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Full Description
Generative AI, Large Language Models, Graph Neural Networks and Knowledge Graphs Applications offers readers a thorough examination of the cutting-edge technologies that are shaping the future of artificial intelligence. Through clear explanations and practical insights, this book bridges theoretical concepts and real-world applications, helping professionals, researchers, and academics understand and implement advanced AI solutions. With a focus on the foundational principles of Generative AI, LLMs, GNNs, and Knowledge Graphs, the text emphasizes their collaborative potential and provides guidance for solving complex problems across multiple disciplines.
The book also stands out for its emphasis on hands-on learning, featuring practical examples and case studies from industries such as healthcare and finance. It highlights interdisciplinary applications, demonstrating how these technologies can be leveraged to address real-world challenges. Readers benefit from comprehensive coverage of each topic, deep dives into their interconnections, and actionable strategies for staying at the forefront of AI advancements.
Contents
PART I: Key Technologies and Methodologies
1. Deep Learning and Neural Networks
2. Generative Models in Depth
3. Transformers and Attention Mechanisms
4. Understanding Large Language Models (LLMs)
5. Graph Neural Networks (GNNs) and Knowledge Graphs
6. Combining Generative AI and Large Language Models
7. Enhancing Knowledge Graphs with Generative AI
8. Graph Neural Networks for Knowledge Representation
9. AI-Powered Multimodal Systems: Combining Text, Graphs, and Images
PART II: Practical Applications and Case Studies in Healthcare
10. Alzheimer's Disease detection using KG, GNN and LLMs
11. Schizophrenia detection using KG, GNN and LLMs
12. Bipolar Disorder detection using KG, GNN and LLMs
13. Depression detection using KG, GNN and LLMs
14. Diabetes detection using KG, GNN and LLMs
15. Obesity detection using KG, GNN and LLMs
16. Coronary Artery Disease detection using KG, GNN and LLMs
17. Hypertension detection using KG, GNN and LLMs
18. Rheumatoid Arthritis detection using KG, GNN and LLMs
19. Systemic Lupus Erythematosus detection using KG, GNN and LLMs
20. Breast Cancer detection using KG, GNN and LLMs
21. Prostate Cancer detection using KG, GNN and LLMs
22. Colorectal Cancer detection using KG, GNN and LLMs
23. Parkinson's Disease detection using KG, GNN and LLMs
24. Amyotrophic Lateral Sclerosis detection using KG, GNN and LLMs
25. scRNA analysis using KG, GNN and LLMs
26. Social Media Text as a Tool for Monitoring Public Mental Health Trends: A Sentiment Analysis
PART III: Practical Applications and Case Studies in Cybersecurity
27. Malware Analysis using KG, GNN and LLMs
28. DDoS attack dataset against EV authentication using KG and GNN
29. Attack detection in Internet of Medical Things using KG and GNN
30. IDS in Internet of Vehicles (IoV) using KGs and GNN
31. EV charger attack detection using KG and GNN
32. Automatic Reasoning for Fact Verification Using KGs and Language Models
33. Attack detection in IoT environment using KGs and LLMs
34. Modbus attack detection using KG and GNN
35. LLM-based models to assess the accuracy of real/fake tweet detection
