id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-295116-eo887olu	Chimmula, Vinay Kumar Reddy	Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks()	2020-05-08		.txt	text/plain	4708	252	50	title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Recurrent LSTM networks has capability to address the limitations of traditional time series forecasting techniques by adapting nonlinearities of given COVID-19 dataset and can result state of the art results on temporal data. Accord-COVID-19 forecasting using LSTM Networks ing to this second model within 10 days, Canada is expected to see exponential growth of confirmed cases.	./cache/cord-295116-eo887olu.txt	./txt/cord-295116-eo887olu.txt