基于深度学习模型的新型冠状病毒阳性病例预测与分析

M. Farhan, Sohail Jabbar, M. R. Shahid
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摘要

2019年12月底,COVID-19病毒是中国武汉市首次报告的病例。2020年3月11日。世界卫生组织(世卫组织)宣布了全球大流行COVID-19。COVID-19在几周内迅速蔓延到世界各地。我们将建议建立一个利用深度学习(DL)模型预测巴基斯坦covid -19阳性病例的预测模型。我们评估了预测模式的主要特征,并指出了巴基斯坦和世界其他国家的新型COVID-19疾病模式。本研究将使用深度学习模型来衡量巴基斯坦的几例COVID-19阳性病例报告。LSTM单元处理时间序列数据预测是非常有效的。递归神经网络过程处理时间依赖和涉及隐藏层被确认和预测阳性病例和每周病例报告在未来。双向LSTM (Bi-LSTM)对数据和信息进行单向处理,以预测和分析每周6-9天的新冠肺炎阳性病例数
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Model
At the end of December 2019, the COVID-19 virus was the initial report case in China Wuhan City. On March 11, 2020. The Department of Health (WHO) announced COVID-19, a global pandemic. The COVID-19 spread rapidly out all over the world within a few weeks. We will propose to develop a forecasting model of COV-19 positive case predict outbreak in Pakistan using Deep Learning (DL) models. We assessed the main features to forecast patterns and indicated The new COVID-19 disease pattern in Pakistan and other countries of the world. This research will use the deep learning model to measure several COVID-19 positive case reports in Pakistan. LSTM cell to process time-series data forecasts is very efficient. Recurrent neural network processes to handle time-dependent and involve hidden layers are confirmed and predict positive cases and weekly cases reported in the future. Bidirectional LSTM (Bi-LSTM) processes data and information in one direction to predict and analyze the weekly 6-9 days readily forecast the number of positive cases of COVID-19
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