DeepELR:基于深度学习的能源和链接稳定性意识的路由在物联网中用于心脏病分类
1Department of Computer Science & Engineering, Kalasalingam Academy of Research Education Krishnankoil, Srivilliputhur, Tamil Nadu 626126, India.
Computational biology and chemistry
|July 8, 2025
概括
这项研究引入了一种新的物联网 (IoT) 方法,用于使用机器学习 (ML) 检测心脏病. 一个新的路由算法,DeepELR,确保可靠的数据传输,用于智能医疗监控.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 是一个快速发展的技术,在医疗保健领域具有重大潜力.
- 物联网支持的健康监测,特别是可穿戴电子产品,为患有慢性病如心脏病的患者提供了巨大的好处.
- 早期诊断和使用机器学习 (ML) 的预测建模对于有效的医学分析和远程患者护理至关重要.
研究的目的:
- 开发一个集成到物联网范式中的心脏病检测系统.
- 设计一个有效的路由算法,从物联网节点传输传感数据到医疗专业人员.
- 在物联网场景中增强远程智能计算,用于健康跟踪.
主要方法:
- 开发了一种新的路由算法,基于深度学习的能源和链接稳定意识路由 (DeepELR).
- 在DeepELR中利用深度循环神经网络 (DRNN) 来预测节点能量和链接稳定性,确保数据完整性.
- 实现基于ASSA的集体学习以进行心脏病分类,使用深度循环神经网络 (DRNN),DEB和循环神经网络 (RNN) 模型.
主要成果:
- 拟议的DeepELR路由算法实现了低能耗 (0.762 J).
- 达到了94.3%的高数据包交付比率 (PDR),表明数据传输可靠.
- 该系统的链路稳定性为31.8%.
结论:
- 开发的物联网系统与DeepELR路由算法和基于ASSA的集体学习有效检测心脏病.
- 该方法确保了可靠和有效的患者数据传输,用于智能医疗监控.
- 这项研究有助于在物联网生态系统中推进远程健康跟踪和预测诊断.
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