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       – In Chapter 1, “Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning,” P. Anbalagan, S. Saravanan and R. Saminathan present a brief guide to the deep reinforcement learning process and its detailed applications and research directions in order to enhance the basics of reinforcement mechanisms.

       – In Chapter 2, “Impact of AI in 5G Wireless Technologies and Communication Systems,” A. Sivasundari and K. Ananthajothi present an in-depth overview of the implementation of AI to improve 5G wireless communication systems, discuss the role and difficulties faced, and highlight suggestions for future studies on integrating advanced AI into 5G wireless communications.

       – In Chapter 3, “Artificial Intelligence Revolution in Logistics and Supply Chain Management,” P.J. Satish Kumar, Ratna Kamala Petla, Elangovan K and P.G. Kuppusamy give a brief description of recent developments and some relevant impacts concerning logistics and supply chain related to AI.

       – In Chapter 4, “An Empirical Study of Crop Yield Prediction Using Reinforcement Learning,” M. P. Vaishnnave and R. Manivannan use reinforcement learning (RL) data technologies and high-performance computing to create new possibilities to activate, calculate, and recognize the agricultural crop forecast.

       – In Chapter 5, “Cost Optimization for Inventory Management in Blockchain Under Cloud,” C. Govindasamy, A. Antonidoss and A. Pandiaraj investigate some of the most prominent bases of blockchain for inventory management of blockchain and cost optimization methods for inventory management in cloud.

       – In Chapter 6, “Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases,” G. Gangadevi and C. Jayakumar describe the deep learning architectures for plant disease prediction and classification using standard techniques like artificial neural network (ANN), k-means classifier (K-means), recurrent neural network (RNN), k-nearest neighbor (K-NN) classifier, and support vector machine (SVM).

       – In Chapter 7, “Generating Art and Music Using Deep Neural Networks,” A. Pandiaraj, Lakshmana Prakash, R. Gopal and P. Rajesh Kanna develop a model using deep neural nets that can imagine like humans and generate art and music of their own. This model can also be used to increase cognitive efficiency in AGI (artificial general intelligence), thereby improving the agent’s image classification and object localization.

       – In Chapter 8, “Deep Learning Era for Future 6G Wireless Communications – Theory, Applications, and Challenges,” S. K. B. Sangeetha, R. Dhaya, S. Gayathri, K. Kamala and S. Divya Keerthi encapsulate the background of 6G wireless communication with details on how deep learning has made a contribution to 6G wireless technology and also highlight future research directions for deep learning-driven wireless technology.

       – In Chapter 9, “Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks,” J. Banumathi, S.K B. Sangeetha and R. Dhaya discuss the use of cooperative spectrum sensing in cognitive radio systems to improve the efficiency of detecting primary users.

       – In Chapter 10, “Natural Language Processing,” S. Meera and B. Persis Urban Ivy elucidate natural language processing (NLP), which is a branch of computer science and artificial intelligence that studies how computers and humans communicate in natural language, with the aim of computers understanding language as well as humans do.

       – In Chapter 11, “Class-Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval,” Sujatha D, Subramaniam M and A. Kathirvel present an efficient class-level multi-feature semantic similarity measure-based approach. The proposed method receives the input query and estimates class-level information similarity, class-level texture similarity, and class-level semantic similarity measures for different classes.

       – In Скачать книгу