id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
american_scientific_journal-11432	Peter Oluwasayo Adigun; Tobi Titus Oyekanmi; Ayodeji Adedotun Adeniyi	Simulation Prediction of Background Radiation Using Machine Learning	2025	26	.pdf	application/pdf	6087	315	48	Random Forest algorithms have the best test accuracy of 94.0%, a trained score of 98%, a K-fold cross validation score of 96.9%, and efficiently classify the effect of background radiation as harmful or harmless. In some parts of the world, little attention or zero attention is given to background radiation, which is hazardous to life and our environment.	cache/american_scientific_journal-11432.pdf	txt/american_scientific_journal-11432.txt