Search Results (182)

Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations’ quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms—extra trees (ET), gradient boosting (GB), and random forest (RF)—to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

Forecasting future solar power plant production is essential to continue the development of photovoltaic energy and increase its share in the energy mix for a more sustainable future. Accurate solar radiation forecasting greatly improves the balance maintenance between energy supply and demand and grid management performance. This study assesses the influence of input selection on short-term global horizontal irradiance (GHI) forecasting across two contrasting Algerian climates: arid Ghardaïa and coastal Algiers. Eight feature selection methods (Pearson, Spearman, Mutual Information (MI), LASSO, SHAP (GB and RF), and RFE (GB and RF)) are evaluated using a Gradient Boosting model over horizons from one to six hours ahead. Input relevance depends on both the location and forecast horizon. At t+1, MI achieves the best results in Ghardaïa (nMAE = 6.44%), while LASSO performs best in Algiers (nMAE = 10.82%). At t+6, SHAP- and RFE-based methods yield the lowest errors in Ghardaïa (nMAE = 17.17%), and RFE-GB leads in Algiers (nMAE = 28.13%). Although performance gaps between methods remain moderate, relative improvements reach up to 30.28% in Ghardaïa and 12.86% in Algiers. These findings confirm that feature selection significantly enhances accuracy (especially at extended horizons) and suggest that simpler methods such as MI or LASSO can remain effective, depending on the climate context and forecast horizon. Full article

Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are also combined to forecast the Standard & Poor’s (S&P) 500 index. Initially, contextual data are scored using TextBlob and pre-trained DistilBERT-base-uncased models, and then a combined dataset is formed. Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). LR and MLP show robust results with high R² scores, close to 0.998, and low error MSE and MAE rates, averaging at 350 and 13 points, respectively, across both training and test datasets, with technical indicators contributing the most to the prediction. While other models also perform very well under different dataset combinations, overfitting challenges are evident in the results, even after additional hyperparameter tuning. Potential limitations are highlighted, motivating further exploration and adaptation techniques in financial modeling that enhance predictive capabilities. Full article

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture. Full article

This study explores the application of machine learning (ML) techniques—gradient boosting (GB), ridge regression (RR), and support vector regression (SVR)—for estimating the consumption of energy (CE) and Blaine fineness (BF) in cement clinker grinding. This study utilizes key clinker grinding parameters, such as maximum ball size, ball filling ratio, clinker mass, rotation speed, and number of revolutions, as input features. Through comprehensive preprocessing, feature selection methods (mutual info regression (MIR), lasso regression (LR), and sequential backward selection (SBS)) were employed to identify the most significant variables for predicting CE and BF. The performance of the models was optimized using a grid search for hyperparameter tuning and validated using k-fold cross-validation (k = 10). The results show that all ML methods effectively estimated the target parameters, with SVR demonstrating superior accuracy in both CE and BF predictions, as evidenced by its higher R² and lower error metrics (MAE, MAPE, and RMSE). This research highlights the potential of ML models in optimizing cement grinding processes, offering a novel approach to parameter estimation that can reduce experimental effort and enhance production efficiency. The findings underscore the advantages of SVR, making it the most reliable method for predicting energy consumption and Blaine fineness in clinker grinding. Full article

Reduced Beam Sections (RBS) are used in steel design to promote ductile behavior by shifting inelastic deformation away from critical joints, enhancing seismic performance through controlled energy dissipation. While current design guidelines assist in detailing RBS connections, moment–rotation curves—essential for understanding energy dissipation—require extensive testing and/or modeling. Machine learning (ML) offers a promising alternative for predicting these curves, yet few studies have explored ML-based approaches, and none, to the best of the authors’ knowledge, have applied Explainable Artificial Intelligence (XAI) to interpret model predictions. This study presents an ML framework using Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), and Ridge Regression (RR) trained on 500 numerical models to predict the moment–rotation backbone curve of RBS connections under cyclic loading. Among all the models applied, the ANN obtained the highest R² value of 99.964%, resulting in superior accuracy. Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. The average SHAP values provide important insights into the performance of RBS connections, revealing that cross-sectional characteristics significantly influence moment capacity. In particular, flange thickness (t_f), flange width (b_f), and the parameter “c” are critical factors, as the flanges contribute the most substantially to resisting bending moments. Full article

This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques. Ten topographic and environmental conditioning factors were selected as model inputs. We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). These models were further fine-tuned using Bayesian optimization via Optuna. To interpret the model outcomes, SHAP (SHapley Additive exPlanations) was applied to analyze both the global and local contributions of each factor. The SHAP analysis revealed that lower elevation, slope, and stream distance, as well as higher stream density and built-up areas, were the most influential factors contributing to flood susceptibility. Moreover, interactions between these factors, such as built-up areas located on gentle slopes near streams, further intensified flood risk. The susceptibility maps were reclassified into five categories (very low to very high), and the GB model identified that approximately 15.047% of the study area falls under very-high-flood-risk zones. Among the models, the GB classifier achieved the highest performance, followed by XGB and RF. The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. These findings support more robust and interpretable flood risk assessments for effective disaster management in urban areas. Full article

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring the potential of incorporating more easily obtainable strength indicators, such as UCS. To evaluate the potential effectiveness of UCS for pavement engineering applications, a dataset of 152 laboratory-tested soil samples was compiled to develop predictive models. For each sample, geotechnical properties including the Atterberg limits, liquid limit (LL), plastic limit (PL), water content (WC), and bulk density (determined using the Harvard miniature compaction apparatus), alongside the UCS, were measured. This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). The aim was to establish a relationship between the dependent variable (UCS) and the independent basic geotechnical properties and to test the effectiveness of each ML algorithm in predicting UCS. The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. The performance ranking of the other models, from best to worst, was RF, GB, SV, KNN, MLR, and MNLR. Full article

This study aims to identify personal, clinical, and acoustic predictors of therapy outcomes based on changes in Korean voice-related quality of life (K-VRQOL) scores, as well as to compare the predictive performance of traditional regression and machine learning models. A total of 102 participants undergoing voice therapy are retrospectively analyzed. Multiple regression analysis and four machine learning algorithms—random forest (RF), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost)—are applied to predict changes in K-VRQOL scores across the total, physical, and emotional domains. The Shapley additive explanations (SHAP) approach is used to evaluate the relative contribution of each variable to the prediction outcomes. Female gender and comorbidity status emerge as significant predictors in both the total and physical domains. Among the acoustic features, jitter, SFF, and MPT are closely associated with improvements in physical voice function. LightGBM demonstrates the best overall performance, particularly in the total domain (R² = 32.54%), while GB excels in the physical domain. The emotional domain shows relatively low predictive power across the models. SHAP analysis reveals interpretable patterns, highlighting jitter and speaking fundamental frequency (SFF) as key contributors in high-performing models. Integrating statistical and machine learning approaches provides a robust framework for predicting and interpreting voice therapy outcomes. These findings support the use of explainable artificial intelligence (AI) to enhance clinical decision-making and pave the way for personalized voice rehabilitation strategies. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security threats include encryption, secure authentication, regular updates, tamper-resistant hardware, and lightweight security protocols. Physical Unclonable Functions (PUFs) are digital fingerprints for device authentication that enhance interconnected devices’ security due to their cryptographic characteristics. PUFs produce output responses against challenge inputs based on the physical structure and intrinsic manufacturing variations of an integrated circuit (IC). These challenge-response pairs (CRPs) enable secure and reliable device authentication. Our work implements the Arbiter PUF (APUF) on Altera Cyclone IV FPGAs installed on the ALINX AX4010 board. The proposed APUF has achieved performance metrics of 49.28% uniqueness, 38.6% uniformity, and 89.19% reliability. The robustness of the proposed APUF against machine learning (ML)-based modeling attacks is tested using supervised Support Vector Machines (SVMs), logistic regression (LR), and an ensemble of gradient boosting (GB) models. These ML models were trained over more than 19K CRPs, achieving prediction accuracies of 61.1%, 63.5%, and 63%, respectively, thus cementing the resiliency of the device against modeling attacks. However, the proposed APUF exhibited its vulnerability to Multi-Layer Perceptron (MLP) and random forest (RF) modeling attacks, with 95.4% and 95.9% prediction accuracies, gaining successful authentication. APUFs are well-suited for device authentication due to their lightweight design and can produce a vast number of challenge-response pairs (CRPs), even in environments with limited resources. Our findings confirm that our approach effectively resists widely recognized attack methods to model PUFs. Full article

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption. Full article

Extraintestinal manifestations (EIMs) significantly increase morbidity in inflammatory bowel disease (IBD) patients. In this study, we examined clinical and genetic factors associated with EIMs in 414 Latin IBD patients, utilizing machine learning for predictive modeling. In our IBD group (314 ulcerative colitis (UC) and 100 Crohn’s disease (CD) patients), EIM presence was assessed. Clinical differences between patients with and without EIMs were analyzed using Chi-square and Mann–Whitney U tests. Based on the genetic data of 232 patients, we identified variants linked to EIMs, and the polygenic risk score (PRS) was calculated. A machine learning approach based on logistic regression (LR), random forest (RF), and gradient boosting (GB) models was employed for predicting EIMs. EIMs were present in 29% (120/414) of patients. EIM patients were older (52 vs. 45 years, p = 0.01) and were more likely to have a family history of IBD (p = 0.02) or use anti-TNF therapy (p = 0.01). EIMs were more common in patients with CD than in those with UC without reaching statistical significance (p = 0.06). Four genetic variants were associated with EIM risk (rs9936833, rs4410871, rs3132680, and rs3823417). While the PRS showed limited predictive power (AUC = 0.69), the LR, GB, and RF models demonstrated good predictive capabilities. Approximately one-third of IBD patients experienced EIMs. Significant risk factors included genetic variants, family history, age, and anti-TNF therapy, with predictive models effectively identifying EIM risk. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article