Enhanced health monitoring in IoT with auto-metric graph neural networks and Archimedes optimisation

IF 1.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-04-15 DOI:10.1080/0952813x.2024.2338495

S.S. Arumugam, T. Sripriya, A. Mudassar Ali, Francis H Shajin

引用次数: 0

Abstract

IoT-based healthcare monitoring systems often lack context-awareness, hindering their ability to provide personalised and accurate healthcare services. The proposed architecture addresses this chal...

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利用自动度量图神经网络和阿基米德优化技术加强物联网健康监测

基于物联网的医疗监控系统往往缺乏情境感知能力，这阻碍了它们提供个性化和准确医疗服务的能力。所提出的架构可解决这一难题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Experimental & Theoretical Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

6.10

自引率

4.50%

发文量

审稿时长

>12 weeks

期刊介绍： Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving