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Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

The implementation of tree‐ensemble models has become increasingly essential in solving classification and prediction problems. Boosting ensemble techniques have been widely used as individual machine learning algorithms in predicting house prices. One of the techniques is LGBM algorithm that employs leaf wise growth strategy, reduces loss and improves accuracy during training which results in overfitting. However, XGBoost algorithm uses level wise growth strategy which takes time to compute resulting in higher computation time. Nevertheless, XGBoost has a regularization parameter, implements column sampling and weight reduction on new trees which combats overfitting. This study focuses on developing a hybrid LGBM and XGBoost model in order to prevent overfitting through minimizing variance whilst improving accuracy. Bayesian hyperparameter optimization technique is implemented on the base learners in order to find the best combination of hyperparameters. This resulted in reduced variance (overfitting) in the hybrid model since the regularization parameter values were optimized. The hybrid model is compared to LGBM, XGBoost, Adaboost and GBM algorithms to evaluate its performance in giving accurate house price predictions using MSE, MAE and MAPE evaluation metrics. The hybrid LGBM and XGBoost model outperformed the other models with MSE, MAE and MAPE of 0.193, 0.285, and 0.156 respectively. The article proposes an integration of advanced ML algorithms, LGBM and XGBoost techniques in predicting house prices. The proposed model is compared to individual boosting ensemble learning algorithms to evaluate its performance. The hybrid LGBM and XGBoost model has better performance accuracy results in predicting house prices compared to the individual models.

Boosting algorithms, as a class of ensemble learning methods, have become very popular in data classification, owing to their strong theoretical guarantees and outstanding prediction performance. However, most of these boosting algorithms were designed for static data, thus they can not be directly applied to on-line learning and incremental learning. In this paper, we propose a novel algorithm that incrementally updates the classification model built upon gradient boosting decision tree (GBDT), namely iGBDT. The main idea of iGBDT is to incrementally learn a new model but without running GBDT from scratch, when new data is dynamically arriving in batch. We conduct large-scale experiments to validate the effectiveness and efficiency of iGBDT. All the experimental results show that, in terms of model building/updating time, iGBDT obtains significantly better performance than the conventional practice that always runs GBDT from scratch when a new batch of data arrives, while still keeping the same classification accuracy. iGBDT can be used in many applications that require in-time analysis of continuously arriving or real-time user-generated data, such as behaviour targeting, Internet advertising, recommender systems, etc.

Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones’ flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the “Okutama-Action” dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias–variance tradeoff.

The present paper deals with performance evaluation of application of three machine learning algorithms such as Deep neural network (DNN), Gradient boosting machine (GBM) and Extreme gradient boosting (XGBoost) to evaluate the ground water indices over a study area of Haryana state (India). To investigate the applicability of these models, two water quality indices, namely Entropy Water Quality Index (EWQI) and Water Quality Index (WQI) are employed in the present study. Analysis of results demonstrated that DNN has exhibited comparatively lower error values and it performed better in the prediction of both indices, i.e. EWQI and WQI. The values of Correlation Coefficient (CC = 0.989), Root Mean Square Error (RMSE = 0.037), Nash–Sutcliffe efficiency (NSE = 0.995), Index of agreement (d = 0.999) for EWQI and CC = 0.975, RMSE = 0.055, NSE = 0.991, d = 0.998 for WQI have been obtained. From variable importance of input parameters, the Electrical conductivity (EC) was observed to be most significant and ‘pH’ was least significant parameter in predictions of EWQI and WQI using these three models. It is envisaged that the results of study can be used to righteously predict EWQI and WQI of groundwater to decide its potability.

The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF + LR, GBDT + LR, and XGB + LR), to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, a landslide factor database was initially established by selecting 15 landslide conditioning factors from the topography, hydrology, climate, geology, and artificial activities. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT + LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB + LR, XGB, RF + LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

Lacosamide, a widely used antiepileptic drug, suffers from poor solubility in conventional solvents, which limits its bioavailability. Supercritical carbon dioxide (SC-CO₂) has emerged as an environmentally friendly substitute solvent for pharmaceutical processing. In this study, the solubility of Lacosamide in SC-CO₂ was modeled and predicted using several machine learning techniques, including Gradient Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XG Boost), and Polynomial Regression (PR). These models have the ability to model nonlinear relationships. Experimental solubility information within a large span of pressures and temperatures were employed for model training and validation. The findings suggested that all applied models were competent in providing reliable predictions, with GBDT (R 2 = 0.9989), XG Boost (R 2 = 0.9986), and MLP (R 2 = 0.9975) exhibiting the highest accuracy, achieving the highest coefficient of determination (R 2 ). Overall, combining experimental data with advanced machine learning algorithms offers a powerful approach for predicting and optimizing drug solubility in supercritical systems, thereby facilitating the design of scalable pharmaceutical processes.

Purpose To develop a machine learning model to evaluate the activity stage of extraocular muscles in thyroid-associated ophthalmopathy (TAO). Methods This study retrospectively analysed data from patients with TAO who underwent contrast-enhanced magnetic resonance imaging (MRI) from 2015 to 2022. Three independent machine learning models, namely, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and deep neural networks (DNNs), were constructed using common clinical features. The performance of these models was compared using evaluation metrics such as the area under the receiver operating curve (AUC), accuracy, precision, recall, and F1 score. The importance of features was explained using Shapley additive explanations (SHAP). Results A total of 2561 eyes of 1479 TAO patients were included in this study. The original dataset was randomly divided into a training set (80%, n  = 2048) and a test set (20%, n  = 513). In the performance evaluation of the test set, the LightGBM model had the best diagnostic performance (AUC 0.9260). According to the SHAP results, features such as conjunctival congestion, swollen caruncles, oedema of the upper eyelid, course of TAO, and intraocular pressure had the most significant impact on the LightGBM model. Conclusion This study used contrast-enhanced MRI as an objective evaluation criterion and constructed a LightGBM model based on readily accessible clinical data. The model had good classification performance, making it a promising artificial intelligence (AI)-assisted tool to help community hospitals evaluate the inflammatory activity of extraocular muscles in TAO patients in a timely manner.

Surface roughness is a critical factor in evaluating the quality of a product’s surface. To predict surface roughness, researchers have employed statistical and empirical methodologies, both of which often lack generalizability when applied to unseen data. To overcome the limitations of existing models, scholars have turned to machine learning and artificial intelligence approaches. Machine learning can accurately predict the surface roughness of machined parts and demonstrates strong generalization ability when applied to new, unseen data. For instance, this research develops a super-learner machine learning model designed to predict surface roughness by leveraging a diverse array of techniques, including kernel ridge regression (KRR), support vector machine (SVM), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB). The optimization of these models was achieved through grid search hyperparameter tuning and K-fold cross-validation. The predictive efficacy of the proposed super-learner model was compared to that of all alternative models. With a coefficient of determination ( R 2 ) of 99.8% between the experimental and predicted values for surface roughness on the test dataset, the super-learner model demonstrated superior predictive capabilities. It emerged as the most accurate model, distinguished by the highest R 2 , the lowest mean absolute error (1.92%), the lowest mean absolute percentage error (1.76%), and the lowest root mean square error (2.29%). Additionally, the model’s predictions were further interpreted using the Shapley additive explanations (SHAP) technique, which provided valuable insight into the significant variables influencing the surface roughness of tempered steel AISI 1060.

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.