Analysing and predicting COVID-19 AI tracking using artificial intelligence

被引:1
|
作者
Alodat, Iyas [1 ]
机构
[1] Jerash Univ, Comp Sci & Informat Technol, Jerash, Jordan
关键词
Deep leaning; mobile application; SPO2; sensors; tensorflow; PNEUMONIA; CLASSIFICATION; CORONAVIRUS;
D O I
10.1142/S1793962321410051
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper, we will discuss prediction methods to restrict the spread of the disease by tracking contact individuals via mobile application to individuals infected with the COVID-19 virus. We will track individuals using bluetooth technology and then we will save information in the central database when they are in touch. Monitoring cases and avoiding the infected person help with social distance. We also propose that sensors used by people to obtain blood oxygen saturation level and their body temperature will be used besides bluetooth monitoring. The estimation of the frequency of the disease is based on the data entered by the patient and also on the data gathered from the users who entered the program on the state of the disease. In this study, we will also propose the best way to restrict the spread of COVID-19 by using methods of artificial intelligence to predict the disease in Jordan using Tensorflow.
引用
收藏
页数:11
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