id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-4243	Alroomi, Azzam 	Forecasting analytics for industry 4	2024	6	.pdf	application/pdf	3801	206	45	The modeling results, as was similarly in a study by Shang, Yang, Huang, & Lyu [14] showed that in cases where limited process knowledge about the process phenomenon exists, data-driven soft sensors could be effective tools for predictive data analytics. Specifically, the platform emphasized the use of machine learning methods for process data analytics while simultaneously taking advantage of big data processing tools and exploiting the currently available industrial grade cloud computing platforms.	cache/easat-4243.pdf	txt/easat-4243.txt