Generative AI: Disruptive Technologies for Innovative Applications
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Add to basketSold by Kennys Bookstore, Olney, MD, U.S.A.
AbeBooks Seller since October 9, 2009
Condition: New
Quantity: Over 20 available
Add to basket2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.
Seller Inventory # V9781394302901
This book is essential for anyone eager to understand the groundbreaking advancements in generative AI and its transformative effects across industries, making it a valuable resource for both professional growth and creative inspiration.
Generative AI: Disruptive Technologies for Innovative Applications delves into the exciting and rapidly evolving world of generative artificial intelligence and its profound impact on various industries and domains. This comprehensive volume brings together leading experts and researchers to explore the cutting-edge advancements, applications, and implications of generative AI technologies. This volume provides an in-depth exploration of generative AI, which encompasses a range of techniques such as generative adversarial networks, recurrent neural networks, and transformer models like GPT-3. It examines how these technologies enable machines to generate content, including text, images, and audio, that closely mimics human creativity and intelligence. Readers will gain valuable insights into the fundamentals of generative AI, innovative applications, ethical and social considerations, interdisciplinary insights, and future directions of this invaluable emerging technology. Generative AI: Disruptive Technologies for Innovative Applications is an indispensable resource for researchers, practitioners, and anyone interested in the transformative potential of generative AI in revolutionizing industries, unleashing creativity, and pushing the boundaries of what’s possible in artificial intelligence.
Audience
AI researchers, industry professionals, data scientists, machine learning experts, students, policymakers, and entrepreneurs interested in the innovative field of generative AI.
N. Gayathri, PhD, is an assistant professor in the Department of Computer Science and Engineering at the Ghandi Institute of Technology and Management, India. She has published several articles in international journals, edited many books, and serves as a guest editor and reviewer for several international journals. Her research interests include big data analytics, Internet of Things, and mobile networks, and sustainable computing.
S Rakesh Kumar, PhD, is an assistant professor in the Department of Computer Science and Engineering at the Ghandi Institute of Technology and Management, India. He has several publications in international journals, conference proceedings, and edited volumes. His research interests include artificial intelligence and machine learning.
Ramesh Chandran, PhD, is an associate professor in the Department of Computer Science and Engineering at the Vellore Institute of Technology, India, with over 19 years of combined teaching and industry experience. He has published over 20 articles in international journals and has presented papers in national and international conferences. His research interests include cloud computing, data mining, artificial intelligence, data analytics, and blockchain.
Pethuru Raj, PhD, is a chief architect at Reliance Jio Platforms Ltd., Bangalore, India with over 30 years of combined industry and research experience in information technology. He has been granted international research fellowships from organizations including the Japan Society for the Promotion of Science and the Japan Science and Technology Agency. His research interests include Internet of Things, artificial intelligence, model optimization techniques, blockchain, digital twins, and cloud computing.
Danilo Pelusi, PhD, is an associate professor of Computer Science in the Department of Communication Sciences, University of Teramo, Italy. He is an editor for several internationally published books and journals and a member of Machine Intelligence Research Labs. His research interests include coding theory, artificial intelligence, signal processing, pattern recognition, fuzzy logic, neural networks, and genetic algorithms.
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