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Generative AI

Disruptive Technologies for Innovative Applications

Edited by N. Gayathri, S Rakesh Kumar, Ramesh Chandran, Pethuru Raj, Danilo Pelusi
Copyright: 2025   |   Expected Pub Date:2024/12/30
ISBN: 9781394302901  |  Hardcover  |  
304 pages

One Line Description
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.

Audience
AI researchers, industry professionals, data scientists, machine learning experts, students, policymakers, and entrepreneurs interested in the innovative field of generative AI.

Description
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.

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Author / Editor Details
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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Table of Contents
Preface
1. Introduction to Generative AI
Ritika Lath, Renuka Patwari, Amit Aylani and Deepak Hajoary
1.1 What is Generative AI
1.2 Difference Between AI, Machine Learning and Generative AI
1.3 History of Generative AI
1.4 Key Milestones and Continued Progress
1.4.1 Generative Adversarial Networks (GANs)
1.4.2 Variational Autoencoders (VAEs)
1.4.3 Autoregressive Models
1.4.4 Transformer Models
1.4.5 Recurrent Neural Networks and Long Short-Term Memory Networks
1.4.6 Energy-Based Models (EBMs)
1.4.7 Flow-Based Models
1.4.8 Diffusion Models
1.5 Exploring the Inner Workings of Generative AI: Understanding Large Language Models (LLMs)
1.6 LLMs vs. Generative AI
1.7 The Impact and Future of LLMs
1.8 Benefits of Generative AI
1.9 Risks of Generative AI
1.10 Evaluating Generative AI Models
1.11 Technical Challenges and Limitations of Gen AI
1.11.1 Continual Reliance on Data
1.11.2 Hallucinations
1.11.3 Lack of Creativity
1.11.4 Ethics and Privacy
1.12 Real Life Use Case of Gen AI
1.13 Conclusion
References
2. Unveiling Generative Artificial Intelligence: Concepts, Mechanisms, and Applications
Divya Bansal and Naboshree Bhattacharya
2.1 Introduction to Generative Artificial Intelligence
2.1.1 Understanding Generative AI: Definition and Scope
2.1.2 Importance and Significance in Contemporary AI Landscape
2.2 Fundamentals of Generative AI
2.3 Mechanisms Behind Generative AI
2.3.1 Probabilistic Models and Bayesian Frameworks
2.3.2 Autoencoders and Variational Inference
2.3.3 Generative Adversarial Networks (GANs)
2.3.4 Reinforcement Learning Approaches
2.3.5 Hybrid and Ensemble Methods
2.4 Applications Across Various Domains
2.4.1 Applications of Generative AI in Biomedical Research and Healthcare
2.4.2 Innovations in Text and Language Generation Models
2.4.3 Recent Advancements in Image Generation and Synthesis Techniques
2.5 Ethical and Societal Implications
2.6 Conclusion
References
3. Generative Adversarial Networks (GANs)
Ankur Chaudhary, Ritesh Rastogi, Aditee Mattoo, Punit Kumar, Tanvi Kumari and Devansh Dubey
3.1 Introduction
3.2 Tale of Two Minds: Unveiling the GAN Mechanism
3.2.1 Origins
3.2.2 Foundational Concepts of Generative Adversarial Network
3.2.3 The Discriminator
3.2.3.1 Theoretical Understanding
3.2.3.2 Objectives and Functionality
3.2.3.3 Mathematical Formulation
3.2.4 The Generator
3.2.4.1 The Generator’s Objective and Mathematical Formulation
3.2.4.2 Creative Dynamics of the Generator
3.2.5 Adversarial Training
3.2.5.1 Fundamental Principles of Adversarial Training
3.2.5.2 Training Dynamics in Adversarial Training
3.2.5.3 Adversarial Training Application
3.2.5.4 Difficulties and Restrictions
3.2.5.5 Future Paths and Prospects for Research
3.3 From Brushstrokes to Breakthroughs: Diverse Canvas of GAN Applications
3.3.1 Dreaming Up New Worlds: Aesthetic Mastery
3.3.1.1 Mastery of Aesthetics Using GANs
3.3.1.2 Improving Realism
3.3.2 Data Augmentation
3.3.2.1 GAN-Based Enrichment of Data
3.3.2.2 Cross-Domain Applications
3.3.3 Unsupervised Learning
3.3.3.1 GANs in Unsupervised Learning
3.4 Challenges and Ethical Considerations
3.4.1 Mode Collapse
3.4.2 Instability During Training
3.4.3 Absence of Convergence
3.5 A Glimpse into the Future: Where GANs Will Lead Us
3.5.1 Customized Content Production
3.5.2 Targeted Image Reproduction
3.5.3 Improved Data Distortion
3.5.4 Conditional Generation
3.5.5 Overcoming Obstacles
3.6 Conclusion: The Revolutionary Impact of Generative Adversarial Networks (GANs)
References
4. Reinforcement Learning in Generative AI
Kanthavel R., Adline Freeda R. and Dhaya R.
4.1 Introduction
4.2 Current State of the Art in Generative AI with Reinforcement Learning
4.3 Different Applications for Generative AI with Reinforcement Learning
4.4 Characteristics of Generative AI with Reinforcement Learning
4.5 Outstanding Problems in Generative AI with Reinforcement Learning
4.6 Limitations of Generative AI with Reinforcement Learning
4.7 Conclusion
References
5. Pix2pix GAN for Image-to-Image Translation: A Comparative Study with Diverse Datasets
Lakshmipriya B., Pankaj Kumar Singh and Jayalakshmy S.
5.1 Introduction
5.1.1 Significance of Generative AI in the Three Use Cases
5.2 Related Works
5.3 Methodology
5.4 Results and Discussions
5.4.1 Dataset
5.4.2 Experimental Assessment
5.5 Conclusion
References
6. Study of State-of-the-Art Performance Metrics in NLP: Specifically for Text Summarization in the Medical Domain Using the SumPubMed Dataset
M. Sridhar, Mohammad Irfan and Sukumar Kathirmani
6.1 Introduction
6.2 Literature Review
6.3 Research Methodology
6.4 Results
6.5 Conclusions
6.6 Future Score of Work
References
7. The Impact of Generative AI in Gaming: Exploring Immersive Experiences
Nalli Vinaya Kumari, G.S. Pradeep Ghantasala, U. Ananthanagu, Pellakuri Vidyullatha and Mudassir Khan
7.1 Introduction
7.2 Realistic Textures and Landscapes
7.3 Intelligent NPCS and Adaptive Behaviors
7.4 Expanding Beyond Traditional Gaming
7.5 Conclusion
References
8. Ethical Dimensions and Societal Effects of Generative AI: The Portrayal of Ethical Issues of ChatGPT, DALL-E, and Other Systems
Bhuvaneswari Sivagnanam, Saranya Rajiakodi, Kathiravan Pannerselvam and Bharathi Raja Chakravarthi
Introduction
Ethical Considerations in AI-Driven Recruitment: Video and Image Interviews
UNESCO
Conclusion
Acknowledgments
References
9. Generative Artificial Intelligence for Social Good and Sustainable Development
Wasswa Shafik
9.1 Introduction
9.1.1 Motivation of Generative Artificial Intelligence
9.1.2 The Contribution of the Chapter
9.1.3 The Organization of the Chapter
9.2 An Overview of Generative Artificial Intelligence and Its Applications
9.2.1 Text Generation
9.2.2 Image Generation
9.2.3 Audio and Music Generation
9.2.4 Data Augmentation
9.2.5 Video and Animation Generation
9.2.6 Drug Discovery
9.2.7 Content Recommendation
9.2.8 Language Translation
9.2.9 Humanoid Robots and Avatars
9.2.10 Design and Creativity
9.3 Generative AI for Social Good
9.3.1 Personalized Healthcare Interventions
9.3.2 Equitable Education Access
9.3.3 Disaster Response and Humanitarian Aid
9.3.4 Accessible Information for Diverse Audiences
9.3.5 Mental Health Support and Therapy
9.3.6 Language Translation for Communication and Diplomacy
9.3.7 Environmental Conservation and Sustainability
9.3.8 Crisis Counseling and Suicide Prevention
9.3.9 Promoting Social Equity Through Policy Insights
9.3.10 Content Creation for Nonprofits and Humanitarian Organizations
9.4 Generative Artificial Intelligence for Sustainable Development
9.4.1 Renewable Energy Optimization
9.4.2 Smart Resource Management
9.4.3 Climate Change Mitigation
9.4.4 Ecosystem Monitoring and Conservation
9.4.5 Circular Economy Promotion
9.4.6 Sustainable Agriculture and Food Security
9.4.7 Urban Planning and Smart Cities
9.4.8 Water Resource Management
9.4.9 Global Supply Chain Sustainability
9.4.10 Environmental Education and Advocacy
9.5 Ethical and Regulatory Considerations
9.5.1 Data Privacy and Security
9.5.2 Fairness and Bias
9.5.3 Explainability and Transparency
9.5.4 Accountability
9.5.5 Environmental Impact
9.5.6 Ethical Use Cases
9.5.7 Global Collaboration
9.5.8 Ethical Artificial Intelligent Education
9.5.9 Public Engagement, Input and Global Governance
9.6 Generative Artificial Intelligence Limitations
9.6.1 Data Dependence
9.6.2 Bias and Fairness
9.6.3 Ethical Concerns
9.6.4 Lack of Creativity and Common Sense
9.6.5 Resource Intensive
9.6.6 Interpretability and Transparency
9.6.7 Overfitting
9.7 Future Research Directions
9.8 Lessons Learned and the Conclusion
9.8.1 Lessons
9.8.2 Conclusion
References
10. Revolutionizing Implementation: Cutting-Edge Tools and Resources in Generative AI
Blessing Takawira and David Pooe
10.1 Introduction
10.2 Foundational Theories and Models
10.3 State-of-the-Art Tools in Generative AI
10.4 Generative AI Tools Strategies
10.5 Literature Review Methodology
10.6 Challenges and Solutions
10.7 Future Directions
10.8 Conclusion
References
11. Applying Fuzzy Data Science in Generative AI for Healthcare
Yogeesh N.
11.1 Introduction
11.1.1 Overview of Fuzzy Data Science and Generative AI
11.1.2 Relevance of These Technologies in Healthcare
11.1.3 Objectives and Structure of the Chapter
11.2 Fundamentals of Fuzzy Data Science
11.2.1 Definition and Principles of Fuzzy Logic and Fuzzy Sets
11.2.2 Importance of Handling Uncertainty and Imprecision in Healthcare Data
11.2.3 Integration of Fuzzy Logic Along with AI Technologies
11.3 Generative AI in Healthcare
11.3.1 Explanation of Generative Models and Their Applications in Healthcare
11.3.2 Benefits of Generative AI for Medical Imaging, Diagnostics, and Treatment Planning
11.4 Synergizing Fuzzy Data Science with Generative AI
11.4.1 Conceptual Framework for Integrating Fuzzy Logic with Generative AI
11.4.2 Techniques and Methodologies for Combining These Technologies
11.4.2.1 Fuzzy Generative Adversarial Networks (Fuzzy GANs)
11.4.2.2 Fuzzy Variational Autoencoders (Fuzzy VAEs)
11.4.2.3 Fuzzy Clustering with Generative Models
11.4.3 Sources of Complexity in Healthcare Data and Uses of the Hybrid Approach
11.4.3.1 Handling Uncertainty and Imprecision
11.5 Case Study 1: Enhancing Diagnostic Accuracy
11.6 Case Study 2: Personalized Treatment Planning
11.7 Challenges and Limitations
11.7.1 Technical Challenges in Integrating Fuzzy Logic and Generative AI
11.7.2 Data Quality and Interpretability Issues
11.7.3 Ethical and Privacy Considerations in Healthcare Applications
11.8 Future Directions
11.8.1 Emerging Trends and Innovations in Fuzzy Data Science and Generative AI
11.8.2 Potential Future Applications in Healthcare
11.8.3 Recommendations for Researchers and Practitioners
11.9 Conclusion
11.9.1 Summary of Key Findings from the Case Studies
11.9.2 Overall Impact of Fuzzy Data Science and Generative AI on Healthcare
11.9.3 Final Thoughts on the Future of These Technologies in Medical Science
References
12. Generative AI in Hospital Industry Transforming Medical Imagining for Patient Diagnosis and Health Data Management
Bhupinder Singh and Christian Kaunert
12.1 Introduction
12.1.1 Overview of Generative AI: Relevance in the Healthcare Sector
12.1.2 Introduction to the Challenges in Medical Imaging and Healthcare Data Management
12.1.3 Objectives of the Chapter
12.1.4 Structure of the Chapter
12.2 Applications of Generative AI in the Healthcare and Hospital Industry
12.3 Generative AI in Medical Imaging: Transforming Visual Representations
12.3.1 Patient Diagnosis: Improving Diagnostic Accuracy
12.4 Personalized Medicine and Treatment Planning
12.5 Revolutionizing Healthcare Data Management- Streamlining Healthcare Data Management
12.6 Challenges and Viable Considerations
12.7 Conclusion, Future Directions and Innovations
References
Index

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