Generative Artificial Intelligence and Explainable Artificial Intelligence for Real-world Applications (Future Generation Information Systems)

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Generative Artificial Intelligence and Explainable Artificial Intelligence for Real-world Applications (Future Generation Information Systems)

Rajendran, Ramkumar (EDT)/ Sweta, Soni (EDT)/ Rohatgi, Divya (EDT)

ウェブストア価格 ¥55,264（本体¥50,240）
CRC Press（2026/08発売）
外貨定価 US$ 250.00
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ポイント 2,510pt

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製本 Hardcover:ハードカバー版／ページ数 512 p.
言語 ENG
商品コード 9781041068761

Full Description

This book integrates the concepts of explainable artificial intelligence, and generative explainable artificial intelligence, showcasing how explainability can be incorporated into generative models. It highlights the importance of making generative models transparent, offering strategies to achieve this through interpretability techniques.

Focuses on the principles and methodologies of explainable artificial intelligence, providing readers with a thorough understanding of how artificial intelligence models can be made transparent and interpretable.
Explores explainable artificial intelligence techniques, including feature importance, SHAP values, LIME, and counterfactual explanations.
Offers an in-depth examination of generative artificial intelligence, covering cutting-edge advancements in generative models like GANs, VAEs, and transformer-based architectures.
Bridges the gap between theoretical concepts and their practical applications, making advanced artificial intelligence technologies accessible to the readers.
Includes numerous case studies and real-world examples that demonstrate the successful application of generative artificial intelligence, and explainable artificial intelligence.

The text is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, biomedical engineering, and information technology.

1. Decoding GenAI: An Introduction to Transformers 2. Bridging the Gap: Integrating Generative AI and Explainability 3. XAI decoded: A systematic literature review on Explainability 4. Applications of Explainable Artificial Intelligence in Healthcare 5. Interactive UI for Captum: Enhancing Explainable AI Accessibility 6. Filling the AI Data Void: Synthesizing Domain-Specific Knowledge using Generative AI 7. Generating Synthetic Images in Multi-Agent Cooperating Environment Focusing on Generalizability 8. Exploring Diffusion Models: Techniques, Applications, and Future Directions 9. Multimodal Generative AI in Healthcare: A Comprehensive Review of Models and Applications 10. Comparative Analysis of Convolutional Neural Network Architectures for Diabetic Retinopathy Classification and Grading in Retinal Images Using Generative AI and Explainable AI 11. From Steps to Signals: Machine Learning for Explainable Gait Analysis 12. A Study on Deep Learning and Machine Learning Methods for Automated Brain Hemorrhage Detection 13. GenAIED: GenAI in Education 14. Generative Intelligence Meets Precision Farming: A Hybrid CNN-RAG Approach for Early Plant Disease Management 15. Applications of Generative AI in Education: A Conceptual Framework for Ethical Implementation in Higher Education Curricula 16. AI in Industry 5.0: Future Trends, Green AI, and Accountability 17. Exploring Multimodal AI Applications in Healthcare, Smart Homes, Autonomous Vehicles, and Speech Recognition