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- MakaleYayıncıda açık erişim2025Explainable artificial intelligence for rock discontinuity detection from point cloud with ensemble methods
Günen, Mehmet Akif
This study presents a framework for the semi-automatic detection of rock discontinuities using a three-dimensional (3D) point cloud (PC). The process begins by selecting an appropriate neighborhood size, a critical step for feature extraction from the PC. The effects of different neighborhood sizes (k = 5, 10, 20, 50, and 100) have been evaluated to assess their impact on classification performance. After that, 17 geometric and spatial features were extracted from the PC. Next, ensemble methods, AdaBoost.M2, random forest, and decision tree, have been compared with Artificial Neural Networks to classify the main discontinuity sets. The McNemar test indicates that the classifiers are statistically significant. The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95% when using a neighborhood size of k = 100, while recall, F-score, and Cohen's Kappa also demonstrate high success. SHapley Additive exPlanations (SHAP), an Explainable AI technique, has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification. The analysis reveals that features such as normal vectors, verticality, and Z-values have the greatest influence on identifying main discontinuity sets, while linearity, planarity, and eigenvalues contribute less, making the model more transparent and easier to understand. After classification, individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets. Finally, the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus (RANSAC). Two real-world datasets (obtained from SfM and LiDAR) and one synthetic dataset were used to validate the proposed method, which successfully identified rock discontinuities and their orientation parameters (dip angle/direction).
- MakaleYayıncıda açık erişim2022Information theory and machine learning based authentication of flaxseed oil using portable and handheld vibrational spectroscopy sensors
Aykas, Didem-Peren; Menevseoglu, Ahmed; Gunes, Nurhan
Flaxseed oil is one of the popular edible oils, and its adulteration has become a concern because of its high commodity value. Therefore, there is a need to detect adulterated flaxseeds with a rapid, non-invasive, and simple method to keep the quality of flaxseed oil. This study aimed to develop a machine learning algorithm and information theory to detect adulterated flaxseed oils by vibrational spectroscopy units combined with chemometrics. Oil spectra were collected with portable/handheld vibrational spectroscopic units and analyzed using conditional entropy, support vector machines (SVM), and soft independent modeling of class analogy (SIMCA) to generate classification models to identify the pureness of oils and partial least square regression (PLSR) to predict the adulterant levels and fatty acid distributions. Conditional entropy showed that two or three wavenumbers/shifts could discriminate the oils using machine learning algorithms with an accuracy of over 95%. SIMCA models determined the oils with an interclass distance (ICD) over 1.1. Specifically, the Fourier transform mid-infrared (FT-MIR) unit provided the best classification of the oils with ICD over 4.9. All instruments showed good performance in predicting fatty acids and adulteration levels with rval≥0.89 and standard error prediction (SEP) ≤1.7%. Overall, these portable/handheld units showed great potential for rapid, simple, and non-invasive monitoring to identify adulterated flaxseed oils.
- DergiJournal of Rock Mechanics and Geotechnical Engineering
- DergiChinese Journal of Analytical Chemistry