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 Ramasamy, Gunavathi; Patkar, Kalpesh Dilip; Chery, Angelo P. Title A name given to the resource Introduction to multimodal learning and heterogenous data 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.1-15 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00002-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00002-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032942844?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032942844?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 Ramasamy G., School of Sciences, Christ University, Karnataka, Bengaluru, India; Patkar K.D., School of Law, CHRIST University, Karnataka, Bengaluru, India; Chery A.P., School of Sciences, Christ University, Karnataka, Bengaluru, India Description An account of the resource With the rising advancements in computation, technology, and many innovative evolutions coming into play, multimodal learning is one of the most rapidly growing fields within the domain of artificial intelligence and Machine Learning. It mainly focuses on integrating information from multiple sources called "modalities," allowing the systems to utilize the varieties of data to furtherenhance their understanding and performance. These so-called modalities make use of various types of data in the form of text, images, audio, and sensor readings. They are able to process complex information due to these modalities and thus provide more insightful results for the tasks that they are assigned. Another important aspect of multimodal learning is heterogeneous datadata that differs significantly in structure, format, and origin. This type of data falls mainly into three categories, comprising structured data, which is quite organized and therefore easy to locate or search, as in the case of the database records. Then comes unstructured data characterized by the free form, which comprises mainly social media posts, videos, and images. In addition, it has been possible to separate semistructured data. It incorporates some features of being ordered, like the metadata included in XML or JSON files; however, a fixed schema does not apply. The understanding of the kind is important because each type calls for a different problem, and each type poses new opportunities in analysis. Handling the heterogeneous data effectively is all the more important because the said system will be fed heterogeneous data, and if its combination and analysis go reasonably logically, it is expected to be a source for multimodal systems. The ability to merge structured, unstructured, and semistructured data improves performance across a wide range of tasks, including but not limited to common applications like image recognition, sentiment analysis, and decision-making processes in autonomous systems. For example, in the multimodal learning case, it would be beneficial for the system that learns customer feedback to merge textual reviews, audio recordings of customer interactions, and visual data from product images. It has been known to yield a much clearer picture of what customers really want and how they actually behave. This chapter introduces notions of multimodal learning as well as heterogeneous datatheir characteristics, types, sources, and practical usage. It will attempt to establish a basic understanding of these two concepts in relation to each other in order to support more advanced applications through machine learning. In a review of the possible compositions between multimodal learning as well as heterogeneous data, the chapter will introduce their importance regarding the creation of intelligent systems that can address complex, intricate tasks across differing fields. As we enter the data age, with multiple sources churning out data at unprecedented rates that appear to have no bounds, integration of multimodal learning with heterogeneous data cannot be ignored. This will be vital for coming up with flexible yet useful applications to real-world problems. This region is promising for systems that perceive, interpret, and respond to the variability of information in a fashion similar to human reasoning and decision-making. Future application of artificial intelligence in the life of man will result from continuous research in the areas of multimodal learning and heterogeneous data. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource artificial intelligence; data science; heterogeneous data; knowledge management; Machine learning; multimodal learning 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