Autofusion: Fusing multi-modalities with interactions

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dc.contributor.authorNguyen, Thuy-Trinhen
dc.contributor.authorDeligianni, Fanien
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-05-29T13:16:00Z
dc.date.available2024-05-29T13:16:00Z
dc.date.issued2024en
dc.description.abstractIn the context of escalating data flows through diverse channels, multimodal machine learning holds the potential to simultaneously process varied data formats from multiple sources, offering robust solutions for uncertainty in various applications. The study highlights the often under-explored correlation information among data modalities and emphasizes the importance of disentangling enriched interactions for more informed decision-making. This paper navigates the burgeoning field of multimodal artificial intelligence (AI) by proposing Autofusion, a pioneering framework addressing representation learning and fusion challenges. Our proposed approach integrates autoencoder structures to address overfitting issues in unimodal machine learning, simultaneously tackling information balancing challenges. The framework’s application in Alzheimer’s disease detection, using DementiaBank’s Pitt corpus, demonstrates promising results, outperforming unimodal methods and showcasing a substantial advantage over traditional fusion techniques. This research significantly contributes by introducing Autofusion as a comprehensive multimodal machine learning solution, demonstrating its efficacy through DementiaBank’s Pitt corpus to detect Alzheimer’s disease, and shedding light on the influential role of cross-modality interaction for enhanced performance in complex applications.en
dc.description.sponsorshipScience Foundation Ireland (Grant 12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationNguyen, T.-T., Deligianni, F. and Nguyen, H. D. (2024) ‘Autofusion: Fusing multi-modalities with interactions’, 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024), Seville, Spain, 11-13 September. Procedia Computer Science, 246, pp. 1029-1038. https://doi.org/10.1016/j.procs.2024.09.522en
dc.identifier.doihttps://doi.org/10.1016/j.procs.2024.09.522
dc.identifier.eissn1877-0509en
dc.identifier.endpage1038
dc.identifier.journaltitleProcedia Computer Scienceen
dc.identifier.startpage1029
dc.identifier.urihttps://hdl.handle.net/10468/15950
dc.identifier.volume246
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartof28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024), Seville, Spain, 11-13 Septemberen
dc.rights© 2024, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectMultimodal interactionsen
dc.subjectMultimodal fusionen
dc.subjectMultimodal machine learningen
dc.subjectAutofusionen
dc.titleAutofusion: Fusing multi-modalities with interactionsen
dc.typeConference itemen
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