SEIR 驱动的语义整合框架:利用递归神经网络在 COVID-19 疫情爆发中加强物联网流行病学监测

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Saket Sarin, Sunil K. Singh, Sudhakar Kumar, Shivam Goyal, Brij B. Gupta, Varsha Arya, Kwok Tai Chui
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引用次数: 0

摘要

在当前 COVID-19 大流行的情况下,复杂的流行病学监测系统比以往任何时候都更加重要,因为传统方法无法应对这一全球紧急事件的范围和复杂性。为了应对这一挑战,作者提出了最先进的 SEIR 驱动语义集成框架(SDSIF),该框架利用物联网(IoT)处理各种数据源。SDSIF 的主要创新之处在于开发了一个广泛的 COVID-19 本体,使无与伦比的数据互操作性和语义推理成为可能。该框架不仅有助于实时数据集成,还能通过使用循环神经网络(RNN)进行高级分析、异常检测和预测建模。SDSIF 具有可扩展性和灵活性,能够适应不同的医疗保健环境和地理区域,为 COVID-19 的疫情管理带来了一场流行病学监测的革命。平均绝对误差 (MAE) 和平均平方误差 (MSE) 等指标被用于严格的评估。评估还包括一个出色的 R 平方得分,这证明了 SDSIF 的有效性和独创性。值得注意的是,8.70 的 RMSE 值适中,凸显了其准确性,而 3.03 的 MSE 值较低,凸显了其较高的预测精度。该框架的 R 方值高达 0.99,更加凸显了其在解释疾病数据变化时的弹性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SEIR-driven semantic integration framework: Internet of Things-enhanced epidemiological surveillance in COVID-19 outbreaks using recurrent neural networks

SEIR-driven semantic integration framework: Internet of Things-enhanced epidemiological surveillance in COVID-19 outbreaks using recurrent neural networks

With the current COVID-19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and complexity of this global emergency. In response to this challenge, the authors present the state-of-the-art SEIR-Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID-19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real-time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID-19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R-squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R-squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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