Search Results (421)

As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing sparse sampling limitations caused by energy-constrained transceiver nodes in Unmanned Aerial Vehicle (UAV) spectrum monitoring, this paper proposes a compressive sensing-based 3D spectrum tensor completion framework for extrapolative reconstruction in obstructed areas (e.g., building occlusions). First, a Sparse Coding Neural Gas (SCNG) algorithm constructs an overcomplete dictionary adaptive to wide-range spectral fluctuations. Subsequently, a Bag of Pursuits-optimized Orthogonal Matching Pursuit (BoP-OOMP) framework enables adaptive key-point sampling through multi-path tree search and temporary orthogonal matrix dimensionality reduction. Finally, a Neural Gas competitive learning strategy leverages intermediate BoP solutions for gradient-weighted dictionary updates, eliminating computational redundancy. Benchmark results demonstrate 43.2% reconstruction error reduction at sampling ratios r ≤ 20% across full-space measurements, while achieving decoupling of highly correlated overlapping subspaces—validating superior estimation accuracy and computational efficiency. Full article

This study addresses the challenge of accurate classification under missing data conditions by integrating multiple imputation strategies with discriminant analysis frameworks. The proposed approach evaluates six imputation methods (Mean, Regression, KNN, Random Forest, Bagged Trees, MissRanger) across several discriminant techniques. Simulation scenarios varied in sample size, predictor dimensionality, and correlation structure, while the real-world application employed the Cirrhosis Prediction Dataset. The results consistently demonstrate that ensemble-based imputations, particularly regression, KNN, and MissRanger, outperform simpler approaches by preserving multivariate structure, especially in high-dimensional and highly correlated settings. MissRanger yielded the highest classification accuracy across most discriminant analysis methods in both simulated and real data, with performance gains most pronounced when combined with flexible or regularized classifiers. Regression imputation showed notable improvements under low correlation, aligning with the theoretical benefits of shrinkage-based covariance estimation. Across all methods, larger sample sizes and high correlation enhanced classification accuracy by improving parameter stability and imputation precision. Full article

Background/Objectives: Mycophenolic acid (MPA) is used as part of first-line combination immunosuppressive therapy for renal transplant recipients. Personalized dosing approaches are needed to balance efficacy and minimize toxicity due to the pharmacokinetic variability of the drug. In this study, population pharmacokinetic (PopPK) modeling and machine learning (ML) techniques are coupled to provide valuable insights into optimizing MPA therapy. Methods: Using data from 76 renal transplant patients, two PopPK models were developed to describe and predict MPA levels for two different formulations (enteric-coated mycophenolate sodium and mycophenolate mofetil). Covariate effects on drug clearance were assessed, and Monte Carlo simulations were used to evaluate exposure under normal and reduced clearance conditions. ML techniques, including principal component analysis (PCA) and ensemble tree models (bagging and boosting), were applied to identify predictive factors and explore associations between MPA plasma/saliva concentrations and the examined covariates. Results: Total daily dose and post-transplant time (PTP) were identified as key covariates affecting clearance. PCA highlighted MPA dose as the primary determinant of plasma levels, with urea and PTP also playing significant roles. Boosted tree analysis confirmed these findings, demonstrating strong predictive accuracy (R² > 0.91). Incorporating saliva MPA levels improved predictive performance, suggesting that saliva may be a complementary monitoring tool, although plasma monitoring remained superior. Simulations allowed exploring potential dosing adjustments for patients with reduced clearance. Conclusions: This study demonstrates the potential of integrating machine learning with population pharmacokinetic modeling to improve the understanding of MPA variability and support individualized dosing strategies in renal transplant recipients. The developed PopPK/ML models provide a methodological foundation for future research toward more personalized immunosuppressive therapy. Full article

During the peak load period, there is a high level of imbalance between power supply and demand, which has become a critical challenge, leading to higher operational costs for power grids. To improve the accuracy of peak load forecasting, this study introduces a novel approach based on Extreme Gradient Boosting Trees (XGBoost) and Multiple Linear Regression (MLR) for daily peak load prediction. The proposed methodology first employs an improved version of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm to decompose the raw load data, subsequently reconstructing each Intrinsic Mode Function (IMF) into high-frequency and stationary components. For the high-frequency components, XGBoost serves as the base predictor within a Bagging-based ensemble structure, while the Sparrow Search Algorithm (SSA) is employed to optimize hyperparameters automatically, ensuring efficient learning and accurate representation of complex peak load fluctuations. Meanwhile, the stationary components are modeled using MLR to provide fast and reliable estimations. The proposed framework was evaluated using actual daily peak load data from Western Inner Mongolia, China. The results indicate that the proposed method successfully captures the peak characteristics of the power grid, delivering both robust and precise predictions. When compared to the baseline model, the RMSE and MAPE are reduced by 54.4% and 87.3%, respectively, underscoring its significant potential for practical applications in power system operation and planning. Full article

Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified exponential growth in scientific publications since 2020, indicating that this field has reached a stage of maturity. The results showed that the predominant techniques for predicting water quality, both for surface and groundwater, fall into three main categories: (i) ensemble models, with Bagging and Boosting representing 43.07% and 25.91%, respectively, particularly random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGB), along with their optimized variants; (ii) deep neural networks such as long short-term memory (LSTM) and convolutional neural network (CNN), which excel at modeling complex temporal dynamics; and (iii) traditional algorithms like artificial neural network (ANN), support vector machines (SVMs), and decision tree (DT), which remain widely used. Current trends point towards the use of hybrid and explainable architectures, with increased application of interpretability techniques. Emerging approaches such as Generative Adversarial Network (GAN) and Group Method of Data Handling (GMDH) for data-scarce contexts, Transfer Learning for knowledge reuse, and Transformer architectures that outperform LSTM in time series prediction tasks were also identified. Furthermore, the most studied water bodies (e.g., rivers, aquifers) and the most commonly used water quality indicators (e.g., WQI, EWQI, dissolved oxygen, nitrates) were identified. The B-SLR and Topic Modeling methodology provided a more robust, reproducible, and comprehensive overview of AI/ML/DL models for freshwater quality prediction, facilitating the identification of thematic patterns and research opportunities. Full article

This study presents a robust and extensible hybrid classification framework for accurately detecting diseases in citrus leaves by integrating transfer learning-based deep learning models with classical machine learning techniques. Features were extracted using advanced pretrained architectures—DenseNet201, ResNet50, MobileNetV2, and EfficientNet-B0—and refined via the minimum redundancy maximum relevance (mRMR) method to reduce redundancy while maximizing discriminative power. These features were classified using support vector machines (SVMs), ensemble bagged trees, k-nearest neighbors (kNNs), and neural networks under stratified 10-fold cross-validation. On the lemon dataset, the best configuration (DenseNet201 + SVM) achieved 94.1 ± 4.9% accuracy, 93.2 ± 5.7% F1 score, and a balanced accuracy of 93.4 ± 6.0%, demonstrating strong and stable performance. To assess external generalization, the same pipeline was applied to mango and pomegranate leaves, achieving 100.0 ± 0.0% and 98.7 ± 1.5% accuracy, respectively—confirming the model’s robustness across citrus and non-citrus domains. Beyond accuracy, lightweight models such as EfficientNet-B0 and MobileNetV2 provided significantly higher throughput and lower latency, underscoring their suitability for real-time agricultural applications. These findings highlight the importance of combining deep representations with efficient classical classifiers for precision agriculture, offering both high diagnostic accuracy and practical deployability in field conditions. Full article

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, F1 score, precision, recall, Cohen’s Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; F1 score: 0.936; AUC: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; F1 score: 0.916; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount. Full article

Basalt fiber-reinforced composites are increasingly utilized in sustainable construction due to their high strength, environmental benefits, and durability. However, the long-term tensile performance of these composites in alkaline environments remains a critical concern. This study investigates the degradation performance of basalt fibers exposed to different alkaline solutions (NaOH, KOH, and Ca(OH)₂) with varying concentrations (5 g/L, 15 g/L, and 30 g/L) over various exposure periods (7, 14, and 28 days). The performance assessment is carried out by mechanical properties, including tensile strength and modulus of elasticity, using experimental techniques and Response Surface Methodology (RSM) to find influential factors on tensile performance. The findings indicate that tensile strength degradation is highly dependent on alkali type and concentration, with Ca(OH)₂-treated fibers exhibiting superior mechanical retention (max tensile strength: 938.94 MPa) compared to NaOH-treated samples, which showed the highest degradation rate. Five machine learning (ML) models, including Tree Random Forest (TRF), Function Multilayer Perceptron (FMP), Lazy IBK, Meta Bagging, and Function SMOreg (FSMOreg), were also implemented to predict tensile strength based on exposure parameters. FSMOreg demonstrated the highest prediction accuracy with a correlation coefficient of 0.928 and the lowest error metrics (RMSE 181.94). The analysis boosts basalt fiber durability evaluations in cement-based composites. Full article

COVID-19 remains a global health challenge, with severe cases often leading to complications and fatalities. The objective of this study was to assess supervised machine learning algorithms for predicting severe COVID-19 based on demographic, clinical, biochemical, and genetic variables, with the aim of identifying the most informative prognostic markers. For Machine Learning (ML) analysis, we utilized a dataset comprising 226 observations with 68 clinical, biochemical, and genetic features collected from 226 patients with confirmed COVID-19 (54—moderate, 142—severe and 30 with mild disease). The target variable was disease severity (mild, moderate, severe). The feature set included demographic variables (age, sex), genetic markers (single-nucleotide polymorphisms (SNPs) in FGB (rs1800790), NOS3 (rs2070744), and TMPRSS2 (rs12329760)), biochemical indicators (IL-6, endothelin-1, D-dimer, fibrinogen, among others), and clinical parameters (blood pressure, body mass index, comorbidities). The target variable was disease severity. To identify the most effective predictive models for COVID-19 severity, we systematically evaluated multiple supervised learning algorithms, including logistic regression, k-nearest neighbors, decision trees, random forest, gradient boosting, bagging, naïve Bayes, and support vector machines. Model performance was assessed using accuracy and the area under the receiver operating characteristic curve (AUC-ROC). Among the predictors, IL-6, presence of depression/pneumonia, LDL cholesterol, AST, platelet count, lymphocyte count, and ALT showed the strongest correlations with severity. The highest predictive accuracy, with negligible error rates, was achieved by ensemble-based models such as ExtraTreesClassifier, HistGradientBoostingClassifier, BaggingClassifier, and GradientBoostingClassifier. Notably, decision tree models demonstrated high classification precision at terminal nodes, many of which yielded a 100% probability for a specific severity class. Full article

Urban traffic noise has become an increasingly significant environmental and public health issue, with many cities—particularly those experiencing rapid urban growth, such as Kuwait—recording levels that often exceed recommended limits. In this study, we present a detailed, data-driven approach for assessing and predicting equivalent continuous noise levels (LAeq) in residential neighborhoods. The analysis draws on measurements taken at 12 carefully chosen sites covering different road types and urban settings, resulting in 21,720 matched observations. A range of predictors was considered, including road classification, traffic composition, meteorological variables, spatial context, and time of day. Four predictive models—Linear Regression, Support Vector Machine (SVM), Gaussian Process Regression, and Bagged Trees—were evaluated through 5-fold cross-validation. Among these, the Bagged Trees model achieved the strongest performance (R² = 0.91, RMSE = 2.13 dB(A)). To better understand how the model made its predictions, we used SHAP (SHapley Additive Explanations) analysis, which showed that road classification, location, heavy vehicle volume, and time of day had the greatest influence on noise levels. The results identify the main determinants of traffic noise in Kuwait’s urban areas and emphasize the role of targeted design and planning in its mitigation. Full article

Tree rings, tree needles, and moss can be used as biomonitors to evaluate atmospheric pollutant concentrations and deposition patterns spanning different timescales. This study compares output from air quality modeling and measurements to patterns observed using a combination of sulfur concentration and isotope composition in moss (using moss bags and controls) as biomonitors in a region of southern Alberta, Canada influenced by industrial emissions. Tree rings allow comparisons of historical to current sulfur deposition patterns. Moss, which integrates atmospheric nutrients during growth, allows for concurrent comparisons. The contrast of inorganic and organic sulfur within conifer tree needles provides a measure of pollutant uptake over their short lifespans. Sulfur uptake within biomonitors in a southern Alberta ecosystem allow assessment of the presence (in moss, needles) and effects (on conifer growth) of atmospheric sulfur deposition from industrial emissions. These data were examined relative to California Puff (CALPuff) model projections and traditional active and passive air quality sampling. Patterns in sulfur isotope abundance (δ³⁴S) from moss bags placed throughout the eastern slopes of the southern Alberta foothills of the Rocky Mountains implicate local industry as the dominant atmospheric sulfur source over winter, with the tissues of conifers (needles and cores) and moss decreasing with distance from industrial emissions. This was consistent with apportionment calculations based on active and passive sampling, which also showed a surprising trend of sulfur deposition upwind of the industrial stack in the mountains to the west. δ³⁴S values for pine needles and tree rings were consistent with greater sulfur stress and reductions in tree growth associated with increased industrial sulfur concentrations and deposition. We conclude that plant biomonitors are effective short-term (tree needles and moss) and long-term (tree cores) indicators of sulfur pollution in a complex, mountainous landscape. Full article

Modern cybersecurity threats continue to evolve in both complexity and prevalence, demanding advanced solutions for intrusion detection. Traditional AI-based detection systems face significant challenges in model selection, as performance varies considerably across different network environments and attack scenarios. To overcome these limitations, we propose a comprehensive ensemble learning approach that systematically integrates feature selection, model optimization, and rigorous evaluation components. Our framework evaluates fourteen distinct machine learning approaches, ranging from individual classifiers to sophisticated ensemble methods including bagging, boosting, and hybrid stacking/blending architectures. These techniques are applied to multiple base algorithms such as neural networks and tree-based models. Extensive testing was conducted on two complementary benchmark datasets (RoEduNet-SIMARGL2021 and CICIDS-2017) to assess detection capabilities across varied threat landscapes. Our experimental results revealed several key findings. Ensemble techniques universally surpass standalone models in detection accuracy, with random forest achieving the best performance on RoEduNet-SIMARGL2021, while the blending and bagging methods approach yielded perfect scores (F1 > 0.996) on CICIDS-2017. Feature selection via information gain demonstrated particular value, reducing model training times by 94% while maintaining detection accuracy. Among ensemble methods, XGBoost showed exceptional computational efficiency, whereas stacking and blending architectures delivered maximum accuracy at the expense of greater resource requirements. This research provides practical guidance for security professionals in model selection based on specific operational constraints and threat profiles. To support community advancement, we have made our complete framework publicly available, facilitating reproducibility and future innovation in intrusion detection systems. Full article

The spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae) (SLF), is a damaging invasive pest and generalist phloem feeder that has been found in 18 states in the United States. It has a complex multimodal communication system involving semiochemicals, emitted both from their honeydew and their bodies, and substrate-borne vibrations. Sensitive and effective traps for detection and survey are essential management tools, but no potent lures for SLF exist yet. We sought to test an alternative that relies on live-trapped SLF acting as lures to improve trap efficacy after the first SLF is captured. SLF circle traps were modified by replacing the commonly used plastic collection bag with a mesh bag pinned to the tree trunk. These allowed the trapped SLF to remain alive and generate signals through the mesh bag, thus leveraging their natural modes of communication to draw additional SLF into the traps. We compared mesh and plastic bags over three years targeting fourth instars and adults and found that prior to oviposition, circle traps with mesh bags captured significantly more fourth instar (70% mesh: 30% plastic) and adult SLF (59% mesh: 41% plastic) compared to plastic bags, but during oviposition time, the results were mixed. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article