id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-152028-c8xit4tf	Javid, Alireza M.	Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University	2020-05-07		.txt	text/plain	2885	226	76	As the number of training data for each country is limited, we use a single-layer neural network called the extreme learning machine (ELM) to avoid over-fitting. We report the average error percentage of ELM time-varying over the last 10 days of the time-series in Table II . We show the reported and estimated number of infection cases for Sweden by using ELM time-varying for different τ 's in Figure 3 . We show the reported and estimated number of infection cases for Sweden by using ELM time-varying for different τ 's in Figure 3 . We increase the prediction range τ in this subsection and we show the reported and estimated number of infection cases for Sweden by using ELM time-varying for τ = 1, 7, and 14 in Figure 5 . The proposed models currently use the only samples of the time-series data to predict the future number of cases.	./cache/cord-152028-c8xit4tf.txt	./txt/cord-152028-c8xit4tf.txt