Multimodal Learning Using Heterogeneous Data
by Saeid Eslamian, Preethi Nanjundan, Jossy George, Faezeh Eslamian
1st Edition
Publisher: Morgan Kaufmann
Book Details
| Print ISBN | 9780443275289 |
| eText ISBN | 9780443275296 |
| Publisher | Morgan Kaufmann |
| Publishing Year | 2025 |
| Edition | 1st Edition |
| Language | English |
| Pages | 290 |
Multimodal Learning Using Heterogeneous Data, 1st Edition, presents a unified framework for combining disparate data types such as text, images, and audio. Tailored for computer science researchers, data science researchers, and academic or industry data analysts, the text establishes core methodologies for working with unstructured heterogeneous information streams.
The text organizes core theoretical principles into systematic analytical workflows. Early sections examine data representation foundations, feature extraction methods, and fusion techniques for multimodal inputs. Subsequent topics address deep learning architectures engineered for fusion tasks, transfer learning paradigms in multimodal environments, and computational methods for cross-modal information retrieval and recommendation systems.
Practical application domains receive dedicated attention throughout the book, including multimodal sentiment analysis and the fusion of medical imaging with clinical data. Additional coverage includes implementations in autonomous systems, robotics, cognitive computing, and multimedia content analysis, supporting advanced study and research across interconnected artificial intelligence fields.
Table of Contents
Chapter 1: Introduction to Multimodal Learning and Heterogenous Data
Chapter 2: Foundations of Multimodal Data Representation
Chapter 3: Modalities in Data: Understanding Text, Images, and Audio
Chapter 4: Feature Extraction and Fusion Techniques for Multimodal Data
Chapter 5: Deep Learning Architectures for Multimodal Fusion
Chapter 6: Transfer Learning in Multimodal Settings
Chapter 7: Challenges in Preprocessing and Normalization of Heterogenous Data
Chapter 8: Cross-Modal Information Retrieval and Recommendation
Chapter 9: Multimodal Sentiment Analysis: Integrating Text, Images, and Audio
Chapter 10: Multimodal Data Generation and Synthesis
Chapter 11: Fusion Techniques for Medical Imaging and Clinical Data
Chapter 12: Ethical Considerations in Multimodal Data Collection and Analysis
Chapter 13: Case Studies: Multimodal Applications in Natural Language Processing
Chapter 14: Visual-Audio Fusion in Multimedia Content Analysis
Chapter 15: Multimodal Learning for Autonomous Systems and Robotics
Chapter 16: Cognitive Computing: Merging Modalities for Human-Like AI
Chapter 17: Multimodal Data Analytics for Social Media and User Behavior
Chapter 18: Surveillance and Security: Integrating Video, Audio, and Sensor Data
Chapter 19: Challenges and Opportunities in Multimodal Learning Research
Chapter 20: Future Trends in Multimodal Learning: From Theory to Practical Applications
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