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 K.V., Thomas; Rattan, Punam; Nair, Raji Ramakrishnan Title A name given to the resource An introduction to multimodal data representation 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.17-30 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00003-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00003-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032985231?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032985231?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 K.V. T., Department of Commerce, CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India; Rattan P., School of Computer Application, Lovely Professional University, Punjab, Phagwara, India; Nair R.R., School of Business and Management (MBA), CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India Description An account of the resource The contemporary digital epoch is characterized by a radical transformation of data representation methodologies that imply increased intricacy as well as an enlarged bulk of data. An unimodal approach focusing on judicious data types, considered in isolation, was the earlier norm. The emphasis was on structured data, which had the advantage of being arranged systematically within relational databases and entity-relationship frameworks. This facilitated efficient data management. With the introduction of the internet and digital communication, such unstructured data as textual content, images, and audio began to be placed up front. But unimodal techniques were not adequately equipped to manage the intricate and interconnected nature of real-world phenomena. The welcome result was the development of multimodal data representation methodologies, which constitute a sophisticated paradigm that integrates data from such varied sources as text, images, audio, video, and sensor data. This results in a more holistic comprehension of complex scenarios. Distinct attributes and inherent challenges characterize each modality. To exemplify, text data need advanced natural language processing strategies to comprehend context and semantics; Image data necessitate methodologies well versed in managing spatial features and elevated dimensionality; audio data requires concentration on temporal patterns and noise; video data, on the contrary, integrates these complexities, leading to efficient processing techniques to accommodate its substantial volume and dynamic characteristics. The unsynchronous and heterogeneous sensor data complicate the integration of diverse data streams. Sophisticated fusion techniques, that is, early fusion, late fusion, and hybrid fusion, capable of integrating features from various modalities, are employed to mitigate the challenges faced by multimodal data representation. It increases interpretative insights and precision. The deep learning technologies, such as convolutional neural networks for image analysis, recurrent neural networks for sequential data processing, and attention mechanisms, have led to advancements in this domain. These models have become competent in recognizing complex patterns across modalities. Naturally, they bring about significant progress in domains such as health care, autonomous systems, multimedia processing, and natural language comprehension. This chapter explores the historical background of data representation, right from the beginnings in unimodal to its advancement in multimodal. The unique characteristics and challenges associated with each modality are scrutinized; Fusion techniques alongside contemporary deep learning models are examined; and underscore real-world applications, which are effective examples of the transformative potential of multimodal data representation. The chapter also emphasizes the necessity of escalating these methodologies in an increasingly data-centric world. It lays the foundation for advancements in the future with the goal of overcoming existing limitations and enlarging the scope of multimodal applications. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource audio recognition; convolutional neural networks (CNNs); data complexity; data fusion; data integration; deep learning; early fusion; feature extraction; hybrid fusion; image processing; late fusion; Multimodal data representation; natural language processing (NLP); recurrent neural networks (RNNs); sensor data; structured data; text analysis; unimodal techniques; unstructured data; video analysis 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