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首页> 外文期刊>Engineering Letters >Emerging Advances in the Internet of Things (IoT) Technology for Fast Response to Covid-19 Outbreak With ANOVA-K-NN Implementation
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Emerging Advances in the Internet of Things (IoT) Technology for Fast Response to Covid-19 Outbreak With ANOVA-K-NN Implementation

机译:互联网(物联网)技术的新兴进步,以快速响应Covid-19与ANOVA-K-NN实施的爆发

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摘要

The internet of things (IoT) is a fast-growing technology that interconnects devices, homes, hospitals, facilities,and other locations together for faster transmission of dataand communication. The present critical situation which is thecoronavirus disease (COVID-19 ) outbreak. This pandemic hascaused a lot of economic meltdowns coupled with instabilitiesand loss of lives worldwide. Several types of research andtechnologies have been implemented to tackle this pandemic.This study describes the vital steps to follow in IoT technologyand the emerging advances it offers for rapid response tothe COVID-19 outbreak in order to save lives and to put anend to the pandemic. Some key threats surrounding the IoTtechnology were highlighted in this study, however, if copedand implemented appropriately, rapid prevention of infection,monitoring of infected and suspected patients, treatment ofinfected patients, and forecasting of adequate solutions for a better environment is assured. Furthermore,Analysis of Variance(ANOVA) algorithm was used to fetch relevant information, andk-nearest neighbor (k-NN) was used as a classifier to predict theperformance of the COVID-19 Iot data system obtained fromSan Francisco University. An accuracy of 79.17% was achieved.Therefore, this study will be proficient especially for cliniciansin making decisions in the post COVID era.
机译:事物互联网(IOT)是一种快速增长的技术,可将设备,房屋,医院,设施和其他位置互连,以便更快地传输数据和通信。目前危重情况是Thecoronavirus疾病(Covid-19)爆发。这种大流行的融资加上了很多经济困境,加上全世界生活的稳定性。已经实施了几种类型的研究和技术来解决这一流行病。本研究描述了在IOT技术中遵循的重要步骤,新兴的进展,它提供了快速反应,以便Covid-19爆发,以拯救生命并促进大流行。在本研究中强调了围绕IOTTEchnology的一些关键威胁,然而,如果副主席进行适当,请快速预防感染,对感染和疑似患者的监测,请确保了对更好的环境预测的预测。此外,使用差异(ANOVA)算法的分析来获取相关信息,并且使用ANDK - 最近邻(K-NN)作为分类器,以预测从SAN Francisco大学获得的CoVID-19物联网数据系统的表现。达到了79.17%的准确性。因此,这项研究将熟练,特别是对于临床医生在后Covid时代做出决定。

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