id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
globalce-261	Lyssas , George; Mitsopoulos, Konstantinos; Zantzas , Dimitris; Kalfas, Anestis; Bamidis, Panagiotis D.	Human Muscle State Machine using Electromyography Classification with Machine Learning	2024	5	.pdf	application/pdf	1999	95	37	In conclusion, the reliability and the adaptability of the Random Forest model is the best choice for the recogni- tion and classification of EMG signals to muscle states, in this application with a small sample size. Gokgoz, E. and Subasi, A. Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed Signal Process Control.	cache/globalce-261.pdf	txt/globalce-261.txt