
Multimodal Learning Using Heterogeneous Data
- 1st Edition - November 1, 2025
- Imprint: Morgan Kaufmann
- Editors: Saeid Eslamian, Preethi Nanjundan, Jossy P. George, Faezeh Eslamian
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 7 5 2 8 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 7 5 2 9 - 6
Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text,… Read more

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Request a sales quoteThe book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.
Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.
- Provides a detailed exploration of multimodal learning techniques with a special focus on handling heterogeneous data sources
- Delves into advanced techniques such as deep fusion, graph-based methods, and attention mechanisms, catering to readers seeking deeper understanding
- Offers code examples, practical guidance, and real-world case studies to bridge the gap between theory and application
- Highlights applications in domains such as healthcare, autonomous vehicles, and multimedia analysis to showcase the practical relevance of multimodal learning
- Discusses emerging trends and challenges, enabling readers to stay ahead in this evolving field
2. Foundations of Multimodal Data Representation
3. Modalities in Data: Understanding Text, Images, and Audio
4. Feature Extraction and Fusion Techniques for Multimodal Data
5. Deep Learning Architectures for Multimodal Fusion
6. Transfer Learning in Multimodal Settings
7. Challenges in Preprocessing and Normalization of Heterogenous Data
8. Cross-Modal Information Retrieval and Recommendation
9. Multimodal Sentiment Analysis: Integrating Text, Images, and Audio
10. Multimodal Data Generation and Synthesis
11. Fusion Techniques for Medical Imaging and Clinical Data
12. Ethical Considerations in Multimodal Data Collection and Analysis
13. Case Studies: Multimodal Applications in Natural Language Processing
14. Visual-Audio Fusion in Multimedia Content Analysis
15. Multimodal Learning for Autonomous Systems and Robotics
16. Cognitive Computing: Merging Modalities for Human-Like AI
17. Multimodal Data Analytics for Social Media and User Behavior
18. Surveillance and Security: Integrating Video, Audio, and Sensor Data
19. Challenges and Opportunities in Multimodal Learning Research
20. Future Trends in Multimodal Learning: From Theory to Practical Applications
- Edition: 1
- Published: November 1, 2025
- Imprint: Morgan Kaufmann
- No. of pages: 250
- Language: English
- Paperback ISBN: 9780443275289
- eBook ISBN: 9780443275296
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Saeid Eslamian
Saeid Eslamian received his PhD in Civil and Environmental Engineering from University of New South Wales, Australia in 1998. Saeid was Visiting Professor in Princeton University and ETH Zurich in 2005 and 2008 respectively. He has contributed to more than 1K publications in journals, conferences, books. Eslamian has been appointed as 2-Percent Top Researcher by Stanford University for several years. Currently, he is full professor of Hydrology and Water Resources and Director of Excellence Center in Risk Management and Natural Hazards. Isfahan University of Technology, His scientific interests are Floods, Droughts, Water Reuse, Climate Change Adaptation, Sustainability and Resilience
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Preethi Nanjundan
Dr. Preethi Nanjundan received her Ph.D. degree in Semantic Web in 2014 and awarded Highly Commended from Bharathiar University, Coimbatore, India. She is currently working as an Assistant Professor in Christ (Deemed to be University), Lavasa campus, Pune. Her research interests are Semantic web, Machine learning, Deep Learning etc. She has published 3 books and 2 chapters.
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Jossy P. George
Dr. Jossy P George has been working with Christ University, Bengaluru, and other associated institutions in various capacities and is currently serving as the Director & Dean at Pune Lavasa Campus. He has a dual master’s degree in computer science and human resources from the USA and has done his FDPM from IIMA. He has been awarded a Doctorate in Computer Science by Christ University, Bengaluru. He is also a member of the IACSIT and Computer Society of India.
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