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A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2021-06-21 , DOI: 10.1007/s11227-021-03935-w
Tariq Ahamed Ahanger 1 , Usman Tariq 1 , Muneer Nusir 1 , Abdulaziz Aldaej 1 , Imdad Ullah 1 , Abdullah Sulman 2
Affiliation  

Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial–temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.



中文翻译:

一种新颖的基于物联网雾云的医疗保健系统,用于监测和预测 COVID-19 的蔓延

病毒性疾病的快速传播是全球范围内正在出现的公共医疗问题。其中,COVID-19 被视为当今最关键和最新颖的感染。目前的调查为监测和预测 COVID-19 病毒感染 (C-19VI) 提供了一个有效的框架。据我们所知,没有研究工作专注于将物联网技术整合到 C-19 在时空模式上的扩展。此外,在预测人类 C-19 以控制 COVID-19 传播方面所做的工作有限。提议的框架包括一个四级架构,用于预期和避免 COVID-19 污染。提出的模型包括 COVID-19 数据收集 (C-19DC) 级别、COVID-19 信息分类 (C-19IC) 级别、COVID-19 挖掘和提取 (C-19ME) 级别、和 COVID-19 预测和决策建模 (C-19PDM) 级别。具体来说,所提出的模型用于授权个人/社区间歇性地筛查 COVID-19 发烧测量 (C-19FM) 并对其进行预测,以便提前采取主动措施。此外,出于先见之明的目的,C-19VI 的概率检查被量化为成员程度,其累积表征为 COVID-19 发热测量 (C-19FM)。此外,预测是利用时间递归神经网络实现的。此外,基于自组织映射技术,C-19VI 的存在是在地理区域上确定的。在四个具有挑战性的数据集上执行模拟。与其他策略相比,在分类效率、预测可行性、

更新日期:2021-06-21
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