Search Results (128)

Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning evaluation indicators for emerging distribution networks. First, based on an existing comprehensive indicator system, key factors of county-level distribution networks are identified to classify typical planning scenarios, and a preliminary scenario-oriented indicator system is established with expert knowledge. Second, data-driven techniques are employed for indicator selection. The maximum relevance and minimum redundancy (mRMR) method and the Random Forest (RF) algorithm are introduced to evaluate indicator relevance and importance, respectively, and a game-theoretic combination method with coefficient-of-variation (CV) correction is used for comprehensive screening. Finally, a county-level case study is conducted to validate the proposed method. The results show that the proposed method can adjust the planning evaluation indicator system according to changes in distribution network characteristics under different scenarios and performs well in the studied cases. This method provides a practical framework for constructing adaptive indicator systems for distribution network planning evaluation. Full article

Accurate crop yield prediction increasingly relies on diverse data streams, including satellite observations, meteorological reanalysis, soil composition, and topographic information. However, despite advances in machine learning, many existing approaches remain crop- or region-specific and require substantial bespoke data engineering, limiting scalability and reproducibility. This study introduces UniCrop, a generalisable, configuration-driven data engineering pipeline that standardises the acquisition, harmonisation, and feature construction of multi-source agro-environmental data. Rather than proposing a new predictive model, UniCrop addresses a key bottleneck in agricultural machine learning: the lack of reproducible and scalable data preparation workflows. For any given location, crop type, and temporal window, the pipeline automatically retrieves, harmonises, and engineers over 160 environmental variables from heterogeneous sources (Sentinel-1/2, MODIS, ERA5-Land, NASA POWER, SoilGrids, and SRTM), reducing them to a compact, analysis-ready feature set using a structured feature selection process based on minimum redundancy maximum relevance (mRMR). The effectiveness of the pipeline is demonstrated through a case study, where the generated datasets enable robust baseline modelling across multiple machine-learning algorithms. Using a selected subset of 15 features, four baseline models (LightGBM, Random Forest, Support Vector Regression, and ElasticNet) were evaluated under rigorous cross-validation. LightGBM achieved the best single-model performance (RMSE = 465.1 kg/ha, $R^2=0.6576$), while a constrained ensemble provided a marginal improvement (RMSE = 463.2 kg/ha, $R^2=0.6604$). SHAP-based analysis further confirms that the selected features capture agronomically meaningful relationships across data modalities. UniCrop contributes a scalable and transparent data engineering pipeline that enables consistent, reproducible, and transferable dataset construction for crop yield prediction. By decoupling data specification from implementation and supporting flexible configuration across crops, regions, and temporal contexts, the framework provides a practical foundation for large-scale agricultural analytics. Full article

Atopic dermatitis (AD) is a chronic inflammatory skin disorder that is significantly contributed to by epigenetics. We developed a machine learning-based framework to identify DNA methylation biomarkers associated with AD classification and severity. Genome-wide methylation data from peripheral blood were processed using four feature selection algorithms: coarse approximation linear function (CALF), elastic net (EN), minimum redundancy maximum relevance (mRMR), and recursive feature elimination with cross-validation (RFECV). The integrative framework identified a central panel of 8 CpG sites that achieved an area under the curve (AUC) of 1.00 in the test set. This panel demonstrated high disease specificity, showing poor classification performance for systemic lupus erythematosus (AUC = 0.46), Crohn’s disease (AUC = 0.50), and oral squamous cell carcinoma (AUC = 0.58). Severity prediction using RFECV-selected 63 CpG sites (RFE63) achieved high accuracy across classifiers, with Random Forest (accuracy = 0.94) outperforming the others. The functional enrichment of CpG-associated genes highlighted key immune-related transcriptional regulators, including STAT5A, RUNX1, MEIS1, and PAX4. These genes are linked to chromatin remodeling, T helper cell differentiation, and interleukin-2 regulation, which are critical in AD pathogenesis and severity. Our findings demonstrate the utility of machine learning-integrated epigenomics in identifying robust, disease-specific biomarkers for AD diagnosis and monitoring, offering new insights into the molecular mechanisms underlying childhood AD. However, further validation in large-scale independent cohorts is required to confirm their clinical robustness and generalizability. Full article

Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation based on frequency response analysis (FRA). To address the existing challenges in transformer winding fault diagnosis, including the absence of a systematic feature evaluation framework for frequency response data and the limited recognition accuracy of machine learning models, a novel hybrid feature selection and diagnostic framework was developed. First, a high-dimensional feature pool comprising 25 numerical indices was extracted from experimental FRA curves. To eliminate feature redundancy and arbitrary selection, a hybrid mechanism integrating maximum-relevance, minimum-redundancy (mRMR) with random forest (RF) was developed to dynamically construct task-specific optimal feature subsets. Furthermore, an inverse-distance-weighted K-nearest neighbors (IKNN) model was introduced to enhance diagnostic sensitivity by accounting for feature-space distance variations. Experimental results obtained from a laboratory winding model demonstrate that the proposed mRMR-RF-IKNN model significantly outperforms traditional and optimized benchmarks across multiple macro-evaluation metrics. This study provides a systematic, intelligent screening mechanism that ensures high-precision identification of both the types and severity of faults in power transformers. Full article

Traffic accidents are among the leading causes of injury worldwide, highlighting the urgent need to better understand the factors that contribute to accident occurrence and severity in order to improve road safety and reduce injuries and fatalities. This study analyzes the U.S. Accidents dataset, comprising data collected from 2016 to 2023, to identify the key determinants of accident severity and to evaluate feature-selection techniques for predictive modeling. To this end, several feature-selection methods are examined, including L1-regularized logistic regression, minimum redundancy maximum relevance (mRMR), conditional mutual information maximization (CMIM), ReliefF, and tree-based importance measures; these are compared with the Weighted Conditional Mutual Information (WCFR). The selected feature subsets are then evaluated using three machine learning models: logistic regression, random forest, and XGBoost. Experimental results show that WCFR consistently outperforms the competing methods, achieving higher validation accuracy (up to approximately 0.84) and Macro-F1 scores (up to approximately 0.55), while using fewer features and maintaining model interpretability. These results indicate that WCFR is particularly effective for accident severity modeling and highlight its potential as a robust feature selection strategy for large-scale transportation safety analytics and future severity prediction studies. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

Multiple Sclerosis (MS) and Myelitis are serious inflammatory spinal cord disorders with overlapping clinical symptoms and radiological characteristics, making accurate differentiation challenging yet clinically essential. Early and precise diagnosis is critical for guiding treatment strategies and improving patient outcomes. In this study, we propose KhayyamNet, a novel hybrid deep learning architecture designed to fuse complementary local and global representations for the accurate diagnosis of MS and Myelitis using spinal MRI. To improve robustness and generalization capability, a comprehensive preprocessing strategy including data augmentation and intensity normalization is also applied to reduce noise and address data variability. The proposed architecture combines three complementary deep learning models for feature extraction composed of Xception for high-level semantic features, Convolutional Neural Networks (CNNs) for fine-grained local patterns, and Vision Transformers (ViTs) for global contextual representations via attention mechanisms. Extracted features are then fused and refined using the Minimum Redundancy Maximum Relevance (MRMR) algorithm to eliminate redundancy and retain the most informative signals. Finally, a Random Forest (RF) classifier utilizes the optimized feature set to achieve accurate and robust differentiation between MS, Myelitis, and control spinal MRIs. Experimental results demonstrate that KhayyamNet outperforms existing methods by achieving an average classification accuracy of $98.15\pm 0.80\%$. This framework demonstrates promising performance for the automated analysis of spinal MRIs and shows potential to assist in the differentiation of MS and Myelitis. While these findings highlight the potential of KhayyamNet for automated MRI interpretation, its evaluation is limited to a single-center dataset, and further validation on external multi-center data is required. Full article

This paper proposes a short-term photovoltaic (PV) power prediction method that integrates aerosol optical feature mining with a dual-channel attention mechanism to address the complex non-linear attenuation effects of atmospheric aerosols and the limitations of existing models in handling sudden meteorological changes and aerosol evolution. Using the optical properties of aerosols and clouds (OPAC) database, a high-dimensional aerosol optical feature set is constructed, which is subsequently optimized using the minimum redundancy maximum relevance (mRMR) algorithm. The prediction scenarios are categorized into polluted and clean regimes through K-means clustering. A dual-channel encoder–decoder network, combining bidirectional long short-term memory (BiLSTM) and iTransformer, is developed to capture high-frequency meteorological volatility and low-frequency aerosol evolution. A bidirectional cross-attention mechanism enables deep feature interaction between the optical and meteorological channels. The method is validated using in situ measurements from a PV station in Hebei, China, along with aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) and meteorological data from the ECMWF Reanalysis v5 (ERA5). Experimental results demonstrate an average reduction of approximately 29.83% in mean absolute error (MAE) on polluted days and 15.22% on clean days. Interpretability analysis reveals distinct physical mechanisms driving the predictions, emphasizing the role of extinction on polluted days and scattering on clean days. Full article

The development of portable electrocardiographic analysis systems necessitates identifying an optimal balance between diagnostic precision and computational efficiency. This research addresses the challenge of optimal feature selection for automated cardiac arrhythmia classification in resource-constrained portable applications. We present a comparative investigation of three distinct feature selection strategies for ECG classification: the MRMR (Minimum Redundancy Maximum Relevance) method, which maximizes relevance while minimizing feature interdependencies; the ReliefF technique, which evaluates discriminative power through proximity analysis in the feature space; and permutation-based importance analysis implemented with neural networks. Utilizing the Large-Scale 12-Lead Electrocardiogram Database for Arrhythmia Study, we construct a hybrid feature space integrating 12 conventional time- and frequency-domain parameters (previously validated and included in the database’s official documentation) with 26 advanced nonlinear descriptors, including the Hurst exponent, DFA scaling parameter, log-absolute correlation measures, mean standard increment from the Poincaré plot, and wavelet entropy. The experimental results demonstrate remarkable convergence among the three paradigms in selecting optimal feature subsets, achieving classification accuracies of 87–89% for four arrhythmia classes using compact configurations of 7–10 features, and 93.57% with an extended 12-parameter set. The 7-feature configuration achieves an 82% complexity reduction compared to the full 38-feature set. Multi-algorithmic analysis confirms the consistent discriminative contribution of the proposed nonlinear descriptors, demonstrating that MRMR, ReliefF, and permutation analyses yield convergent rankings of critical parameters for automated cardiac pathology diagnosis. Full article

Series arc faults often occur in aging or faulty electrical systems due to insulation degradation, poor contact, or corrosion. These faults typically generate low current signatures, which are difficult to detect with traditional overcurrent protection methods. To address this measurement challenge, this paper proposes a systematic fault detection framework that combines discriminative feature extraction, statistical validation, and optimized classification. To comprehensively characterize arc fault signals, a diverse set of time- and frequency-domain features is extracted, and composite multiscale entropy is introduced to quantify nonlinear and transient fault dynamics more effectively. The MRMR (Maximum Relevance Minimum Redundancy) algorithm is applied to select features with high information content and low redundancy, thereby improving model generalization. A random search algorithm is used to adaptively optimize the random forest hyperparameters, establishing a high-accuracy fault diagnosis model. The experimental setup was established based on the UL1699B standard using a 115 V/400 Hz arc fault platform, and 1800 sets of data under nine different load types were collected for training and validation. Experimental results show that the proposed method outperforms five mainstream machine learning algorithms in terms of fault detection accuracy and performance. The results confirm its metrological robustness and its potential for deployment in waveform-based fault electrical monitoring systems. Full article

In vertical mine hoisting systems, the rigid guide serves as a critical safety component whose failure may induce severe dynamic disturbances and potentially trigger cascading safety incidents. Existing data-driven diagnosis methods for rigid guides often lack robustness under variable operating conditions and require substantial labeled data. Yet in practical mine hoisting operations, variations in hoisting speed and lifting mass are inevitable, and acquiring sufficient fault samples is challenging due to safety constraints. To address these problems, this paper proposes a novel fault diagnosis framework that integrates a physics-informed feature-extraction pipeline with the zero-space observer theory. Vibration signals are processed to extract dimensionless and relative features, which are deliberately designed based on the dynamic mechanisms underlying different fault states. These features rely solely on the geometric characteristics of the waveform at the fault location, rendering them sensitive to fault types while remaining robust to variations in operating conditions. The feature set is subsequently optimized using the minimum redundancy maximum relevance (mRMR) algorithm to enhance computational efficiency, mitigate overfitting, and improve the generalization ability of the method. A set of zero-space observers is then constructed to perform efficient fault classification through geometric operations in the feature space, with each observer specifically sensitive to its corresponding health state while remaining insensitive to others. Experimental validation across multiple health states and operational variations demonstrates that the proposed method outperforms four widely used intelligent models in both classification accuracy and computational efficiency, showing strong suitability for real-world deployment in coal mining applications. Full article

This study aimed to develop a non-invasive blood glucose estimation method by integrating wearable multimodal signals, including photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature (ST), with food log–derived nutritional features, and to validate its clinical reliability. We analyzed data from 16 adults who underwent continuous glucose monitoring (CGM) while multimodal physiological signals were collected over 8–10 consecutive days, yielding more over 20,000 paired samples. Features from food logs and physiological signals were extracted, followed by feature selection using Boruta and minimum Redundancy Maximum Relevance (mRMR). Five machine learning models were trained and evaluated using five-fold cross-validation. Food log features alone demonstrated stronger predictive power than unimodal physiological signals. The fusion of nutritional, physiological, and temporal features achieved the best accuracy using LightGBM, reducing the RMSE to 12.9 mg/dL, with a MARD of 7.9%, a MAE of 8.82 mg/dL, and R² of 0.69. SHapley Additive exPlanations (SHAP) analysis revealed that 24-h carbohydrate and sugar intake, time since last meal, and short-term EDA features were the most influential predictors. By integrating multimodal wearable and dietary information, the proposed framework significantly enhances non-invasive glucose estimation. The interpretable LightGBM model demonstrates promising clinical utility for continuous monitoring and early dysglycemia management. Full article

In the context of the convergence of multiple energy systems, the risk of power quality degradation across different stages of energy generation and distribution has become increasingly significant. Accurate identification of power quality disturbances is crucial for improving power quality and ensuring the stable operation of power grids. However, existing disturbance identification methods struggle to balance accuracy and computational efficiency, limiting their applicability in real-time monitoring scenarios. To address this issue, this paper proposes a novel disturbance recognition framework called ST-mRMR-RF. The method first applies the S-transform to convert the time-domain signal into the time-frequency domain. It then extracts spectrum, low-frequency, mid-frequency, and high-frequency components as frequency-domain features from this domain. These are fused with time-domain features to form a multi-scale feature set. To reduce feature redundancy, the Maximum Relevance Minimum Redundancy (mRMR) algorithm is applied to select the optimal feature subset, ensuring maximum category relevance and minimal redundancy. Based on this foundation, four classifiers—Random Forest (RF), Partial Least Squares (PLS), Extreme Learning Machine (ELM), and Convolutional Neural Network (CNN)—are employed for disturbance identification. Experimental results show that the feature subset selected via mRMR reduces the model’s training time by 88.91%. When tested in a white noise environment containing 21 types of power quality disturbance signals, the ST-mRMR-RF method achieves a recognition accuracy of 99.24% at a 20dB signal-to-noise ratio. Overall, this framework demonstrates outstanding performance in noise resistance, classification accuracy, and computational efficiency. Full article

Designing cost-effective, reliable diagnostic sensor suites for complex assets remains challenging due to conflicting objectives across stakeholders. A holistic framework that integrates the Normalised Diagnostic Contribution Index (NDCI)—which scores sensors by separation power, severity sensitivity, and uniqueness—with a Multi-Objective Sensor Optimisation Framework (MOSOF) is presented. Using a high-fidelity virtual aircraft model coupling engine, fuel, electrical power system (EPS), and environmental control system (ECS), NDCI against minimum Redundancy-maximum Relevance (mRMR) is benchmarked under a rigorous nested cross-validation protocol. Across subsystems, NDCI yields more compact suites and higher diagnostic accuracy, notably for engine (88.6% vs. 69.0%) and ECS (67.7% vs. 52.0%). Then, a multi-objective optimisation reflecting an airline use-case (diagnostic performance, cost, reliability, and benefit-to-cost) is executed, identifying a practical Pareto-optimal ‘knee’ solution comprising 12–14 sensors. The recommended suite delivers a normalised performance of ≈0.69 at ≈USD36k with ≈145 kh MTBF, balancing the cross-subsystem information value with implementation constraints. The NDCI-MOSOF workflow provides a transparent, reproducible pathway from raw multi-sensor data to stakeholder-aware design decisions, and constitutes transferable evidence for model-based safety and certification processes in Integrated Vehicle Health Management (IVHM). The limitations (simulation bias, cost/MTBF estimates), validation on rigs or in-service fleets, and extensions to prognostics objectives are discussed. Full article

Accurate recognition of complex human activities from wearable sensors plays a critical role in sports analytics and human performance monitoring. However, the high dimensionality and redundancy of raw inertial data can hinder model performance and interpretability. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (MRMR) and Regularized Neighborhood Component Analysis (RNCA) to improve classification accuracy while reducing computational complexity. Multi-sensor inertial data were collected from field hockey players performing six activity types. Time- and frequency-domain features were extracted from four body-mounted inertial measurement units (IMUs), resulting in 432 initial features. MRMR, combined with Pearson correlation filtering (|ρ| > 0.7), eliminated redundant features, and RNCA further refined the subset by learning supervised feature weights. The final model achieved a test accuracy of 92.82% and F1-score of 86.91% using only 83 features, surpassing the MRMR-only configuration and slightly outperforming the full feature set. This performance was supported by reduced training time, improved confusion matrix profiles, and enhanced class separability in PCA and t-SNE visualizations. These results demonstrate the effectiveness of the proposed two-stage feature selection method in optimizing classification performance while enhancing model efficiency and interpretability for real-time human activity recognition systems. Full article