论文标题
使用LSTM深度学习模型对COVID19引起的恢复案例的影响分析
Impact analysis of recovery cases due to COVID19 using LSTM deep learning model
论文作者
论文摘要
当今世界受到新颖的冠状病毒（Covid-19）的严重影响。使用医疗套件来识别冠状病毒受影响的人非常慢。接下来会发生什么，没人知道。世界正面临着不稳定的问题，不知道在不久的将来会发生什么。本文试图使用LSTM（长期记忆）对冠状病毒恢复病例进行预后。这项工作利用了258个地区的数据，其纬度和经度以及403天的死亡人数范围从22-01-2020到27-02-2021。具体而言，被称为LSTM的先进基于深度学习的算法对为时间序列数据（TSD）分析提取高度必不可少的特征产生了极大的影响。有很多方法已经用于分析传播预测。本文的主要任务最终是使用基于LSTM深度学习的体系结构来分析冠状病毒在全球恢复案例中的传播。
The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and do not know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM (Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
