HAWKFOG - 一个增强的深度学习框架用于雾物联网环境
1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Frontiers in artificial intelligence
|July 15, 2024
概括
这项研究介绍了HAWKFOGS,这是一种使用深度学习与边缘和雾计算来准确预测心脏病的新框架. 它达到99.7%的准确性,超过现有方法.
科学领域:
- 心脏病学 心脏病学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 心脏病是全球死亡的主要原因,需要先进的诊断工具.
- 目前基于物联网 (IoT) 的医疗系统由于预测局限性而难以有效诊断心脏病.
- 边缘和雾计算为实时健康监测和预测提供了潜在的进步.
研究的目的:
- 提出一个新的框架,HAWKFOGS,将深度学习与边缘和雾计算相结合,以改善心脏病诊断.
- 为了解决当前基于物联网的健康监管系统的预测能力限制.
主要方法:
- 开发了一个新的框架,HAWKFOGS,将深度学习与边缘和雾计算结合起来.
- 使用了带有心电图和血压传感器的物联网设备的数据.
- 基于物流混乱的哈里斯·霍克 (Harris Hawk) 优化增强门式循环神经网络被用于预测.
- 使用物联网节点和嵌入式Jetson基于纳米的雾网关进行了废弃实验.
主要成果:
- 霍克福格斯模型实现了高性能指标:准确率为99.7%,精度为99.65%,回忆率为99.7%,特异性为99.7%,F1得分为99.69%.
- 与其他算法相比,该模型展示了优越的预测能力.
- 模型构建时间特别低,为1.16秒.
结论:
- 该HAWKFOGS框架有效地提高心脏病诊断使用深度学习,边缘和雾计算.
- 拟议的模型为实时心脏问题诊断提供了实用和高效的解决方案.
- 这种方法显著提高了预测准确度,并减少了诊断时间.
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