Generative AI

Disruptive Technologies for Innovative Applications
1. Auflage 2025
ISBN: 978-1-394-30290-1
Verlag: Wiley

Buch, Englisch, 288 Seiten

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.

Autoren/Hrsg.

Gayathri, N.

Kumar, S Rakesh

Chandran, Ramesh

Raj, Pethuru

Pelusi, Danilo

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensorganisation, Corporate Responsibility Unternehmensethik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Geisteswissenschaften Philosophie Angewandte Ethik & Soziale Verantwortung Wirtschaftsethik, Unternehmensethik

Inhaltsverzeichnis

Preface xiii

1 Introduction to Generative AI 1
Ritika Lath, Renuka Patwari, Amit Aylani and Deepak Hajoary

2 Generative Adversarial Networks (GANs) 29
Ankur Chaudhary, Ritesh Rastogi, Aditee Mattoo, Punit Kumar, Tanvi Kumari and Devansh Dubey

3 Reinforcement Learning in Generative AI 57
Kanthavel R., Adline Freeda R. and Dhaya R.

4 Pix2pix GAN for Image-to-Image Translation: A Comparative Study with Diverse Datasets 73
Lakshmipriya B., Pankaj Kumar Singh and Jayalakshmy S.

5 Study of State-of-the-Art Performance Metrics in NLP: Specifically for Text Summarization in the Medical Domain Using the SumPubMed Dataset 91
M. Sridhar, Mohammad Irfan and Sukumar Kathirmani

6 The Impact of Generative AI in Gaming: Exploring Immersive Experiences 107
Nalli Vinaya Kumari, G.S. Pradeep Ghantasala, U. Ananthanagu, Pellakuri Vidyullatha and Mudassir Khan

7 Ethical Dimensions and Societal Effects of Generative AI: The Portrayal of Ethical Issues of ChatGPT, DALL-E, and Other Systems 131
Bhuvaneswari Sivagnanam, Saranya Rajiakodi, Kathiravan Pannerselvam and Bharathi Raja Chakravarthi

8 Generative Artificial Intelligence for Social Good and Sustainable Development 153
Wasswa Shafik

9 Revolutionizing Implementation: Cutting-Edge Tools and Resources in Generative AI 187
Blessing Takawira and David Pooe

10 Applying Fuzzy Data Science in Generative AI for Healthcare 215
Yogeesh N.

11 Generative AI in Hospital Industry Transforming Medical Imagining for Patient Diagnosis and Health Data Management 249
Bhupinder Singh and Christian Kaunert

References 262

Index 271

Über Autor(innen)

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.