Explore the vast range of titles available.

The prediction of human mobility can facilitate resolving many kinds of urban problems, such as reducing traffic congestion, and promote commercial activities, such as targeted advertising. However, the requisite personal GPS data face privacy issues. Related organizations can only collect limited data and they experience difficulties in sharing them. These data are in “isolated islands” and cannot collectively contribute to improving the performance of applications. Thus, the method of federated learning (FL) can be adopted, in which multiple entities collaborate to train a collective model with their raw data stored locally and, therefore, not exchanged or transferred. However, to predict long-term human mobility, the performance and practicality would be impaired if only some models were simply combined with FL, due to the irregularity and complexity of long-term mobility data. Therefore, we explored the optimized construction method based on the high-efficient gradient-boosting decision tree (GBDT) model with FL and propose the novel federated voting (FedVoting) mechanism, which aggregates the ensemble of differential privacy (DP)-protected GBDTs by the multiple training, cross-validation and voting processes to generate the optimal model and can achieve both good performance and privacy protection. The experiments show the great accuracy in long-term predictions of special event attendance and point-of-interest visits. Compared with training the model independently for each silo (organization) and state-of-art baselines, the FedVoting method achieves a significant accuracy improvement, almost comparable to the centralized training, at a negligible expense of privacy exposure.

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

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.

A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. The model is trained using a symmetric tree as the base model, utilizing ordered boosting to replace gradient estimation, which enhances prediction accuracy. Comparative models including Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Logistic Regression (LR) were introduced. Five performance evaluation metrics are utilized to assess the predictive capabilities of the CatBoost model. Based on CatBoost model, the predicted probability of slope instability is calculated, and the early warning model of slope instability is further established. The results suggest that the CatBoost model demonstrates a 6.25% disparity in accuracy between the training and testing sets, achieving a precision of 100% and an Area Under Curve (AUC) value of 0.95. This indicates a high level of predictive accuracy and robust ordering capabilities, effectively mitigating the problem of overfitting. The slope instability warning model offers reasonable classifications for warning levels, providing valuable insights for both research and practical applications in the prediction of slope stability and instability warning.

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.

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.

Several bridges failed because of scouring and erosion around the bridge elements. Hence, precise prediction of abutment scour is necessary for the safe design of bridges. In this research, experimental and computational investigations have been devoted based on 45 flume experiments carried out at the NIT Warangal, India. Three innovative ensemble-based data intelligence paradigms, namely categorical boosting (CatBoost) in conjunction with extra tree regression (ETR) and K-nearest neighbor (KNN), are used to accurately predict the scour depth around the bridge abutment. A total of 308 series of laboratory data (a wide range of existing abutment scour depth datasets (263 datasets) and 45 flume data) in various sediment and hydraulic conditions were used to develop the models. Four dimensionless variables were used to calculate scour depth: approach densimetric Froude number (Fd50), the upstream depth (y) to abutment transverse length ratio (y/L), the abutment transverse length to the sediment mean diameter (L/d50), and the mean velocity to the critical velocity ratio (V/Vcr). The Gradient boosting decision tree (GBDT) method selected features with higher importance. Based on the feature selection results, two combinations of input variables (comb1 (all variables as model input) and comb2 (all variables except Fd50)) were used. The CatBoost model with Comb1 data input (RMSE = 0.1784, R = 0.9685, MAPE = 10.4724) provided better accuracy when compared to other machine learning models.

Identifying lithology from well logs is an important step in deep prospecting and resource estimation. Various machine learning algorithms have been adopted to identify lithology in oil and gas fields. Such algorithms, however, are rarely used for mineral deposits because of their complex geological conditions. In this paper, we propose an application framework using the gradient boosting decision tree (GBDT) algorithm to identify lithology from well logs in a mineral deposit. The GBDT classifier was built via the procedure of grid search and cross-validation to optimize the hyperparameters. In the Zhaoxian gold deposit, as the study area, an optimized GBDT classifier was built to fit the association between a set of well logs and ten lithological classes. The results demonstrate that the GBDT classifier has good classification performance in lithology identification, with a precision of 93.55%, a recall of 93.49% and an F1-score of 93.27%. The GBDT classification results also indicate that the major features contributing to the lithology classification are resistivity, followed by spontaneous potential and natural gamma according to the model interpretation of feature importance and partial dependence plots. The study demonstrates that the GBDT model can enhance our understanding of lithology identification from well logs in mineral deposits, which provides significant implications for further exploration targeting the deep-seated parts of mineral deposits.

Landslides can cause severe damage to both the environment and society, and many statistical, index-based, and inventory-based methods have been developed to assess landslide susceptibility; however, it is still challenging to choose the most effective method and properly identify major driving factors for specific regions. Here, we applied four machine learning algorithms, adaptive boosting (AdaBoost), gradient-boosting decision tree (GBDT), multilayer perceptron (MLP), and random forest (RF), to predict the landslide susceptibility at 30 m spatial scale based on thirteen landslide conditioning factors (LCFs) in a landslide-vulnerable region. Based on inventory landslide points, the classification results were evaluated, and indicated that the performance of the RF (F1-score: 0.85, AUC: 0.92), AdaBoost (F1-score: 0.83, AUC: 0.91), and GBDT (F1-score: 0.83, AUC: 0.88) methods were significantly better than the MLP (F1-score: 0.76, AUC: 0.79) method. The results further indicated that the areas with high and very high landslide risk (susceptibility greater than 0.5) accounted for about 40% of the study region. All four models matched well and predicted similar spatial distribution patterns in landslide susceptibility, with the very high risk areas mostly distributed in the western and southeastern regions. Daoshi, Qingliangfeng, Jinnan, and Linglong towns have the highest landslide risk, with mean susceptibility levels greater than 0.5. The leading contributing factors to landslide susceptibility were slightly different for the four models; however, population density, distance to road, and relief amplitude were generally among the top leading factors for most towns. Our study provided significant information on the highly landslide-prone areas and the major contributing factors for decision-makers and policy planners, and suggested that different areas should take unique precautions to mitigate or avoid severe damage from landslide events.

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.