id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-238342-ecuex64m	Fong, Simon James	Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction	2020-03-22		.txt	text/plain	9520	481	54	Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. Section 2 describes the proposed methodology called GROOMS+CMCM, followed by introduction of two key soft computing algorithms -BFGS-PNN and FRI which is adopted for forecasting some particular future trends as inputs to the MC model and generating fuzzy decision rules respectively. Being able to work with limited data, flexible in simulating input variables (hybrid deterministic and its counterpart), and informative outcomes coupled with fuzzy rules and risks, would be useful for experts making sound decision at the critical time.	./cache/cord-238342-ecuex64m.txt	./txt/cord-238342-ecuex64m.txt