Comparison of spatial and aspatial logistic regression models for landmine risk mapping

Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules

“…35 We repeated five times random test/training splits to avoid sampling bias and the average of evaluation metrics was reported. After the database was established, a broad set of nine machine learning models, including Logistic Regression (LR), 30 Gaussian Naï ve Bayes (GNB), 31 k-Nearest Neighbors (KNN), 32 Support Vector Machine (SVM), 33 Decision Tree (DT), 34 Random Forests (RF), 35 Adaptive Boosting (ADA), 36 eXtreme Gradient Boosting (XGB) 37 and Multilayer Perceptron (MLP) 38 were trained by a grid-search cross-validation (5-fold GridSearchCV) method and the hyperparameters of a single-shot trial was summarized in Table S2. The evaluation metrics including accuracy, precision, recall, F1, receiver operating characteristic (ROC) curve were obtained by comparing the predicted results and the ground truths (Table S3) Although these machine learning models offer individual advantages, such as high accuracy for classification, easiness to operate or good interpretability, they must be weighed carefully for a new application.…”

“…Table S5). C-propan-3- Logistic Regression (LR), 30 Gaussian Naï ve Bayes (GNB), 31 k-Nearest Neighbors (KNN), 32 Support Vector Machine (SVM), 33 Decision Tree (DT), 34 Random Forest (RF), 35 Adaptive Boosting (ADA), 36 eXtreme Gradient Boosting (XGB), 37 and Multilayer Perceptron (MLP) 38 are trained on historical datasets for predicting crystallization propensity of MONCs.…”

Development of the TIRAMISU Advanced Intelligence Decision Support System

“…In order to reduce or eliminate it, that surface area must be defined and positioned. Thus, the mine risk can be visualised (Schultz, Alegría, Cornelis, and Sahli 2016;Alegria, Zimanyi, Cornelis, and Sahli 2017;Lacroix et al 2013) and experts given the opportunity to make decisions. One key issue in humanitarian demining is the choice of areas to clear.…”

“…One key issue in humanitarian demining is the choice of areas to clear. Using GIS, airborne and satellite data combined with GNSS in a non-technical survey, it is possible to georeference all existing and additionally gathered data in MIS (2013;Schultz et al 2016;Mather 2000). GIS methods can be used to integrate the SHA history data (changing of defensive lines of battlefield during conflict years) to synthesise and interpret it (Nolan 2009;Heymans and Claassens 2015).…”

“…GIS methods can be used to integrate the SHA history data (changing of defensive lines of battlefield during conflict years) to synthesise and interpret it (Nolan 2009;Heymans and Claassens 2015). In previous studies, remnants of war indicators defined as "visual signs that mines or ERW may be near" were extracted from satellite and airborne images and their spatial relationships examined as a basis for risk assessment (Schultz et al, 2016). Testing GIS methods in combination with geostatistical methods for modelling mine risks is on the rise for the purpose of integrating data on demining activities (Alegria et al, 2011); Chan, Alegría, Veratelli, Folegani, and Sahli 2012;Lacroix et al 2013).…”