报告详情
An Attention-based Fusion Multimodal Predictive Model for Heart Rate Deviation: A Simulative Design Study
编号：62
访问权限：仅限参会人
更新：2025-12-21 12:27:00 浏览：115次
拓展类型2
报告开始：2025年12月30日 16:30（Asia/Amman）
报告时间：15min
所在会场：[S5] Track 5: Emerging Trends of AI/ML [S5-2] Track 5: Emerging Trends of AI/ML
摘要
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.
关键词
intelligent systems,,multimodal predictive models,gated attention fusion,heart rate deviation
报告人
Tefo Kgosietsile
PhD Student University of Botswana稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
-
02月10日 2026
初稿截稿日期
-
02月10日 2026
注册截止日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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