id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-10037	Waeto, Salwa; Chuarkham, Khanchit; Riyapan, Pakwan; Intarasit, Arthit	Predictive modeling of unrest situation in a Southern province of Thailand using machine learning models	2025	10	.pdf	application/pdf	4782	229	49	Forecasting is conducted using SVR, ANA, and ARIMA models. Thung Yang Daeng 0.7436 1.1798 0.0000 1.0000 0.0000 7.0000 Yarang 2.128 2.199 2.000 3.000 0.000 13.000 Yaring 0.9615 1.1799 1.0000 1.7500 0.0000 5.0000 Results of data compaction based on the feature level, slope, and epsilon in the structured model, along with the best predictions for forecasting between NN, SVM, and ARIMA models under appropriate conditions, are shown in Figure 1 and Table 1. 1021 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1015-1024, 2025 DOI: 10.55214/2576-8484.v9i9.10037 © 2025 by the authors;	cache/easat-10037.pdf	txt/easat-10037.txt