Presentation Details
An Attention-based Fusion Multimodal Predictive Model for Heart Rate Deviation: A Simulative Design Study
ID：62
View Protection：ATTENDEE
Updated Time：2025-12-21 12:27:00 Hits：388
Online
Start Time：2025-12-30 16:30（Asia/Amman）
Duration：15min
Session：S5 Track 5: Emerging Trends of AI/ML »
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
PhD Student University of BotswanaSubmission Author
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Important Date
-
Conference Date
Dec 29
2025
toDec 31
2025
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Dec 20 2025
Draft paper submission deadline
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Dec 31 2025
Contribution Submission Deadline
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Dec 31 2025
Registration deadline
Sponsored By
United Societies of Science
Organized By
Zarqa University
Contact Information
- Miss AsianComNet
- as******@usssociety.org
Previous Conferences
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2024-10-24 Thailand Bangkok
2024 Asian Conference on Communication and Networks
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