K. Ananthajothi</b>, PhD is an assistant professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. He has published a book on "Theory of Computation and Python Programming" and holds 2 patents.</p> <p><b>S. Balamurugan</b>, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.</p> <p><b>R. Karthik Ganesh</b>, PhD is an associate professor in the Department of Computer Science and Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, Tamilnadu, India. His research interests are in wireless communication, video and audio compression, image classification, and ontology techniques.</p>

<p><b>The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. </b> </p> <p>Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. <p><b> Audience</b> <p>Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.

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