Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Savithri, M.; Arul Selvan Gnanamonickam, A. Title A name given to the resource Deep learning architectures for multimodal fusion Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.57-68 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00004-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00004-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032924333?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032924333?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Savithri M., Department of Data Science, Christ University, Karnataka, Bengaluru, India; Arul Selvan Gnanamonickam A., Department of Software System and Computer Science (PG), KG College of Arts and Scienc, Tamil Nadu, Coimbatore, India Description An account of the resource The advancement in technology during the recent years has provided deep learning technology as an emerging and powerful paradigm which can be used for processing and understanding complex data across various domains. Multimodal fusion is integrating the information that is collected from various sources or modalities, which requires a comprehensive understanding of data like autonomous driving, medical diagnosis, etc. In this chapter, we will explore the various advanced deep learning architectures that have been specially designed based on the multimodal fusion. The various challenges that are being faced in multimodal data, which include heterogeneity, noise, reliability of data, etc. Various deep learning architectures that are built to address the various challenges, like convolutional neural networks, recurrent neural networks, are reviewed, and the suitability of the fusion strategies is highlighted. The various techniques that are used for combining the information from disparate modalities, like early fusion, late fusion, and hybrid approaches, are also discussed with their pros and cons. Various real-time applications in the field of healthcare, multimedia, robotics, etc., are demonstrated based on the impact of the architectures. Finally, the potential of deep learning architecture based on the revolutionary multimodal fusion will be discussed. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource convolutional neural networks; deep learning; explainable AI (XAI); Multimodal fusion; multimodal generative adversarial networks; recurrent neural networks Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online