id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-324230-nu0pn2q8	Ardabili, S. F.	COVID-19 Outbreak Prediction with Machine Learning	2020-04-22		.txt	text/plain	7335	451	53	This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). In the present study, the frequently used algorithms, (i.e., genetic algorithm (GA), particle swarm optimizer (PSO) and grey wolf optimizer (GWO)) are employed to estimate the parameters by solving a cost function. In the present research, two frequently used ML methods, the multi-layered perceptron (MLP) and adaptive network-based fuzzy inference system (ANFIS) are employed for the prediction of the outbreak in the five countries. According to Tables 5 to 12 , GWO provided the highest accuracy (smallest RMSE and largest correlation coefficient) and smallest processing time compared to PSO and GA for fitting the logistic, linear, logarithmic, quadratic, cubic, power, compound, and exponential-based equations for all five countries.	./cache/cord-324230-nu0pn2q8.txt	./txt/cord-324230-nu0pn2q8.txt