Cognitive Analytics and Reinforcement Learning: Theories, Techniques and Applications - Tapa dura

Acerca del autor

Elakkiya R., PhD, is an assistant professor in the Department of Computer Science at the Birla Institute of Technology & Science in Dubai, UAE. She received a Ph.D. in 2018 and did her doctoral research in sign language recognition. Her research focuses on addressing trending issues in computer science, mathematics, and engineering. Along with publishing two books, 50 research articles, and three patents, she is an editor of the Information Engineering and Applied Computing journal. She received the Young Achiever Award in 2019.

Subramaniyaswamy V., PhD, is a professor at the School of Computing at SASTRA Deemed University in Tamilnadu, India. He received a Ph.D. from Anna University in 2013. His research areas include cognitive computing, reinforced learning, recommender systems, artificial intelligence, and the Internet of Things. He has published more than 200 research papers and book chapters in international journals and books.

De la contraportada

The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research.

Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications.

Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems.

The book’s real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning.

Audience

The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.