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Xu and Wang weighted feature selection algorithm based on random forest (RF-WFS) with XGBoost classifier PIDD Achieved an accuracy of 93.75% Materials and Methods Dataset.
Debelee, Detection of bacterial wilt on enset crop using deep learning approach, in: International Journal of Engineering Research in Africa, Vol. 51, Trans Tech Publ, 2020, pp. 131–146
Yohannes, Classification of mammograms using texture and cnn based extracted features, in: Journal of Biomimetics, Biomaterials and Biomedical Engineering, Vol. 42, Trans Tech Publ, 2019, pp. 79–97
Yohannes, Classifier based breast cancer segmentation, in: Journal of Biomimetics, Biomaterials and Biomedical Engineering, Vol. 47, Trans Tech Publ, 2020, pp. 41–61
Pradeep, A review of ensemble machine learning approach in prediction of diabetes diseases, International Journal on Future Revolution in Computer Science & Communication Engineering 4 (3) (2018) 463–466

Knowing the radon concentration of the near-surface (surficial) materials may give important information also on geology.
Detection and quantification of tracks in etched LR-115 detectors is a key step in employing the SSNTD method for applied radon studies.
Phillip, Dependence of the radon emanation coefficient on radium distribution and internal structure of the material, Geochim.
Roberts, Behaviour of radon in the geological environment: a review, Engineer.
Yu, Formation and growth of tracks in nuclear track materials, Mater.