Search Results (3,812)

Background/Objectives: Non-variceal upper gastrointestinal bleeding (NVUGIB) is associated with considerable morbidity and mortality, particularly in emergency department (ED) settings. While traditional clinical scores such as the Glasgow-Blatchford Score (GBS), AIMS65, and Pre-Endoscopic Rockall are widely used for risk stratification, their accuracy in mortality prediction is limited. This study aimed to evaluate the performance of multiple supervised machine learning (ML) models in predicting 30-day all-cause mortality in NVUGIB and to compare these models with established risk scores. Methods: A retrospective cohort study was conducted on 1233 adult patients with NVUGIB who presented to the ED of a tertiary center between January 2022 and January 2025. Clinical and laboratory data were extracted from electronic records. Seven supervised ML algorithms—logistic regression, ridge regression, support vector machine, random forest, extreme gradient boosting (XGBoost), naïve Bayes, and artificial neural networks—were trained using six feature selection techniques generating 42 distinct models. Performance was assessed using AUROC, F1-score, sensitivity, specificity, and calibration metrics. Traditional scores (GBS, AIMS65, Rockall) were evaluated in parallel. Results: Among the cohort, 96 patients (7.8%) died within 30 days. The best-performing ML model (XGBoost with univariate feature selection) achieved an AUROC > 0.80 and F1-score of 0.909, significantly outperforming all traditional scores (highest AUROC: Rockall, 0.743; p < 0.001). ML models demonstrated higher sensitivity and specificity, with improved calibration. Key predictors consistently included age, comorbidities, hemodynamic parameters, and laboratory markers. The best-performing ML models demonstrated very high apparent AUROC values (up to 0.999 in internal analysis), substantially exceeding conventional scores. These results should be interpreted as apparent performance estimates, likely optimistic in the absence of external validation. Conclusions: While machine-learning models showed markedly higher apparent discrimination than conventional scores, these findings are based on a single-center retrospective dataset and require external multicenter validation before clinical implementation. Full article

Background: The purpose of this study was to investigate the value of machine learning models for preoperative non-invasive prediction of International Society of Urological Pathology (ISUP) grading in clear cell renal cell carcinoma (ccRCC) based on CT urography (CTU)-related peritumoral area (PAT) radiomics features. Methods: We retrospectively analysed 328 ccRCC patients from our institution, along with an external validation cohort of 175 patients from The Cancer Genome Atlas. A total of 1218 radiomics features were extracted from contrast-enhanced CT images, with LASSO regression used to select the most predictive features. We employed four machine learning models, namely, Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for training and evaluation using Receiver Operating Characteristic (ROC) analysis. The model performance was assessed in training, internal validation, and external validation sets. Results: The XGBoost model demonstrated consistently superior discriminative ability across all datasets, achieving AUCs of 0.95 (95% CI: 0.92–0.98) in the training set, 0.93 (95% CI: 0.89–0.96) in the internal validation set, and 0.92 (95% CI: 0.87–0.95) in the external validation set. The model significantly outperformed LR, MLP, and SVM (p < 0.001) and demonstrated prognostic value (Log-rank p = 0.018). Transcriptomic analysis of model-stratified groups revealed distinct biological signatures, with high-grade predictions showing significant enrichment in metabolic pathways (DPEP3/THRSP) and immune-related processes (lymphocyte-mediated immunity, MHC complex activity). These findings suggest that peritumoral imaging characteristics provide valuable biological insights into tumor aggressiveness. Conclusions: The machine learning models based on PAT radiomics features of CTU demonstrated significant value in the non-invasive preoperative prediction of ISUP grading for ccRCC, and the XGBoost modeling had the best predictive ability. This non-invasive approach may enhance preoperative risk stratification and guide clinical decision-making, reducing reliance on invasive biopsy procedures. Full article

Background/Objectives: Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods: Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results: According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions: Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs. Full article

Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R² = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha⁻¹ per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 10⁶ mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article

This study presents a gradient boosting-based machine learning (ML) approach developed to predict cutting speed and feed rate in band sawing operations. The model was built using a dataset of 1701 experimental samples from three industrially common material types: AISI 304, CK45, and AISI 4140. Each sample was defined by key process parameters, namely, material type, a hardness range of 15–44 HRC, and a diameter range of 100–500 mm, with cutting speed and feed rate as target variables. Five ML models were examined and compared in this study, including linear regression (LR), support vector regression (SVR), random forest regression (RFR), least squares boosting (LSBoost), and extreme gradient boosting (XGBoost). Model training and validation were carried out using five-fold cross-validation. The results show that the XGBoost model offers the highest accuracy. For cutting speed estimation, the performance values of XGBoost are an RMSE of 0.213, an MAE of 0.140, an R² of 0.999, and an MAPE of 0.407%; and for feed rate estimation, an RMSE of 0.259, an MAE of 0.169, an R² of 0.999, and a MAPE of 1.14%. These results indicate that gradient-based ensemble methods capture the nonlinear behavior of cutting parameters more effectively than linear or kernel-driven techniques, providing a practical and robust approach for data-driven optimization in intelligent manufacturing. Full article

This research seeks to investigate extremely efficient catalysts for the nitrogen reduction process (NRR), utilizing machine learning (ML)-aided density functional theory (DFT) computations. Specifically, we investigate dual transition metal atoms anchored on hexagonal nitrogen-doped graphene (TM₁-TM₂@N₆G) as prospective high-activity catalysts for the NRR. The findings indicate that the synergistic effect of dual transition metal atoms in the TM₁-TM₂@N₆G catalyst overcomes the intrinsic constraints of the linear relationship among intermediates, facilitating the activation and adsorption of N₂, thereby exhibiting significant potential for ammonia synthesis through N₂ reduction. Particularly, four catalysts screened by ML and DFT exhibit good stability and excellent selectivity and activation towards N₂. Among them, the catalysts Ti-Cr@N₆G, Ti-Mo@N₆G, and Ti-Pd@N₆G possess two reaction pathways with minimum reaction energies of 0.55 eV, 0.50 eV, and 0.40 eV, respectively. Remarkably, Ti-Co@N₆G, which features a single reaction pathway, exhibits a reaction energy lower than 0.05 eV, allowing the NRR to proceed spontaneously. It is noteworthy that incorporating ML into DFT calculations facilitates the rapid screening of all transition metal combinations, significantly accelerating the research on catalytic performance and optimizing the selection of catalysts. Full article

Extreme sea conditions caused by tropical cyclones pose significant risks to coastal safety, infrastructure, and ecosystems. Although existing models have advanced in predicting Significant Wave Height (SWH), their performance in predicting Maximum Wave Height (MWH) remains limited, particularly in capturing rapid wave fluctuations and localized meteorological dynamics. This study proposes a novel MWH prediction framework that integrates high-resolution buoy observations with deep learning. A moored buoy deployed in the Qiongzhou Strait provides precise nearshore observations, compensating for limitations in reanalysis datasets. Light Gradient Boosting Machine (LightGBM) is employed for key feature selection, and a hybrid Bidirectional Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (BiTCN-BiGRU) model is constructed to capture both short- and long-term temporal dependencies. The results show that BiTCN-BiGRU outperforms BiGRU, reducing MAE by 6.11%, 5.41%, and 14.09% for 1-h, 3-h, and 6-h forecasts. This study also introduces the Time Distortion Index (TDI) into MWH prediction as a novel metric for evaluating temporal alignment. This study offers valuable insights for disaster warning, coastal protection, and risk mitigation under extreme marine conditions. Full article

Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R² to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article

In a digital landscape marked by the exponential growth of cyber threats, the development of automated domain reputation systems is extremely important. Emerging technologies such as artificial intelligence and machine learning now enable proactive and scalable approaches to early identification of malicious or suspicious domains. This paper presents an adaptive domain name reputation system that integrates advanced machine learning to enhance cybersecurity resilience. The proposed framework uses domain data from .ro domain Registry and several other sources (blacklists, whitelists, DNS, SSL certificate), detects anomalies using machine learning techniques, and scores domain security risk levels. A supervised XGBoost model is trained and assessed through five-fold stratified cross-validation and a held-out 80/20 split. On an example dataset of 25,000 domains, the system attains accuracy 0.993 and F1 0.993 and is exposed through a lightweight Flask service that performs asynchronous feature collection for near real-time scoring. The contribution is a blueprint that links list supervision with registry/DNS/TLS features and deployable inference to support proactive domain abuse mitigation in ccTLD environments. Full article

This study employed machine learning to establish an intelligent model for rapid and accurate seismic damage assessment of steel bundled-tube stories. The study built a 100-story elastoplastic steel bundled-tube model based on an actual engineering case, and then extracted and labeled data. Eight machine learning algorithms were employed to assess the seismic damage states of the steel bundled-tube stories. Hyperparameter optimization was performed on the two best-performing algorithms, and Shapley Additive Explanations (SHAP) analysis was used to investigate the influence of input variables on the five damage states. Using original parameters, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) showed highest accuracies (94.6% and 94.3%). After optimization, XGBoost’s accuracy rose by 2.2% to 96.5%, outperforming RF, and is thus recommended as the final model. This study fills the gap in story-level damage assessment using machine learning. SHAP analysis revealed peak acceleration and story load-bearing capacity as core variables. Displacement is more crucial in the low-damage state, while energy dissipation plays a dominant role in the high-damage state, which poses a challenge to the traditional seismic design that only limits displacement. The method identifies weak stories for targeted reinforcement, optimizing seismic performance of steel bundled-tube structures. Full article

Facility agriculture cultivation is the main production form of the vegetable industry in the world. As an important vegetable crop, hot peppers are easily threatened by many diseases in a facility microclimate environment. Traditional disease detection methods are time-consuming and allow the disease to proliferate, so timely detection and inhibition of disease development have become the focus of global agricultural practice. This article proposed a generalizable and explainable machine learning model for hot pepper damping-off in intensive seedling production under the condition of ensuring the high accuracy of the model. Through Kalman filter smoothing, SMOTE-ENN unbalanced sample processing, feature selection and other data preprocessing methods, 19 baseline models were developed for prediction in this article. After statistical testing of the results, Bayesian Optimization algorithm was used to perform hyperparameter tuning for the best five models with performance, and the Extreme Random Trees model (ET) most suitable for this research scenario was determined. The F₁-score of this model is 0.9734, and the AUC value is 0.9969 for predicting the severity of hot pepper damping-off, and the explainable analysis is carried out by SHAP (SHapley Additive exPlanations). According to the results, the hierarchical management strategies under different severities are interpreted. Combined with the front-end visualization interface deployed by the model, it is helpful for farmers to know the development trend of the disease in advance and accurately regulate the environmental factors of seedling raising, and this is of great significance for disease prevention and control and to reduce the impact of diseases on hot pepper growth and development. Full article

Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems. Full article

Natural hazards such as landslides are among the most harmful and recurring hazards to infrastructure, communities, and the environment around the world. In Pakistan, the Balakot Valley is prone to severe landslides, especially along the Balakot–Naran route, which is a major economic and tourist route. This route requires accurate landslide susceptibility mapping (LSM) to mitigate landslide risk. However, existing approaches mainly rely on statistical methods, which do not sufficiently address the complexity of spatial patterns and characteristics between landslide conditioning factors (LCFs) and their prevalence. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements of slope deformation (Vslope) were employed to update the landslide inventory. Following this update, an LSM was generated to examine the causal variables that are associated with landslide occurrences. Several machine learning (ML) classifiers, which include Adaptive Boosting (AdaBoost), Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and a hybrid (ADA + LGBM + XGB), are utilized for mapping landslide susceptibility. A total of 14 LCFs were considered, with 70% of the dataset being trained and 30% tested. To evaluate the significance of these variables, Recursive Feature Elimination (RFE) and the Shapley Additive Explanations (SHAP) were used. Results indicate that the hybrid model exhibits superior efficiency in the area under the curve (AUC) (88.00%), precision (84.69%), accuracy (84.52%), F1-score (84.69%), and recall (84.70%). The hybrid classifier, when combined with InSAR predictions, generates an improved LSM for the route. In conclusion, the improved LSM can effectively identify areas that are prone to landslides along the Balakot–Naran route. Full article

Background: Diabetes mellitus is a significant primary global health concern that requires accurate diagnosis at an early stage to prevent severe complications. However, accurate prediction remains challenging due to limited, noisy, and imbalanced datasets. This study proposes a novel machine learning framework for improved diabetes prediction, addressing key challenges such as inadequate feature selection, class imbalance, and data preprocessing. Methods: This proposed work systematically evaluates five feature selection algorithms—Recursive Feature Elimination, Grey Wolf Optimizer, Particle Swarm Optimizer, Genetic Algorithm, and Boruta—using cross-validation and SHAP analysis to enhance feature interpretability. Classification is performed using two boosting algorithms: the light gradient boosting machine algorithm (LGBM) and the extreme gradient boosting algorithm (XGBoost). Results: The proposed framework, using the five most important features selected by the Boruta feature selection algorithm, outperformed other configurations with the LightGBM classifier, achieving an accuracy of 85.16%, an F1-score of 85.41%, and a 54.96% reduction in training time. Conclusions: Additionally, we have benchmarked our approach against recent studies and validated its effectiveness on both the Pima Indian Diabetes Dataset and the newly released DiaHealth dataset, demonstrating robust and accurate early diabetes detection across diverse clinical datasets. This approach offers a cost-effective, interpretable, and clinically relevant solution for early diabetes detection by reducing the number of input features, providing transparent feature importance, and achieving high predictive accuracy with efficient model training. Full article

The accurate real-time forecasting and impact factor identification of air pollutant levels are critical for effective pollution control and management. In this study, we implemented three machine learning algorithms, namely, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Fully Connected Neural Network (FCNN), to predict PM_2.5 and O₃ concentrations in the Beijing–Tianjin–Hebei region from 2019 to 2023. XGBoost outperformed the other algorithms and was further utilized to predict PM_2.5 and O₃ concentrations and identify their controlling factors. The models could efficiently capture the spatial and temporal variations in the pollutants in the study area, and it was found that both anthropogenic sources and weather conditions can have significant impacts on air pollutant levels. PM₁₀ and CO were significantly correlated to PM_2.5 levels, which could be attributed to their similar emission sources and dispersion characteristics in air. O₃ concentrations were greatly influenced by temperature and NO₂ due to their significant impacts on O₃ generation. This study demonstrates that XGBoost-based models are cost-effective tools for predicting PM_2.5 and O₃ levels and identifying their controlling factors. These findings provide valuable insights for formulating effective air pollution prevention policies. Full article