使用鱼松鼠水母的COVID-19预测搜索优化技术在基于雾云架构的雾云架构中
Shanthi Amgothu1, Srinivas Koppu1
1School of Computer Science Engineering and Information Systems, Vellore, India.
PloS one
|December 21, 2023
概括
一个新的白松鼠水母搜索优化 (CSJSO) 计划使用雾云架构增强了COVID-19的预测. 这种方法改善了医疗保健机构的患者数据管理和诊断准确性.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 由于数据管理挑战,COVID-19患者面临长时间的医院等待时间.
- 可穿戴设备生成初步的患者数据,需要强大的存储和处理.
- 现有的医疗保健系统在处理大量患者数据的复杂性方面扎.
研究的目的:
- 提出一种基于雾云的新方案,即鱼松鼠水母搜索优化 (CSJSO),用于高效的COVID-19预测.
- 整合医疗保健物联网传感器,雾计算和云计算,以改善患者数据分析.
- 提高COVID-19检测和管理的准确性和速度.
主要方法:
- 开发了CSJSO,结合了CAViar松鼠搜索算法 (CSSA) 和水母搜索优化 (JSO).
- 在Remora Namib Beetle JSO (RNBJSO) 训练的雾层中实现了一个深度神经模糊网络 (DNFN).
- 在云层中利用深度长期短期内存 (Deep LSTM),与CSJSO一起训练用于COVID-19检测.
主要成果:
- 在确诊病例 (数据库-1) 中,CSJSO实现了0.062的平均平方误差 (MSE) 和0.252的根平均平方误差 (RMSE).
- 在数据库-2中,CSJSO表现出高性能,精度为 (0.925),灵敏度为 (0.928),特异性为 (0.925).
- 拟议的架构有效地管理患者数据,并改善COVID-19诊断能力.
结论:
- 该CSJSO计划为COVID-19预测在雾云环境中提供了一个强有力的解决方案.
- 这种方法解决了数据的复杂性,并提高了医疗保健的诊断效率.
- 先进的优化算法和深度学习模型的整合显示出对抗流行病的重大前景.
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