id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-7976	DIDI, Ahmed Ali ; Reguig, Fethi Bereksi 	Modeling local muscle fatigue phases from EMG: A comparative study of classification and regression approaches	2025	14	.pdf	application/pdf	5703	263	40	Their insensitivity to outliers and strong performance on small datasets make them well-suited for biomedical applications such as fatigue phase classification. Across the board, the three classifiers—LDA, QDA, and linear SVM—were selected due to a balance struck among interpretability, computational efficiency, and classification strength; their relative effectiveness was assessed to identify the best fit for fatigue phase classification using EMG- derived features.	cache/easat-7976.pdf	txt/easat-7976.txt