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Generative AI : Disruptive Technologies for Innovative Applications
| Generative AI : Disruptive Technologies for Innovative Applications |
| Autore | Gayathri N |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (291 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
KumarS. Rakesh
ChandranRamesh RajPethuru PelusiDanilo |
| Soggetto topico | Artificial intelligence |
| ISBN |
1-394-30291-6
1-394-30293-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Generative AI -- 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 -- Chapter 2 Generative Adversarial Networks (GANs) -- 2.1 Introduction -- 2.2 Tale of Two Minds: Unveiling the GAN Mechanism -- 2.2.1 Origins -- 2.2.2 Foundational Concepts of Generative Adversarial Network -- 2.2.3 The Discriminator -- 2.2.3.1 Theoretical Understanding -- 2.2.3.2 Objectives and Functionality -- 2.2.3.3 Mathematical Formulation -- 2.2.4 The Generator -- 2.2.4.1 The Generator's Objective and Mathematical Formulation -- 2.2.4.2 Creative Dynamics of the Generator -- 2.2.5 Adversarial Training -- 2.2.5.1 Fundamental Principles of Adversarial Training -- 2.2.5.2 Training Dynamics in Adversarial Training -- 2.2.5.3 Adversarial Training Application -- 2.2.5.4 Difficulties and Restrictions -- 2.2.5.5 Future Paths and Prospects for Research.
2.3 From Brushstrokes to Breakthroughs: Diverse Canvas of GAN Applications -- 2.3.1 Dreaming Up New Worlds: Aesthetic Mastery -- 2.3.1.1 Mastery of Aesthetics Using GANs -- 2.3.1.2 Improving Realism -- 2.3.2 Data Augmentation -- 2.3.2.1 GAN-Based Enrichment of Data -- 2.3.2.2 Cross-Domain Applications -- 2.3.3 Unsupervised Learning -- 2.3.3.1 GANs in Unsupervised Learning -- 2.4 Challenges and Ethical Considerations -- 2.4.1 Mode Collapse -- 2.4.2 Instability During Training -- 2.4.3 Absence of Convergence -- 2.5 A Glimpse into the Future: Where GANs Will Lead Us -- 2.5.1 Customized Content Production -- 2.5.2 Targeted Image Reproduction -- 2.5.3 Improved Data Distortion -- 2.5.4 Conditional Generation -- 2.5.5 Overcoming Obstacles -- 2.6 Conclusion: The Revolutionary Impact of Generative Adversarial Networks (GANs) -- References -- Chapter 3 Reinforcement Learning in Generative AI -- 3.1 Introduction -- 3.2 Current State of the Art in Generative AI with Reinforcement Learning -- 3.3 Different Applications for Generative AI with Reinforcement Learning -- 3.4 Characteristics of Generative AI with Reinforcement Learning -- 3.5 Outstanding Problems in Generative AI with Reinforcement Learning -- 3.6 Limitations of Generative AI with Reinforcement Learning -- 3.7 Conclusion -- References -- Chapter 4 Pix2pix GAN for Image-to-Image Translation: A Comparative Study with Diverse Datasets -- 4.1 Introduction -- 4.1.1 Significance of Generative AI in the Three Use Cases -- 4.2 Related Works -- 4.3 Methodology -- 4.4 Results and Discussions -- 4.4.1 Dataset -- 4.4.2 Experimental Assessment -- 4.5 Conclusion -- References -- Chapter 5 Study of State-of-the-Art Performance Metrics in NLP: Specifically for Text Summarization in the Medical Domain Using the SumPubMed Dataset -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Research Methodology -- 5.4 Results. 5.5 Conclusions -- 5.6 Future Score of Work -- References -- Chapter 6 The Impact of Generative AI in Gaming: Exploring Immersive Experiences -- 6.1 Introduction -- 6.2 Realistic Textures and Landscapes -- 6.3 Intelligent NPCS and Adaptive Behaviors -- 6.4 Expanding Beyond Traditional Gaming -- 6.5 Conclusion -- References -- Chapter 7 Ethical Dimensions and Societal Effects of Generative AI: The Portrayal of Ethical Issues of ChatGPT, DALL-E, and Other Systems -- Introduction -- Ethical Considerations in AI-Driven Recruitment: Video and Image Interviews -- UNESCO -- Conclusion -- Acknowledgments -- References -- Chapter 8 Generative Artificial Intelligence for Social Good and Sustainable Development -- 8.1 Introduction -- 8.1.1 Motivation of Generative Artificial Intelligence -- 8.1.2 The Contribution of the Chapter -- 8.1.3 The Organization of the Chapter -- 8.2 An Overview of Generative Artificial Intelligence and Its Applications -- 8.2.1 Text Generation -- 8.2.2 Image Generation -- 8.2.3 Audio and Music Generation -- 8.2.4 Data Augmentation -- 8.2.5 Video and Animation Generation -- 8.2.6 Drug Discovery -- 8.2.7 Content Recommendation -- 8.2.8 Language Translation -- 8.2.9 Humanoid Robots and Avatars -- 8.2.10 Design and Creativity -- 8.3 Generative AI for Social Good -- 8.3.1 Personalized Healthcare Interventions -- 8.3.2 Equitable Education Access -- 8.3.3 Disaster Response and Humanitarian Aid -- 8.3.4 Accessible Information for Diverse Audiences -- 8.3.5 Mental Health Support and Therapy -- 8.3.6 Language Translation for Communication and Diplomacy -- 8.3.7 Environmental Conservation and Sustainability -- 8.3.8 Crisis Counseling and Suicide Prevention -- 8.3.9 Promoting Social Equity Through Policy Insights -- 8.3.10 Content Creation for Nonprofits and Humanitarian Organizations. 8.4 Generative Artificial Intelligence for Sustainable Development -- 8.4.1 Renewable Energy Optimization -- 8.4.2 Smart Resource Management -- 8.4.3 Climate Change Mitigation -- 8.4.4 Ecosystem Monitoring and Conservation -- 8.4.5 Circular Economy Promotion -- 8.4.6 Sustainable Agriculture and Food Security -- 8.4.7 Urban Planning and Smart Cities -- 8.4.8 Water Resource Management -- 8.4.9 Global Supply Chain Sustainability -- 8.4.10 Environmental Education and Advocacy -- 8.5 Ethical and Regulatory Considerations -- 8.5.1 Data Privacy and Security -- 8.5.2 Fairness and Bias -- 8.5.3 Explainability and Transparency -- 8.5.4 Accountability -- 8.5.5 Environmental Impact -- 8.5.6 Ethical Use Cases -- 8.5.7 Global Collaboration -- 8.5.8 Ethical Artificial Intelligent Education -- 8.5.9 Public Engagement, Input and Global Governance -- 8.6 Generative Artificial Intelligence Limitations -- 8.6.1 Data Dependence -- 8.6.2 Bias and Fairness -- 8.6.3 Ethical Concerns -- 8.6.4 Lack of Creativity and Common Sense -- 8.6.5 Resource Intensive -- 8.6.6 Interpretability and Transparency -- 8.6.7 Overfitting -- 8.7 Future Research Directions -- 8.8 Lessons Learned and the Conclusion -- 8.8.1 Lessons -- 8.8.2 Conclusion -- References -- Chapter 9 Revolutionizing Implementation: Cutting-Edge Tools and Resources in Generative AI -- 9.1 Introduction -- 9.2 Foundational Theories and Models -- 9.3 State-of-the-Art Tools in Generative AI -- 9.4 Generative AI Tools Strategies -- 9.5 Literature Review Methodology -- 9.6 Challenges and Solutions -- 9.7 Future Directions -- 9.8 Conclusion -- References -- Chapter 10 Applying Fuzzy Data Science in Generative AI for Healthcare -- 10.1 Introduction -- 10.1.1 Overview of Fuzzy Data Science and Generative AI -- 10.1.2 Relevance of These Technologies in Healthcare -- 10.1.3 Objectives and Structure of the Chapter. 10.2 Fundamentals of Fuzzy Data Science -- 10.2.1 Definition and Principles of Fuzzy Logic and Fuzzy Sets -- 10.2.2 Importance of Handling Uncertainty and Imprecision in Healthcare Data -- 10.2.3 Integration of Fuzzy Logic Along with AI Technologies -- 10.3 Generative AI in Healthcare -- 10.3.1 Explanation of Generative Models and Their Applications in Healthcare -- 10.3.2 Benefits of Generative AI for Medical Imaging, Diagnostics, and Treatment Planning -- 10.4 Synergizing Fuzzy Data Science with Generative AI -- 10.4.1 Conceptual Framework for Integrating Fuzzy Logic with Generative AI -- 10.4.2 Techniques and Methodologies for Combining These Technologies -- 10.4.2.1 Fuzzy Generative Adversarial Networks (Fuzzy GANs) -- 10.4.2.2 Fuzzy Variational Autoencoders (Fuzzy VAEs) -- 10.4.2.3 Fuzzy Clustering with Generative Models -- 10.4.3 Sources of Complexity in Healthcare Data and Uses of the Hybrid Approach -- 10.4.3.1 Handling Uncertainty and Imprecision -- 10.5 Case Study 1: Enhancing Diagnostic Accuracy -- 10.6 Case Study 2: Personalized Treatment Planning -- 10.7 Challenges and Limitations -- 10.7.1 Technical Challenges in Integrating Fuzzy Logic and Generative AI -- 10.7.2 Data Quality and Interpretability Issues -- 10.7.3 Ethical and Privacy Considerations in Healthcare Applications -- 10.8 Future Directions -- 10.8.1 Emerging Trends and Innovations in Fuzzy Data Science and Generative AI -- 10.8.2 Potential Future Applications in Healthcare -- 10.8.3 Recommendations for Researchers and Practitioners -- 10.9 Conclusion -- 10.9.1 Summary of Key Findings from the Case Studies -- 10.9.2 Overall Impact of Fuzzy Data Science and Generative AI on Healthcare -- 10.9.3 Final Thoughts on the Future of These Technologies in Medical Science -- References. Chapter 11 Generative AI in Hospital Industry Transforming Medical Imagining for Patient Diagnosis and Health Data Management. |
| Record Nr. | UNINA-9911025992103321 |
Gayathri N
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Revolution with Generative AI: Trends and Techniques / / edited by N Gayathri, S. Rakesh kumar, Alvaro Rocha
| Revolution with Generative AI: Trends and Techniques / / edited by N Gayathri, S. Rakesh kumar, Alvaro Rocha |
| Autore | Gayathri N |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (258 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
Rakesh kumarS
RochaAlvaro |
| Collana | Information Systems Engineering and Management |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| ISBN | 3-031-91660-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Generative Adversarial Networks (GANs) -- Applications of GANs in high-resolution image synthesis, domain adaptation, and image-to-image translation -- Reinforcement Learning in Generative AI: State-of-the-Art Performance -- The Impact of Generative AI on Agriculture Environment. |
| Record Nr. | UNINA-9911011772303321 |
|
Gayathri N
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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