id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fis-5480	Shaikh, Abdul Aabid	Prediction of crack length in thin-walled plates under different mode conditions using machine learning algorithms	2025	21	.pdf	application/pdf	9186	548	56	10.3221/IGF-ESIS.75.06 59 Figure 2: ML process for crack length prediction. To quantitatively assess the performance of the ML algorithms developed for crack length prediction, four standard evaluation matrix were adopted: mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R² Score), and classification accuracy (%).	cache/fis-5480.pdf	txt/fis-5480.txt