Enhancing predictive modeling of interrill and rill erosion susceptibility in the Eastern Mediterranean using stacking ensemble machine learning algorithms

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

This paper analyses the recent evolution of the electricity price of one of the EastEuropean countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.

Rainfall-triggered landslides pose significant risks in hilly and mountainous areas, necessitating advanced susceptibility and hazard modeling in GIS environments. This review examines research trends, influential authors, and emerging themes in this field through bibliometric analysis and content review, covering publications from 2007 to 2025 with a focus on the last five years. Recent research trends reveal a shift towards complex, physically based models incorporating multiple rainfall variables and their interactions with local conditions. Emerging trends include machine learning applications, increased spatiotemporal analysis, and rising interest in climate change impacts on landslide occurrence. However, there is a lack of attention given to how topography and other factors might explain spatial differences in rainfall. Research output has increased since 2020, driven by initiatives like the Kyoto Landslide Commitment 2020. While progress has been made in landslide susceptibility modeling, several challenges persist, such as developing standardized protocols for case study research to enhance comparability; exploring methods to integrate multiple case studies and improve generalizability; investigating emerging technologies like big data analytics to address sample limitations; improving translation of research findings for practitioners and policymakers; and enhancing methods to incorporate topographic effects on rainfall variability in susceptibility models. Many studies rely on single-site case studies, limiting result generalizability. Better integration of topographic influences on rainfall spatial variability is needed, particularly in mountainous regions. Addressing these challenges will be crucial for advancing landslide risk assessment. Future research should focus on developing robust, transferable models that account for complex environmental interactions and provide practical decision-support tools.

Series dc arc fault creates a fire hazard and negative impacts on the distribution bus if not detected and isolated quickly. However, the detection of a series arc fault is challenging due to the low fault current, lack of zero-crossing, and the erratic behavior of arc discharge based on different power electronic loads and controllers in modern power applications. This article presents a practical and versatile series dc arc fault detection method based on ensemble machine learning (EML) algorithms. A buck converter constant power load (CPL) and a boost converter CPL are designed and built to study the different arc fault behaviors and generate training data for the machine learning algorithms. A set of time domain features is extracted from the experimental data and analyzed using the feature importance attribute. An adaptive normalization function then processed the features to mitigate false positive classification caused by load changes. A two-step algorithm is proposed to recognize the arc fault in different load types. Various EML algorithms and the associated hyperparameters are benchmarked to select the most accurate hyperparameters for a detection algorithm for low-cost hardware implementation. Finally, the detection algorithm's effectiveness is verified with CPL testbed.

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE = 0.384; MSE = 0.148; MAE = 0.147) outperformed the AdaBoost model (RMSE = 0.410; MSE = 0.168; MAE = 0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm.

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique’s performance through hyperparameter tuning on the Arabic cyberbullying data set.

Machine Learning is a branch of Artificial Intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze the performances of single and ensemble machine learning algorithms on the Cleveland Heart disease data set. Experimental study proves that the accuracy score and area under the ROC curve in the ensemble machine learning model is higher than the single machine learning model in predicting non-CVD and CVD patients.

Utilizing an intelligent machine learning strategy for predicting the tensile strength and ductility properties of nitrogen doped CoCrFeMnNi based high entropy alloys

Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm

Fiber-reinforced polymer (FRP)–concrete bonding is widely adopted for structural strengthening, yet its durability is highly vulnerable to freeze–thaw (FT) degradation. This study combines experimental testing with interpretable machine learning (ML) to reveal the degradation mechanism and predict the interfacial behavior of FRP–concrete systems under FT exposure. Single-lap shear tests showed that all specimens failed through interfacial debonding accompanied by partial concrete peeling. The ultimate bond strength decreased by 6.0–18.5%, and the peak shear stress dropped by 53–80%, indicating a pronounced loss of ductility and adhesion. To extend the analysis, experimental data were integrated with literature datasets, and three ensemble ML algorithms—AdaBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to predict key bond–slip parameters including ultimate bond strength, local maximum bond stress, slip values, and interfacial fracture energy. Among them, XGBoost achieved the highest predictive accuracy, with R2 values exceeding 0.94 for most output parameters and consistently low RMSE values. Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) further identified adhesive tensile strength, fiber modulus, FRP thickness, and concrete strength as dominant factors and defined their optimal ranges. The findings offer a scientific foundation for evaluating and predicting the long-term bond durability of FRP–concrete systems and support the development of reliable reinforcement strategies for infrastructure in cold and severe environments.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of ’very high’ and ’high’ class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Ensemble machine learning (ML) algorithms, such as bagging and boosting, are powerful decision-support tools that enhance disease prediction and risk management in the veterinary field. Lumpy Skin Disease (LSD) poses a significant threat to livestock health and results in substantial economic losses. This study aims to predict LSD using 1,041 data records collected from six Egyptian governorates between June 2020 and October 2022. The dataset exhibits a multiclass imbalance with three outcome classes: Dead (6%), Diseased (32%), and Healthy (62%). To address this imbalance, we applied SMOTE, Random Oversampling (ROS), and Random Undersampling (RUS). Five ensemble models: Decision Tree (DT), Random Forest (RF), AdaBoost, Gradient Boosting (GBoost), and XGBoost were evaluated on both imbalanced and balanced datasets, with hyperparameter tuning via grid search and 10-fold cross-validation. Our findings highlight the superior performance of the RF model combined with ROS (RF-ROS), achieving the highest accuracy (82%) and AUC (0.93), followed by balanced XGBoost (81.25%, AUC = 0.93). AdaBoost and GBoost also improved significantly after oversampling and tuning. SHAP analysis identified vaccination status as the most important predictor, emphasizing targeted interventions. These results demonstrate that combining resampling with hyperparameter tuning enhances ML performance on imbalanced veterinary data.

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach