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An Attention-based Fusion Multimodal Predictive Model for Heart Rate Deviation: A Simulative Design Study
ID：62 View protection：Participant Only Updated time：2025-12-21 12:27:00 Views：104 Online

Start Time：2025-12-30 16:30

Duration：15min

Session：[S5] Track 5: Emerging Trends of AI/ML [S5-2] Track 5: Emerging Trends of AI/ML

Slide
Abstract
Intelligent health analysis models improve the health sector, particularly in monitoring heart rate. Traditional unimodal systems struggle to satisfactorily provide a comprehensive and accurate prediction of heart rate deviation (HRD) as they learn from limited data features. This research study argues that an attention-based multimodal fusion model trained on authorized medical datasets remains the best possible solution, as it can manage HRD complexity. The study employs a simulation experiment methodology to compare the response accuracy of the unimodal to the attention-based multimodal model. A matrix comparison of six dataset features was selected to test a prediction of accurate and comprehensive HRD. The simulative experimental findings demonstrate that a gated attention-fused predictive multimodal system outperforms traditional unimodal systems, as heart rate deviation involves complex complementary signals. 
 
Keywords
intelligent systems,,multimodal predictive models,gated attention fusion,heart rate deviation
Speaker
Tefo Kgosietsile
University of Botswana

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Important Dates

  • Conference date

    12-29

    2025

    -

    12-31

    2025

  • 12-30 2025

    Presentation submission deadline

  • 02-10 2026

    Draft paper submission deadline

  • 02-10 2026

    Registration deadline

Sponsored By

United Societies of Science

Organized By

扎尔卡大学

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