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Table of Contents
1 Cover
4 Preface
5 1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning 1.1 Introduction 1.2 Comprehensive Study 1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 1.5 Conclusion References
6 2 Impact of AI in 5G Wireless Technologies and Communication Systems 2.1 Introduction 2.2 Integrated Services of AI in 5G and 5G in AI 2.3 Artificial Intelligence and 5G in the Industrial Space 2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 2.5 Conclusion References
7 3 Artificial Intelligence Revolution in Logistics and Supply Chain Management 3.1 Introduction 3.2 Theory—AI in Logistics and Supply Chain Market 3.3 Factors to Propel Business Into the Future Harnessing Automation 3.4 Conclusion References
8 4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning 4.1 Introduction 4.2 An Overview of Reinforcement Learning in Agriculture 4.3 Reinforcement Learning Startups for Crop Prediction 4.4 Conclusion References
9 5 Cost Optimization for Inventory Management in Blockchain and Cloud 5.1 Introduction 5.2 Blockchain: The Future of Inventory Management 5.3 Cost Optimization for Blockchain Inventory Management in Cloud 5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 5.5 Conclusion References
10 6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases 6.1 Introduction 6.2 Literature Review 6.3 Proposed Idea 6.4 Reference Gap 6.5 Conclusion References
11 7 Generating Art and Music Using Deep Neural Networks 7.1 Introduction 7.2 Related Works 7.3 System Architecture 7.4 System Development 7.5 Algorithm-LSTM 7.6 Result 7.7 Conclusions References
12 8 Deep Learning Era for Future 6G Wireless Communications—Theory, Applications, and Challenges 8.1 Introduction 8.2 Study of Wireless Technology 8.3 Deep Learning Enabled 6G Wireless Communication 8.4 Applications and Future Research Directions Conclusion References
13 9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks 9.1 Introduction 9.2 Spectrum Sensing in Cognitive Radio Networks 9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments
